Jay Shah Podcast: Recent Episodes
Jay Shah
Interviews with scientists and engineers working in Machine Learning and AI, about their journey, insights, and discussion on latest research topics.
Meredith is an associate professor at New York University and research director at the NYU Alliance for Public Interest Technology. Her research interests include using data analysis for good and ethical AI. She is also the author of the book “More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech” and we will discuss more about this with her in this podcast.
Time stamps of the conversation 00:42 Introduction 01:17 Background 02:17 Meaning of “it is not a glitch” in the book title 04:40 How are biases coded into AI systems? 08:45 AI is not the solution to every problem 09:55 Algorithm Auditing 11:57 Why do organizations don't use algorithmic auditing more often? 15:12 Techno-chauvinism and drawing boundaries 23:18 Bias issues with ChatGPT and Auditing the model 27:55 Using AI for Public Good - AI on context 31:52 Advice to young researchers in AI
Meredith's homepage: https://meredithbroussard.com/ And her Book: https://mitpress.mit.edu/9780262047654/more-than-a-glitch/
About the Host: Jay is a Ph.D. student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Part-2 of my podcast with David Stutz. (Part-1: https://youtu.be/J7hzMYUcfto) David is a research scientist at DeepMind working on building robust and safe deep learning models. Prior to joining DeepMind, he was a PhD student at the Max Plank Institute of Informatics. He also maintains a fantastic blog on various topics related to machine learning and graduate life which is insightful to young researchers out there.
00:00:00 Working at DeepMind 00:08:20 Importance of Abstraction and Collaboration in Research 00:13:08 DeepMind internship project 00:19:39 What drives research projects at DeepMind 00:27:45 Research in Industry vs Academia 00:30:45 Interview tips for research roles, at DeepMind or other companies 00:44:38 Finding the right Advisor & Institute for PhD 01:02:12 Do you really need a Ph.D. to do AI/ML research? 01:08:28 Academia vs Industry: Making the choice 01:10:49 Pressure to publish more papers 01:21:35 Artificial General Intelligence (AGI) 01:33:24 Advice to young enthusiasts on getting started
David's Homepage: https://davidstutz.de/ And his blog: https://davidstutz.de/category/blog/ Research work: https://scholar.google.com/citations?user=TxEy3cwAAAAJ&hl=en
About the Host: Jay is a Ph.D. student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Rora helps top AI researchers and professionals negotiate their pay -- often as they transition from academia into industry. Moving into tech is a huge transition for many PhDs and post-docs -- the pay is much more significant and the terms of employment are often quite different.
In the past 5 years, the Rora team has helped over 1000 STEM professionals negotiate more than $10M in additional earnings from companies like DeepMind, OpenAI, Google Brain, and Anthropic -- and advocate for better roles, more alignment with their managers, and more flexible work.
Referral link: https://teamrora.com/jayshah
Guide to STEM Ph.D. AI Researcher + Research Scientist pay: https://www.teamrora.com/post/ai-researchers-salary-negotiation-report-2023 (the majority of the STEM PhDs we support are going into tech roles) Rora's negotiation philosophy: https://www.teamrora.com/post/the-biggest-misconception-about-negotiating-salaryhttps://www.teamrora.com/post/job-offer-negotiation-lieshttps://www.teamrora.com/post/roras-3-keys-to-negotiating-a-new-job-offer00:00 Highlights 00:55 Introduction 01:42 About Rora 05:40 Myths in Job Negotiations 08:58 Fear of losing job offers 12:36 30-60-90 day roadmap for negotiation 15:28 Knowing if you should negotiate 20:46 Negotiating with only one offer 24:40 What to negotiate? 29:00 Knowing if you're low-balled in offers 31:31 When negotiations don't workout 35:00 When & How to Negotiate? 43:00 Negotiating promotions 46:45 Is there always room for Negotiation? 49:42 Quick advice to people who have offers in hand 55:32 Wrong assumptions
Learn more about Jordan: https://www.linkedin.com/in/jordansale And Rora: https://teamrora.com/jayshah
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host: Jay is a Ph.D. student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Part-1 of my podcast with David Stutz. (Part-2: https://youtu.be/IumJcB7bE20) David is a research scientist at DeepMind working on building robust and safe deep learning models. Prior to joining DeepMind, he was a Ph.D. student at the Max Plank Institute of Informatics. He also maintains a fantastic blog on various topics related to machine learning and graduate life which is insightful to young researchers out there.
Check out Rora: https://teamrora.com/jayshah Guide to STEM Ph.D. AI Researcher + Research Scientist pay: https://www.teamrora.com/post/ai-researchers-salary-negotiation-report-202300:00:00 Highlights and Sponsors 00:01:22 Intro 00:02:14 Interest in AI 00:12:26 Finding research interests 00:22:41 Robustness vs Generalization in deep neural networks 00:28:03 Generalization vs model performance trade-off 00:37:30 On-manifold adversarial examples for better generalization 00:48:20 Vision transformers 00:49:45 Confidence-calibrated adversarial training 00:59:25 Improving hardware architecture for deep neural networks 01:08:45 What's the tradeoff in quantization? 01:19:07 Amazing aspects of working at DeepMind 01:27:38 Learning the skills of Abstraction when collaborating
David's Homepage: https://davidstutz.de/ And his blog: https://davidstutz.de/category/blog/ Research work: https://scholar.google.com/citations?user=TxEy3cwAAAAJ&hl=en
About the Host: Jay is a Ph.D. student at Arizona State University. Linkedin: https://www.linkedin.com/in/shahjay22/ Twitter: https://twitter.com/jaygshah22 Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Part-1 of my podcast with David Stutz. (Part-2: https://youtu.be/IumJcB7bE20)
David is a research scientist at DeepMind working on building robust and safe deep learning models. Prior to joining DeepMind, he was a Ph.D. student at the Max Plank Institute of Informatics. He also maintains a fantastic blog on various topics related to machine learning and graduate life which is insightful to young researchers out there.
Check out Rora: https://teamrora.com/jayshah
Guide to STEM Ph.D. AI Researcher + Research Scientist pay: https://www.teamrora.com/post/ai-researchers-salary-negotiation-report-2023
00:00:00 Highlights and Sponsors
00:01:22 Intro
00:02:14 Interest in AI
00:12:26 Finding research interests
00:22:41 Robustness vs Generalization in deep neural networks
00:28:03 Generalization vs model performance trade-off
00:37:30 On-manifold adversarial examples for better generalization
00:48:20 Vision transformers
00:49:45 Confidence-calibrated adversarial training
00:59:25 Improving hardware architecture for deep neural networks
01:08:45 What's the tradeoff in quantization?
01:19:07 Amazing aspects of working at DeepMind
01:27:38 Learning the skills of Abstraction when collaborating
David's Homepage: https://davidstutz.de/
And his blog: https://davidstutz.de/category/blog/
Research work: https://scholar.google.com/citations?user=TxEy3cwAAAAJ&hl=en
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/
Dr. Subbarao Kambhampati is a Professor of Computer Science at Arizona State University and the director of the Yochan lab where his research focuses on decision-making and planning, specifically in the context of human-aware AI systems. He has been named a fellow of AAAI, AAAS, and ACM in recognition of his research contributions and also received a distinguished alumnus award from the University of Maryland and IIT Madras.
Check out Rora: https://teamrora.com/jayshah
Guide to STEM Ph.D. AI Researcher + Research Scientist pay: https://www.teamrora.com/post/ai-researchers-salary-negotiation-report-2023
Rora's negotiation philosophy:
https://www.teamrora.com/post/the-biggest-misconception-about-negotiating-salary
https://www.teamrora.com/post/job-offer-negotiation-lies
00:00:00 Highlights and Intro
00:02:16 What is chatgpt doing?
00:10:27 Does it really learn anything?
