DSI Seed Funds Past Projects
All Projects
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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.
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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.
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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.
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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.
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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.
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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, NursingThis 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.
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James Anderson, Electrical Engineering
Michael Mauel, Applied Physics
Jeffrey Levesque, Applied PhysicsFusion 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.
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Billy Caceres, Nursing
Ipek Ensari, Data Science Institute
Kasey Jackman, NursingThis 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.
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Sean Luo, Psychiatry
Min Qian, Biostatistics
Kara Rudolph, EpidemiologyPharmacologic 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.
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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 InstituteSocial 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 protests (e.g., Black Lives Matter).
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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.
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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.
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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.
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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.
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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.
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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.
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Rene Hen, Neuroscience and Psychiatry
Sergey Kalachikov, Chemical EngineeringMajor 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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.