Air pollution-ecosystem feedbacks: Unmanned aerial vehicles and ecosystem models to quantify ozone-forest interactions

Barry Munyan Meng

Jessica Munyan (Environmental Sciences), Tingyang Meng (Electrical and Computer Engineering), and Laura Barry (Environmental Sciences)

This project is investigating ozone emissions and photosynthesis rates by using the eddy-covariance (EC) technique, which is a statistical method used to measure trace exchange rates and gases in an environment and an unmanned aerial vehicle (UAV), or drone.

Faculty Advisors: Zongli Lin (School of Engineering) and Sally Pusede (College of Arts and Sciences)


Mothers, Infants, Microbiome and Mental Health (MIMMs): A social neuroscience perspective of maternal-child health to unlock the potential of bio-banked data

Dreisbach and Kelsey

Caitlin Dreisbach (Nursing) and Caroline Kelsey (Psychology)

This project explores the interplay between the intestinal microbiome, anxiety and depression during pregnancy, and subsequent maternal-child attachment. 

Faculty Advisors: Jeanne Alhusen (School of Nursing) and Tobias Grossman (College of Arts and Sciences)


Echo Chambers, Audience, and Community: Studying the democratic use of social media with Big Data

Gampa and Kielty

Anup Gampa (Psychology) and Colin Kielty (Politics)

This project, using a dataset consisting of 3.8 million Twitter users and 150 million tweets and an additional dataset containing more than 40 million tweets corresponding to 45 keywords centered around the Black Lives Matter movement, explores online political discourse from the perspective of those engaged in it. 

Faculty Advisors: Brian Nosek (College of Arts and Sciences) and Nicholas Winter (College of Arts and Sciences)


Modeling Microbial Stability via Semiparametric High Dimensional Graphical Models

Gao and Tian

Yingnan Gao (Biology) and Lu Tian (Systems and Information Engineering)

This project uses high dimensional graphical models to explicitly model the joint distribution of the abundances of all bacteria species in the human gut microbiome, which is more comprehensive than previous approaches, in an effort to develop novel methods and tools that will allow us to synthesize the overarching principles governing the microbial stability and make broad and meaningful predictions that will improve human health.

Faculty Advisors: Quanquan Gu (School of Engineering) and Martin Wu (College of Arts and Sciences)


Ethically Paternal: Value Directed Algorithms

Lam and Rucker

Derek Lam (Philosophy) and Mark Rucker (Systems and Information Engineering)

This project explores the design of an algorithm that can push back against an individual’s immediate desires (i.e., act paternalistically) in an ethically defensible manner using statistical analysis and Inverse Reinforcement Learning to distinguish between a person’s values and desires and developing a recommendation app.

Faculty Advisors: Talbot Brewer (College of Arts and Sciences) and Matthew Gerber (School of Engineering)


Censorship and Detecting Deception: A Data-Driven Look at Obfuscation in Soviet Dissident Writing Versus Misinformation in the USSR and Post-Truth Journalism in America

Maxwell and McEleney

Alex Maxwell (Slavic Languages and Literatures) and Sarah McEleney (Slavic Languages and Literatures)

This project explores how well machine learning and other data science approaches work in detecting fake news in the ideologically distinct environments of the United States and the Soviet-era USSR.

Faculty Advisor: Dariusz Tolczyk (College of Arts and Sciences)


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