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: [ CV ] [ Google Scholar ] [ Research Statement ] [ Teaching Statement ]

Conference Publications

[ASPLOS21]
 Yipeng Huang, Steven Holtzen, Todd Millstein, Guy Van den Broeck, and Margaret R. Martonosi. Logical Abstractions for Noisy Variational Quantum Algorithm Simulation. In Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2021.
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[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.
ACM SIGPLAN Distinguished Paper Award.
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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. 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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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

  • (2020) ACM SIGPLAN Distinguished Paper Award for Scaling Exact Inference for Discrete Probabilistic Programs!
  • (2020) UCLA Dissertation Year Fellowship
  • (2018 – 2019) UCLA Computer Science Outstanding Teaching Assistant Honorable Mention
  • (2017) UCLA Computer Science Oustanding Master's Student Award.
  • (2015) Recipient of NPSC Fellowship, fully funded Master's degree.
  • Officer for Eta Kappa Nu UCLA (Webmaster, Mentorship Chair)
  • Member of Upsilon Pi Epsilon UCLA
Updated October, 2020. © Steven Holtzen.