Summer Research Program with
Columbia University

McGregor's research primarily centers on security and privacy issues affecting journalists and media organizations. Her current work includes an NSF-funded project to provide readers with stronger guarantees about digital media by incorporating cryptographic signatures into digital publishing workflows. She works with natural language processing experts to develop novel classifiers for detecting abusive speech on Twitter, and is researching effective models for peer support with a focus on the media space. Her work in these and related areas has received support from the NSF, the Knight Foundation, Google, Columbia University, among others.

As an educator, McGregor is committed to increasing the reach of data science education to help ensure that essential literacies about data-driven systems are accessible to learners of every age and background. Prior to her work at Columbia University, McGregor was the senior programmer for the News Graphics team at the Wall Street Journal (WSJ), a front-end programmer at the photo wire service MediaVast (acquired by Getty Images), and a reporter for The New York Amsterdam News.

His research interests are in understanding and discovering materials and processes for clean-energy applications using atomic-scale modeling and data science. Prior to joining the Department of Chemical Engineering at Columbia University in 2019, Alex was an independent University Fellow in the School of Chemistry at the University of St Andrews (UK). Alex obtained his PhD in 2012 from FAU Erlangen-Nuremberg in Germany and conducted postdoctoral research at MIT and UC Berkeley. In 2019, Alex was named a Scialog Fellow for Advanced Energy Storage by the Research Corporation for Science Advancement.

Prior to joining Columbia, she was a Postdoctoral Research Fellow at the University of Michigan Transportation Research Institute. She received her Ph.D. degree from the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities in 2014. Dr. Di directs the DitecT (Data and innovative technology-driven Transportation) Lab @ Columbia University. Her research lies at the intersection of game theory, dynamic control, and machine learning. She is specialized in emerging transportation systems optimization and control, shared mobility modeling, and data-driven urban mobility analysis. Details about DitecT Lab and Prof. Sharon Di's research can be found in the following link: http://sharondi-columbia.wixsite.com/ditectlab.

His research interests lie at the interface of machine learning, operations research, and causal inference, with a particular emphasis on developing reliable learning methods for decision-making problems. He received his Ph.D. from Stanford University and worked as a research scientist at Facebook Core Data Science before joining Columbia. Hong is a recipient of several awards and fellowships, including best paper awards at the Neural Information Processing Systems conference and the International Conference on Machine Learning, and the INFORMS Applied Probability Society.

He is interested in exploring the intersection of data science and physical systems, and uses python and julia for his research. He is also excited about data visualization, and trying to present findings in interesting ways.

He is currently writing a mixed-methods dissertation on local gerrymandering in U.S. cities using Bayesian simulations and expert interviews to determine how local gerrymandering is different from what we see at the federal level. In his free time he likes climbing, playing guitar, and gardening.

While she has a diverse research background, her main focus is in stellar astrophysics, galactic archaeology and the stellar-exoplanetary synergy. She is interested in using large-scale photometric, spectroscopic, and astrometric surveys - such as Kepler, TESS, Gaia, APOGEE, LAMOST and GALAH - to study the formation and evolution of the Milky Way. She is also passionate about mentorship and outreach.