Steve Holtzen
(he/him/his)
Assistant Professor
Research interests
- Artificial intelligence
- Programming languages
- Formal methods
Education
- PhD in Computer Science, UCLA
- MS in Computer Science, UCLA
- BS in Computer Science, UCLA
Biography
Steve Holtzen is an assistant professor in the Khoury College of Computer Sciences at Northeastern University, based in Boston.
Holtzen's research — which lies at the intersection of artificial intelligence, machine learning, and programming languages — focuses on systems of probabilistic modeling and reasoning. He designs systems that make probabilistic modeling fast, accessible, and useful for solving everyday reasoning tasks. In doing so, Holtzen tackles automated reasoning, probabilistic verification, probabilistic inference, tractable probabilistic modeling, and probabilistic programming languages. He teaches courses in artificial intelligence, programming languages, and machine learning, and is affiliated with the Programming Research Laboratory.
Before joining Khoury College in 2021, Holtzen earned his doctorate in computer science from UCLA, where he worked as a research assistant. At the same time, he served on the technical staff in the cyber data analytics department at Sandia National Laboratories.
Holtzen was named Outstanding Graduating PhD Student by the UCLA Computer Science Department and won the ACM SIGPLAN Distinguished Paper Award at OOPSLA 2020. He has published at UAI, ICML, CAV, and ASPLOS.
Recent publications
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A Nominal Approach to Probabilistic Separation Logic
Citation: John M. Li, Jon Aytac, Philip Johnson-Freyd, Amal Ahmed , Steven Holtzen. (2024). A Nominal Approach to Probabilistic Separation Logic LICS, 55:1-55:14. https://doi.org/10.1145/3661814.3662135 -
Ahead-of-time Compilation for Diverse Samplers of Constrained Design Spaces
Citation: Abdelrahman Madkour, Ross Mawhorter, Stacy Marsella, Adam M. Smith , Steven Holtzen. (2024). Ahead-of-time Compilation for Diverse Samplers of Constrained Design Spaces FDG, 54. https://doi.org/10.1145/3649921.3656986 -
Probabilistic Logic Programming Semantics For Procedural Content Generation
Citation: Abdelrahman Madkour, Chris Martens , Steven Holtzen, Casper Harteveld, Stacy Marsella. (2023). Probabilistic Logic Programming Semantics For Procedural Content Generation AIIDE, 295-305. https://doi.org/10.1609/aiide.v19i1.27525 -
Scaling integer arithmetic in probabilistic programs
Citation: William X. Cao, Poorva Garg, Ryan Tjoa, Steven Holtzen, Todd D. Millstein, Guy Van den Broeck. (2023). Scaling integer arithmetic in probabilistic programs UAI, 260-270. https://proceedings.mlr.press/v216/cao23b.html -
Lilac: a Modal Separation Logic for Conditional Probability
Citation: John M. Li, Amal Ahmed , Steven Holtzen. (2023). Lilac: a Modal Separation Logic for Conditional Probability CoRR, abs/2304.01339. https://doi.org/10.48550/arXiv.2304.01339 -
Model Checking Finite-Horizon Markov Chains with Probabilistic Inference
Citation: Model Checking Finite-Horizon Markov Chains with Probabilistic Inference. Steven Holtzen, Sebastian Junges, Marcell Vazquez-Chanlatte, Todd Millstein, Sanjit A. Seshia, and Guy Van den Broeck. In International Conference on Computer-Aided Verification (CAV), 2021. -
Logical Abstractions for Noisy Variational Quantum Algorithm Simulation
Citation: Yipeng Huang , Steven Holtzen, Todd D. Millstein, Guy Van den Broeck, Margaret Martonosi. (2021). Logical Abstractions for Noisy Variational Quantum Algorithm Simulation CoRR, abs/2103.17226. https://arxiv.org/abs/2103.17226 -
Scaling Exact Inference for Discrete Probabilistic Programs
Citation: Steven Holtzen, Guy Van den Broeck, and Todd Millstein. 2020. Scaling exact inference for discrete probabilistic programs. Proc. ACM Program. Lang. 4, OOPSLA, Article 140 (November 2020), 31 pages. DOI:https://doi.org/10.1145/3428208 -
Generating and Sampling Orbits for Lifted Probabilistic Inference
Citation: Generating and Sampling Orbits for Lifted Probabilistic Inference Steven Holtzen, Todd Millstein, Guy Van den Broeck Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, PMLR 115:985-994, 2020. -
Sound Abstraction and Decomposition of Probabilistic Programs
Citation: Sound Abstraction and Decomposition of Probabilistic Programs. Steven Holtzen, Guy Van den Broeck, and Todd Millstein. In International Conference on Machine Learning (ICML), 2018.
