Feras Saad فِرَاسْ سَعَد

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

My research explores the design and implementation of scalable probabilistic computing systems, theoretical analysis of their properties, and practical applications to challenging problems in modeling and inference. This work integrates ideas from programming languages, probability, and computation—which together give us the building blocks for engineering principled probabilistic reasoning systems with a high degree of automation, accuracy, and scale.

I explore diverse applications of these ideas in areas such as automated statistical model discovery, spatiotemporal data science, and random variate generation. I also maintain open-source software libraries to help practitioners use these methods in their own areas. See research and software for an overview.

Background

I received the PhD in Computer Science and the 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 Opportunities

I am actively recruiting students and postdocs at all levels.

  • Prospective Students: If you are interested in working with me as a graduate student, please apply to the CMU CS PhD program and mention my name in your application.
  • Current CMU Students: If you are already a graduate, masters, or undergraduate student at CMU, please send me an email and we can find a time to chat.
  • Postdocs: Please send me an email with your CV, two representative papers, and a description of your research background and interests.