Alane Suhr, PhD candidate
Department of Computer Science, Cornell University
Fatema Tuz Zohora, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ming Li
Smith Oduro-Marfo, University of Victoria
Mohammadkazem (Kazem) Taram, PhD candidate
Department of Computer Science and Engineering, University of California, San Diego
The tension between security and performance has become more painful in recent years. In the context of processor architecture, we are observing a large influx of new attacks that appear regularly, each exploiting a crucial performance optimization, threatening to unwind decades of architectural gains.
Hasti Seifi, Assistant Professor
Department of Computer Science, University of Copenhagen
Come see six groups of fourth-year CS and SE students who took CS 497: Computing and Discrimination — a unique course offered for the first time this Winter by Computer Science Professors Dan Brown and Maura R. Grossman — as they showcase their final projects.
Learn more about this course from its instructors.
Andrew Begel, Principal Researcher
Human-AI eXperiences Team, Microsoft Research
Assistive technologies help people with disabilities to adapt to a world that is not designed to accommodate them. My research aims to create the socio-technical infrastructure underpinning accessible technology and inclusive workplaces to provide opportunity, eliminate bias, and empower people with disabilities to fully engage and collaborate equitably with their non-disabled colleagues.
Chengcheng Hu, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Jimmy Lin
Shahab Asoodeh, Assistant Professor
Department of Computing and Software, McMaster University
Lizhe Chen, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Olga Veksler
In recent research, the self-supervised video representation learning methods have achieved improvement by exploring video’s temporal properties, such as playing speeds and temporal order. These works inspire us to exploit a new artificial supervision signal for self-supervised representation learning: the change of video playing speed.