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Events for February 26, 2025
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EiS Communications Hub - Tutoring for Engineering Ph.D. StudentsWed, Feb 26, 2025 @ 10:00 AM - 12:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Viterbi Ph.D. students are invited to drop by the Hub for instruction on their writing and speaking tasks! All tutoring is one-on-one and conducted by Viterbi faculty.
Location: Ronald Tutor Hall of Engineering (RTH) - 222A
Audiences: Viterbi Ph.D. Students
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home
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: Paul Bogdan (USC / ECE) - Theoretical Foundations of NeuroAI: Challenges and A Gedanken Modeling Framework Motivated by Living Neuronal Networks Dynamics
Wed, Feb 26, 2025 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Paul Bogdan, USC / ECE
Talk Title: Theoretical Foundations of NeuroAI: Challenges and A Gedanken Modeling Framework Motivated by Living Neuronal Networks Dynamics
Abstract: Brains build compact models or discover governing laws of the world from just a few assumptions or noisy and conflicting observations. Biological brains can also predict uncanny events via memory-based analogies even when resources are limited. The ability of biological intelligence to discover, generalize, hierarchically reason and plan, and complete a wide range of unknown heterogeneous tasks calls for a comprehensive understanding of how distributed networks of interactions among neurons, glia, and vascular systems enable animal and human cognition. Such an understanding can serve as a basis for advancing the design of artificial general intelligence (AGI). In this talk, we will discuss the challenges and potential solutions for inferring the theoretical foundations of biological intelligence and NeuroAI which can guide the design of future A(G)I, expanding the limit of human discovery. To infer network structures from very scarce and noisy data, we propose a new mathematical framework capable of learning the emerging causal fractal memory from biological neuronal spiking activity. This framework offers insight into the topological properties of the underlying neuronal networks and helps us predict animal behavior during cognitive tasks. We will also discuss an AI framework for mining the optical imaging of brain activity and reconstructing the weighted multifractal graph generators governing the neuronal networks from very scarce data. This network generator inference framework can reproduce a wide variety of network properties, differentiate varying structures in brain networks and chromosomal interactions, and detect topologically associating domain regions in conformation maps of the human genome. We will discuss how network science-based AI can discover the phase transitions in complex systems and help with designing protein–nanoparticle assemblies. To infer the objectives and rules by which distributed networks of neurons attain intelligent decisions, we discuss an AI framework (multiwavelet-based neural operator) capable of learning, solving, and forecasting sets of coupled governing laws. We thus learn the operator kernel of an unknown partial differential equation (PDE) from noisy scarce data. For time-varying PDEs, this model exhibits 2-10X higher accuracy than state-of-the-art machine learning tools. Inspired by the multifractal formalism for detecting phase transitions in biological neuronal networks, we explore the principles of self-organization in Large Language Models (LLMs). Through the lens of multifractal analysis, we reveal the intricate dynamics of neuron interactions, showing how self-organization facilitates the emergence of complex patterns and intelligence within LLMs.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Paul Bogdan is the Jack Munushian Early Career Chair associate professor in the Ming Hsieh Department of Electrical and Computer Engineering at University of Southern California. He received his Ph.D. degree in Electrical & Computer Engineering at Carnegie Mellon University. His work has been recognized with a number of honors and distinctions, including the 2021 DoD Trusted Artificial Intelligence (TAI) Challenge award, the USC Stevens Center 2021 Technology Advancement Award for the first AI framework for SARS-CoV-2 vaccine design, the 2019 Defense Advanced Research Projects Agency (DARPA) Director’s Fellowship award, the 2018 IEEE CEDA Ernest S. Kuh Early Career Award, the 2017 DARPA Young Faculty Award, the 2017 Okawa Foundation Award, the 2015 National Science Foundation (NSF) CAREER award, the 2012 A.G. Jordan Award from Carnegie Mellon University for an outstanding Ph.D. thesis and service, and several best paper awards. His research interests include cyber-physical systems, new computational cognitive neuroscience tools for deciphering biological intelligence, the quantification of the degree of trustworthiness and self-optimization of AI systems, new machine learning techniques for complex multi-modal data, the control of complex time-varying networks, the modeling and analysis of biological systems and swarms, new control techniques for dynamical systems exhibiting multi-fractal characteristics, performance analysis and design methodologies for heterogeneous manycore systems.
