As extreme weather events such as flooding and drought become more common in a climate impacted by humans, the ability to understand and predict water resources and systems is becoming more important than ever. To that end, a team of Princeton University and University of Arizona researchers has received a $5 million grant from the National Science Foundation (NSF) for the HydroGEN (Hydrologic Scenario Generation) project, which will use machine learning and artificial intelligence to develop simulated models of the nation’s watershed systems.

Nick McGreivy is interested using machine learning as a tool to accelerate the computational simulation of partial differential equations in plasma physics. “Plasma physicists have spent a lot of time and effort developing equations that describe the behavior of plasma within a nuclear fusion reactor,” said McGreivy. “One problem that I am working on is that often those equations take a really long time to solve. The goal is to speed that process up dramatically, maybe even by a couple of orders of magnitude, through a clever application of machine learning. And then that will allow us to test much more quickly and a much wider range of different possible scenarios, which would be useful for the optimization of future fusion reactors.”

Barkachi is interested in using data science techniques for practical applications and having it potentially spun off into a start-up business. His independent project for the CSML certificate was developed along those lines: an e-learning market place called LiveShare.

Kreisch research interests lie in cosmology, a branch of astronomy that concerns itself with the universe’s origin and its evolution. She marries this interest with machine learning, which scientists have increasingly come to rely in recent years in order to interpret cosmological data. “I work at the intersection of theory and computation and cosmology,” said Kreisch. Her thesis is made up of three parts. The first part is theory and computationally-driven and concerns itself with neutrinos and their interactions in the early universe. This research, “The Neutrino Puzzle: Anomalies, Interactions, and Cosmological Tensions,” appeared in the journal Physical Review D in April 2019 and garnered 170 citations. Kreisch is first author.

Since our last newsletter, the Center for Statistics and Machine Learning has been a hive of activity and growth, despite a pandemic hampering many in-person engagements. We marked the end of the spring semester with our annual poster session and were pleased to celebrate the accomplishments of 100 undergraduates and 16 graduate students earning SML certificates. Other notable events included new faculty joining the center, the publication of exciting research, enriching online events, and the expansion of educational opportunities for students and the larger data science community on campus. Check out our highlights below.

Ten new interdisciplinary research projects have won funding from Princeton University’s Schmidt DataX Fund, with the goal of spreading and deepening the use of artificial intelligence and machine learning across campus in order to accelerate discovery. The 10 faculty projects, supported through a major gift from Schmidt Futures, involve 19 researchers and several departments and programs, from computer science to politics. The projects explore a variety of subjects, including an analysis of how money and politics interact, discovering and developing new materials exhibiting quantum properties, and advancing natural language processing through the automatic construction of novel knowledge bases.

In his independent project for the CSML certificate, Alan Ding decided to look into the deeply polarizing COVID-19 discourse on Twitter. He wanted to analyze any trends and see if it was possible to ameliorate polarization on the social media platform.

Francisco Carrillo’s main research focus is on computational fluid dynamics, specifically flow of fluid material through a deformable porous medium such as fine-grained soils and sedimentary rocks.

Timothy D. Kim, 28, doctoral student

Studies:

Kim is a doctoral student at the Princeton Neuroscience Institute, where he has been since 2015. He also recently completed the graduate certificate program at the Center for Statistics and Machine Learning…

In the summer after her freshman year, Roshini Balasubramanian interned at Children’s National Health System, a non-profit pediatric healthcare provider, through Princeton Internships in Civic Service. It was during this internship where she witnessed firsthand the transformational power of big data in healthcare. Healthcare practitioners were using data science to track patients and uncover new insights in other medical data they were collecting.