00:17:28 Chatgpt hallucinations & getting facts wrong
00:23:29 Generative vs Predictive Modeling in AI
00:41:51 Learning common patterns from Language
00:57:00 Implications in society
01:03:28 Can we fix chatgpt hallucinations?
01:26:24 RLHF is not enough
01:32:47 Existential risk of AI (or chatgpt)
01:49:04 Open sourcing in AI
02:04:32 OpenAI is not "open" anymore
02:08:51 Can AI program itself in the future?
02:25:08 Deep & Narrow AI to Broad & Shallow AI
02:30:03 AI as assistive technology - understanding its strengths & limitations
02:44:14 Summary
Articles referred to in the conversation
https://thehill.com/opinion/technology/3861182-beauty-lies-chatgpt-welcome-to-the-post-truth-world/
More about Prof. Rao
Homepage: https://rakaposhi.eas.asu.edu/
Twitter: https://twitter.com/rao2z
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/
Karyna Naminas is the CEO of Label Your Data which provides data annotation services to different organizations interested in developing AI-based solutions.
Check out Rora: https://teamrora.com/jayshah
Guide to STEM Ph.D. AI Researcher + Research Scientist pay: https://www.teamrora.com/post/ai-researchers-salary-negotiation-report-2023
Rora's negotiation philosophy:
https://www.teamrora.com/post/the-biggest-misconception-about-negotiating-salary
https://www.teamrora.com/post/job-offer-negotiation-lies
00:00:00 Introduction and Sponsors
00:02:28 Background before being a CEO
00:06:38 Fascinating aspects of AI
00:09:10 Data annotation outside of AI
00:10:21 Effect of COVID, Russia-Ukraine War, and economic crisis on Business
00:18:47 Sourcing data annotators
00:22:40 Challenges in annotation
00:31:00 Data annotation for Military applications in Ukraine
00:41:42 Tools used for annotation
00:44:56 Segment anything and chatgpt to facilitate annotation
00:51:00 Key responsibilities as a CEO
00:53:58 Metrics for performance evaluation
00:59:56 Building leadership
01:06:06 Advice to aspiring entrepreneurs
01:09:34 Dealing with failures as a CEO
Learn more about Karyna: https://www.linkedin.com/in/karyna-naminas-923908200
Label Your Data: https://labelyourdata.com/
LinkedIn: https://www.linkedin.com/company/label-your-data/
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/
Amey Dharwadker works as a Machine Learning Tech Lead Manager at Meta, supporting Facebook's Video Recommendations Ranking team and working on building and deploying personalization models for billions of users. He has also been instrumental in driving a significant increase in user engagement and revenue for the company through his work on News Feed and Ads ranking ML models. As an experienced researcher, he has co-authored publications at various AI/ML conferences and patents in the fields of recommender systems and machine learning. He has undergraduate and graduate degrees from the National Institute of Technology Tiruchirappalli (India) and Columbia University.
Time stamps of the conversation
00:00:46 Introduction
00:01:46 Getting into recommendation systems
00:05:25 Projects currently working on at Facebook, Meta
00:06:55 User satisfaction to improve recommendations
00:08:25 Implicit Metrics to improve engagement
00:11:34 Video vs product recommendations based on fixed attributes
00:13:20 Understanding video content
00:15:55 Working at Scale
00:20:02 Cold start problem
00:22:41 Data privacy concerns
00:24:36 Challenges of deploying machine learning models
00:30:56 Trade-off in metrics to boost user engagement
00:33:47 Introspecting recommender systems - Interpretability
00:37:14 Long video vs short video - how to adapt algorithms?
00:42:17 Being a Machine Learning Tech Lead Manager at Meta - work routine
00:45:00 Transitioning to leadership roles
00:50:55 Tips on interviewing for Machine Learning roles
00:57:23 Machine Learning job interviews
01:02:30 Finding your interest in AI/machine learning
01:05:24 Transitioning to ML roles within the industry
01:08:36 Remaining updated to research
01:12:00 Advice to young computer science students
More about Amey: https://research.facebook.com/people/dharwadker-amey-porobo/
Linkedin: https://www.linkedin.com/in/ameydharwadker/
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/
Dr. Aparna Taneja works at Google Research in India on innovative projects driving real-world social impact. Her team collaborates with an NGO called ARMMAN with the mission to improve maternal and child health outcomes in underserved communities of India. Prior to Google she was a Post-Doc at Disney Research, Zurich, and has a PhD from the Computer Vision and Geometry Group in ETH Zurich and a Bachelor's in Computer Science from the Indian Institute of Technology, Delhi.
Time stamps of the conversation
00:00:46 Introductions
00:01:20 Background and Interest in AI
00:03:59 Satellite imaging and AI at Google
00:08:30 Multi-Agent systems for social impact - part of AI for social good
00:10:30 Awareness of AI benefits in non-tech fields
00:13:42 Project SAHELI - improving maternal and child health using AI
00:20:05 Intuition for methodology
00:22:07 Measuring impact on health
00:27:42 Challenges when working with real-world data
00:32:58 Problem scoping and defining research statements
00:38:16 Disconnect between tech and non-tech communities while collaborating
00:43:22 What motivates you, the theoretical or application side of research
00:47:17 What research skills are a must when working on real-world challenges using AI
00:50:33 Factors considered before doing a PhD
00:54:08 Significance of Ph.D. for research roles in the industry
00:58:15 Choosing industry vs Academia
01:02:38 Managing personal life with a research career
01:07:58 Advice to young students interested in AI on getting started
Learn more about Aparna here: https://research.google/people/106890/
Research: https://scholar.google.com/citations?user=XtMi1L0AAAAJ&hl=en
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/
Dr. Srijan Kumar is an Assistant professor at Georgia Tech with research interests in combating misinformation and harmful content on online platforms, building robust AI models prone to adversarial attacks, and behavior modeling for more accurate recommender systems. Before joining Georgia Tech, he was a postdoctoral fellow at Stanford University and completed his Ph.D. in computer science from the University of Maryland. He has received multiple awards for his research work, including Forbes 30u30 and being named a Kavli Fellow by the National Academy of Sciences.
Time stamps of the conversation
00:01:00 Introductions
00:01:45 Background and Interest in AI
00:05:27 Current research interests
00:09:50 What is misinformation?
00:15:07 ChatGPT and misinformation
00:23:40 How can AI help detect misinformation?
00:39:15 Twitter's Birdwatch platform to detect fake/misleading news
00:56:38 Detecting fake bots on Twitter
01:03:39 Adversarial training to build robust AI models
01:05:31 Robustness vs Generalizability in machine learning
01:11:40 Navigating your interest in the field of AI/machine learning
01:19:22 Doing a Ph.D. and working in Industry vs Academia
01:24:22 Focusing on Quality of Research rather than Quantity
01:31:23 Advice to young people interested in AI
Dr. Kumar's homepage: https://cc.gatech.edu/~srijan/
Twitter: https://twitter.com/srijankedia
Linkedin: https://www.linkedin.com/in/srijankr
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/
Emma is a final-year medical student at the University of Cambridge and also pursuing her Ph.D. in Machine Learning. With her knowledge of clinical decision-making, she is working on research projects that leverage machine-learning techniques to improve clinical workflow. She will be taking her role as an academic doctor post her graduation.
Time stamps of the conversation
00:00:00 Introduction
00:02:08 From clinical science to learning AI
00:13:15 Learning the basics of Artificial Intelligence
00:20:12 Promise of AI in medicine
00:30:13 Do we really need interpretable AI models for clinical decision-making?
