Willie Neiswanger

Machine learning at Stanford Computer Science SAIL / StatsML

I am a postdoc in computer science at Stanford University, working with Stefano Ermon and affiliated with the StatsML Group, Stanford AI Lab, and SLAC.

Research: I develop methods for distributed inference and probabilistic programming to help scale and automate machine learning. My interests include computer vision, computational biology, materials science, and analysis of text and network data. I also work on sequential decision making under uncertainty, which I apply to problems in science and engineering.

Education: I completed my PhD in Machine Learning at Carnegie Mellon University, where I was advised by Eric Xing and collaborated with Jeff Schneider and Barnabas Poczos.

Previously, I studied at Columbia University, where I worked with the Wiggins Lab and Frank Wood.


  • Mar 16, 2021 New paper on uncertainty quantification with martingales for GPs in ALT 2021 (+ code).
  • Mar 9, 2021 New paper on active classification for catalyst discovery in the Journal of Chemical Physics.
  • Jan 12, 2021 New paper on interactive weak supervision in ICLR 2021 (+ code).
  • Dec 22, 2020 Released Uncertainty Toolbox, for predictive UQ, calibration, metrics, and visualization.
  • Dec 2, 2020 New paper on BANANAS for neural architecture search in AAAI 2021 (+ code).
  • Sep 25, 2020 New paper on encodings for neural architecture search in NeurIPS 2020 (spotlight).
  • July 10, 2020 New paper on understanding the prevalence of SARS-CoV-2 on medRxiv (+ MLD blog).
  • June 1, 2020 I co-organized the Real World Experiment Design and Active Learning Workshop at ICML 2020.
  • Mar 6, 2020 New paper on uncertainty quantification for materials property predictions in MLST.
  • Mar 5, 2020 New paper on Dragonfly, a system for scalable and robust Bayesian optimization in JMLR.
  • Jan 7, 2020 New paper on molecular optimization and synthesis route design in AISTATS 2020.
  • Dec 13, 2019 I co-organized the Learning with Rich Experience (LIRE) Workshop at NeurIPS 2019.
  • Sep 3, 2019 New paper on contextual optimization for nuclear fusion applications in NeurIPS 2019.
  • Aug 15, 2019 I defended my PhD thesis and graduated 🎉.
  • Aug 1, 2019 New paper on T cell synapse propensity in the Journal of Immunology.
  • Apr 21, 2019 New paper on experimental design, optimization, active learning, hybrid tasks, in ICML 2019.



A full list of my publications can be found here.