Justin Cosentino

📍 San Francisco
✉️ name.first at name.last dot io

about

I’m currently a research engineer on the Google Health AI Genomics team, working to better understand the genetic basis of disease. Recently, I graduated with a Master’s in Computer Science from Tsinghua University. I was supervised by Professor Jun Zhu in the Tsinghua Statistical Artificial Intelligence and Learning Group.

Previously, I was a research intern in Uber’s Advanced Technologies Group under the supervision of Professor Raquel Urtasun, and an intern with the Google Brain Genomics team. Before Tsinghua, I was a Senior Software Engineer working on Search at Salesforce. I studied Computer Science at Swarthmore College.

I like drinking too much coffee and playing (subpar) blitz and bullet chess. I also enjoy downhill skiing, distance running, and lacrosse.

research interests

papers

Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology
Use machine learning to generate more accurate phenotypes, leading to the discovery of novel loci.
{Babak Alipanahi, Farhad Hormozdiari, Babak Behsaz, Justin Cosentino, Zachary R. McCaw}, Emanuel Schorsch, D. Sculley, Elizabeth H. Dorfman, Paul J. Foster, Lily H. Peng, Sonia Phene, Naama Hammel, Andrew Carroll, and {Anthony P. Khawaja, Cory Y. McLean}.
American Journal of Human Genetics (AJHG), 2021.
[ paper ] [ code ] [ bibtex ] 

Generative Well-intentioned Networks
A novel framework for leveraging uncertainty and rejection-based classifiers.
Justin Cosentino and Jun Zhu.
Neural Information Processing Systems (NeurIPS), 2019.
[ paper ] [ poster ] [ slides ] [ bibtex ] 

The Search for Sparse, Robust Neural Networks
Showing that winning Lottery Tickets account for the robustness of a network.
{Justin Cosentino, Federico Zaiter}, and {Dan Pei, Jun Zhu}.
Safety and Robustness in Decision Making Workshop @ Neural Information Processing Systems (NeurIPS), 2019.
[ paper ] [ poster ] [ code ] [ bibtex ] 

Last updated in Sep, 2021.