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.

Anthony Cilluffo tackled a thorny and hot button subject for his research project: police misconduct. “Police departments are grappling with how to respond to public demands for accountability and reform. One way to improve policing is to provide targeted training to officers most likely to commit actions that breach the public’s trust in police,” he said. In order find police officers who could benefit from additional training, he turned to data science. He took a public data set of NYPD complaints from the 1980s to 2019 and developed a model to predict officers most likely to have a substantiated complaint against them. “I trained random forest and naive Bayes classifiers for this task,” said Cilluffo about some of the data science techniques he used. “Overall, the results using publicly available data offer a promising view of the possible results using more detailed personnel data inside the NYPD.”

Data-driven public policy depends on data. And, in the area of technology policy, access to data has been a significant barrier to research. Concerned about how online services might intrude on privacy, push hyper-partisan misinformation, or disadvantage their competitors? Those services aren’t sharing the relevant data with researchers.

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Olga Russakovsky, an assistant professor of computer science is a forward-thinking expert in computer vision systems. In this video short in the “Forward Thinkers” series, Russokovsky acknowledges the amazing breakthroughs in computer vision that have powered important applications in areas such as disaster relief, autonomous transportation, or medical diagnostics. She also voices concern for the potential for bias within these systems and speaks about her vision for “AI for All,” and her work in identifying a more diverse generation to work on artificial intelligence systems.