Sam Stites [AA9F1] #

[meta] Created:

My name is Sam Stites (he/him). I'm an Assistant Professor at Roosevelt University, studying Bayesian inference and the semantics of probabilistic programming languages (PPLs). Before joining Roosevelt, I was at Northeastern's PRL (2022-2026) and was part of the data@NU lab (2019-2022) researching deep probabilistic programming. Prior to my doctoral studies I was in industry for a 7-year stretch.

Contact: The best way to contact me is by email. Apply ROT13 to the following: [email protected]. I'm also reachable as .stites on discord. Feel free to reach out if want to talk about stenography, racket, agda, or grad school.

Code #
  • (in progress) forester.el – how this site is built! It is very "in the works"
  • (in progress) Quick-checking MCMC algorithms using exact inference.
  • multippl, a rust compiler and formalization that prototypes a multi-language approaches to probabilistic inference.
  • Inference combinators, reborn in coix, is actively developed by my equally-contributing first author Heiko Zimmerman
  • redirect-to-abstract (github)
  • Hasktorch is now in long-term maintenance mode with Junji Hashimoto
  • reinforce reinforcement learning in Haskell.
  • CSSR (v2). Causal State Splitting Reconstruction of recursive hidden Markov models. I coded this up the successor to CSSR with Cosma Shalizi before starting my Ph.D. studies. It is currently in an unpublished state but if this work is relevant to you, please reach out to Cosma and cc me.
Teaching #
Roosevelt University #
Northeastern University #
Publication and Talks #
Ongoing work: #

Sam Stites, Steven Holtzen. Quick-checking Markov Chain Monte Carlo with Probabilistic Circuits.

Lisa Oakley, Sam Stites, Cameron Moy, Steven Holtzen, Alina Oprea, Marco Gaboardi. A Bayesian Approach to Membership Inference.

Peer-reviewed work: #

Sam Stites, John M. Li, Steven Holtzen. Multi-Language Probabilistic Programming. Proceedings of the ACM on Programming Languages, Volume 9, Issue OOPSLA1. Article No.: 124, Pages 1239 - 1266. (doi, pdf, pdf-full, code)

Sam Stites, Steven Holtzen. A Multi-Language Approach to Probabilistic Program Inference. The Languages for Inference (LAFI) Workshop at POPL 2024 (abs, talk).

Heiko Zimmermann, Hao Wu, Babak Esmaeili, Sam Stites, Jan-Willem van de Meent. Nested Variational Inference. Third Symposium on Advances in Approximate Bayesian Inference. 2021 (openreview, AABI2021)

Sam Stites, Heiko Zimmermann, Hao Wu, Eli Sennesh, Jan-Willem van de Meent. Learning Proposals for Probabilistic Programs with Inference Combinators. Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1056-1066, 2021. (abstract, pdf-full, code)

Sam Stites, Austin Huang. Hasktorch: A Comprehensive Haskell Library for Differentiable Functional Programming. Poster, Pytorch DevCon, 2018. (poster)

Sam Stites, Austin Huang. Hasktorch: A Comprehensive Haskell Library for Differentiable Functional Programming. The Numerical Programming in Functional Languages workshop (NPFL) at the International Conference of Functional Programming, 2018 (NPFL, talk)

Service #
  • SIGPLAN-M Mentor (2023-)
  • OOPSLA1 2024 Sub-Reviewer
  • PLDI 2023 Student Volunteer
  • AISTATS 2022 Reviewer
  • Northeastern 2022 Faculty Admissions Volunteer
  • Northeastern 2021 Ph.D. Review Committee Volunteer
  • Google Summer of Code Mentor (2019)