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Events for December 02, 2022
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PhD Thesis Proposal - Zunchen Huang
Fri, Dec 02, 2022 @ 09:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Zunchen Huang
Title: Constraint Based Analysis for Persistent Memory Programs
Time: Friday, December 2, 9:00 AM-11:00 AM PST
Committee: Chao Wang (chair), William GJ Halfond, Mukund Raghothaman, Srivatsan Ravi, and Pierluigi Nuzzo.
Abstract: Emerging persistent memory (PM) technologies are beginning to bridge the gap between volatile memory and non-volatile storage in computer systems, by allowing high-speed memory access, byte-addressability, and persistency at the same time. However, PM programming remains a challenging and error-prone task due to reliance on ordinary developers to write correct and efficient PM software code. In this presentation, I propose a framework to detect and repair PM bugs in software code automatically using a set of new symbolic analysis techniques. Unlike existing methods, which rely on patterns and heuristics to detect and repair a small subset of PM bugs, the proposed symbolic analysis framework is able to handle a wide range of PM bugs uniformly. This is achieved by first encoding the program semantics, correctness properties, and PM requirements as a set of logical constraints, and then solving these constraints using off-the-shelf solvers. By reasoning about these logical constraints symbolically, our method can detect, diagnose, and repair PM bugs both efficiently and automatically. It can also infer PM requirements automatically. Finally, I will discuss potential extensions of the framework to programs that rely on both PM and multi-threading to reason about persistency and concurrency simultaneously.
Zoom link: https://usc.zoom.us/j/4326990557
WebCast Link: https://usc.zoom.us/j/4326990557
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Thesis Proposal - Ehsan Qasemi
Fri, Dec 02, 2022 @ 11:00 AM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Candidate: Ehsan Qasemi
Title: Multi-Modal Preconditioned Commonsense Inference
Committee:
Muhao Chen, Aiichiro Nakano, Daniel O'Leary, Fred Morstatter, Luis Garcia
Humans can seamlessly reason with circumstantial preconditions of commonsense knowledge. We understand that "a glass is used for drinking water", unless "the glass is broken" or "the water is toxic". Despite state-of-the-art (SOTA) models' impressive performance in inferring commonsense knowledge, it is unclear whether they understand the circumstantial preconditions.
In this dissertation, I initially propose a novel challenge of reasoning with preconditions attributed to commonsense knowledge, design three tasks based on the challenge in text-only setup, and show there is a significant gap between SOTA language models' performance and human's on our tasks. I then use weak supervision in a combination of targeted fine-tuning strategies to improve the language model's performance on the preconditioned inference task. Finally, I go beyond the text-only setup and investigate the problem of preconditioned inference in a multi-modal setup when the model is challenged to infer the preconditions from an image.
Zoom link: https://usc.zoom.us/j/92119832136 Date: Friday Dec 2nd, 11AM-12 PM
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
