Ke Alexander Wang

Hey, I’m Alex Wang!

I’m a third-year PhD candidate in Computer Science at Stanford University, working with Emily B. Fox on machine learning for time series in the context of personalized healthcare. I’m also affiliated with Stanford Data Science as a Stanford Data Science Scholar.

I’m thankful that I’ve had many supportive mentors in my life, including Stefano Ermon, Chris Ré, and Tatsunori Hashimoto at Stanford, Andrew Gordon Wilson and Jacob R. Gardner at Cornell, and numerous talented collaborators.

I graduated with a M. Sci. in Computer Science at Cornell University with a minor in applied math and a B.A. in Math and Computer Science also at Cornell Universtiy with a minor in Physics.

Publications

Is Importance Weighting Incompatible with Interpolating Classifiers? [paper] *Ke Alexander Wang, *Niladri S. Chatterji, Saminul Haque, Tatsunori Hashimoto ICLR 2022 NeurIPS 2021 DistShift Workshop Spotlight Presentation

GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics [paper] Ke Alexander Wang, Danielle C. Maddix, Bernie Wang NeurIPS 2021 ICBINB Workshop Spotlight Presentation

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes [paper] *Sanyam Kapoor, *Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson ICML 2021 Long Oral Presentation

Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information [paper] [blog] Willie Neiswanger, Ke Alexander Wang, Stefano Ermon ICML 2021

Simplifying Lagrangian and Hamiltonian Neural Networks via Explicit Constraints [paper] *Marc Finzi, *Ke Alexander Wang, Andrew Gordon Wilson NeurIPS 2020 Spotlight Presentation

DC2: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering [paper] *Ke Alexander Wang, *Xinran Bian, Pan Liu, Donghui Yan IEEE Big Data 2019

Exact Gaussian Processes on a Million Data Points [paper] [code] [poster] *Ke Alexander Wang, *Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson NeurIPS 2019

Model-based Policy Gradients with Entropy Exploration through Sampling [paper] [poster] Samuel Stanton, Ke Alexander Wang, Andrew Gordon Wilson ICML 2019 Generative Modeling and Model-Based Reasoning for Robotics and AI Workshop