00:38:47 Using AI for more clinically-useful problems
00:50:55 Facilitating interdisciplinary efforts
00:54:06 Predicting length of stay in ICUs using convolutional neural networks
01:03:04 AI for improving clinical workflows and biomarker discovery
01:07:55 Clustering disease trajectories in mechanically ventilated patients using machine learning
01:16:37 ChatGPT for medical research or clinical decision making
01:25:21 Quality over quantity of AI works published nowadays
01:31:07 Advice to researchers
Emma's Homepage: https://emmarocheteau.com/
LinkedIn: https://www.linkedin.com/in/emma-rocheteau-125384132/
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/
Understanding why and how transformers are so efficient in large language models nowadays such as #chatgpt and more.
Watch the full podcast with Dr. Surbhi Goel here: https://youtu.be/stB0cY_fffo
Find Dr. Goel on social media
Website: https://www.surbhigoel.com/
Linkedin: https://www.linkedin.com/in/surbhi-goel-5455b25a
Twitter: https://twitter.com/surbhigoel_?lang=en
Learning Theory Alliance: https://let-all.com/index.html
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/
Anupam is the co-founder and President of TruEra and prior to that, he was a Professor at Carnegie Mellon University for 15 years. TruEra provides AI solutions that help enterprises use machine learning, improve and monitor model quality, and build trust. His research and other efforts are focused on privacy, fairness, and building trustworthy machine-learning models. He holds a Ph.D. in computer science from Stanford University and Bachelor’s degree in same from IIT Kharagpur in India.
Time stamps of the conversation
00:50 Introductions
01:45 Background and TruEra
05:30 Trustworthy AI
11:55 Validating Large models in the real world
16:15 History of NLP and large language models
29:25 Opportunities and challenges with ChatGPT
36:52 Evaluating the reliability of ChatGPT
39:10 Existing tools that aid explainability
43:12 AI trends to look for in 2023
More about Dr. Datta
Website: https://www.andrew.cmu.edu/user/danupam/
Linkedin: https://www.linkedin.com/in/anupamdatta
Research: https://scholar.google.com/citations?user=oK3QM1wAAAAJ&hl=en
About TruEra: https://truera.com/
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/
Surbhi is an Assistant Professor at the University of Pennsylvania. She got her Ph.D. in Computer Science from UT Austin and prior to joining UPenn as an Assistant Professor, she was a postdoctoral researcher at Microsoft Research NYC in the Machine Learning group. She has research expertise in theoretical computer science & machine learning, with a particular focus on developing theoretical foundations for modern deep learning paradigms. She also is a part of building the Learning Theory Alliance community that organizes and conducts several events useful for researchers and students in their careers.
Time stamps of the conversation
00:00:54 Introduction
00:01:54 Background and research interests
00:05:03 Interest in Machine Learning Theory
00:13:02 Understanding how deep learning works
00:16:30 Transformer architecture
00:25:40 Scale of data and big models
00:31:28 Reasoning in deep learning
00:38:52 Theoretical perspective on AGI, consciousness, and sentience in AI
00:46:00 Remaining updated to the latest research
00:53:38 Should one do a Ph.D.?
00:57:45 Is a Ph.D. mandatory for machine learning industry positions?
01:01:38 What makes a good research thesis?
01:05:30 Some best practices in research
01:12:20 Learning Theory Alliance Group
01:14:25 Job interviews in academia for researchers
01:20:00 Advice to young researchers and students
01:25:02 Decision to become a Professor
Find Dr. Goel on social media
Website: https://www.surbhigoel.com/
Linkedin: https://www.linkedin.com/in/surbhi-goel-5455b25a
Twitter: https://twitter.com/surbhigoel_?lang=en
Learning Theory Alliance: https://let-all.com/index.html
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/
Sebastian Raschkais the lead AI educator at GridAI. He is the author of the book "Machine Learning with PyTorch and Scikit Learn" and also a few other books that cover the fundamentals of #machinelearning and #deeplearning techniques and implementing them with Python. He is also an Assistant Professor of Statistics at the University of Wisconsin-Madison and has been actively involved in making ML more accessible to beginners through his blogs, video tutorials, tweets and of course his books. He also holds a doctorate in Computational and Quantitative Biology from Michigan State University.
Time Stamps of the Podcast
00:00:00 Introductions
00:02:40 Entry point in AI/ML that made you interested in it
00:05:30 How did you go about learning the basics and implementation of various methods?
00:11:45 What makes Python ideal for learning Machine Learning recently?
00:21:54 What is your book about and who is this for?
00:33:55 What goes into writing a good technical book?
00:40:50 Applying ML to toy datasets vs real-world research problems
00:47:40 Choosing b/w machine learning methods & deep learning methods
00:56:22 Large models vs architecture efficient models
01:01:25 Interpretability & Explainability in AI
01:08:45 Insights for people interested in machine learning research, academia or PhD
01:14:17 Keeping up with research in deep learning
Sebastian's homepage: https://sebastianraschka.com/
Twitter: https://mobile.twitter.com/rasbt
LinkedIn: https://www.linkedin.com/in/sebastianraschka/
His book: https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-scikit-learn-ebook-dp-B09NW48MR1/dp/B09NW48MR1/
Video Tutorials: @SebastianRaschka
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/
Dr. Matthew Lungren is currently the Chief Medical Information Officer at Nuance Communications - Microsoft company, and also holds part-time appointments with the University of California San Francisco as an Associate Clinical Professor and also as adjunct faculty at Stanford and Duke University. He is a radiologist by training and has led and contributed to multiple projects that use AI and deep learning for medical imaging and precision medicine.
Time stamps from the conversation
00:00:55 Introduction
00:01:46 Role as a Chief Medical Information Officer
00:05:25 Leading research projects in the industry
00:08:45 Is AI ready for primetime use cases in the real world?
00:12:40 Regulations on AI systems in healthcare
00:17:25 Interpretability vs a robust validation framework
00:25:22 Promising directions to mitigate data issues in medical research
00:32:24 Stable diffusion models
00:34:06 Making datasets public
00:39:00 Vision transformers for multi-modal models
00:44:35 Biomarker discovery
00:48:20 Sentiment of AI in medicine
00:53:26 Bridging the communication gap between computer scientists and medical experts
01:01:42 Advice to young researchers from medical and engineering schools
Find Dr. Lungren on social media
Twitter: https://twitter.com/mattlungrenmd
LinkedIn: https://www.linkedin.com/in/mattlungrenmd/
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahml
About the author: https://www.public.asu.edu/~jgshah1/
Dr. Charles Fisher is the CEO and Founder of Unlearn(dot)AI which helps in faster drug development and efficient clinical trials. This year they also raised a series B funding of 50 million dollars. Charles holds a Ph.D. in biophysics from Harvard University and prior to founding Unlearn, he did his Postdoctorate at Boston University, followed by being a principal scientist at Pfizer and a machine learning engineer at a virtual reality company in silicon valley.
Time stamps of the conversation
00:00:30 Introduction
00:01:16 What got you into Machine Learning?
00:04:10 Learning the basics and implementation
00:07:55 Digital twins for clinical trials and drug development
00:13:06 Patient heterogeneity in medical research
00:16:05 Error quantification of models
00:17:17 ML models for drug development
00:22:45 Adoption of AI in medical applications
00:25:35 Building trust in AI systems
00:35:10 How to show AI models are safe in the real world?
00:38:38 Moving from academia to industry to entrepreneurship
00:45:08 Research projects in startups vs academia vs big companies
00:53:12 Routine as a CEO
00:57:50 Is a Ph.D. necessary for a research career in the industry?
01:01:20 Taking inspiration from biology to improve machine learning
01:05:25 Advice to young people
About Charles:
LinkedIn: https://www.linkedin.com/in/drckf/
More about Unlearn: https://www.unlearn.ai/
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Mina Ghashami is an Applied Scientist in the Alexa Video team at Amazon Science alongside being a lecturer at Stanford University. Prior to joining Amazon, she was a Research Scientist at Visa Research working on recommendation systems built on transactions from users and a few other projects. She completed her Ph.D. in Computer Science from the University of Utah followed by a PostDoctoral position at Rutgers University. At Amazon, she is mainly focused on Video-based ranking recommendation systems, something we talk about in detail in this conversation.
