The Data Science for Social Good summer program trains aspiring researchers to work on data science projects with social impact. Working closely with governments and nonprofits, participants take on real-world problems in education, health, energy, public safety, transportation, economic development, international development, and more. Participants include a diverse and inclusive cohort of students who spend the summer on campus working with the program.
This third summer of the Stanford Data Science for Social Good (DSSG) program ran from June 21st to August 13th, 2021.
The goal of the DSSG program is to train the next generation of ethically aware data scientists and to provide measurable impact for projects with social impact. This summer's program had nine student fellows from a variety of backgrounds, ranging from computer science to statistics to sociology. This year, Stanford also invited fellows from other US universities! The fellows divided into three teams, each worked with a different partner organization to bring critical insights into a core data science challenge.
- View the intro to the program and all final presentations (Final Presentations were Wednesday, August 11, 2021 from 10:00 - 11:30 am)
Faidra Monachou(Stanford University) is a final-year Ph.D. candidate in Management Science and Engineering at Stanford University, a Stanford Data Science Scholar, and Stanford HAI Graduate Fellow. She is interested in market and information design, with a particular focus on the role of discrimination, diversity, and information in education, labor, and sharing economy. In Summer 2021, she served as a co-organizer of the Stanford Data Science for Social Good program.
Kiran Shiragur(Stanford University) is a final-year Ph.D. candidate at Stanford co-advised by Moses Charikar and Aaron Sidford. Kiran is affiliated with the Stanford Theory Group and Stanford Operations Research Group. His broad research interest is in theoretical computer science and his focus is on fundamental mathematical problems in data science at the intersection of computer science, statistics, optimization, biology and economics.
Armin Thomas(Stanford University) is a Ram and Vijay Shriram Data Science Fellow at Stanford Data Science, where he works with Russ Poldrack on projects that broadly connect deep learning, neuroscience, and psychology. He is also associated with the Stanford Data Science Center for Open and Reproducible Science as well as Stanford's newly founded Center for Research on Foundation Models. Prior to coming to Stanford, Armin obtained a PhD in machine learning from Technische Universität Berlin, as well as a MSc in cognitive neuroscience and a BSc in psychology from Freie Universität Berlin.
Forecasting Aids for COVID-19 Research
The Delphi Research Group at Carnegie Mellon University is one of two influenza forecasters in the United States. In addition to maintaining the largest public repository of real-time indicators of COVID-19 activity, it has also been making foresasts of COVID-19 cases and deaths since March 2020. In this project, we design a customizable, interactive, parameterized report for evaluating and comparing performance of several COVID-19 forecasters for cases, deaths, and hospitalizations. Such a report helps provides Delphi insight into the performance of several forecasters over time periods, geographic locations using a choice of metrics. Our approach can be used to generate other informative reports in a production environment.
Shilaan Alzahawi (Stanford University) is a Master's student in Statistics at Ghent University and a Ph.D. candidate in Organizational Behavior at the Stanford Graduate School of Business. Her research focuses on the science of team science: the coordination and effectiveness of large-scale science collaborations. In addition, Shilaan is interested in open, reproducible, and rigorous science, and is affiliated with the Stanford Center for Open and Reproducible Science. In her free time, she enjoys deadlifting, hiking, and taking board games much too seriously.
Michelle Lee (Columbia University) recently graduated with her Master’s in Public Health at Columbia University (Department of Population and Family and Biostatistics). Her long term goal is to explore the potential for Artificial Intelligence and advanced technologies to fill the gaps in humanitarian aid settings. She loves to travel, playing the guitar, and recently picked up ballet.
Taha Bouhoun (Minerva University) graduated with a bachelor's degree in Computational Sciences with a minor in Econometrics. He is particularly interested in Causal Inference and Statistical Modeling and has worked on projects ranging from Optimizing Ambulance Response Time to Modeling Opinion Change in Social Networks. Taha loves discussing his unpopular opinion on art, playing tennis on weekends, and attending comedy shows.
