
Anupam Datta
Anupam Datta is a Principal Research Scientist and Snowflake AI Research Lead at Snowflake. He joined Snowflake as part of the acquisition of TruEra where he served as Co-Founder, President, and Chief Scientist from 2019-2024. Datta was on the faculty at Carnegie Mellon University from 2007-2022, most recently as a tenured Professor of Electrical & Computer Engineering and Computer Science. Datta's current research focuses on Trustworthy AI, especially evaluation, optimization, and security for data and coding agents. Prior results include early work on Shapley Values & gradient-based explanations, fairness assessments, robustness of classical machine learning and deep learning models for natural language processing and computer vision. These research results have had a significant impact on products at TruEra and Snowflake.
Datta has published over 100 research papers, served as Chair of the National Academies Workshop on Assessing and Improving AI Trustworthiness, on the Steering Committee of of the ACM Conference on Fairness, Accountability, and Transparency, and the IEEE Computer Security Foundations Symposium, and as an Editor-in-Chief of Foundations and Trends in Privacy and Security. He received the 2018 David P. Casasent Outstanding Research Award from the CMU College of Engineering, a 2020 Young Alumni Achiever Award from IIT Kharagpur, a 2021 Google Faculty Research Award, and several awards for top papers at conferences. Datta obtained a B.Tech. from IIT Kharagpur, and Ph.D. and M.S. degrees from Stanford University in Computer Science, where he currently teaches a course on Trustworthy AI.
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NOV 04, 2025Gen AI
What’s Your Agent’s GPA? A Framework for Evaluating AI Agent Reliability

NOV 04, 2025Gen AI
Optimizing Query Execution in Cortex AISQL

JUN 03, 2025Gen AI
Inside Snowflake Intelligence: Five Pillars of Enterprise-Grade Agentic AI

FEB 04, 2025Gen AI
Eval-Guided Optimization of LLM Judges for the RAG Triad

JAN 31, 2025Gen AI
Benchmarking LLM-as-a-Judge for the RAG Triad Metrics

JAN 23, 2025Machine Learning
Machine Learning Models Require the Right Explanation Framework — and It’s Easy to Get Wrong
