Alex Zhang, GFW Report
Zhenbo Li, Master’s candidate
David R. Cheriton School of Computer Science
Supervisors: Professors Bin Ma, Yang Lu
Ajay Singh, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Trevor Brown
In this presentation, we introduce Neutralization Based Reclamation (NBR), a novel technique that helps concurrent data structures with non-synchronized traversals to safely free objects. Additionally, we explore optimization possibilities, examining the efficiency of the technique.
Edward Lee, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ondřej Lhoták
Reasoning about the use of external resources is an important aspect of many practical applications. Effect systems enable tracking such information in types, but at the cost of complicating signatures of common functions. Capabilities coupled with escape analysis offer safety and natural signatures, but are often overly coarse grained and restrictive.
Researchers have created a new AI-assisted digital art tool designed to help art therapy patients better express themselves while maintaining the efficacy of the process.
The tool, dubbed DeepThInk, was designed by researchers at the Cheriton School of Computer Science and the Southern University of Science and Technology in collaboration with art therapists. DeepThInk grew out of the challenges the therapists faced when the COVID-19 pandemic forced them to conduct their work virtually.
Cancer is the leading cause of death in Canada. According to the Canadian Cancer Society, an estimated 230,000 people are diagnosed with the disease every year.
University Professor Ming Li, the Canada Research Chair in Bioinformatics, is using deep learning technology to make personalized cancer vaccines accessible to everyone. He began doing cancer research when his wife, Jessie W. H. Zou, was diagnosed with breast cancer. Though she died in 2010, her legacy continues in his research.
Professor Shlomi Steinberg has a PhD in computer science from the University of California, Santa Barbara. While pursuing his doctoral degree he was a recipient of an NVIDIA PhD fellowship. He received his MSc in mathematics and computer science from the Weizmann Institute of Science in Israel under the supervision of Professor David Harel. His master’s research centred on efficient execution and distribution of formally verifiable software paradigms.
Ruixue Zhang, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ming Li
Shiori Sagawa, PhD candidate
Department of Computer Science, Stanford University
Machine learning systems are powerful, but they can fail due to distribution shifts: mismatches in the data distribution between training and deployment. Distribution shifts are ubiquitous and have real-world consequences: models can fail on subpopulations (e.g., demographic groups) and on new domains unseen during training (e.g., new hospitals).
Siddhartha Sahu, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Semih Salihoğlu