Seed Fund Projects

Past Seed Fund Projects

  • Valerie Purdie-Greenaway, Psychology

    Alfredo Spagna, Psychology

    Peter Bearman, Sociology

    Jennifer Manly, Neurology

    Smaranda Muresan, DSI, Computer Science


    This team will develop a shared understanding of how diversity and inclusion (D&I) is conceptualized and studied in the academic literature and compare academic research on D&I to what is found in popular press outlets. The project will draw from social psychology, organizational behavior, and social-cognitive neuroscience to create a baseline for understanding the structure of scientific knowledge related to D&I and to understand what kinds of D&I research finds its way into the popular press.

  • Gerard Torrats-Espinosa, Sociology

    Kara Rudolph, Public Health


    This team proposes to create a novel linkage of police administrative records that capture highly detailed information on all search warrants that the Chicago Police Department executed from 2012 to 2020. They will document spatial and temporal patterns of search warrant use across Chicago’s neighborhoods.

  • Jeffrey A. Fagan, Law, Public Health

    Rajiv Sethi, Barnard, Economics

    Elizabeth Ananat, Barnard, Economics

    Morgan C. Williams, Jr., Barnard, Economics

    Brendan O’Flaherty, Economics

    José Luis Montiel Olea, Economics


    This project will create a data archive on non-fatal injuries and fatalities from police encounters—data that may be harmonized and integrated with other increasingly detailed datasets on police killings—and provide estimates of a continuum of police use of force. The new database will provide capacity and research opportunities for departments, schools, laboratories, and students across the university on an urgent public policy issue.

  • Upmanu Lall, Earth and Environmental Engineering

    Bolun Xu, Earth and Environmental Engineering


    This project combines data-driven renewable energy simulations with model-based storage pricing models to quantify the financial value of various energy storage technologies in integrating renewables and mitigating climate change in a decarbonizing electric power system.

  • Xiaofan (Fred) Jiang, Electrical Engineering

    Daniel Westervelt, Lamont-Doherty Earth Observatory


    This team will develop and apply a novel, globally applicable, bias correction algorithm to a fast-growing global network of consumer grade, low-cost air quality sensors. This method will allow users to obtain high-quality data from raw, unvalidated sensor data, thereby empowering communities to better understand their air pollution exposure and take action.

  • Veronica Barcelona, Nursing
    Kenrick Cato, Nursing
    Dena Goffman, Obstetrics and Gynecology
    Coretta Green, New York-Presbyterian
    Anita Holman, Obstetrics and Gynecology
    Janice James Aubey, Obstetrics and Gynecology
    Bernadette Khan, New York-Presbyterian
    Kenya Robinson, New York-Presbyterian
    Maxim Topaz, Nursing


    This team will examine the association between linguistic bias and pregnancy-related morbidity among birthing people from 2017-2019 at two hospitals. They will use natural language processing approaches to: 1) identify stigmatizing language in clinical notes, 2) examine patterns of language use by race and ethnicity, and 3) study associations between language use and pregnancy-related morbidity.

  • James Anderson, Electrical Engineering
    Michael Mauel, Applied Physics
    Jeffrey Levesque, Applied Physics


    Fusion science seeks to advance our fundamental understanding of physics and make plasma fusion viable for applications such as clean energy production. Tokamak fusion reactors generate vast and rich data sets obtained through multiple sensing modalities. The goal of this project is to develop new robust and efficient methods rooted in randomized numerical linear algebra for analyzing and characterizing complex fusion discharge dynamics.

  • Billy Caceres, Nursing
    Ipek Ensari, Data Science Institute
    Kasey Jackman, Nursing


    This pilot study will use data science techniques to leverage ecological momentary assessment and consumer sleep technology to phenotype sleep health profiles in Black and Latinx sexual and gender minority adults. The investigators will use 30 days of daily electronic diaries and actigraphy to examine the associations of daily exposure to minority stressors (such as experiences of discrimination and anticipated discrimination) with sleep health among Black and Latinx sexual and gender minority adults.

  • Sean Luo, Psychiatry
    Min Qian, Biostatistics
    Kara Rudolph, Epidemiology


    Pharmacologic treatment of opioid use disorder (OUD) is complicated by the likely absence of a one-size-fits-all best approach; rather, “​optimal​”​ dose and dose adjustment are hypothesized to depend on person-level factors, including factors that change over time, reflecting how well the individual is responding to treatment. ​This team will use harmonized data from multiple existing clinical trials with natural variability in OUD medication dose adjustments over time to 1) learn optimal dosing strategies​,​ and 2) estimate the extent to which such optimal dosing strategies could reduce risk of treatment drop-out and relapse.

  • Colin Wayne Leach, Psychology, Africana Studies
    Courtney Cogburn, Social Work
    Sining Chen, Industrial Engineering and Operations Research
    Kathleen McKeown, Computer Science
    Susan McGregor, Data Science Institute


    Social media is a powerful means of individual expression, and collective consolidation, of people’s sentiment about the most important issues in our society.​ ​​This transdisciplinary​ ​project marries the latest advances in computational and statistical techniques of language use over time with social behavioral theories of emotion and stress to examine the temporal dynamics of ​tweets surrounding police killings of Black people and subsequent protest​s​ (e.g., Black Lives Matter).

  • 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.

  • Jacqueline Gottlieb, Neuroscience

    Vince Dorie, Associate Research Scientist, Data Science Institute


    In this project, online behavioral data will be collected from a large sample of participants, using a battery of tasks that probe different theories of how information is prioritized and used. This combined data set will allow an analysis of the latent factors that shape human-information demand while also unifying those theories.

  • 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.

  • 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.

  • 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.

  • 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.

  • Rene Hen, Neuroscience and Psychiatry
    Sergey Kalachikov, Chemical Engineering


    Major depressive disorder is a debilitating illness that affects more than 350 million people around the world. The most common treatments are drugs such as Prozac. About half of the patients who take the pills, however, do not respond to treatment. This team is thus trying to understand the molecular mechanisms of such treatment resistance. Ultimately, they would like to be able to predict which people will respond to antidepressant drugs before they begin treatment, and to develop new treatments that can circumvent antidepressant resistance in the millions of people who do not respond now to antidepressants.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.