I am a second year PhD student (MEng/SB 2016) in Computer Science and Artificial Intelligence. I am a member of the Probabilistic Computing Project, advised by Vikash Mansinghka. My academic research is centered around the design and implementation of probabilistic computing systems, as well as algorithmic approaches to statistical inference in noisy, sparse, and multivariate data. From an applied perspective, I am interested in deploying these methods to public interest data.
A Bayesian Nonparametric Method for Clustering, Imputation,
and Forecasting in Multivariate Time Series
Saad, F.A. and Mansinghka, V.K. arXiv preprint, arXiv:1611.07051, 2017. [Abstract, Paper, Supplement]
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
Saad, F.; Casarsa, L.; and Mansinghka, V. arXiv preprint, arXiv:1704.01087, 2017. [Abstract, Paper, Supplement]
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Nonparametric Bayes
Saad, F. and Mansinghka, V. Artificial Intelligence and Statistics (AISTATS), 2017. [Abstract, Paper, Supplement]
Charles & Jennifer Johnson Computer Science Master of Engineering Thesis Award, MIT EECS 2017.
Summer 2017: Instructor at the
Probabilistic Programming for Advanced Machine Learning Summer School
in Washington, DC.
Fall 2016: TA for 9.S915, Introduction to Probabilistic Programming, a graduate seminar at MIT.
Summer 2015: Instructor at the Probabilistic Programming for Advanced Machine Learning Summer School in Portland, Oregon.