Malik Magdon-Ismail

Professor, Computer Science Rensselaer Polytechnic Institute

  • Troy NY

Learning from data and decision-making in complex data systems

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Spotlight

2 min

A Free App Can Help School and College Administrators Contain COVID-19

With COVID-19 infection rates rising across the country as students return to school for the spring semester, how will schools and colleges control the spread? COVID Back-to-School can help. It’s a free online tool that predicts the outcome of taking specific measures to curtail the spread of the virus. The algorithm powering the app was developed by Rensselaer Polytechnic Institute computer science professor Malik Magdon-Ismail and builds upon the success of COVID War Room, an algorithm that can predict the spread of COVID-19 in smaller cities and counties across the United States and select international locations. Administrators at Rensselaer consulted COVID Back-to-School when devising a COVID-19 management plan that successfully kept the infection rate on campus well below 0.5% during the fall 2020 semester, even with most students attending in-person classes. Magdon-Ismail, an expert in machine learning, designed the algorithm to allow administrators at schools of all levels, as well as ordinary citizens, to quantitatively analyze various strategies for containing the virus. Users can enter details about their institution — like the zip codes students come from, the size of the school, how often students are tested, the number of expected interactions during a class or meal — and COVID Back-to-School will project outcomes like the proportion of students likely to arrive infected, the proportion of students likely to be infected over time, and the number of likely new infections every 14 days. “This is a publicly available tool that we’re hoping schools can use to quantitatively analyze re-opening strategies,” Magdon-Ismail said. “Schools can use it, at least, to evaluate how their current strategy will play out assuming an infection on campus. Better still, COVID Back-to-School allows schools to try out various strategies before actually implementing them, to see what works and what doesn’t.” Magdon-Ismail is available to discuss how the algorithm works and the utility it may provide to colleges and universities across the country in the spring semester.

Malik Magdon-Ismail

Areas of Expertise

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

Education

Yale University

B.S.

Physics

California Institute of Technology

M.S.

Physics

California Institute of Technology

Ph.D.

Electrical Engineering

Media Appearances

How Your Brain Functions in Certain Ways Could Be Key to Quantum Computing

Lifewire  online

2022-10-27

The mysterious actions of quantum mechanics may be at work within your brain, and recent research supporting the idea could lead to improved quantum computers.

A new study published in the peer-reviewed Journal of Physics Communications suggests that the human brain has much in common with a quantum computer. Experts say it's part of a growing body of evidence that quantum mechanisms could explain how the brain works.

"Understanding quantum computing and the associated algorithms could reveal some of the deeper workings in the brain," Malik Magdon-Ismail, a professor of computer science at Rensselaer Polytechnic Institute, told Lifewire in an email interview.

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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

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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.

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Articles

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.

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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.

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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.

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