David Eriksson

Research Scientist - Core Data Science

I do research in machine learning with a focus on scaling Bayesian optimization and Gaussian processes to complex high-dimensional problems. I was previously a Sr. Research Scientist at Uber AI Labs and before that I received my Ph.D. in Applied Mathematics from Cornell University.


Bayesian optimization, AutoML, Gaussian processes, scientific computing

Latest Publications

UAI - July 28, 2021

A Nonmyopic Approach to Cost-Constrained Bayesian Optimization

Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias Seeger

UAI - July 23, 2021

High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces

David Eriksson, Martin Jankowiak

ICML - July 18, 2021

Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization

David Eriksson, Pierce I-Jen Chuang, Samuel Daulton, Peng Xia, Akshat Shrivastava, Arun Babu, Shicong Zhao, Ahmed Aly, Ganesh Venkatesh, Maximilian Balandat

NeurIPS - July 16, 2021

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization

Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner

AISTATS - July 9, 2021

Scalable Constrained Bayesian Optimization

David Eriksson, Matthias Poloczek