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Student Activity
Events for March
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PhD Thesis Proposal- Yuzhong Huang
Tue, Mar 19, 2024 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Student Activity
PhD Thesis Proposal- Yuzhong Huang
Title: Explicit Control in the Understanding and Generation of 3D world
Committee Members: Fred Morstatter (Chair), Yue Wang, Aiichiro Nakano, Antonio Ortega
Abstract:
Understanding and recreating our living environment has been a key topic in scientific research, ranging from virtual reality, autonomous driving, and generative AI tools. Recent advancements have significantly improved machine model’s capability to recognize and generate visually similar 3D objects.
However, existing approaches often lack explicit control mechanisms, limiting their adaptability and interpretability. This thesis proposal addresses this gap by focusing on three crucial aspects: (1) Explicit control in understanding 3D worlds, achieved through the imposition of planar priors and plane-splatting volume rendering method. (2) Explicit control in generating 3D worlds, enabled by an orientation-conditioned diffusion model. (3) Explicit control in modifying 3D objects, enabled by projecting text-guided 2D segmentation map onto 3D models.
These advancements pave the way for more intuitive and precise manipulation of 3D environments.
Location: Charles Lee Powell Hall (PHE) - 325
Audiences: Everyone Is Invited
Contact: Yuzhong Huang
Event Link: https://usc.zoom.us/j/99330288526
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PhD Thesis Proposal- Xin Qin
Wed, Mar 27, 2024 @ 12:45 PM - 01:45 PM
Thomas Lord Department of Computer Science
Student Activity
PhD Thesis Proposal- Xin Qin
Title: Data-driven and Logic-based Analysis of Learning-enabled Cyber-Physical Systems
Committee: Jyotirmoy Deshmukh, Chao Wang, Souti Chattopadhyay, Yan Liu and Paul Bogdan
Abstract: Rigorous analysis of cyber-physical systems (CPS) is becoming increasingly important, especially for safety-critical applications that use learning-enabled components. In this proposal, we will discuss various pieces of a broad framework that enable scalable reasoning techniques tuned to modern software design practices in autonomous CPS applications. The proposal will center around three main pillars: (1) Statistical verification techniques to give probabilistic guarantees on system correctness; here, we treat the underlying CPS application as a black-box and use distribution-free and model-free techniques to provide probabilistic correctness guarantees. (2) Predictive monitoring techniques that use physics-based or data-driven models of the system to continuously monitor logic-based requirements of systems operating in highly uncertain environments; this allows us to design runtime mitigation approaches to take corrective actions before a safety violation can occur. (3) Robust testing for CPS using reinforcement learning. We train an agent to produce a policy to initiate unsafe behaviors in similar target systems without the need for retraining, thereby allowing for the elicitation of faulty behaviors across various systems. The proposal hopes to demonstrate the scalability of our approaches on various realistic models of autonomous systems.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 349
Audiences: Everyone Is Invited
Contact: Xin Qin
