Sotabase

Career

· Chief Technical Advisor, AI & Machine Learning, insitro2024–
· Professor, Department of Statistics and Department of Computer Science, Stanford University2021–2021
· Distinguished Engineer and Lead of Health AI, Apple2018–2021
· Amazon Professor of Machine Learning, University of Washington2016–2021
· Associate Professor, University of Washington2016–2020
· Assistant Professor, University of Washington2012–2016
· Assistant Professor, University of Pennsylvania2011–2012
· Postdoctoral Researcher, Duke University2009–2011
· Master of Engineering, Massachusetts Institute of Technology (MIT)2004–2005
· Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology (MIT)2004–2009
· Bachelor of Science in Electrical Engineering, Massachusetts Institute of Technology (MIT)2000–2004

Publications (195)

Stochastic Gradient Hamiltonian Monte Carlo
International Conference on Machine Learning · 2014
976
cited
A Complete Recipe for Stochastic Gradient MCMC
Neural Information Processing Systems · 2015
516
cited
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
Journal of machine learning research · 2020
454
cited
Granger Causality: A Review and Recent Advances
Annual Review of Statistics and Its Application · 2021
422
cited
A Sticky HDP-HMM With Application to Speaker Diarization
2009
398
cited
An HDP-HMM for systems with state persistence
International Conference on Machine Learning · 2008
328
cited
Neural Granger Causality
IEEE Transactions on Pattern Analysis and Machine Intelligence · 2021
310
cited
Bayesian Nonparametric Inference of Switching Dynamic Linear Models
IEEE Transactions on Signal Processing · 2010
257
cited
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
Neural Information Processing Systems · 2008
254
cited
Sparse graphs using exchangeable random measures
Journal of The Royal Statistical Society Series B-statistical Methodology · 2014
241
cited
A Bayesian Approach for Predicting the Popularity of Tweets
arXiv.org · 2013
175
cited
Sharing Features among Dynamical Systems with Beta Processes
Neural Information Processing Systems · 2009
161
cited
Bayesian nonparametric covariance regression
Journal of machine learning research · 2011
107
cited
Control variates for stochastic gradient MCMC
Statistics and computing · 2017
105
cited
Expectation-Maximization for Learning Determinantal Point Processes
Neural Information Processing Systems · 2014
103
cited
Learning the Parameters of Determinantal Point Process Kernels
International Conference on Machine Learning · 2014
102
cited
Neural Granger Causality for Nonlinear Time Series
2018
102
cited
JOINT MODELING OF MULTIPLE TIME SERIES VIA THE BETA PROCESS WITH APPLICATION TO MOTION CAPTURE SEGMENTATION
2013
100
cited
Stochastic variational inference for hidden Markov models
Neural Information Processing Systems · 2014
95
cited
The Sticky HDP-HMM: Bayesian Nonparametric Hidden Markov Models with Persistent States
2009
88
cited
Sotabase