Carnegie Mellon University

Adjunct & Visiting Faculty

The ordering on this page is randomized (as opposed to ordering alphabetically). Read more about biases due to alphabetical ordering.
1 Photo of Eunsu Kang, CMU

Eunsu Kang

Visiting Professor, Art and Machine Learning, Carnegie Mellon University

Eunsu Kang is an artist, a researcher, and an educator who explores the intersection of art and machine learning. She has been making interactive art installations and performances, teaching artmaking using machine learning methods, and now she is also looking into the possibility of creative AI. She was a tenured art professor and now is teaching at Carnegie Mellon University’s School of Computer Science. She started her artist career with video installations and single-channel videos. After more than 100 exhibitions, her works have transformed into interactive and interdisciplinary art projects, which currently focuses on the new area of AI art.
Her work has been invited to numerous places around the world including Korea, Japan, China, Switzerland, Sweden, France, Germany, and the US. All ten of her past solo shows, consisting of individual or collaborative projects, were invited or awarded. She has won the Korean National Grant for Arts three times. Her research has been presented at conferences such as ACM, ICMC, ISEA, and NeurIPS. Kang earned her Ph.D. in Digital Arts and Experimental Media from DXARTS at the University of Washington. She received an MA in Media Arts and Technology from UCSB and an MFA from Ewha Womans University.

28 Photo of Ryan Tibshirani

Ryan Tibshirani

Adjunct Faculty, Machine Learning, School of Computer Science

Research Interests

  • Convex Optimization
  • Forecasting Epidemics
  • High-dimensional Statistics
  • Nonparametric Statistics

Dr. Tibshirani's research interests lie broadly in statistics, machine learning, and optimization. More specifically, he is interested in high-dimensional statistics, post-selection inference, nonparametric estimation, convex optimization, and convex geometry. He likes to think about problems from different angles: applied, theoretical, and computational. He is also interested in developing methods for epidemiological forecasting, particularly flu forecasting.

Adam Berger

Adam Berger

Adjunct Faculty, Machine Learning, School of Computer Science, Carnegie Mellon University, Fall 2024