Time stamps of the conversation
00:00:50 Introductions
00:01:40 Alexa Video - Ranking and Recommendation research
00:05:25 Feature engineering for recommendation systems
00:08:30 Ground truth for training recommendation systems
00:12:46 What does an Applied Scientist do? (at Amazon)
00:19:17 What got you into AI? And specifically recommendation systems
00:24:30 Matrix approximation
00:27:15 Challenges in recommendation research
00:32:00 What's more interesting, theoretical or applied side of research?
00:37:10 Over parametrization vs generalizability
00:39:55 Managing academic and industry positions at the same time
00:46:26 Should one do a Ph.D. for research roles in the industry?
00:50:00 Skills learned while pursuing a PhD
00:54:22 Deciding industry vs academia
00:56:20 Coping up with research in deep learning
01:02:14 What makes a good research dissertation?
01:04:16 Advice to young students navigating their interest in machine learning
To learn more about Mina:
Homepage: https://mina-ghashami.github.io/
Linkedin: https://www.linkedin.com/in/minaghashami
Research: https://scholar.google.com/citations?user=msJHsYcAAAAJ&hl=en
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Alberto Santamaria-Pang is a Principal Applied Data Scientist at Microsoft. He did his Ph.D. in computer science from the University of Houston and has a long experience in research and development on various AI projects including but not limited to medical imaging and deep learning. Prior to Microsoft, he was a principal scientist at GE research. He has led many research projects in industry and also government-funded projects, a few of which we will be discussing today.
Time stamps of conversations:
00:00:37 Introduction
00:01:25 Background before you got into the industry
00:04:17 Interest in AI and Medical Imaging
00:05:54 What does a Principal Scientist do?
00:10:00 What drives research in industry? Product or Theoretical pursuit?
00:11:35 Learning skills relevant to a principal scientist
00:15:14 Principal Investigator vs Principal Scientist
00:21:00 How do industry and academia collaborate on research projects?
00:25:30 Promise & challenges of AI in medical research and applications
00:31:53 What should explainable AI look like?
00:38:35 Adoption of AI in medical research
00:43:00 Is AI generalizable?
00:44:36 AI for biomarker discovery
00:51:42 Are large models useful in AI & Med space
00:58:00 Why is there a lack of datasets?
01:01:02 Do you think AI is scary?
01:04:00 Where do we need innovation in AI precisely?
01:10:20 Getting inspiration from bio-research to improve algorithms
01:13:19 AI and molecular pathology for cancer research
00:20:30 Should one get a Ph.D.?
01:27:38 Advice for young people
About Alberto:
His research works: https://scholar.google.com/citations?user=sVahJxsAAAAJ&hl=en
LinkedIn: https://www.linkedin.com/in/alberto-santamaria
About the Host:
Jay is a Ph.D. student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Are large language models really sentient or conscious? What is explainability (XAI) and how can we create human-aware AI systems for collaborative tasks? Dr. Subbarao Kambhampati sheds some light on these topics, generating explanations for human-in-loop AI systems and understanding 'intelligence' in context to AI systems. He is a Prof of Computer Science at Arizona State University and director of the Yochan lab at ASU where his research focuses on decision-making and planning specifically in the context of human-aware AI systems. He has received multiple awards for his research contributions. He has also been named a fellow of AAAI, AAAS, and ACM and also a distinguished alumnus from the University of Maryland and also recently IIT Madras.
Time stamps of conversations:
00:00:40 Introduction
00:01:32 What got you interested in AI?
00:07:40 Definition of intelligence that is not related to human intelligence
00:13:40 Sentience vs intelligence in modern AI systems
00:24:06 Human aware AI systems for better collaboration
00:31:25 Modern AI becoming natural science instead of an engineering task
00:37:35 Understanding symbolic concepts to generate accurate explanations
00:56:45 Need for explainability and where
01:13:00 What motivates you for research, the application associated or theoretical pursuit?
01:18:47 Research in academia vs industry
01:24:38 DALL-E performance and critiques
01:45:40 What makes for a good research thesis?
01:59:06 Different trajectories of a good CS PhD student
02:03:42 Focusing on measures vs metrics
02:15:23 Advice to students on getting started with AI
Articles referred in the conversation
AI as Natural Science?: https://cacm.acm.org/blogs/blog-cacm/261732-ai-as-an-ersatz-natural-science/fulltext
Polanyi's Revenge and AI's New Romance with Tacit Knowledge: https://cacm.acm.org/magazines/2021/2/250077-polanyis-revenge-and-ais-new-romance-with-tacit-knowledge/fulltext
More about Prof. Rao
Homepage: https://rakaposhi.eas.asu.edu/
Twitter: https://twitter.com/rao2z
About the Host:
Jay is a PhD student at Arizona State University.
Linkedin: https://www.linkedin.com/in/shahjay22/
Twitter: https://twitter.com/jaygshah22
Homepage: https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Divy is a Program Manager and one of the founding members of Google research in India. He is actively involved and leading strategic programs that connect academia and research at Google, programs that focus on AI-for-Social-Good initiatives, and educational programs for schools in India with a particular focus on building computer science foundations.
00:00:12 Introductions
00:01:12 Background prior to joining Google
00:08:40 Programs you are working on as a Program Manager at Google Research and what are your responsibilities
00:14:35 Lifecycle of a program & various phases
00:17:55 Getting involved in research while being a Program Manager
00:25:55 Learning skills for strategic thinking as a PM
00:35:08 How did you get your PM role at Google and what was the interview like?
00:40:35 Resources people can use to prepare for PM interviews
00:41:58 Difference b/w Product vs Program vs Technical Manager
00:46:10 Previous experiences that helped develop skills for Program Manage role
00:53:58 Tips on being more organized with work
Divy's Homepage: https://sites.google.com/view/divythakkar
Twitter: https://twitter.com/divy93t
LinkedIn: https://www.linkedin.com/in/divythakkar/
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
research #programmanager #googleresearch #india #aiforsocialgood #manager
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Watch the full conversation with Dr. Manish Gupta here: https://youtu.be/-Tl6-DKxEMU
Dr. Manish Gupta is currently the Director of Google Research in India. Prior to that he was the Vice-president and led the Xerox Research Center in India majorly working on data analytics and mobile computing. Before that, he was also at IBM research in India leading the efforts and building a lab focused on high-performance computing and business analytics. He also led the efforts at Goldman Sachs developing technologies relating to cloud, databases, and networking aiding business functions. We also co-founded and was the CEO of an educational technology startup called VideoKen.
Dr. Manish Gupta's Homepage
https://www.iiitb.ac.in/faculty/manish-gupta
https://research.google/people/106704/
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
research #ai #machinelearning #googleresearch #india #aiforsocialgood
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Check out the full conversation with Hanie here: https://youtu.be/hFJLuqaSakA
Hanie is a senior research scientist at Google Brain working on research problems related to understanding and improving deep learning techniques. She works on designing algorithms with theoretical guarantees such that they work efficiently in real-world applications. Prior to that, she was a research scientist at Allen Institute of AI, and before that she was a Post-Doc fellow at UC Irvine. She graduated from USC with a Ph.D. with minors in Mathematics.
