Vivek F. Farias

About

Vivek Farias is a professor whose research and teaching focus on operations management, decision-making under uncertainty, and data-driven optimization. His work involves learning from commerce data, experimentation, control in online platforms, and large-scale optimization problems. He has mentored numerous students, many of whom have gone on to become assistant professors at leading universities or hold prominent roles in industry and research. His academic contributions include developing algorithms for large-scale personalization, revenue management, and fairness in operations, with recognition such as the INFORMS Dantzig Dissertation Award, Third Prize.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Political Science
• Econometrics
• Economics
• Mathematics
• Geography
• Medicine
• Business

Selected publications

• Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona

  Nature Communications · 2020 · 447 citations

  Large-scale, unbiased proteomics studies are constrained by the complexity of the plasma proteome. Here we report a highly parallel protein quantitation platform integrating nanoparticle (NP) protein coronas with liquid chromatography-mass spectrometry for efficient proteomic profiling. A protein corona is a protein layer adsorbed onto NPs upon contact with biofluids. Varying the physicochemical properties of engineered NPs translates to distinct protein corona patterns enabling differential and…

  DOI

• Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

  Proceedings of the National Academy of Sciences · 2022 · 311 citations

  Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hu…

  DOI

• The United States COVID-19 Forecast Hub dataset

  Scientific Data · 2022 · 126 citations

  Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hosp…

  DOI

• Assortment Optimization Under Consider-Then-Choose Choice Models

  Management Science · 2020 · 92 citations

  Consider-then-choose models, borne out by empirical literature in marketing and psychology, explain that customers choose among alternatives in two phases, by first screening products to decide which alternatives to consider and then ranking them. In this paper, we develop a dynamic programming framework to study the computational aspects of assortment optimization under consider-then-choose premises. Although nonparametric choice models generally lead to computationally intractable assortment o…

  DOI

• Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

  medRxiv (Cold Spring Harbor Laboratory) · 2021 · 77 citations

  Abstract Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Fo…

  DOI

Recent grants

• An Optimization Framework for Dynamic A-B Testing

  NSF · $472k · 2017–2022

• CAREER: Large Scale Stochastic Control: A Math Programming and Discrete Optimization Lens

  NSF · $400k · 2011–2017

Frequent coauthors

• Tianyi Peng

  Zhejiang Sci-Tech University

  19 shared

• Devavrat Shah

  18 shared

• Srikanth Jagabathula

  14 shared

• Andrew A. Li

  Carnegie Mellon University

  14 shared

• Ciamac C. Moallemi

  13 shared

• Andrew Zheng

  12 shared

• Deeksha Sinha

  Massachusetts Institute of Technology

  12 shared

• Retsef Levi

  12 shared

Labs

• Vivek F. Farias LabPI

  Not provided

Awards & honors

• INFORMS Fellow (2025)
• Pierskalla Best Paper Award from the Health Applications Soc…
• Daniel H. Wagner Prize for Excellence in the Practice of Adv…
• Institute for Operations Research and the Management Science…
• INFORMS MSOM Best Publication Award in Management Science (2…

Similar researchers at Massachusetts Institute of Technology

• Steven Eppingerh-49
• Stephen C. Gravesh-46