Steven Holtzen

PhD. Student at U.C.L.A.

About

Welcome to my academic homepage. I am a PhD. candidate at U.C.L.A., co-advised by Guy Van den Broeck and Todd Millstein. My research focuses on the intersection between machine learning (tractable probabilistic modeling, statistical relational learning, graphical models) and programming languages. I am particularly interested in probabilistic programming languages, a probabilistic modeling framework that specifies probability models using programming languages.

I would love to hear from you:

Conference Publications

[OOPSLA20] Steven Holtzen, Guy Van den Broeck, and Todd Millstein. Scaling Exact Inference for Discrete Probabilistic Programs. In Proc. ACM Program. Lang. 4 (OOPSLA), 2020.
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[UAI20] Honghua Zhang, Steven Holtzen, and Guy Van den Broeck. On the Relationship Between Probabilistic Circuits and Determinantal Point Processes. In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), 2020.
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[UAI19] Steven Holtzen, Todd Millstein, and Guy Van den Broeck. Generating and Sampling Orbits for Lifted Probabilistic Inference. In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), 2019. Invited Oral Presentation (35/450).
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[ICML18] Steven Holtzen, Guy Van den Broeck, and Todd Millstein. Sound Abstraction and Decomposition of Probabilistic Programs. In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018.
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[UAI17] Steven Holtzen, Todd Millstein, and Guy Van den Broeck. Probabilistic Program Abstractions. In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 2017.
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[IROS16] Steven Holtzen*, Yibiao Zhao*, Tao Gao, Josh Tenenbaum, and Song-Chun Zhu. Inferring Human Intent from Video by Sampling Hierarchical Plans. In IEEE International Conference on Intelligent Robots and Systems (IROS), 2016.
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Talks & Presentations

  1. LAFI 2019 for our work on exact symbolic inference for probabilistic programs.
  2. POPL PPS 2018 for our work on probabilistic program abstractions for probabilistic program inference.
  3. StarAI 2017 for our UAI work on probabilistic program abstractions.

Teaching

Professional Service

Bio

Education

  • Sept. 2017 – Present.
    PhD., Computer Science. UCLA.
  • Sept. 2015 – Jul. 2017.
    M.S., Computer Science. UCLA.
  • Sept. 2011 – Jun. 2015.
    B.S., Computer Science. UCLA.

Employment

  • July 2015 – Present.
    Sandia National Laboratories. Member of technical staff.
  • June 2014 – Sept. 2014
    Palantir Technologies. Forward Deployed Engineering Intern on the Healthcare Team (Helix).
  • Apr. 2013 – Sept. 2013
    Symantec Corporation. Security technology and response team intern.

Awards & Honors

Updated October, 2020. © Steven Holtzen.