Dr. Hanie Sedghi's links
Twitter: https://twitter.com/haniesedghi?ref_src=twsrc%5Etfw
Homepage: https://haniesedghi.com/
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
theoryofmachinelearning #deeplearning #ai #machinelearning #fundamentals
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Dr. Manish Gupta is currently the Director of Google Research in India. Prior to that he was the Vice-president and led the Xerox Research Center in India majorly working on data analytics and mobile computing. Before that he also at IBM research in India leading the efforts and building a lab focused on high-performance computing and business analytics. He also worked on the IBM Blue Gene supercomputer project in the early 2000s at IBM TJ Watson research center for which IBM received the National Medal of Technology and Innovation from the President of the US. He also led the efforts at Goldman Sachs developing technologies relating to cloud, databases, and networking aiding business functions. We also co-founded and was the CEO of an educational technology startup called VideoKen. He has received many distinguished awards for his efforts and has coauthored many academic papers in the domains of computer science.
Time-Stamps
00:00:00 Introductions
00:01:50 What kind of research projects are you currently spearheading at Google Research and what does your work routine look like?
00:06:30 What was your thought process prior to joining Google Research?
00:13:40 What’s the difference between a Researcher | Senior Researcher | Director of research?
00:23:00 What should robust AI systems look like?
00:33:22 How do you decide which research problems to work on?
00:46:46 What kind of challenges have you encountered while working on AI research problems specific to India?
00:56:15 How do you design and evaluate the impact of these AI projects?
00:59:27 What made you consider shifting back to India after a long streak of a career in the USA? What factors did you consider?
01:03:06 Any skills you would suggest students nurture apart from technical expertise?
01:05:40 There’s always a concern about AI usage and automation. Where do you think the balance lies?
Dr. Manish Gupta's Homepage
https://www.iiitb.ac.in/faculty/manish-gupta
https://research.google/people/106704/
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
research #ai #machinelearning #googleresearch #india #aiforsocialgood
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Hanie is a senior research scientist at Google Brain working on research problems related to understanding and improving deep learning techniques. She works on designing algorithms with theoretical guarantees such that they work efficiently in real-world applications. Prior to that, she was a research scientist at Allen Institute for AI, and before that she was a Post-Doc fellow at UC-Irvine. She graduated from USC with a Ph.D. with minors in Mathematics.
Dr. Hanie Sedghi's links
Twitter: https://twitter.com/haniesedghi?ref_src=twsrc%5Etfw
Homepage: https://haniesedghi.com/
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
theoryofmachinelearning #deeplearning #ai #machinelearning #fundamentals
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Do Vision Transformers work in the same way as CNNs? Do the internal representational structures of ViTs and CNNs differ? An in-depth analysis article: https://arxiv.org/pdf/2108.08810.pdf
Listen to the full conversation here: https://youtu.be/htnJxcwJqeA
Dr. Maithra Raghu is a senior research scientist at Google working on analyzing the internal workings of deep neural networks so that we can deploy them better keeping humans in the loop. She recently graduated from Cornell University with a Ph.D. in CS and previously graduated from Cambridge University with BA and Masters in Mathematics. She has received multiple awards for her research work including the Forbes 30 under 30.
Maithra's Homepage: https://maithraraghu.com
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
explainableai #reliableai #robustai #machinelearning
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
How to decide and choose a research/thesis to work on that interests you and is also relevant to current research directions.
Full episodes with
Maithra, Google: https://youtu.be/htnJxcwJqeA
Natasha Google: https://youtu.be/8XpCnmvq49s
Milind Google: https://youtu.be/eqwF3NpZFb4
Hima Harvard University: https://youtu.be/8Ym4oYTd8Fo
Ishan Facebook AI: https://youtu.be/Pb5RQAEtznk
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
machinelearning #ai #phd #research #thesis
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Dr. Maithra Raghu is a senior research scientist at Google working on analyzing the internal workings of deep neural networks so that we can deploy them better keeping humans in the loop. She recently graduated from Cornell University with a PhD in CS and previously graduated from Cambridge University with BA and Masters in Mathematics. She has received multiple awards for her research work including the Forbes 30 under 30.
Questions that we cover
00:00:00 Introductions
00:01:00 To understand more about your research interests, can you tell us what kind of research questions you are interested in while working at Google Brain?
00:04:45 What interested you about it and how did you get started?
00:15:00 What is one thing that surprises/puzzles you about deep learning effectiveness to date?
00:22:05 What’s the difference between being a researcher in academia/PhD student vs being a researcher at a big organization (Google)?
00:28:35 In what use cases do you think ViTs might be a good choice to perform image analysis over CNN vs where do you think CNNs still have an undoubted advantage?
00:37:15 Why does ViT perform better than ResNet only on larger datasets and not on mid-sized datasets or smaller?
00:43:55 In regards to medical imaging tasks, would it be theoretically wrong to pre-train the model on dataset A and fine-tune it on dataset B?
00:47:35 Do you think ViT or transformer-based models already have/have the potential to cause a paradigm shift in the way we approach imaging tasks? Why?
00:5:25 Medical datasets are often limited in size, what are your views on tackling these problems in the near future
00:55:55 From an internal representation perspective, do you think deep neural networks can have the ability of reasoning?
00:58:20 How did you decide on your own PhD research topic? Advice you would give to graduate researchers trying to find a research problem for their thesis?
01:04:00 Many times researchers/students feel stuck/overwhelmed with a particular project they are working on, how do you suggest based on experience to tackling that?
01:10:35 How do you now/as a graduate student used to keep up with the latest research in ML/DL?
Maithra's Homepage: https://maithraraghu.com
Blogpost talked about: https://maithraraghu.com/blog/2020/Reflections_on_my_Machine_Learning_PhD_Journey/
Her Twitter: https://twitter.com/maithra_raghu
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
explainableai #reliableai #robustai #machinelearning
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
How can you students and researchers work on projects using AI that creates a real impact and difference in society?
Watch the full podcast with Dr. Tambe here: https://youtu.be/eqwF3NpZFb4
Dr. Milind Tambe is a Professor of Computer Science at Harvard University and Director of the Center for Research in Computation and Society. He is also the Director of AI for Social Good at Google Research in India. He has been leading and working on projects that are creating an impact, ranging from wildlife conservation, public health, and safety using AI techniques.
Prof. Milind Tambe's Homepage: https://teamcore.seas.harvard.edu/tambe
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
aiforsocialgood #googleai #ai #machinelearning #socialimpact
***Disclaimer: The information contained in this video represents the views and opinions
Watch the full conversation with Nasrin here: https://youtu.be/59kRUmhA5yI
Nasrin is the co-founder of a deep-tech startup Verneek and has been in the space of AI startups for the past 5 years now. Before that, she was a senior research scientist at Elemental Cognition & BenevolentAI, and prior to which she graduated with a Ph.D. from the University of Rochester and her major research interests are in building intelligent systems that can demonstrate commonsense reasoning & generate causal explanations in order to improve human-AI collaborations. She was featured in Forbes 30u30 for her work in NLU.
Nasrin's LinkedIn Profile: https://www.linkedin.com/in/nasrinm/
Her startup Verneek: https://www.linkedin.com/company/verneek/
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming podcasts!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
How can we build intuition for interdisciplinary fields in order to tackle challenges in social reinforcement learning?
Natasha Jaques is currently a Research Scientist at Google Brain and a post-doc fellow at UC Berkeley, where her research interests are in designing multi-agent RL algorithms while focusing on social reinforcement learning. She received her Ph.D. from MIT and has also received multiple awards for her research works submitted to venues like ICML and NeurIPS She has interned at DeepMind, Google Brain, and is an OpenAI Scholars mentor.
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Why and where do companies fail at productionizing ML models?
Watch the full podcast with Aarti here: https://youtu.be/VWJXiszQpTU
Aarti is a machine learning engineer at Snorkel AI. Prior to that, she worked closely with Andrew Ng in various capacities. She graduated with a master’s in CS from Stanford, and bachelor's in CS and Computer Engineering from @New York University, and at @Microsoft Research as a research intern for John Langford, where she contributed to Vowpal Wabbit, an open-source project.
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Deciding whether to do a Ph.D. or not and things to focus on while writing your research thesis.
