PeopleofSuperintelligence

Researcher

Feras A. Saad

Also known as Feras Saad

Feras Saad is a computer scientist at Carnegie Mellon University who develops probabilistic programming systems for AI and data analysis. He earned electrical engineering and computer science degrees at MIT by 2016 and completed his computer science PhD there in 2022 under Vikash Mansinghka. His work includes Gen (2019), the Fast Loaded Dice Roller (2020), and SPPL (2021), which answers questions about probabilistic models through exact inference. In 2024, he coauthored GenSQL, which connects database queries with probabilistic models, and research on Bayesian neural fields for predictions across space and time. He is an assistant professor in CMU's Computer Science Department, which he joined in 2023 after working as a visiting research scientist at Google.

Works
54
Citations
401
h-index
8
Badges

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2016–2017

    MIT degrees and thesis prize

    By 2016, he earns bachelor's and Master of Engineering degrees in electrical engineering and computer science at MIT. In 2017, his work on probabilistic programming systems receives the Charles & Jennifer Johnson MEng Thesis Award in Computer Science from MIT's Department of Electrical Engineering and Computer Science.

  2. 2019

    The Gen programming system

    As a graduate student in MIT's Probabilistic Computing Project, he coauthors Gen in 2019 with Marco Cusumano-Towner, Alexander Lew, and Vikash Mansinghka. The system lets users combine models and inference methods for tasks in computer vision, robotics, and statistics.

  3. 2020

    The Fast Loaded Dice Roller

    In 2020, he helps create the Fast Loaded Dice Roller with Cameron Freer, Martin Rinard, and Vikash Mansinghka. The algorithm generates random integers with specified probabilities, providing a building block for Monte Carlo simulation and probabilistic inference.

  4. 2021

    SPPL for exact inference

    He coauthors the Sum-Product Probabilistic Language, or SPPL, with Martin Rinard and Vikash Mansinghka in 2021. The system computes exact answers to questions about probabilistic models, including questions about the fairness of loan decisions.

  5. 2022–2023

    Doctorate and CMU appointment

    In 2022, he completes a computer science PhD at MIT under Vikash Mansinghka. His thesis, Scalable Structure Learning, Inference, and Analysis with Probabilistic Programs, receives MIT's Sprowls PhD Thesis Award in Artificial Intelligence and Decision Making in 2023. After working as a visiting research scientist at Google, he joins Carnegie Mellon's Computer Science Department as an assistant professor in 2023.

  6. 2023

    Learning time series models

    At ICML in 2023, he coauthors Sequential Monte Carlo Learning for Time Series Structure Discovery. The method learns Gaussian process models as new observations arrive, and the team evaluates its forecasting on 1,428 econometric datasets.

  7. 2024

    GenSQL for database analysis

    In 2024, he coauthors GenSQL, a system that combines database queries with generative probabilistic models. It helps users make predictions, detect unusual values, and generate synthetic data from tables while accounting for uncertainty.

  8. September 2024

    The Bayesian Neural Field

    In September 2024, he coauthors Scalable Spatiotemporal Prediction with Bayesian Neural Fields in Nature Communications and helps design and implement its statistical model. The Bayesian Neural Field combines a deep neural network with hierarchical Bayesian inference to make predictions across space and time and estimate their uncertainty.

  9. 2026

    Assistant professor at CMU

    In 2026, he continues as an assistant professor in Carnegie Mellon's Computer Science Department, affiliated with its Principles of Programming, Artificial Intelligence, and Theory groups. His Probabilistic Computing Systems Lab develops methods for probabilistic modeling and inference; he teaches Probabilistic Programming Languages in spring 2026 and Probability and Computing in fall 2026.

Career

  1. Since 2023Carnegie Mellon UniversityAssistant Professor, Computer Science
  2. 2024GoogleVisiting Research Scientist

Education

  1. 2022Massachusetts Institute of TechnologyPhD in computer science
  2. Massachusetts Institute of TechnologyBachelor’s degree in electrical engineering and computer science
  3. Massachusetts Institute of TechnologyMaster of Engineering in electrical engineering and computer science

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Feras Saad | Carnegie Mellon University Computer Science Department csd.cmu.edu. Checked 27 October 2026.
  2. New AI programming language goes beyond deep learning | MIT News | Massachusetts Institute of Technology news.mit.edu. Checked 27 October 2026.
  3. Algorithm quickly simulates a roll of loaded dice | MIT News | Massachusetts Institute of Technology news.mit.edu. Checked 27 October 2026.
  4. Exact symbolic artificial intelligence for faster, better assessment of AI fairness | MIT News | Massachusetts Institute of Technology news.mit.edu. Checked 27 October 2026.
  5. 2023 EECS Awards - MIT EECS eecs.mit.edu. Checked 27 October 2026.
  6. Sequential Monte Carlo Learning for Time Series Structure Discovery proceedings.mlr.press. Checked 27 October 2026.
  7. MIT researchers introduce generative AI for databases | MIT News | Massachusetts Institute of Technology news.mit.edu. Checked 27 October 2026.
  8. Scalable spatiotemporal prediction with Bayesian neural fields | Nature Communications nature.com. Checked 27 October 2026.
  9. Feras Saad | Carnegie Mellon University cs.cmu.edu. Checked 27 October 2026.