Bijan Mazaheri

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Ph.D. Candidate, NSF Graduate Research Fellow
Department of Computing & Mathematical Sciences
California Institute of Technology
Contact: bmazaher@caltech.edu

About me

I am a computer scientist and mathematician interested in hidden variables in causal structures: when and if they can be learned, and how they affect networks of opinions. I am currently pursuing a Ph.D. in Computing and Mathematical Sciences at Caltech supported by a NSF Graduate Research Fellowship.

I am also a competitive runner. My personal best for the marathon is 2:15:26 at the Chicago Marathon and I was one of the youngest athletes at the 2020 US Olympic Marathon Trials.

Research

I work with Prof. Jehoshua Bruck and Prof. Leonard Schulman on topics related to machine learning including:
  • Expert Graphs: Developing a structure to study consistency in overlapping expertise in the presence of uncertainty.
  • Source Identification: Learning an unobservable confounding variable given assumptions about the visible variables.
  • Covariate Shift: Preparing a machine learning classifier for demographic differences between training data and deployment.

Publications

  • B. Mazaheri, S. Gordon, Y. Rabani, L. Schulman. "Identifying Discrete Mixtures of Bayesian Networks." [ARXIV Coming]
  • B. Mazaheri, S. Jain, J. Bruck. “Expert Graphs: Synthesizing New Expertise via Collaboration.” IEEE ISIT 2021. [arXiv]
  • S. Gordon, B. Mazaheri, Y. Rabani, L. Schulman. "Source Identification for Mixtures of Product Distributions." COLT 2021. [arXiv]
  • B. Mazaheri, S. Jain, J. Bruck. “Robust Correction of Sampling Bias using Cumulative Distribution Functions.” NeurIPS 2020. [NeurIPS 2020, arXiv]
  • S. Gordon, B. Mazaheri, Y. Rabani, L. Schulman. “The Sparse Hausdorff Moment Problem, with Applications to Topic Models.” 2020. [arXiv]
  • S. Jain, B. Mazaheri, N. Raviv, J. Bruck. “Cancer Classification from Healthy DNA using Machine Learning.” 2019. [bioRxiv, Patent]
  • S. Jain, B. Mazaheri, N. Raviv, J. Bruck. “Short Tandem Repeats Information in TCGA is Statistically Biased by Amplification.” 2019. [bioRxiv]

Projects

Education

  • California Institute of Technology - Ph.D., Computing and Mathematical Sciences
  • Cambridge University (Emmanuel College) - Studied mathematics under a Herchel Smith Fellowship
  • Williams College - B.A., physics and computer science