Bio


https://web.stanford.edu/~bayati/bio.html

Academic Appointments


Honors & Awards


  • National Science Foundation CAREER Award, Stanford University (2016)
  • William Pierskalla best paper award, INFORMS Health Applications Society (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


https://web.stanford.edu/~bayati/pub-chron.html

2019-20 Courses


Stanford Advisees


  • Doctoral Dissertation Reader (AC)
    Mona Azadkia, Ahmadreza Momeni
  • Doctoral Dissertation Advisor (AC)
    Wanning Chen, Nima Hamidi, Ali Sharafat
  • Doctoral Dissertation Co-Advisor (AC)
    Danqi Luo
  • Doctoral (Program)
    Abdullah Coskun, Carolyn Kim, Giacomo Mantegazza, Bryce McLaughlin, Ilan Morgenstern, Daniil Vishnev, Alper YILMAZ, Mingxi Zhu

Graduate and Fellowship Programs


  • Biomedical Informatics (Phd Program)

All Publications


  • Online Decision Making with High-Dimensional Covariates OPERATIONS RESEARCH Bastani, H., Bayati, M. 2020; 68 (1): 276–94
  • Evidence of Upcoding in Pay-for-Performance Programs MANAGEMENT SCIENCE Bastani, H., Goh, J., Bayati, M. 2019; 65 (3): 1042–60
  • Scalable Approximations for Generalized Linear Problems JOURNAL OF MACHINE LEARNING RESEARCH Erdogdu, M. A., Bayati, M., Dicker, L. H. 2019; 20
  • Personalizing Many Decisions with High-Dimensional Covariates Advances in Neural Information Processing Systems 32 Hamidi, N., Bayati, M., Gupta, K. 2019
  • Generating Random Networks Without Short Cycles OPERATIONS RESEARCH Bayati, M., Montanari, A., Saberi, A. 2018; 66 (5): 1227–46
  • Data Uncertainty in Markov Chains: Application to Cost-Effectiveness Analyses of Medical Innovations OPERATIONS RESEARCH Goh, J., Bayati, M., Zenios, S. A., Singh, S., Moore, D. 2018; 66 (3): 697–715
  • 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
  • Statistical analysis of a low cost method for multiple disease prediction. Statistical methods in medical research Bayati, M., Bhaskar, S., Montanari, A. 2016: 962280216680242-?

    Abstract

    Early identification of individuals at risk for chronic diseases is of significant clinical value. Early detection provides the opportunity to slow the pace of a condition, and thus help individuals to improve or maintain their quality of life. Additionally, it can lessen the financial burden on health insurers and self-insured employers. As a solution to mitigate the rise in chronic conditions and related costs, an increasing number of employers have recently begun using wellness programs, which typically involve an annual health risk assessment. Unfortunately, these risk assessments have low detection capability, as they should be low-cost and hence rely on collecting relatively few basic biomarkers. Thus one may ask, how can we select a low-cost set of biomarkers that would be the most predictive of multiple chronic diseases? In this paper, 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 over a statistical benchmark.

    View details for DOI 10.1177/0962280216680242

    View details for PubMedID 27932665

  • Scaled Least Squares Estimator for GLMs in Large-Scale Problems Erdogdu, M. A., Bayati, M., Dicker, L. H., Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2016
  • 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): e109264

