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About

Edric (Ed) Tam is a statistician and AI researcher currently working as postdoctoral fellow at Stanford University (Advisor: Barbara Engelhardt). He received his PhD in Statistical Science from Duke University (Advisor: David Dunson). His research lies at the intersection of AI, Bayesian Statistics and Biomedical Applications. His postdoctoral research has been supported by a Croucher Foundation Postdoctoral Fellowship as well as a Warren Alpert Fellowship in AI and Computational Biology.

Research Interests

Bayesian inference, computation and decision-making aided by neural networks and synthetic data, motivated by biomedicine.

  • Bayes: Robust Bayes, Generalized Bayes, Synthetic and Simulated Data (e.g. from AI systems and scientific simulators), Bayesian Computation, (Markov Chain) Monte Carlo
  • AI: Embeddings, Deep Generative Models, AI Evaluation
  • Biomedical Applications: Medical Imaging, Pharmacology, Neuroscience, Genomics etc

Highlights

  • Holds degrees in engineering, computer science and statistics from Duke, Chicago, Yale and Johns Hopkins
  • Research experience at Google (Search Team), Meta, Amazon, Apple (Siri NLP) and Pinterest Labs
  • Taught/TA’ed 10+ courses; research mentor to multiple undergraduate and MS students; teaching and mentoring recognized by awards three years in a row

Selected Recognitions

  • Croucher Foundation Postdoctoral Fellowship (2025-2027)
  • Stanford Warren Alpert Fellowship in AI and Computational Biology (2024-2025)
  • Stanford Emerson Consequential Scholar (2026)
  • IMS New Researchers Award (2025)
  • Best Poster Award, Bayesian Nonparametrics (BNP) Conference (2025)
  • Assistant Program Chair, NeurIPS (2025)
  • Duke Statistics Outstanding Mentor of Undergraduate Researchers (2024)
  • Duke Statistics Teaching Assistant of the Year, Honorable Mention (2022, 2023)

Personal

Contact

Email: edrictam@stanford.edu

Last updated: 2026-10-12