Watch the full podcast here: https://youtu.be/8Ym4oYTd8Fo
Dr. Himabindu Lakkaraju is an Assistant Professor at Harvard University and her major research interests are along the lines of explainability, fairness, and robustness in AI systems. Prior to that she graduated with a Ph.D. from @Stanford and has received multiple awards for her research work.
Dr. Lakkaraju's homepage: https://himalakkaraju.github.io/
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
explainableai #reliableai #robustai #machinelearning
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Dr. Geneviève Patterson is the head of applied research at VSCO. Prior to that, she was the CTO of a video editing company she co-founded called TRASH - later acquired by VSCO. She holds a Ph.D. in CS from Brown University with a focus on research in video understanding. She was also a postdoctoral researcher at Microsoft Research where she worked on interpreting deep neural networks and much more.
Geneviève Homepage: http://genp.github.io/
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Dr. Milind Tambe is a Professor of Computer Science at Harvard Universityand Director of the Center for Research in Computation and Society. He is also the Director of AI for Social Good at Google Research in India. He has been leading and working on projects that are creating an impact, ranging from wildlife conservation, public health, and safety using AI techniques.
Time Stamps:
00:00 Introductions
01:05 In concrete terms what projects are you currently working on?
03:18 Do you think there is a disconnect between the scientific/tech communities and the social sector while trying to make use of AI? If so, who should be taking more lead for making that gap small?
05:26 Do these applications in any way inspire novelty in the theoretical aspects of Machine Learning research?
08:15 How do you design and evaluate the impact of these projects?
11:20 How do you define Interpretable or Explainable AI, at the intersection of social sciences and AI?
16:50 Concern of AI usage and Automation. Where do you think the balance lies?
19:50 What bits students can do researchers to work on projects that have a real impact and just pure novelty?
23:45 Roadblocks to more widespread adoption of AI tools for social good?
29:18 What motivates you personally about using AI for social good and not just theoretical exploration of new techniques?
Prof. Milind Tambe's Homepage: https://teamcore.seas.harvard.edu/tambe
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
aiforsocialgood #googleai #ai #machinelearning #socialimpact
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
“What got you into AI? And what part of it really interests you about it?” — a simple question that I have asked more than 30 researchers from different backgrounds and applications on my podcasts to learn more about their motivation. Thought of compiling them into one; to whosoever, it proves to be insightful with the aim to learn more what drives researchers explore and work in the domain of Machine Learning and what got them started in it!
About the Host:
I am a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
LinkedIn: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming podcasts!
machinelearning #artificialintelligence #research
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Read more about this here in her Twitter thread: https://twitter.com/nasrinmmm/status/1374372131207806976
Nasrin is the co-founder of a deep-tech startup Verneek and has been in the space of AI startups for the past 5 years now. Before that, she was a senior research scientist at Elemental Cognition & BenevolentAI, and prior to which she graduated with a Ph.D. from the University of Rochester and her major research interests are in building intelligent systems that can demonstrate commonsense reasoning & generate causal explanations in order to improve human-AI collaborations. She was featured in Forbes 30u30 for her work in NLU.
We talk about her background and story in AI, some details of her research work, and insights about being in the AI startup space.
Nasrin's LinkedIn Profile: https://www.linkedin.com/in/nasrinm/
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming podcasts!
machinelearning #artificialintelligence #aistartups #buildingastartup
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Three research scientists from Google share their journey about interest in Machine Learning research and how they got started with it.
Watch full podcasts with each of these speakers:
Azalia Mirhoseini: https://youtu.be/5LCfH8YiOv4
Sara Hooker: https://youtu.be/MHtbZls2uts
Natasha Jacques: https://youtu.be/8XpCnmvq49s
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Shreya is currently a graduate student at Stanford and also working as an ML engineer at Viaduct.ai. She has previously interned at Google-Brain and at Facebook. She talks about her experience as an applied ML engineer and making ML models work in the real world.
Shreya's homepage: https://www.shreya-shankar.com
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Full episode available here: https://youtu.be/V1mDR4x_JY0
Jineet, a Data Scientist at Intuit and a graduate of Carnegie Mellon University shares some amazing insights about data science and machine learning probing his experiences.
Our aim of these webinars is to connect you with the brightest minds in the field of Machine Learning/Data Science so that you can learn how to break into the field and build an incredible career!
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
What really drives the innovation in research projects? Is it a pure pursuit of theory or application-oriented?
Ishan is a Research Scientist at @Facebook AI. Much of his recent research work revolves around self-supervised learning is known for this works like including SwAV and PIRL and,
Michal Drozdzal is also a Research Scientist at @Facebook AI with major research interests in computer vision, Machine Learning, and medical image analysis.
The full conversation with Ishan: https://youtu.be/uOVxndMyasc
And Michal: https://youtu.be/9gKwux0r0KY
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming podcasts!
artificialintelligence #machinelearning #aiinhealthcare #medicalimaging
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Po-Shen Loh is a Professor of Mathematics at Carnegie Mellon University with research interests in combinatorics, probability theory, and computer science. He is also the coach of the United States International Math Olympiad team and it was under his guidance he led his teams to secure first place in 2015, 16, 18, and 19. He is also the founder of Expii, an online platform that teaches young students basic concepts of math and science. He is also the founder of the NOVID app that has an innovative way of contact tracing in order to reduce the spread of coronavirus.
NOVID App: https://www.novid.org/
More about Po: https://www.poshenloh.com/
About the Host:
I am a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
LinkedIn: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming podcasts!
mathematics #research #coronavirus #covid
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Michal Drozdzal is a Research Scientist at Facebook AI with major research interests in computer vision. Machine Learning and medical image analysis. He received a Ph.D. in Computer Science from the University of Barcelona. We talk about his journey into research, some open research questions, how research projects look at the intersection of AI and medical research, and more about the democratization of AI research.
Michal's LinkedIn account: https://www.linkedin.com/in/michal-drozdzal-a36b9b42/?originalSubdomain=ca
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming podcasts!
artificialintelligence #machinelearning #aiinhealthcare #medicalimaging
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Nasrin is the co-founder of a deep-tech startup Verneek and has been in the space of AI startups for the past 5 years now. Before that, she was a senior research scientist at Elemental Cognition & BenevolentAI, and prior to which she graduated with a Ph.D. from the University of Rochester and her major research interests are in building intelligent systems that can demonstrate commonsense reasoning & generate causal explanations in order to improve human-AI collaborations. She was featured in Forbes 30u30 for her work in NLU.
We talk about her background and story in AI, some details of her research work, and insights about being in the AI-startup space.
Nasrin's LinkedIn Profile: https://www.linkedin.com/in/nasrinm/
Her startup Verneek: https://www.linkedin.com/company/verneek/
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming podcasts!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
How to deal and Interpret failures in research? Developing guts for which research will work out and which will not over course of time, from being a researcher in academia as a Ph.D. student to now being a research scientist at Facebook.
Check out the whole podcast here: https://youtu.be/Pb5RQAEtznk
Ishan is a Research Scientist at @Facebook AI. Much of his recent research work revolves around self-supervised learning is known for this works like including SwAV and PIRL. He completed his Ph.D. from @Carnegie Mellon University with Martial Hebert and Abhinav Gupta. and his thesis was titled “Visual Learning with Minimal Human Supervision”
Also check out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Ishan is a Research Scientist at Facebook AI. Much of his recent research work revolves around self-supervised learning is known for this works like including SwAV and PIRL. He completed his Ph.D. from CMU with Martial Hebert and Abhinav Gupta. and his thesis was titled “Visual Learning with Minimal Human Supervision”
Ishan's homepage: http://imisra.github.io/
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Linda is Product Lead at FAIRE and has in the past interned at Snap Inc., Microsft, BAIN, and graduate from Harvard University. She has a tremendous amount of experience in Product Management and openly shares her insights through newsletters here https://www.productlessons.xyz/ for people to get started with it.