Measuring spatial-temporal change of physical conditions in neighborhoods with street view imagery
Neighborhood environmental characteristics play an essential role in shaping the health of individuals and communities, consequently contributing to inequality in the U.S. However, studies on neighborhoods have been constrained by limited data, limited methods, and extensive costs for capturing neighborhood environments characteristics at a large spatial-temporal scale. Partnering with Changing Cities Research Lab, we build a deep learning pipeline to systematically and automatically identify the physical conditions of neighborhood environments at a large scale, across multiple cities and over 10 years, using innovative street view images and crowd-sourcing data. This project will further help researchers to analyze how spatial-temporal changes of physical neighborhood conditions affect individual- and community- level of health.
Lijing Wang (Stanford University) is a Ph.D. candidate in Geological Sciences at Stanford Earth and a Stanford Data Science Scholar. Her research focuses on using geostatistics, computer vision, and Bayesian inference methods for efficient and sustainable earth resource exploration and exploitation. She is also passionate about teaching data science methods to geoscience audiences and the broader scientific community.
TingYan Deng (Vanderbilt University) is a senior at Vanderbilt University triple majoring in computer science, math, and economics. He is particularly interested in applying deep learning, big data analytics for social good and improving the well-being of the society as a whole. His hobbies include lifting, playing basketball, and League of Legends.
Aidan Fitzgerald (Cornell University) recently graduated with an MEng in Computer Science from Cornell University. He is interested in applying machine learning to the world’s most pressing problems – such as global poverty and existential risks to humanity – using the lens of effective altruism. His hobbies include rewatching Avatar: The Last Airbender and blogging.
Daniel Chen(Stanford University) is a first-year student in Stanford’s Master of Science in Statistics program. His academic interests include cultural psychology and causal inference, and he is broadly interested in the intersection between social science and quantitative methods. In his free time he enjoys cooking and playing video games.
Operationalizing Equity Tiebreaker in San Francisco Student School Assignment
The San Francisco Unified School District (SFUSD) has partnered with the SDS DSSG team to develop a policy recommendation for how students are assigned to public elementary schools in SFUSD. This zone-based assignment policy incorporates an equity tiebreaker priority to improve access to schools for historically underserved communities. In this project, the team identified appropriate geographic proxies for assigning equity tiebreakers and explored the effect of the equity tiebreaker on improving equity of access. This project helps to inform the implementation of the equity tiebreaker in the assignment policy in the school year 2023-24.
Qian Zhao (Stanford University) is a fifth year Ph.D. candidate in the Department of Statistics studying properties of statistical methods when applied to high-dimensional data. In her research, she develops methods to provide valid inference for high-dimensional generalized linear models. She loves teaching, mentoring and using statistics to answer scientific questions. In her free time, she enjoys reading, hiking and swimming.
Gabriel Agostini (Columbia University) is an undergraduate student at Columbia pursuing a Bachelor of Science in Applied Mathematics and a Bachelor of Arts in Urban Studies. He is interested in using data science to address social inequalities and improve urban design. In his free time (and also work time), he can be found playing with his cat Mezcal.
Riya Berry (University of California, Berkeley) is a rising senior at UC Berkeley, where she is studying Data Science and Interdisciplinary Studies, concentrating on Inequalities in Education. She is passionate about leveraging data science for social good and policy impact. In her spare time, she enjoys cooking, playing with her yellow lab, and curating her Pinterest board.
Juan Langlois (Stanford University) is pursuing a Master of Science in Management Science and Engineering (Computational Social Science track). He is interested in addressing the technical as well as the behavioral challenges of running organizations and complex systems. His academic focus lies at the intersection of incentives and algorithms.
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Are you interested in becoming a student fellow or mentor next summer? Add yourself to the mailing list and we’ll contact you when next summer’s applications for fellows and mentors are up in early spring. Summer 2022 will be open to non-Stanford affiliated students!
Do you have a social good project that you think DSSG could help with? If you’re interested in partnering with us, please add your name to this list, and we will notify you later this winter when the application for partnerships for next summer goes live.