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Events for April 03, 2023

  • ECE-S Seminar - Dr Ruohan Gao

    Mon, Apr 03, 2023 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr Ruohan Gao, Postdoctoral Research Fellow | Department of Computer Science, Stanford University

    Talk Title: Multisensory Machine Intelligence

    Abstract: The future of Artificial Intelligence demands a paradigm shift towards multisensory perception-”to systems that can digest ongoing multisensory observations, that can discover structure in unlabeled raw sensory data, and that can intelligently fuse useful information from different sensory modalities for decision making. While we humans perceive the world by looking, listening, touching, smelling, and tasting, traditional form of machine intelligence mostly focuses on a single sensory modality, particularly vision. My research aims to teach machines to see, hear, and feel like humans to perceive, understand, and interact with the multisensory world. In this talk, I will present my research of multisensory machine intelligence that studies two important aspects of the multisensory world: 1) multisensory objects, and 2) multisensory space. In both aspects, I will talk about how I design systems to reliably capture multisensory data, how I effectively model them with new differentiable simulation algorithms and deep learning models, and how I explore creative cross-modal/multi-modal applications with sight, sound, and touch. In the end, I will conclude with my future plans.

    Biography: Ruohan Gao is a Postdoctoral Research Fellow working with Prof. Fei-Fei Li, Prof. Jiajun Wu, and Prof. Silvio Savarese in the Vision and Learning Lab at Stanford University. He obtained his Ph.D. advised by Prof. Kristen Grauman at The University of Texas at Austin and B.Eng. at The Chinese University of Hong Kong. Ruohan mainly works in the fields of computer vision and machine learning with particular interests in multisensory learning with sight, sound, and touch. His research has been recognized by the Michael H. Granof Award which is designated for UT Austin\'s Top 1 Doctoral Dissertation, the Google PhD Fellowship, the Adobe Research Fellowship, a Best Paper Award Runner Up at British Machine Vision Conference (BMVC) 2021, and a Best Paper Award Finalist at Conference on Computer Vision and Pattern Recognition (CVPR) 2019.

    Host: Dr Antonio Ortega, aortega@usc.edu

    Webcast: https://usc.zoom.us/j/93551506449?pwd=SzF2UTRRL1ZSQjF4N3VMdDlsOEJwUT09

    More Information: ECE Seminar Announcement 04.03.2023 Ruohan Gao.pdf

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248

    WebCast Link: https://usc.zoom.us/j/93551506449?pwd=SzF2UTRRL1ZSQjF4N3VMdDlsOEJwUT09

    Audiences: Everyone Is Invited

    Contact: Miki Arlen

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  • CS Colloquium: Tian Li (CMU) - Scalable and Trustworthy Learning in Heterogeneous Networks

    Mon, Apr 03, 2023 @ 11:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Tian Li, CMU

    Talk Title: Scalable and Trustworthy Learning in Heterogeneous Networks

    Series: CS Colloquium

    Abstract: To build a responsible data economy and protect data ownership, it is crucial to enable learning models from separate, heterogeneous data sources without data centralization. For example, federated learning aims to train models across massive networks of remote devices or isolated organizations, while keeping user data local. However, federated networks introduce a number of unique challenges such as extreme communication costs, privacy constraints, and data and systems-related heterogeneity.

    Motivated by the application of federated learning, my work aims to develop principled methods for scalable and trustworthy learning in heterogeneous networks. In the talk, I discuss how heterogeneity affects federated optimization, and lies at the center of accuracy and trustworthiness constraints in federated learning. To address these concerns, I present scalable federated learning objectives and algorithms that rigorously account for and directly model the practical constraints. I will also explore trustworthy objectives and optimization methods for general ML problems beyond federated settings.




    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Tian Li is a fifth-year Ph.D. student in the Computer Science Department at Carnegie Mellon University working with Virginia Smith. Her research interests are in distributed optimization, federated learning, and trustworthy ML. Prior to CMU, she received her undergraduate degrees in Computer Science and Economics from Peking University. She received the Best Paper Award at the ICLR Workshop on Security and Safety in Machine Learning Systems, was invited to participate in the EECS Rising Stars Workshop, and was recognized as a Rising Star in Machine Learning/Data Science by multiple institutions.

    Host: Dani Yogatama

    Location: Ronald Tutor Hall of Engineering (RTH) - 115

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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  • CS Colloquium: Willie Neiswanger (Stanford University) - AI-Driven Experimental Design for Accelerating Science and Engineering

    Mon, Apr 03, 2023 @ 02:00 PM - 03:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Willie Neiswanger, Stanford University

    Talk Title: AI-Driven Experimental Design for Accelerating Science and Engineering

    Series: CS Colloquium

    Abstract: AI-driven experimental design methods have the potential to accelerate costly discovery and optimization tasks throughout science and engineering-”from materials design and drug discovery to computer systems tuning and instrument control. These methods are promising as they provide the intelligent decision making needed for use in complex real-world problems where experiments are time-consuming or expensive, and efficiency is paramount. In the first part of my talk, I will discuss challenges that I encountered while applying these methods to new types of scientific optimization problems being pursued at national labs. I will then introduce an information-based framework for flexible experimental design, which overcomes these challenges by enabling easy customization to new problem settings. This framework is theoretically principled, and has been used by scientists for efficient materials synthesis and optimization in large scientific instruments. Along the way, I will discuss my vision for reliable systems that expand the scope of AI-driven experimental design and make it easier to use, so that it can be put in the hands of scientists, engineers, and other practitioners everywhere.


    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Willie Neiswanger is a postdoctoral scholar in the Computer Science Department at Stanford University. Previously, he completed his PhD in machine learning at Carnegie Mellon University. He develops machine learning techniques to perform optimization and experimental design in costly real-world settings, where resources are limited. His work spans topics in active learning, uncertainty quantification, Bayesian decision making, and reinforcement learning, and he applies these methods downstream to solve problems in science and engineering. Willie\'s work has received honors including a Best Paper Award at OSDI\'21, and has been published in top machine learning venues (e.g., NeurIPS, ICML, ICLR, AAAI, AISTATS) and natural science journals (e.g., J Chem Physics, J Immunology, Cell Reports, Nucl Fusion). He has also collaborated with the SLAC National Accelerator Laboratory and the Princeton Plasma Physics Laboratory, where his methods have been run live on particle accelerators and tokamak machines for optimization/control tasks.

    Host: Dani Yogatama

    Location: Ronald Tutor Hall of Engineering (RTH) - 105

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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