You can reach out to her and follow more of her updates at https://twitter.com/thelindazhang
Also check out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Aarti is a machine learning engineer at Snorkel AI. Prior to that, she worked closely with Andrew Ng in various capacities - at AI Fund helping build ML companies from scratch internally, as well as investing in ML companies, as a machine learning engineer at his startup Landing AI, as head TA for his deep learning class at Stanford University (CS230), and in his research lab at Stanford. She graduated with a master’s in CS from Stanford, and with bachelors in CS and Computer Engineering from New York University where she worked in David Sontag’s lab on applications of machine learning to clinical medicine, and at Microsoft Research as a research intern for John Langford, where she contributed to Vowpal Wabbit, an open-source project.
Reach out to her at
LinkedIn: https://www.linkedin.com/in/aartibagul
Twitter: https://twitter.com/aarti_bagul
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
What kind of challenges have you noticed while working with ML applications in production, that most newbies don't know as students or fresh graduates? And what ML applications production pipeline look like?
Watch the full podcast here: https://youtu.be/VWJXiszQpTU
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Watch the full podcast with Natasha here: https://youtu.be/8XpCnmvq49s
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Watch the full podcast with Linda here: https://youtu.be/0Yrt2lBzsNk
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Watch the full podcast here: https://youtu.be/8XpCnmvq49s
Natasha Jaques is currently a Research Scientist at @Google Brain and a post-doc fellow at @UC Berkeley, where her research interests are in designing multi-agent RL algorithms while focusing on social reinforcement learning, that can improve generalization, coordination between agents, and collaboration between human and AI agents. She received her PhD from the @Massachusetts Institute of Technology (MIT) where she focused on Affective Computing and other techniques for deep/reinforcement learning. She has also received multiple awards for her research works submitted to venues like ICML and NeurIPS She has interned at @DeepMind, Google Brain, and is an @OpenAI Scholars mentor.
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Natasha Jaques is currently a Research Scientist at Google Brain and a post-doc fellow at UC-Berkeley, where her research interests are in designing multi-agent RL algorithms while focusing on social reinforcement learning, that can improve generalization, coordination between agents, and collaboration between human and AI agents. She received her Ph.D. from MIT where she focused on Affective Computing and other techniques for deep/reinforcement learning. She has also received multiple awards for her research works submitted to venues like ICML and NeurIPS She has interned at DeepMind, Google Brain, and is an OpenAIScholars mentor.
00:00 Introductions
01:25 Can you tell us a bit about what projects you are working on at Google currently? And what does the work routine look like as a Research Scientist?
06:25 You have worked as a researcher at many diverse backgrounds who are leading in the domain of machine learning: MIT, Google Brain, DeepMind - what are the key differences you have noticed while doing research in academia vs industry vs research lab?
10:00 About your paper, social influence as intrinsic motivation for multi-agents deep reinforcement learning, can you tell us more about how you are trying to leverage intrinsic rewards for better coordination?
12:00 Game Theory and Reinforcement Learning: discussion
16:00 What was the intuition behind that approach - did you resort to cognitive psychology to get this idea and later on the model it using standard DRL principles or something else?
20:00 Crackpot-y motivation behind the intuition of modeling social influence in MARL
24:00 What applications did you have in mind while working on that approach? What could be the potential domains you see people can use that approach?
25:35 Do you think generalization in RL is close enough to have an ImageNet moment?
28:35 Inspiration from social animals for better architectures - Yay/Nay?
30:20 How far are we in terms of using systems with DeepRL in day-to-day use? Or are there any such applications already in use?
34:40 Do you think these DRL can be made interpretable to some extent?
39:00 What really intrigued you to pursue a Ph.D. after your master's and not a job?
40:30 How did you go about deciding the topic for your Ph.D. thesis?
47:40 How do you typically go about segmenting a research topic into smaller segments, from the initial stage when it's more of an abstract and no connections to theory too much more implementable?
50:00 What are currently exploring and optimistic about?
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Learn more about what product management really is and the skills required for the role with Renee Yao, who leads global healthcare AI startups at @NVIDIA, managing 1000+ healthcare startups. Prior to that, she worked as a senior PM and product marketing manager for AI Systems at NVIDIA. She graduated from Haas School of Business at @UC Berkeley with a focus in Economics and IT.
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Cade Metz is a reporter at The New York Times where he frequently covers emerging areas of artificial intelligence, from self-driving cars to virtual reality and much more. Prior to that, he was also a senior staff writer at the WIRED magazine covering stories about big tech companies, bitcoins, and AI. He has a strong background in reporting and is also coming out with his own book called “Genius Makers” on March 16th.
Slate Star Codex, NYT: https://www.nytimes.com/2021/02/13/technology/slate-star-codex-rationalists.html
Book Genius Makers: https://cademetzauthor.com/order/
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Check out the full podcast here: https://youtu.be/t8YCzcWSUrg
Akshay is an Assistant Professor at @Stanford University in the Radiology department. He has a strong background in bioengineering with a bachelor's from @UC San Diego and Ph.D. from Stanford followed by a Postdoctoral experience in radiology at Stanford. We discuss more how machine learning is leveraged for faster medical image acquisition and post-processing methods.
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Akshay is an Assistant Professor at Stanford University in the Radiology department. He has a strong background in bioengineering with a bachelor's from UC San Diego and Ph.D. from Stanford followed by a Postdoctoral experience in radiology at Stanford. We discuss more how machine learning is leveraged for faster medical image acquisition and post-processing methods.
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Sharon is a Stanford Computer Science Ph.D. student advised by Andrew Ng and Michael Bernstein working on generative models. Popularly known for her Coursera course on building GANs, she talks more about the use of AI in medicine, what product management is about, some philosophical fun chat and interpretability, and the future of AI in healthcare.
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Learn more about what product management really is and the skills required for the role with Renee Yao, who leads global healthcare AI startups at @NIVIDIA, managing 1000+ healthcare startups. Prior to that, she worked as a senior PM and product marketing manager for AI Systems at NVIDIA. She graduated from Haas School of Business at @UCBerkley with a focus in Economics and IT.
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Azalia is a Research scientist at the Google Brain team, where she leads machine learning for systems moonshot projects. Her research interests include and not limited to exploring deep reinforcement learning for optimizing computer systems. She has a Ph.D. in Electrical and Computer Engineering from Rice University and has received many awards for her contributions including the MIT Technology Review 35 under 35.
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Sara Hooker on getting started with learning machine learning, best resources and learning to implement models from scratch.
Watch the full episode with Sara here: https://youtu.be/MHtbZls2uts
Sara is a research scholar at the @Google-Brain team working on building interpretable machine learning models for reliability and robustness. We talk about how she transitioned from economics to now pure research at the Brain team. We also talk in detail about what interpretability means, what are the state-of-art techniques and what are some of the most important things any machine learning researcher must know.
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Things that I learned apart from research, non-technical traits I did not know you would but I am glad now I did.
Full podcast with Geneviève here: https://youtu.be/AA1Co0XBcKg
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
How do medical professionals see ML fitting into medical research - Biomarker discovery? Confirming hypothesis with accuracy? A tool to manage much data?
Check out the full podcast here: https://youtu.be/qnOvXrkt7zE
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Dr. Lungren is the Co-Director of the Stanford Center for Artificial Intelligence in Medicine and Imaging, @Stanford AIMI and an Associate Professor at @Stanford Medical Center. His research interest is in the field of AI and deep learning in medical imaging, precision medicine, and predictive health outcomes.
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Check the full podcast with Debarghya Das: https://youtu.be/Vn79br3mKZM
Debarghya (Deedy), currently a Founding Engineer at a Stealth Startup is a computer science graduate from Cornell University. He has previously worked and interned at Google, Facebook and Coursera. In this podcast we talk about various computer science roles in industry: research, software engineering, product management, and doing a PhD or not.
