Erik M Lindgren

I am a Software Engineer at Databricks in New York. Before that I worked at Google Research. I recieved my PhD from the University of Texas at Austin in the department of Electrical and Computer Engineering, where I was advised by Prof. Alex Dimakis. I am interested in machine learning, combinatorial optimization, and information theory. I received my bachelor's degree from Boston University.


Efficient Training of Retrieval Models Using Negative Cache
E. M. Lindgren, S. Reddi, R. Guo, S. Kumar
Neural Information Processing Systems, 2021

Composing Normalizing Flows for Inverse Problems
J. Whang, E. M. Lindgren, A. G. Dimakis
International Conference on Machine Learning, 2021
+ Best Paper Award at UAI 2021 Workshop on Tractable Probabilistic Modeling

Accelerating Large-Scale Inference with Anisotropic Vector Quantization
R. Guo, P. Sun, E. Lindgren, Q. Geng, D. Simcha, F. Chern, S. Kumar
International Conference on Machine Learning, 2020
[paper] [code]

On Robust Learning of Ising Models
E. M. Lindgren, V. Shah, Y. Shen, A. G. Dimakis, A. Klivans
NeurIPS Workshop on Relational Representation Learning, 2018

Experimental Design for Cost-Aware Learning of Causal Graphs
E. M. Lindgren, M. Kocaoglu, A. G. Dimakis, S. Vishwanath
Neural Information Processing Systems, 2018
[paper] [code] [video] [poster]

Exact MAP Inference by Avoiding Fractional Vertices
E. M. Lindgren, A. G. Dimakis, A. Klivans
International Conference on Machine Learning, 2017

Leveraging Sparsity for Efficient Submodular Data Summarization
E. M. Lindgren, S. Wu, A. G. Dimakis
Neural Information Processing Systems, 2016
[paper] [video]

A Rule-Based Design Specification Language for Synthetic Biology
E. Oberortner, S. Bhatia, E. M. Lindgren, D. Densmore
ACM Journal on Emerging Technologies in Computing Systems, 2014