WebNotice

Please note: This PhD seminar will take place online.

Yen-Ting (Allen) Yeh, PhD candidate
David R. Cheriton School of Computer Science

Please note: This seminar will take place in DC 1304 and virtually over Zoom.

Jason Hartford, Postdoctoral researcher
Mila, Quebec

Causal inference provides a powerful suite of tools through which economists, epidemiologists, and the social sciences understand the world. But textbook causal inference methods limit the questions that scientists can ask because they rely on classical statistical estimation techniques.

Please note: This seminar will take place in DC 1304 and virtually over Zoom.

Swati Mishra, PhD candidate
Computing and Information Science Department, Cornell University

Please note: This seminar will take place in DC 3317.

Christian Janos Lebeda, PhD candidate
Basic Algorithms Research Copenhagen
Algorithms Group, IT University of Copenhagen

Please note: This seminar will take place in DC 1304 and virtually over Zoom.

Shlomi Steinberg, PhD candidate
University of California, Santa Barbara

Rendering and path-tracing techniques power most of the complex computer-generated content we see in films and movies, visualizations and even video games. However, these techniques are strictly confined to ray optics, while many applications often require simulating the interference and diffraction phenomena, that arise from the wave nature of light.

Please note: This PhD seminar will take place in DC 2314 and virtually over Zoom.

Aarti Malhotra, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jesse Hoey

Please note: This distinguished lecture will take place in DC 1302 and virtually over Zoom.

Tanya Berger-Wolf
Director, Translational Data Analytics Institute
Professor, Computer Science and Engineering | Electrical and Computer Engineering | Evolution, Ecology, and Organismal Biology
Director, Imageomics Institute
Ohio State University

Please note: This seminar will take place online.

Saikat Dutta, PhD candidate
Department of Computer Science, University of Illinois Urbana-Champaign

Please note: This seminar will take place in DC 1304 and virtually over Zoom.

Stephanie Wang, PhD candidate
University of California, Berkeley

Scaling applications with distributed execution has become the norm. With the rise of big data and machine learning, more and more developers must build applications that involve complex and data-intensive distributed processing.

Please note: This seminar will take place online.

Ananya Kumar, PhD candidate
Department of Computer Science, Stanford University