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Debarghya (Deedy), currently a Founding Engineer at a Stealth Startup is a computer science graduate from Cornell University. He has previously worked and interned at Google, Facebook and Coursera. In this podcast we talk about various computer science roles in industry: research, software engineering, product management, and doing a PhD or not.
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Watch the full episode with Sara here: https://youtu.be/MHtbZls2uts
Sara is a research scholar at @Google-Brain team working on building interpretable machine learning models for reliability and robustness. We talk about how she transitioned from economics to now pure research at the Brain team. We also talk in detail about what interpretability means, what are the state-of-art techniques, and what are some of the most important things any machine learning researcher must know.
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Sharon is a Stanford Computer Science Ph.D. student advised by Andrew Ng and Michael Bernstein working on generative models. Popularly known for her Coursera course on building GANs, she talks more about the use of AI in medicine, what product management is about, some philosophical fun chat and interpretability, and the future of AI in healthcare.
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Sharon is a Stanford Computer Science Ph.D. student advised by Andrew Ng and Michael Bernstein working on generative models. Popularly known for her Coursera course on building GANs, she talks more about the use of AI in medicine, what product management is about, some philosophical fun chat, and interpretability, and the future of AI in healthcare.
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Sara is a research scholar at the Google-Brain team working on building interpretable machine learning models for reliability and robustness. We talk about how she transitioned from economics to now pure research at the Brain team. We also talk in detail about what interpretability means, what are the state-of-art techniques, and what are some of the most important things any machine learning researcher must know.
About the Host:
Jay is a Ph.D. student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Siddha is a self-driving architect at NVIDIA and she also guides teams at NASA as an AI domain expert . She was featured on the Forbes 30 under 30 list in 2019.
Previously, she developed deep learning models for resource-constrained edge devices at DeepVision. She earned her Master's degree from Carnegie Mellon University, and her work ranges from visual question answering to generative adversarial networks to gathering insights from CERN’s petabyte-scale data and has been published at top-tier conferences including CVPR and NeurIPS.
In this interview she shares her insights on:
1. How she got started with the domain ML and Computer Vision.
2. How graduate study and internships helped her gain a perspective of theoretical and practical knowledge in AI.
3. How she keeps herself updated to this every-day changing research domain.
4. What her book, she recently wrote, is about and who should read it.
5. What are the new trends to look for in AI in 2020 and forward.
6. General advice to any beginners in ML, some DO's and DON'T's
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Full podcast with Shreya available here: https://www.youtube.com/watch?v=LojomxFnjBY&t=439s
Shreya is currently a graduate student @Stanford and also working as an ML engineer @Viaduct.ai. She has previously interned at @Google -Brain and @Facebook . She talks about her experience as an applied ML engineer and making ML models work in the real-world.
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
About the Host:
Jay is a PhD student at Arizona State University, doing research on building Interpretable AI models for Medical Diagnosis.
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Barkha shares her experience and useful tips for landing good jobs and internships in the CS domain in the states, starting from being a Deputy Convener of Student Placement Cell in DAIICT, India to being an intern at Intuit and an SDE at LinkedIn.
Learn more about her: https://www.linkedin.com/in/barkhabhojak/
Our aim of these webinars is to connect you with the brightest minds in the field of CS/Machine Learning/Data Science so that you can learn how to break into the field and build an incredible career!
Hosted by:
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Siddha is a self-driving architect at NVIDIA and she also guides teams at NASA as an AI domain expert. She was featured on the Forbes 30 under 30 list in 2019.
Previously, she developed deep learning models for resource-constrained edge devices at DeepVision. She earned her Master's degree from Carnegie Mellon University, and her work ranges from visual question answering to generative adversarial networks to gathering insights from CERN’s petabyte-scale data and has been published at top-tier conferences including CVPR and NeurIPS.
Hosted by:
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Vidhan shares what intrigued him about Machine Learning as a student at CMU and how he built up his profile learning from experiences and exploring landing him a job in research at Microsoft.
Our aim of these webinars is to connect you with the brightest minds in the field of Machine Learning/Data Science so that you can learn how to break into the field and build an incredible career!
Hosted by:
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Full episode with Deedy available here: https://youtu.be/Vn79br3mKZM
Debarghya (Deedy) currently a Founding Engineer at a Stealth Startup is a computer science graduate from Cornell University. He has previously worked and interned at Google, Facebook and Coursera. In this podcast we talk about various computer science roles in industry: research, software engineering, product management, and doing a PhD or not.
Also check-out these talks on all available podcast platforms: https://jayshah.buzzsprout.com
Hosted by:
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Full episode with Shashank available here: https://youtu.be/jysvEYGggFE
About the Speaker:
Shashank works with several other Machine Learning Engineers at @Google on Computer Vision and Perception projects. He graduated from@Arizona State University in 2018 and has a great experience working in various domains of Machine Learning and Data Science.
Our aim of these webinars is to connect you with the brightest minds in the field of Machine Learning/Data Science so that you can learn how to break into the field and build an incredible career!
Hosted by:
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Ganesh works at MGH and BWH Center for Clinical Data Science, revolutionizing healthcare using Artificial Intelligence. He has experience working with Deep Learning algorithms, Data Visualizations and scalable distributed systems.
Our aim of these webinars is to connect you with the brightest minds in the field of Machine Learning/Data Science so that you can learn how to break into the field and build an incredible career!
Hosted by:
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
As a Machine Learning Instructor, Ajinkya talks about how building applications and complementing your engineering skills with creativity and interest is more important than merely focusing on the mathematics of algorithms. And that is what the new every day changing ML industry might demand!
Our aim of these webinars is to connect you with the brightest minds in the field of Machine Learning/Data Science so that you can learn how to break into the field and build an incredible career!
Hosted by:
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Jineet, a Data Scientist at Intuit and a graduate from Carnegie Mellon University shares some amazing insights about data science and machine learning probing his experiences.
LinkedIn: https://www.linkedin.com/in/jineetdoshi/
Our aim of these webinars is to connect you with the brightest minds in the field of Machine Learning/Data Science so that you can learn how to break into the field and build an incredible career!
Hosted by:
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Why being a Tech-God is not enough. Thoughts from Shraddha Patel, CMU Grad, and a Business Analytics Consultant at Servian, Australia.
Our aim of these webinars is to connect you with the brightest minds in the field of Machine Learning/Data Science so that you can learn how to break into the field and build an incredible career!
About the Speaker:
Shraddha is a recent graduate of CMU with a focus on Business Intelligence and Data Analytics. She has work experience as a Software Development Engineer and Machine Learning Engineer and shared with us some of the insights as she paved her way to being a consultant.
Hosted by:
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Shashank works with several other Machine Learning Engineers at Google on Computer Vision and Perception projects. He graduated from Arizona State University in 2018 and has great experience working in various domains of Machine Learning and Data Science.
Hosted by:
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
Vaibhavi Desai, Community Manager, Developer Relations Southeast Asia at Google. She designs programs on Machine Learning and Data Science at Google, working with several Machine Learning Engineers and nurturing communities around various other developer programs.
Hosted by:
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.
SHOW LESS
Interview with Devanshu Jain, Software Engineer at YouTube, on our first episode of Machine Learning for Beginners!
Our aim of these webinars is to connect you with the brightest minds in the field of ML/DS so that you can learn how to break into the field and build an incredible career!
Speaker:
Devanshu works on Home Page recommendations at YouTube and has held experience working in Uber and Amazon before.
Hosted by:
Jay Shah: https://www.linkedin.com/in/shahjay22/
You can reach out to https://www.public.asu.edu/~jgshah1/ for any queries.
Stay tuned for upcoming webinars!
Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.