Current students

Please note: This master’s thesis presentation will take place online.

Mofetoluwa Adeyemi, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jimmy Lin

Please note: This PhD seminar will take place in DC 2585 (NOTE NEW ROOM).

Zeynep Korkmaz, PhD candidate
David R. Cheriton School of Computer Science

Supervisors: Professors M. Tamer Özsu, Khuzaima Daudjee

Please note: This PhD seminar will take place in DC 2310.

Yudong Luo, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Pascal Poupart

Please note: This PhD seminar will take place online.

Valerie Platsko, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Kate Larson

Please note: This master’s thesis presentation will take place online.

Kritika Iyer, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Ian Goldberg

Please note: This PhD seminar will take place online.

Justin Tracey, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Ian Goldberg

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

Yash Pote, PhD candidate
School of Computing, National University of Singapore

Please note: This seminar will take place in DC 1304.

Xupeng Miao, Postdoctoral Researcher
Computer Science Department, Carnegie Mellon University

In this talk, I will introduce my work on machine learning (ML) parallelization, a critical endeavor to bridge the significant gap between diverse ML programs and multitiered computing architectures. Specifically, I will explore ML parallelization at three distinct yet interconnected levels.

Please note: This seminar will take place in DC 1304.

Misha Khodak, PhD candidate
Computer Science Department, Carnegie Mellon University

Advances in machine learning (ML) have led to skyrocketing demand across diverse applications beyond vision and text, resulting in unique theoretical and practical challenges. The vastness of use cases calls for general-purpose yet customizable tools for tackling large subclasses of such problems.

CS/FINE 383 is a third-year studio course where students work in an interdisciplinary environment to combine computer science principles with fine art technical and conceptual skills. Experience novel computational art works and aesthetic experiences using generative agents, advanced computer vision, distributed computing, and more.

Where is the Computational Art Exhibition?