About

Professor David Chan is the Mark and Stephanie Robinson Chancellor’s Chair and faculty director for the Robinson Life Science, Business, and Entrepreneurship Program at UC Berkeley Haas. His research draws on labor and organizational economics to study how information and workforce dynamics influence decision-making and outcomes in healthcare. He is an investigator at the Department of Veterans Affairs, co-director of the VA Center for Policy Evaluation, and a research associate at the National Bureau of Economic Research. His work has been recognized with several awards, including the NIH Director's Early Independence Award, the 2023 American Society of Health Economists (ASHEcon) Medal, and the 2024 NIHCM Research Award. Prior to his current role, he was an associate professor of Health Policy at Stanford University, with academic credentials including a medical degree from UCLA, a PhD in economics from MIT, and master's degrees from the London School of Economics and Oxford University. His training includes internal medicine at Brigham and Women’s Hospital and an instructor position at Harvard Medical School. His research focuses on health economics, labor economics, and organizational economics, with significant contributions to understanding healthcare decision-making, team dynamics in medical settings, and policy evaluation.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• Human–computer interaction
• Data science
• Speech recognition
• Psychology
• Cognitive science
• Epistemology

Selected publications

• Multi-Modal Pre-Training for Automated Speech Recognition

  ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · 2022 · 13 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Speech recognition

  Traditionally, research in automated speech recognition has focused on local-first encoding of audio representations to predict the spoken phonemes in an utterance. Unfortunately, approaches relying on such hyper-local information tend to be vulnerable to both local-level corruption (such as audio-frame drops, or loud noises) and global-level noise (such as environmental noise, or background noise) that has not been seen during training. In this work, we introduce a novel approach that leverages a self-supervised learning technique based on masked language modeling to compute a global, multi-modal encoding of the environment in which the utterance occurs. We then use a new deep-fusion framework to integrate this global context into a traditional ASR method, and demonstrate that the resulting method can outperform baseline methods by up to 7% on Librispeech; gains on internal datasets range from 6% (on larger models) to 45% (on smaller models).

  DOI

• Exploring Exploration: Comparing Children with RL Agents in Unified Environments

  arXiv (Cornell University) · 2020 · 7 citations

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

  Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn. In turn, this early learning supports more robust generalization and intelligent behavior later in life. While much work has gone into developing methods for exploration in machine learning, artificial agents have not yet reached the high standard set by their human counterparts. In this work we propose using DeepMind Lab (Beattie et al., 2016) as a platform to directly compare child and agent behaviors and to develop new exploration techniques. We outline two ongoing experiments to demonstrate the effectiveness of a direct comparison, and outline a number of open research questions that we believe can be tested using this methodology.

Frequent coauthors

• John Canny

  13 shared

• Shalini Ghosh

  10 shared

• Björn Hoffmeister

  8 shared

• Ariya Rastrow

  Amazon (United States)

  6 shared

• Hitesh Tulsiani

  4 shared

• Sudheendra Vijayanarasimhan

  4 shared

• Roshan Rao

  4 shared

• Trevor Darrell

  4 shared

Education

• Ph.D., Business Administration

  University of California, Berkeley

  1999

• M.D., Medicine

  University of California, San Francisco

  1994

• B.A., Economics

  University of California, Berkeley

  1990

Awards & honors

• NIH Director's Early Independence Award (2014)
• American Society of Health Economists (ASHEcon) Medal (2023)
• National Institute of Health Care Management (NIHCM) Researc…
• VA Health Systems Research (HSR) Best Paper Award (2024)
• Neil R. Powe Award, Johns Hopkins University School of Medic…

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• John F. Hartwigh-180
• Rasmus Nielsenh-180