Erik M Lindgren

I am a graduate student at the University of Texas at Austin in the department of Electrical and Computer Engineering, where I am 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.

In 2018, I interned at Google Research, where I worked on neural networks for recommender systems. In 2017, I interned at Google, where I worked on machine learning with the Ground Truth team. In 2014, I interned at Lattice Automation, where I worked on algorithms for synthetic biology.

You can contact me at


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
[arxiv] [code] [video] [poster]
(Contributed talk in NeurIPS DISCML Workshop, 2017)

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
[journal link]