Data Science Seminar
Hosted by Department of Mathematical Sciences
Reception at CW-112 at 4:30 pm with refreshment available, allowing the audience and the speaker to mingle after the talk.
RSVP at http://bit.ly/DS-TAE-RSVP.
To answer scientific questions, and reason about data, we must build models and perform inference within those models. But how should we approach model construction and inference to make the most successful predictions? How do we represent uncertainty and prior knowledge? How flexible should our models be? Should we use a single model, or multiple different models? Should we follow a different procedure depending on how much data are available?
In this talk I will present a philosophy for model construction, grounded in probability theory. I will exemplify this approach for scalable kernel learning and Gaussian processes, Bayesian deep learning, and understanding human learning.
Bio: Andrew Gordon Wilson is faculty in the Courant Institute and Center for Data Science at NYU. Before joining NYU, he was an assistant professor at Cornell University from 2016-2019. He was a research fellow in the Machine Learning Department at Carnegie Mellon University from 2014-2016, and completed his PhD at the University of Cambridge in 2014. Andrew's interests include probabilistic modelling, scientific computing, Gaussian processes, Bayesian statistics, and loss surfaces and generalization in deep learning. His webpage is https://cims.nyu.edu/~andrewgw.
The Interdisciplinary Dean's Speaker Series in Data Sciences is supported by the:
For questions, contact Ken Kurtz (email@example.com) or Xingye Qiao (firstname.lastname@example.org). Contact Ken Kurtz to request meeting time with the speaker.