Feras Saad فِرَاسْ سَعَد

I am an Assistant Professor in the Computer Science Department at Carnegie Mellon, affiliated with the Principles of Programming and Artificial Intelligence groups.

My research explores the design and implementation of principled probabilistic inference systems that operate with a high degree of automation, accuracy, and scale. My interests span multiple levels of the computation stack, including:

statistical inference and data science, e.g., Bayesian methods, nonparametrics;
probabilistic programming languages, e.g., semantics, compilers, program synthesis;
mathematical foundations, e.g., computability and complexity of stochastic simulation.

My group also maintains open-source software libraries to help practitioners use these techniques in their own problems. See publications, research, software for an overview.

Background

I received the PhD in Computer Science, and MEng/SB degrees in Electrical Engineering and Computer Science, from MIT. My theses on probabilistic programming were recognized with the Sprowls PhD Thesis Award in AI+Decision Making and Johnson MEng Thesis Award in Computer Science. Prior to CMU, I was a Visiting Research Scientist at Google.

Contact

Email Address:	fsaad@cmu.edu
Telephone:	(412) 268-1805
Admin. Assistant:	Oliver Moss

Office:	Gates Hillman Complex 9225
Address:	Carnegie Mellon University
	5000 Forbes Avenue
	Pittsburgh, PA 15213

Research

My work combines ideas from probability and programming languages to build scalable systems for sound probabilistic modeling and inference. Current research themes include:

Probabilistic Programming. Researchers in diverse areas (e.g., econometrics, cognitive science, bioinformatics) routinely develop computational models of uncertain data and perform inference from observed data to learn about the underlying data-generating process. Probabilistic programs, which produce outputs by making random choices, are a unifying and uniquely expressive approach to modeling and inference over these processes. These languages aim to rigorously formalize the semantics of probabilistic models, automate the process of discovering models for complex data, and scale-up the hard aspects of probabilistic inference in high-dimensional settings. [PLDI-24] [PLDI-21] [POPL-19] [PLDI-19] [AISTATS-17] [NIPS-16]

Automated Probabilistic Model Discovery. A grand challenge in AI is automating the process of discovering accurate and interpretable probabilistic models for reasoning about data. I am interested in studying this problem through the lens of probabilistic program synthesis, formalized as Bayesian structure learning over rich generative model families. [ICML-23] [UAI-21] [POPL-19] [AISTATS-18]

Statistical Estimators & Tests. As probabilistic programs become a more widespread representation, new statistical techniques are needed for inferring their properties through the black-box computational inferences they expose. While static or analytical analysis is sometimes possible, a more flexible approach is to infer program properties using simulation-based statistical analyses of sampled execution traces. [PLDI-24] [AISTATS-22] [AISTATS-19]

Random Variate Generation. Generating random variables is a fundamental operation in scientific computation. This work explores (1) computational and information-theoretic limits of random sampling algorithms; and (2) developing new samplers that are theoretically optimal or near-optimal (in entropy consumption and/or statistical sampling error) while also extremely efficient in practice. [POPL-20] [AISTATS-20].

Software + Applications. A central motivation of my work is to develop performant, freely available software systems that make probabilistic inference more broadly accessible to everyday users while also helping domain-experts solve meaningful problems in the sciences, engineering, and public interest. [eLife-2019]

Feras Saad فِرَاسْ سَعَد

Background

Contact

Research

Probabilistic Computing Systems Lab

Publications

Software

Talks

Press