Data Science Lunch: Hancheng Min

Hancheng Min is a machine learning theorist whose research centers around building mathematical principles that facilitate the interplay between machine learning and dynamical systems, working with Prof. René Vidal. His recent research focus has been on understanding the inductive bias of the training algorithms on promoting certain structural properties in the neural networks and connecting these theoretical findings to practical issues in ML such as the adversarial robustness of neural networks. Hancheng is also an AI x Science Fellow. 

During the academic year, our postdoctoral fellows meet for an informal lunch with a Penn faculty guest speaker. Each speaker then leads the group in a casual discussion regarding the use of data science in their research. Discussions vary in content, but tend to focus on the applications of machine learning and artificial intelligence in various academic disciplines.

We encourage open conversation and the exchange of ideas at each lunch, thus fostering a collaborative environment among fellows and faculty alike. As such, these lunches provide an excellent opportunity for our fellows to make interdisciplinary connections and discover new ways to utilize data science in their own research.