hero image
Malik Magdon-Ismail - Rensselaer Polytechnic Institute. Troy, NY, US

Malik Magdon-Ismail Malik Magdon-Ismail

Professor, Computer Science | Rensselaer Polytechnic Institute

Troy, NY, UNITED STATES

Learning from data and decision-making in complex data systems

Spotlight

Areas of Expertise (8)

Probability and Stochastic Processes

Pattern Recognition

Machine Learning

Big Data

Algorithms

Data Mining

Social Networks

Computational Finance

Biography

Malik Magdon-Ismail, a professor of computer science at Rensselaer Polytechnic Institute, is an expert in learning from data and decision-making in complex data systems. In his work, Magdon-Ismail mixes theory with validation through experiment or practice.

During the COVID-19 pandemic, he developed algorithms predicting aspects of the pandemic in small cities and school campuses. COVID War Room predicts the spread and severity of COVID-19 in small cities and counties through the United States and in select international locations. Building on the predictive power of the COVID War Room algorithms, Magdon-Ismail built COVID Back-to-School, a series of algorithms that make it possible to quantify the impacts of policy choices on the spread of COVID in a campus environment.

Other work include contributions to an intriguing project modeling stellar tidal streams from galactic collisions, mathematical models for optimal decision in a representative democracy, separating terrorist-like topological signatures embedded in benign networks, and understanding supercomputer failures using neuromorphic computing.

Media

Publications:

Documents:

Photos:

Videos:

Machine Learning Models Predict COVID-19 Impact in Smaller Cities

Audio:

Education (3)

Yale University: B.S., Physics

California Institute of Technology: M.S., Physics

California Institute of Technology: Ph.D., Electrical Engineering

Media Appearances (4)

How RPI fought the coronavirus and won

Times Union  online

2020-11-12

Aggressive testing strategy and algorithm helped keep COVID-19 at bay, university officials say

view more

RPI launches ‘COVID Back to School’ response aid app

News10  tv

2020-11-12

The more you know, the better you plan. The better you plan, the more likely you keep COVID-19 at bay. That’s the idea behind an all-new app Rensselaer Polytechnic Institute released on Thursday: “COVID Back to School.

view more

RPI Develops App to Limit COVID Spread

Spectrum News  online

2020-11-13

Rensselaer Polytechnic Institute hasn’t had some of the COVID-19 problems other college campuses have and those at the school credit an algorithm and app for keeping cases to a minimum.

view more

An Update On RPI's COVID-19 Model And The Coming "War"

WAMC  radio

2020-05-13

We get an update from Dr. Malik Magdon-Ismail, Professor of Computer Science at RPI, who has been working on a machine learning model for predicting the impacts of the pandemic.

view more

Articles (3)

AI and ML for Predicting COVID-19

AAAI Symposium on AI for Social Good

Malik Magdon-Ismail

Keynote address on using AL and machine learning for predicting spread of COVID-19.

view more

Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics

arXiv

Malik Magdon-Ismail

We present a robust data-driven machine learning analysis of the COVID-19 pandemic from its early infection dynamics, specifically infection counts over time. The goal is to extract actionable public health insights. These insights include the infectious force, the rate of a mild infection becoming serious, estimates for asymtomatic infections and predictions of new infections over time. We focus on USA data starting from the first confirmed infection on January 20 2020. Our methods reveal significant asymptomatic (hidden) infection, a lag of about 10 days, and we quantitatively confirm that the infectious force is strong with about a 0.14% transition from mild to serious infection. Our methods are efficient, robust and general, being agnostic to the specific virus and applicable to different populations or cohorts.

view more

Nonlinear Dynamics from Incomplete Networks

AAAI

Chunheng Jiang, Jianxi Gao, Malik Magdon-Ismail

We study nonlinear dynamics on complex networks. Each vertex i has a state xi which evolves according to a networked dynamics to a steady-state x ∗i. We develop fundamental tools to learn the true steady-state of a small part of the network, without knowing the full network. A naive approach and the current state-of-the-art is to follow the dynamics of the observed partial network to local equilibrium. This dramatically fails to extract the true steady state. We use a mean-field approach to map the dynamics of the unseen part of the network to a single node, which allows us to recover accurate estimates of steady-state on as few as 5 observed vertices in domains ranging from ecology to social networks to gene regulation. Incomplete networks are the norm in practice, and we offer new ways to think about nonlinear dynamics when only sparse information is available.

view more