Funded Research Projects

• Machine Learning to the Rescue: Eliminating the Damage Caused by TseTse Fly in Sub-Saharan Africa Project

  Szabolcs Marka, Physics; Zsuzsanna Marka, Physics; Zelda Moran, Public Health; John Wright, Electrical Engineering

  This team is pioneering a machine-learning based imaging and sorting solution that aims to drastically reduce Africa’s tsetse population. The solution, which allows for the sorting of male and female tsetse flies, to support the Sterile Insect Technique, which the IAEA has used to eradicate tsetse populations in Zanzibar and other countries.

• Tracking Air Pollutants and Reconstructing 3D Scalar Fields from 2D Satellite Images via Machine Learning Project

  Pierre Gentine, Earth and Environmental Engineering; Marco Giometto, Civil Engineering and Engineering Mechanics; Mostaf Momen, Civil Engineering and Engineering Mechanics; Carl Vondrick, Computer Science

  This team is developing machine-learning models and improved satellite-imaging techniques that will help environmental officials locate and characterize hazardous pollutants in the lower atmosphere, allowing them to design strategies to mitigate pollution.

• Nudging New York: Using Data Science to Increase Healthcare Access in Underserved Communities Project

  Marianthi-Anna Kioumourtzoglou, Environmental Health Sciences; John Paisley, Electrical Engineering; Kai Ruggeri, Health Policy and Management

  This research team intends to reduce missed appointments at community clinics by using big data and Bayesian machine learning techniques to understand why patients miss appointments and what can be done to help them keep them.

• AI-Assisted Identification of Child Abuse and Neglect in Hospital Settings with Implications for Bias Reduction and Future Interventions Project

  Aviv Landau, Data Science Institute; Desmond Patton, Social Work; Maxim Topaz, Nursing

  This team is developing an innovative artificial intelligence system to detect and assess risk for child abuse and neglect within hospital settings that would prioritize the prevention and reduction of bias against Black and Latinx communities.

• Data-Driven Modeling and Estimation of Li-Ion Battery Properties Project

  Matthias Preindl, Electrical Engineering; Alan West, Chemical Engineering

  This engineering team is developing a machine-learning model that can estimate a Li-Ion battery’s charge level with greater accuracy, aiming for an error rate of just one percent.

• Predicting Personalized Cancer Therapies Using Deep Probabilistic Modeling Project

  David Blei, Statistics; Anna Lasorella, Pediatrics; Raul Rabadan, Systems Biology; Wesley Tansey, Systems Biology

  This team aims to model, predict, and target therapeutic sensitivity and resistance of cancer. They will integrate Bayesian modeling with recently developed variational inference and deep learning methods and apply them to large scale genomic and drug sensitivity data across many cancer types.

• A Game-Theoretical Framework for Modeling Strategic Interactions Between Autonomous and Human-Driven Vehicles Project

  Xi Chen, Computer Science; Sharon Di, Civil Engineering and Engineering Mechanics; Qiang Du, Applied Physics and Applied Mathematics; Eric Talley, Law

  This team is developing a fundamental framework using the game theoretic approach to model the strategic interactions of conventional human-driven vehicles and autonomous and/or connected vehicles. Other than technical advances, this project will also address the Trolley Problem (i.e., ethical sense development) in AV algorithm design.

• Planetary Linguistics Project

  Michael Collins, Computer Science; David Kipping, Astronomy

  This team will build predictive models capable of intelligently optimizing telescope resources, and uncover the rules and regularities in planetary systems, specifically through the application of grammar induction methods used in computational linguistics.

• Enabling Small: Data Medical Research with Private Transferable Knowledge Project

  Roxana Geambasu, Computer Science; Daniel Hsu, Computer Science; Nicholas Tatonetti, Biomedical Informatics

  This team is building an infrastructure system for sharing privacy-preserving machine learning models of large-scale, dynamic, clinical datasets. The system will enable medical researchers in small clinics or pharmaceutical companies to incorporate multitask feature models learned from big clinical datasets to bootstrap their own machine learning models on top of their (potentially much smaller) clinical datasets. The multitask feature models protect the privacy of individual records in the large datasets through a rigorous method called differential privacy.

• p(true): Distilling Truth by Community Rating of Claims on the Web Project

  Trenton Jerde, Zuckerman Institute; Nikolaus Kriegeskorte, Zuckerman Institute; Nima Mesgarani, Electrical Engineering; Chris Wiggins, Applied Physics and Applied Mathematics

  This team will build a complementary mechanism for web-based sharing of reasoned judgments to perform probabilistic inference on contentious claims with machine learning algorithms and bring rationality to the social web.

• Detecting and Attributing Spatiotemporal Variations in Sources of Ground-Level Project

  Ruth DeFries, Ecology, Evolution and Environmental Biology; Arlene Fiore, Earth and Environmental Sciences; Jeff Goldsmith, Biostatistics; Marianthi-Anna Kioumourtzoglou, Environmental Health Sciences; Daniel Westervelt, Lamont-Doherty Earth Observatory; John Wright, Electrical Engineering

  This team will develop methods to extract patterns from multiple datasets and identify the dominant sources of air pollution across India and how they vary in space and time. Their work is a step towards the overarching goal of informing effective clean air solutions and reducing public health burdens associated with exposure to air pollution in India.

• Gender and Racial/Ethnic Inequality in High-Skilled Labor Market: Gaining New Insights from Online Resume and Reviews Database Project

  Kriste Krstovski, Data Science Institute; Yao Lu, Sociology

  This team combines new sources of labor market data with data science methods to identify factors and environments that shape gender and racial inequality in high-skilled labor market. The team will chart long-term career trajectories of a large number of high-skilled American workers and examine gender and racial variations; and construct measures of company environment, especially that pertains to gender and racial equity, and assess its consequences for the career path of different groups of skilled workers.

• Interpretable Microbiome Dynamics in Liver Transplant Recipients Project

  Itsik Pe’er, Computer Science; Anne-Catrin Uhlemann, Medicine

  This team is developing methods for temporal analysis of gut microbiome compositions to better define the risk of infections in liver transplant recipients. They will integrate existing coarse resolution data with newly collected deep metagenomics and metabolomics data.

• Modeling the Dynamics of Young Onset Colorectal Cancer using Big Population Data Project

  Piero Dalerba, Pathology and Cell Biology; Jianhua Hu, Biostatistics; Mary Beth Terry, Epidemiology; Wan Yang, Epidemiology

  This team will build a novel model-inference system to study the dynamics of colorectal cancer, test a range of risk mechanisms over the life course, and identify key risk factors underlying the recent increase in young onset colorectal cancer incidence in the United States to support more effective early prevention.

• Probabilistic Modeling of Intercellular Interactions that Drive Ferroptosis Project

  Elham Azizi, Biomedical Engineering; Jellert Gaublomme, Biological Sciences; Brent Stockwell, Biological Sciences

  This team will develop probabilistic models to elucidate the role of intercellular interactions in driving susceptibility of treatment-resistant mesenchymal tumor cells to a newly discovered ferroptotic vulnerability, which could offer a therapeutic avenue to prevent survival of these cancer cells that are prone to metastasis.