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Events for March 27, 2024
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CS Colloquium: Paul Liang - Foundations of Multisensory Artificial Intelligence
Wed, Mar 27, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Paul Liang, CMU
Talk Title: Foundations of Multisensory Artificial Intelligence
Abstract: Building multisensory AI systems that learn from multiple sensory inputs such as text, speech, video, real-world sensors, wearable devices, and medical data holds great promise for impact in many scientific areas with practical benefits, such as in supporting human health and well-being, enabling multimedia content processing, and enhancing real-world autonomous agents. In this talk, I will discuss my research on the machine learning principles of multisensory intelligence, as well as practical methods for building multisensory foundation models over many modalities and tasks. In the first half, I will present a theoretical framework formalizing how modalities interact with each other to give rise to new information for a task. These interactions are the basic building blocks in all multimodal problems, and their quantification enables users to understand their multimodal datasets and design principled approaches to learn these interactions. In the second part, I will present my work in cross-modal attention and multimodal transformer architectures that now underpin many of today’s multimodal foundation models. Finally, I will discuss our collaborative efforts in scaling AI to many modalities and tasks for real-world impact on mental health, cancer prognosis, and robot control. This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Paul Liang is a Ph.D. student in Machine Learning at CMU, advised by Louis-Philippe Morency and Ruslan Salakhutdinov. He studies the machine learning foundations of multisensory intelligence to design practical AI systems that integrate, learn from, and interact with a diverse range of real-world sensory modalities. His work has been applied in affective computing, mental health, pathology, and robotics. He is a recipient of the Siebel Scholars Award, Waibel Presidential Fellowship, Facebook PhD Fellowship, Center for ML and Health Fellowship, Rising Stars in Data Science, and 3 best paper/honorable mention awards at ICMI and NeurIPS workshops. Outside of research, he received the Alan J. Perlis Graduate Student Teaching Award for instructing courses on multimodal ML and advising students around the world in directed research.
Host: Willie Neiswanger / Xiang Ren
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
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Computer Science General Faculty Meeting
Wed, Mar 27, 2024 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Invited Faculty Only
Contact: Assistant to CS Chair
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
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
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
CS Colloquium: Teodora Baluta - New Algorithmic Tools for Rigorous Machine Learning Security Analysis
Wed, Mar 27, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Teodora Baluta, National University of Singapore
Talk Title: New Algorithmic Tools for Rigorous Machine Learning Security Analysis
Abstract: Machine learning security is an emerging area with many open questions lacking systematic analysis. In this talk, I will present three new algorithmic tools to address this gap: (1) algebraic proofs; (2) causal reasoning; and (3) sound statistical verification. Algebraic proofs provide the first conceptual mechanism to resolve intellectual property disputes over training data. I show that stochastic gradient descent, the de-facto training procedure for modern neural networks, is a collision-resistant computation under precise definitions. These results open up connections to lattices, which are mathematical tools used for cryptography presently. I will also briefly mention my efforts to analyze causes of empirical privacy attacks and defenses using causal models, and to devise statistical verification procedures with ‘probably approximately correct’ (PAC)-style soundness guarantees. This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Teodora Baluta is a Ph.D. candidate in Computer Science at the National University of Singapore. She enjoys working on security problems that are both algorithmic in nature and practically relevant. She is one of the EECS Rising Stars 2023, a Google PhD Fellow, a Dean’s Graduate Research Excellence Award recipient and a President’s Graduate Fellowship recipient at NUS. She interned at Google Brain working in the Learning for Code team. Her works are published in security (CCS, NDSS), programming languages/verification conferences (OOPSLA, SAT), and software engineering conferences (ICSE, ESEC/FSE). More details are available on her webpage: https://urldefense.com/v3/__https://teobaluta.github.io/__;!!LIr3w8kk_Xxm!pCgCXC327otABpiCTruPDSq7pyOXJEWhQ5X0UekIkZhAzt8Q0u0y5QtnemfzYURw7fop1LHm8tR_SY5JCA$ .
Host: Mukund Raghothaman
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
