What is Machine Learning and Can it Help Advance My Research?

The Center for Statistics and Machine Learning proposes a three-hour wintersession workshop (including lunch). The workshop aims to increase awareness of how machine learning could aid faculty, postdoc, and student research. No detailed prior knowledge of machine learning is assumed. The workshop will begin with an overview of crucial machine learning ideas and address three questions: What is machine learning? Where has it been particularly successful? What can it not do well (yet)? Then five faculty, from various parts of the university will give 20-30 minute presentations on how they are incorporating machine learning into their research. The workshop will then move into a question, answer, and discussion session with a boxed lunch provided. Several data scientists and a research software engineer will attend this part of the session to answer questions concerning datasets, dataset curation, and software tools for machine learning. The session will target faculty, postdocs, and advanced students wondering if machine learning can help their research program. However, space permitting, the session is open to all wintersession participants.

What to expect:
Single workshop (one-off workshop –" 3 hours total)

Meet the facilitator:
Peter Ramadge (ECE/CSML): is engaged in research and teaching in signal processing and machine learning with applications in neuroscience. He is the Director of the Center for Statistics and Machine Learning (CSML) at Princeton University. (0rganizer). Waheed Bajwa (CSML): is a visiting faculty fellow at CSML on leave from Rutgers University. His research interests include inverse problems, compressed sensing, and applications in biological sciences and complex networked systems. Filiz Garip (SOC): is engaged in research on migration, economic sociology, and inequality. She studies the mechanisms that enable or constrain mobility and lead to greater or lesser degrees of social and economic inequality. Tom Griffiths (PSY/COS) is interested in developing mathematical models of higher-level cognition and understanding the formal principles that underlie our ability to solve the computational problems we face in everyday life. Ching Yao Lai (GEO): uses idealized mathematical models, laboratory experiments, machine learning and simulations to explore the rich physics governing the interplay between fluids and structures with applications in geophysics and climate science. Brandon Stewart (SOC): develops new quantitative statistical methods for applications across computational social science.

To request accommodations for this event, please contact the workshop or event facilitator at least 3 working days prior to the event.

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