Host: CS Department
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone (USC) 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. -
Computer Science General Faculty Meeting
Wed, Feb 26, 2025 @ 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 and staff only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Invited Faculty Only
Contact: Julia Mittenberg-Beirao
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. -
AME Seminar
Wed, Feb 26, 2025 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering, USC School of Advanced Computing
Conferences, Lectures, & Seminars
Speaker: Jessica Zhang, Carnegie Mellon University
Talk Title: Generative Manufacturing: AI + IGA, Digital Twins and Reduced Order Modeling for Applications in Additive Manufacturing
Abstract: Abstract: Generative manufacturing applies the power of artificial intelligence (AI) to generate and execute optimal solutions given customer-defined constraints and parameters, such as functional specifications, cost, and lead time, by exploring vast combinations of design and production alternatives based on material and process availability. In this talk, I will present our latest research on combining AI with isogeometric analysis (IGA) for applications in additive manufacturing (AM). It includes a machine learning (ML) framework for inverse design and manufacturing of self-assembling fiber-reinforced composites in 4D printing, IGA-based topology optimization for AM of heat exchangers, as well as data-driven residual deformation prediction to enhance metal component printability and lattice support structure design in the laser powder bed fusion (LPBF) AM process. By speeding up geometry distortion predictions from several hours to mere seconds, our model can be deployed to prevent generation of infeasible designs. Our on-going efforts also include developing digital twins to enable rapid prediction of stress-induced build failures in LPBF manufacturing using dynamic neural surrogates, where reduced order modeling is a key technique to efficiently simulate underlying physics.
Biography: Bio: Jessica Zhang is the George Tallman Ladd and Florence Barrett Ladd Professor of Mechanical Engineering at Carnegie Mellon University, with a courtesy appointment in Biomedical Engineering. She earned her B.Eng. in Automotive Engineering and M.Eng. in Engineering Mechanics from Tsinghua University, and her M.Eng. in Aerospace Engineering and Ph.D. in Computational Engineering and Sciences from The University of Texas at Austin. Her research interests include computational geometry, isogeometric analysis, the finite element method, data-driven simulations, and image processing, with a strong focus on their applications in computational biomedicine and engineering. Zhang has co-authored over 240 publications in peer-reviewed journals and conference proceedings and is the author of the book Geometric Modeling and Mesh Generation from Scanned Images (CRC Press). Her work spans both theoretical development and practical applications, contributing significantly to advancements in both fields. She is a Fellow of prominent societies, including ASME, SIAM, IACM, USACM, IAMBE, AIMBE, SMA, IMR and ELATES at Drexel, highlighting her distinguished reputation in the field. Currently, she serves as the Editor-in-Chief of Engineering with Computers, further establishing her leadership in computational science and engineering research. Zhang has received numerous awards, including two recent major recognitions: 2025 ASME Van C. Mow Medal for her meritorious contributions to the field of bioengineering and 2025 AWM-SIAM Sonia Kovalevsky Lecture Award for her achievements in applied and computational mathematics.
Host: The School of Advanced Computing
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1Location: James H. Zumberge Hall Of Science (ZHS) - 252
WebCast Link: https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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. -
DEN@Viterbi - How To Apply - Online Graduate Engineering Virtual Information Session
Wed, Feb 26, 2025 @ 05:00 PM - 06:00 PM
DEN@Viterbi, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi School of Engineering for a virtual information session via WebEx, providing an introduction to DEN@Viterbi, our top-ranked online delivery system. Discover the 40+ graduate engineering and computer science programs available entirely online. Attendees will have the opportunity to connect directly with USC Viterbi representatives during the session to discuss the admission process, program details, and the benefits of online delivery.
WebCast Link: https://uscviterbi.webex.com/weblink/register/refdcdc2f7dde9be7d6455c572f54a13a
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
Event Link: https://uscviterbi.webex.com/weblink/register/refdcdc2f7dde9be7d6455c572f54a13a
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.
