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Wednesday, February 7, 2024 9:00 am - 10:00 am EST (GMT -05:00)

PhD Seminar • Bioinformatics • Mitigating the Missing-fragmentation Problem in De Novo Peptide Sequencing with a Two-stage Graph-based Deep Learning Model

Please note: This PhD seminar will take place online.

Ruixue Zhang, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Ming Li

Wednesday, February 7, 2024 10:30 am - 11:30 am EST (GMT -05:00)

Seminar • Artificial Intelligence • Learning Generative Models from a Control Perspective for Scientific Discovery

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

Dinghuai Zhang, PhD candidate
Mila

Advancements in scientific discovery have always been at the forefront of human endeavor, particularly in complex domains such as molecule synthesis. The intrinsic challenges in these fields stem from two main factors: the vast and combinatorially complex high-dimensional search spaces, and the costly evaluation of scientific hypotheses. Therefore, leveraging machine learning offers a promising avenue to expedite the scientific discovery process.

Wednesday, February 7, 2024 12:30 pm - 1:30 pm EST (GMT -05:00)

PhD Seminar • Information Retrieval | Natural Language Processing | Deep Learning • Heterogenous Benchmarking across Domains and Languages: The Key to Enable Meaningful Progress in IR Research

Please note: This PhD seminar will take place online.

Nandan Thakur, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jimmy Lin

Wednesday, February 7, 2024 2:00 pm - 3:00 pm EST (GMT -05:00)

PhD Seminar • Programming Languages • Effects, Capabilities, and Boxes: From Scope-based Reasoning to Type-based Reasoning and Back

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

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.

Friday, February 9, 2024 1:30 pm - 2:30 pm EST (GMT -05:00)

PhD Seminar • Data Systems | Programming Languages • Reclaiming Memory in Concurrent Data Structures: A Neutralization-Based Approach

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

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.