Ali Jadbabaie

About

Ali Jadbabaie is the Department Head and JR East Professor at the Massachusetts Institute of Technology's Department of Civil and Environmental Engineering. He holds a B.S. degree from Sharif University of Technology, an M.S. from the University of New Mexico, and a Ph.D. from the California Institute of Technology. His research interests encompass network science, network economics, consensus and information aggregation in social networks, and the cooperative control of multi-agent systems. He also focuses on applications of algebraic topology in sensor network coverage and deployment, as well as the analysis, optimization, and control of networked dynamical systems in physics, engineering, and biology. His work includes motion coordination and vision-based control of unmanned air and ground vehicles, robust control, and spectral graph theory. Dr. Jadbabaie is a core faculty member at the Institute for Data, Systems and Society and a faculty/PI at the Laboratory for Information and Decision Systems, contributing significantly to advancing knowledge in these areas.

Research topics

• Computer Science
• Artificial Intelligence
• Political Science
• Business
• Economics
• Medicine
• Econometrics
• Mathematics
• Geography
• Actuarial science

Selected publications

• 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

• Random walks on simplicial complexes and the normalized Hodge 1-Laplacian

  SIAM Review · 217 citations

  Senior authorCorresponding

  <p>Using graphs to model pairwise relationships between entities is a ubiquitous framework for studying complex systems and data. Simplicial complexes extend this dyadic model of graphs to polyadic relationships and have emerged as a model for multinode relationships occurring in many complex systems. For instance, biological interactions occur between sets of molecules and communication systems include group messages that are not pairwise interactions. While Laplacian dynamics have been i…

  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

• 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

• Robust Federated Learning: The Case of Affine Distribution Shifts

  arXiv (Cornell University) · 2020 · 64 citations

  Senior authorCorresponding

  Federated learning is a distributed paradigm that aims at training models using samples distributed across multiple users in a network while keeping the samples on users' devices with the aim of efficiency and protecting users privacy. In such settings, the training data is often statistically heterogeneous and manifests various distribution shifts across users, which degrades the performance of the learnt model. The primary goal of this paper is to develop a robust federated learning algorithm…

  DOI

Recent grants

• CAREER: Distributed Coordination Strategies for Mobile Autonomous Agents

  NSF · $400k · 2004–2010

• Topological Methods for Distributed Coverage Problems in Mobile Sensing Networks

  NSF · $270k · 2007–2012

Frequent coauthors

• George J. Pappas

  79 shared

• Amir Ajorlou

  62 shared

• Víctor M. Preciado

  University of Pennsylvania

  51 shared

• Alireza Tahbaz-Salehi

  Northwestern University

  51 shared

• Vasileios Tzoumas

  43 shared

• Devavrat Shah

  40 shared

• Shahin Shahrampour

  36 shared

• Suvrit Sra

  36 shared

Education

• Ph.D., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  2006

• M.S., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  2002

• B.S., Electrical Engineering

  Sharif University of Technology

  1999

Similar researchers at Massachusetts Institute of Technology

• Eric J. Almh-125
• Markus J. Buehlerh-121
• Dara Entekhabih-94