Monday, February 6, 2023, 11am

Anomaly detection (AD) algorithms are widely used for data-driven decision support in domains where quantifying risk is critical, such as identifying fraudulent healthcare providers in public health insurance, consumer lending, and detecting aberrant patterns in human electroencephalography (EEG) records. However, AD in decision support is challenging due to the multitude of data modalities (e.g. time-series, or structural data) and data scale, unavailability of ground truth labels for learning and evaluation, and difficulty in yielding human interpretable results for domain-specific problems. This thesis proposes to address the challenges and build intelligent detection systems with the following desirable properties: unsupervised, explainable, scalable, and equitable. Throughout,  we propose novel AD algorithms that enable better decision support by addressing domain-specific key challenges such as including domain or expert knowledge, mitigating bias that may adversely affect minority groups, and handling aberrant behavior involving a group of actors. We present applications in public healthcare fraud, and health monitoring in critical care.

Thesis Committee:
Leman Akoglu (Co-chair)
Christos Faloutsos (Co-chair)
Daniel Nagin
David Choi
Jetson Leder-Luis (Boston University)

Additional Information

Zoom Participation. See announcement.

Event Type: Thesis Proposals
Room Number: Virtual Presentation - ET
Building: Remote Access - Zoom
Speaker's Name: SHUBHRANSHU SHEKHAR
Speaker Website: shubhranshu-shekhar.github.io
Speaker's Professional Title: Ph.D. Student, Joint Ph.D. Program in Machine Learning and Public Policy, Carnegie Mellon University
Talk Title: Data-driven Decisions — An Anomaly Detection Perspective
For More Information: stidle@andrew.cmu.edu
Affiliations: Heinz College, Machine Learning Department (MLD)
Organization(s): SCS