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Events for April 30, 2024
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PhD Dissertation Defense - Alan Romano
Tue, Apr 30, 2024 @ 09:30 AM - 11:30 AM
Thomas Lord Department of Computer Science
University Calendar
Title: Static Program Analyses for WebAssembly
Committee Members: Weihang Wang (Chair), Chao Wang, and Pierluigi Nuzzo
Date/Time: Tuesday, April 30th, 9:30am - 11:30am
Abstract: WebAssembly is a recent standard for the web that aims to enable high-performance web applications that can run at near-native speeds. The standard has gained attention in both academia and industry for its ability to speed up existing user-facing web applications. Due to its well-defined and sound design, many static program analysis techniques have been developed to accomplish various purposes of WebAssembly analysis. However, we identify gaps in the static program analysis tools of the current WebAssembly ecosystem. We find that current program optimizations applied on WebAssembly modules may lead to diminished performance. We also identify a lack of tools that help developers understand WebAssembly modules through robust binary decompilation. Finally, we find a gap in the ability to analyze cross-language WebAssembly applications across the two languages they are typically implemented in, i.e., WebAssembly and JavaScript.
In this thesis, we present a novel WebAssembly Analysis Framework, or WAF . WAF is a static program analysis framework for WebAssembly modules that consists of multiple intermediate representations. Inspired by frameworks made for Java, the core of our framework lies in our three intermediate representations that each model the WebAssembly module at a different semantic level. This structure enables WAF to serve in multiple use cases, including program optimizations, binary decompilation, cross-language program analysis, and malware detection. We aim to show that our framework can improve static program analysis in the areas that the WebAssembly ecosystem is lacking.Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Alan Romano
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PhD Thesis Proposal - Tian Ye
Tue, Apr 30, 2024 @ 03:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Enhancing Adversarial Training in Low-Label Regimes
Committee Members: Viktor Prasanna (Chair), Paul Bogdan, Jyotirmoy Deshmukh, Rajgopal Kannan, Cauligi Raghavendra
Data & Time: April 30, 3:00 PM - 4:00 PM Location: EEB 219
Abstract: As machine learning models are increasingly deployed in critical real-world applications, ensuring their robustness against adversarial attacks is essential to prevent potentially severe consequences. Adversarial training, which involves teaching models to recognize and resist adversarial perturbations, is a key strategy for building such robustness. This thesis explores the enhancement of adversarial robustness in scenarios characterized by low-label regimes, where extensive labeled training data are not accessible, by addressing several challenges in existing semi-supervised adversarial training methods. Specifically, the proposed research focuses on: (1) optimizing the generation of adversarial samples to reduce the risk of overfitting, (2) enhancing the reliability of pseudo-labels to mitigate confirmation bias, and (3) simplifying the optimization of training processes to enhance accessibility and efficiency. These improvements will contribute to strengthening the security and functionality of machine learning applications against adversarial threats in a broader range of applications.Location: Hughes Aircraft Electrical Engineering Center (EEB) - 219
Audiences: Everyone Is Invited
Contact: Ellecia Williams
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PhD Thesis Proposal - Nan Xu
Tue, Apr 30, 2024 @ 04:30 PM - 06:30 PM
Thomas Lord Department of Computer Science
University Calendar
Committee Members: Xuezhe Ma (chair), Muhao Chen, Jonathan May, Ram Nevatia, Daniel O’Leary
Title: Decoding Recipes for Coherent and Factual Text Generation
Abstract: While Large language models (LLMs) have demonstrated increasing power in generating texts, they have also called upon studies on their degeneration problems such as repetition, incoherence, hallucination, etc. My PhD thesis outlines my research aiming to tackle these challenges from the perspective of decoding, which is train-free and driven by models' own understanding of seen and generated texts. Specifically, I focus on 1) reducing undesired repetitions and off-topic generations by analyzing probability distribution of decoding steps for open-ended text generation and 2) mitigating hallucinations by studying models' uncertainty against user prompts for false-premise question answering. Motivated by the emergent ability of Large Vision Language Models (LVLMs) to perceive and understand visual signals, I will also introduce my proposal to mitigate hallucinations with effective decoding strategies given multimodal inputs.
Venue: RTH 306 and Zoom https://usc.zoom.us/j/97468606369?pwd=a2ovTlYweE1neGpTMHFtUlNrcVVnQT09
Date: 04/30/2024, 4:30-6:30PMLocation: Ronald Tutor Hall of Engineering (RTH) - 306
Audiences: Everyone Is Invited
Contact: Ellecia Williams
