Shubhranshu Shekhar

Shubhranshu Shekhar is an Assistant Professor of Data Science in the Brandeis International Business School at Brandeis University. He obtained his Ph.D. in Machine Learning and Public Policy from Carnegie Mellon University, along with an MS in Machine Learning Research from CMU and an MS in Computer Science from IIT Madras.

Educational Background:

• Ph.D. in Machine Learning and Public Policy, Carnegie Mellon University
• MS in Machine Learning Research, Carnegie Mellon University
• MS in Computer Science, IIT Madras

Dr. Shekhar's research focuses on unsupervised and explainable machine learning methods, particularly in high-stakes domains like healthcare and finance. His work aims to build intelligent systems that are not only accurate but also transparent and equitable, enabling better human decision-making. Recent research has concentrated on fraud detection in healthcare, macroeconomic forecasting using large language models, and fairness-aware outlier detection systems. His approach often combines theoretical rigor with practical applications, addressing real-world challenges where machine learning can have significant impact.

Dr. Shekhar's publication record shows a consistent focus on anomaly detection, network analysis, and explainable AI, with recent work extending into healthcare applications and economic forecasting. His research bridges computer science, public policy, and domain-specific applications, demonstrating interdisciplinary expertise.

Scientific Awards:

• George Duncan Award for PhD 2nd Paper at Heinz College
• Best Student Machine Learning Paper Runner-up Award

Dr. Shekhar has extensive teaching experience, having served as an Instructor for "Machine Learning for Problem Solving" at Carnegie Mellon University's Heinz College and as a Teaching Assistant for various machine learning and statistics courses at CMU and IIT Madras. His teaching portfolio includes some of the most popular graduate courses in machine learning and statistics at these institutions.

Dr. Shekhar leads research in the intersection of machine learning and high-stakes decision making, with ongoing projects in healthcare analytics, financial fraud detection, and equitable AI systems. His work often involves collaboration across disciplines, reflecting the complex nature of real-world problems that require both technical and policy-oriented solutions.