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Events for January 24, 2024
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PhD Thesis Defense - Jiao Sun
Wed, Jan 24, 2024 @ 11:00 AM - 01:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense - Jiao Sun
Committee members: Xuezhe (Max) Ma (Chair), Nanyun (Violet) Peng, Johnathan May, Emilio Ferrara, Dan O’Leary
Title: Emphasizing the Importance of Data and Evaluation in the Era of Large language Models
Abstract: Large-scale models have marked the beginning of a new era, significantly transforming language understanding, text and image generation, and complex decision-making tasks. For example, they may perpetuate stereotypes or produce misleading information. Nevertheless, due to the limitations of existing evaluation methods, these problems are often overlooked. My research highlights the imperative need for more careful and nuanced model evaluation and assessment. Upon investigation, large models may have generated biased or inappropriate content due to inadequacies in their training data. Identified through trustworthy evaluation methods, I address these challenges with a focus on aligning large models with human intentions from a data-centric perspective.Location: Ronald Tutor Hall of Engineering (RTH) - 306
Audiences: Everyone Is Invited
Contact: CS Events
Event Link: https://usc.zoom.us/my/jiaosun
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PhD Thesis Proposal - Haowen Lin
Wed, Jan 24, 2024 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
Committee: Cyrus Shahabi (Chair), Bistra Dilkina, Muhao Chen, Xiong Li, Marlon Boarnet
Title: Accurate and controllable trajectory generation
Abstract : In various application domains like transportation, urban planning, and public health, analyzing human mobility, represented as a sequence of consecutive visits (aka trajectories), is crucial for uncovering essential mobility patterns. Due to privacy and commercial concerns, real-world trajectories are not readily available, giving rise to an important research area of generating synthetic but realistic trajectories. This thesis addresses the challenge of trajectory generation using data-driven approaches, integrating both explicit and implicit constraints within a continuous spatiotemporal domain. First, I present a framework based on generative adversarial imitation learning that synthesizes realistic trajectories that preserve moving behavior patterns (.g., work commute, shopping purpose) in real data. Next, I explore the hypothesis that grouping trajectories governed by similar dynamics into clusters before trajectory modeling could enhance modeling effectiveness. I present a framework that can simultaneously model trajectories in continuous space and time while clustering them. Finally, we discuss the proposed work that will incorporate explicit spatial and temporal constraints that will potentially generate more representative and realistic trajectories.
Zoom link: https://usc.zoom.us/j/95828555243Location: https://usc.zoom.us/j/95828555243
Audiences: Everyone Is Invited
Contact: CS Events
Event Link: zoom link: https://usc.zoom.us/j/95828555243