    Abstract

    Several studies have focused on stratifying patients according to their level of readmission risk, fueled in part by incentive programs in the U.S. that link readmission rates to the annual payment update by Medicare. Patient-specific predictions about readmission have not seen widespread use because of their limited accuracy and questions about the efficacy of using measures of risk to guide clinical decisions. We construct a predictive model for readmissions for congestive heart failure (CHF) and study how its predictions can be used to perform patient-specific interventions. We assess the cost-effectiveness of a methodology that combines prediction and decision making to allocate interventions. The results highlight the importance of combining predictions with decision analysis.We construct a statistical classifier from a retrospective database of 793 hospital visits for heart failure that predicts the likelihood that patients will be rehospitalized within 30 days of discharge. We introduce a decision analysis that uses the predictions to guide decisions about post-discharge interventions. We perform a cost-effectiveness analysis of 379 additional hospital visits that were not included in either the formulation of the classifiers or the decision analysis. We report the performance of the methodology and show the overall expected value of employing a real-time decision system.For the cohort studied, readmissions are associated with a mean cost of $13,679 with a standard error of $1,214. Given a post-discharge plan that costs $1,300 and that reduces 30-day rehospitalizations by 35%, use of the proposed methods would provide an 18.2% reduction in rehospitalizations and save 3.8% of costs.Classifiers learned automatically from patient data can be joined with decision analysis to guide the allocation of post-discharge support to CHF patients. Such analyses are especially valuable in the common situation where it is not economically feasible to provide programs to all patients.

    View details for PubMedID 25295524

  • 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

  • Message-Passing Algorithms for Sparse Network Alignment ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA Bayati, M., Gleich, D. F., Saberi, A., Wang, Y. 2013; 7 (1)
  • Estimating LASSO Risk and Noise Level Neural Information Processing Systems 26 Bayati, M., Erdogdu, M., Montanari, A. 2013
  • The LASSO Risk for Gaussian Matrices IEEE TRANSACTIONS ON INFORMATION THEORY Bayati, M., Montanari, A. 2012; 58 (4): 1997-2017
  • The Dynamics of Message Passing on Dense Graphs, with Applications to Compressed Sensing IEEE International Symposium on Information Theory Bayati, M., Montanari, A. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2011: 764–85
  • BELIEF PROPAGATION FOR WEIGHTED b-MATCHINGS ON ARBITRARY GRAPHS AND ITS RELATION TO LINEAR PROGRAMS WITH INTEGER SOLUTIONS SIAM JOURNAL ON DISCRETE MATHEMATICS Bayati, M., Borgs, C., Chayes, J., Zecchina, R. 2011; 25 (2): 989-1011

    View details for DOI 10.1137/090753115

    View details for Web of Science ID 000292302000033

  • A Sequential Algorithm for Generating Random Graphs ALGORITHMICA Bayati, M., Kim, J. H., Saberi, A. 2010; 58 (4): 860-910
  • A rigorous analysis of the cavity equations for the minimum spanning tree JOURNAL OF MATHEMATICAL PHYSICS Bayati, M., Braunstein, A., Zecchina, R. 2008; 49 (12)

    View details for DOI 10.1063/1.2982805

    View details for Web of Science ID 000262225000007

  • Statistical mechanics of Steiner trees PHYSICAL REVIEW LETTERS Bayati, M., Borgs, C., Braunstein, A., Chayes, J., Ramezanpour, A., Zecchina, R. 2008; 101 (3): 037208

    Abstract

    The minimum weight Steiner tree (MST) is an important combinatorial optimization problem over networks that has applications in a wide range of fields. Here we discuss a general technique to translate the imposed global connectivity constrain into many local ones that can be analyzed with cavity equation techniques. This approach leads to a new optimization algorithm for MST and allows us to analyze the statistical mechanics properties of MST on random graphs of various types.

    View details for DOI 10.1103/PhysRevLett.101.037208

    View details for Web of Science ID 000258184500053

    View details for PubMedID 18764290

  • On the exactness of the cavity method for weighted b-matchings on arbitrary graphs and its relation to linear programs JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT Bayati, M., Borgs, C., Chayes, J., Zecchina, R. 2008
  • Max-product for maximum weight matching: Convergence, correctness, and LP duality Bayati, M., Shah, D., Sharma, M. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2008: 1241–51
  • Iterative scheduling algorithms 26th IEEE Conference on Computer Communications (INFOCOM 2007) Bayati, M., Prabhakar, B., Shah, D., Sharma, M. IEEE. 2007: 445–453
  • 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