Bio


See Personal Homepage.

Academic Appointments


Honors & Awards


  • National Science Foundation CAREER Award, Stanford University (2016)
  • Best paper award, INFORMS Applied Probability Society (2015)
  • William Pierskalla best paper award, INFORMS Health Applications Society (2014)
  • Gold Medal, International Mathematics Olympiad (1997)

Current Research and Scholarly Interests


For a full description of some of my current projects, please visit my Personal Homepage.

2017-18 Courses


Stanford Advisees


Graduate and Fellowship Programs


  • Biomedical Informatics (Phd Program)

All Publications


  • Accurate Emergency Department Wait Time Prediction M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT Ang, E., Kwasnick, S., Bayati, M., Plambeck, E. L., Aratow, M. 2016; 18 (1): 141-156
  • Active Postmarketing Drug Surveillance for Multiple Adverse Events OPERATIONS RESEARCH Goh, J., Bjarnadottir, M. V., Bayati, M., Zenios, S. A. 2015; 63 (6): 1528-1546
  • UNIVERSALITY IN POLYTOPE PHASE TRANSITIONS AND MESSAGE PASSING ALGORITHMS ANNALS OF APPLIED PROBABILITY Bayati, M., Lelarge, M., Montanari, A. 2015; 25 (2): 753-822

    View details for DOI 10.1214/14-AAP1010

    View details for Web of Science ID 000350708000012

  • Bargaining dynamics in exchange networks JOURNAL OF ECONOMIC THEORY Bayati, M., Borgs, C., Chayes, J., Kanoria, Y., Montanari, A. 2015; 156: 417-454
  • A Low-Cost Method for Multiple Disease Prediction. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Bayati, M., Bhaskar, S., Montanari, A. 2015; 2015: 329-338

    Abstract

    Recently, in response to the rising costs of healthcare services, employers that are financially responsible for the healthcare costs of their workforce have been investing in health improvement programs for their employees. A main objective of these so called "wellness programs" is to reduce the incidence of chronic illnesses such as cardiovascular disease, cancer, diabetes, and obesity, with the goal of reducing future medical costs. The majority of these wellness programs include an annual screening to detect individuals with the highest risk of developing chronic disease. Once these individuals are identified, the company can invest in interventions to reduce the risk of those individuals. However, capturing many biomarkers per employee creates a costly screening procedure. We propose a statistical data-driven method to address this challenge by minimizing the number of biomarkers in the screening procedure while maximizing the predictive power over a broad spectrum of diseases. Our solution uses multi-task learning and group dimensionality reduction from machine learning and statistics. We provide empirical validation of the proposed solution using data from two different electronic medical records systems, with comparisons to a statistical benchmark.

    View details for PubMedID 26958164

  • Data-Driven Decisions for Reducing Readmissions for Heart Failure: General Methodology and Case Study PLOS ONE Bayati, M., Braverman, M., Gillam, M., Mack, K. M., Ruiz, G., Smith, M. S., Horvitz, E. 2014; 9 (10)
  • COMBINATORIAL APPROACH TO THE INTERPOLATION METHOD AND SCALING LIMITS IN SPARSE RANDOM GRAPHS ANNALS OF PROBABILITY Bayati, M., Gamarnik, D., Tetali, P. 2013; 41 (6): 4080-4115

    View details for DOI 10.1214/12-AOP816

    View details for Web of Science ID 000328255600008

  • Iterative scheduling algorithms 26th IEEE Conference on Computer Communications (INFOCOM 2007) Bayati, M., Prabhakar, B., Shah, D., Sharma, M. IEEE. 2007: 445–453
  • A sequential algorithm for generating random graphs 10th Int Workshop on Approximation Algorithms for Combinatorial Optimization Problems/11th Int Workshop on Randomization and Computation Bayati, M., Kim, J. H., Saberi, A. SPRINGER-VERLAG BERLIN. 2007: 326–340
  • Simple Deterministic Approximation Algorithms for Counting Matchings STOC 07: PROCEEDINGS OF THE 39TH ANNUAL ACM SYMPOSIUM ON THEORY OF COMPUTING Bayati, M., Gamarnik, D., Katz, D., Nair, C., Tetali, P. 2007: 122-127
  • A simpler max-product Maximum Weight Matching algorithm and the auction algorithm IEEE International Symposium on Information Theory Bayati, M., Shah, D., Sharma, M. IEEE. 2006: 557–561
  • Maximum weight matching via max-product belief propagation IEEE International Symposium on Information Theory and Its Applications Bayati, M., Shah, D., Sharma, M. IEEE. 2005: 1763–1767
  • Achieving stability in networks of input-queued switches using a local online scheduling policy GLOBECOM '05: IEEE GLOBAL TELECOMMUNICATIONS CONFERENCE, VOLS 1-6 Nabar, S. U., Kumar, N., Bayati, M., Keshavarzian, A. 2005: 694-698