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Conferences, Lectures, & Seminars
Events for September
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Epstein Institute, ISE 651 Seminar Class
Tue, Sep 03, 2024 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Yao Xie, Coca-Cola Foundation Chair and Professor, H. Milton Stewart School of Industrial & Systems Engineering, Georgia Tech
Talk Title: Connecting the Dots: Learning Point Processes via Monotone Variational Inequalities
Host: Dr. Qiang Huang
More Information: Dr. Yao Xie .jpg
Location: Social Sciences Building (SOS) - B2
Audiences: Everyone Is Invited
Contact: Casi Jones/ ISE
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MFD Distinguished Lecture Series: Leslie Abdul-Aziz
Tue, Sep 03, 2024 @ 04:00 PM - 05:20 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Dr. Leslie Abdul-Aziz, USC WiSE Gabilan Assistant Professor of Civil and Environmental Engineering
Talk Title: How to Make Carbon Capture Smarter: A Deep Dive Into Materials Chemistry for Integrated Carbon Capture and Utilization
Series: Mork Family Department Distinguished Lecture Series
Abstract: The conventional carbon capture and utilization (CCU) process involves separating CO2 from waste streams,transporting it through a pipeline, and then converting it into fuels and chemical commodities. On the other hand,Integrated Carbon Capture and Utilization (ICCU) or thermocatalytic reactive capture offers a more efficient approachwith lower energy requirements. This method directly transforms captured CO2 into methanol, synthesis gas, andcarbon monoxide.
At the heart of an ICCU scheme are dual-functional materials comprised of a catalyst affixed to a solid sorbent,enabling the selective capture and conversion of CO2 within the same reactor. This seminar will delve into efforts toenhance these materials, beginning with the example of Zr-modified Ni/CaO DFMs, which exhibit improved CO2 capture and conversion capabilities. Additionally, there will be a discussion on multifunctional materials such as self-regenerative Ni-doped CaTiO3/CaO nanocomposites, explored for CO2 capture and subsequent dry reforming of methane (ICCDRM). These materials have demonstrated stable CO2 capture capacity and syngas productivity overmultiple cycles, with reduced coke deposition due to small exsolved Ni nanoparticles and their strong interaction withthe host material.
Biography: Dr. Kandis Leslie Gilliard-AbdulAziz is an Assistant Professor of Civil and Environmental Engineering. She joined theUSC faculty in January 2024. Before joining the Viterbi School of Engineering, Dr. Gilliard-AbdulAziz was anAssistant Professor of Chemical and Environmental Engineering at the University of California, Riverside, where shedirected the Sustainable Lab at the University of California, Riverside, between 2018 – 2023. She earned her Ph.D. inChemistry from the University of Illinois at Urbana-Champaign in 2017 and was a Provost postdoctoral fellow at theUniversity of Pennsylvania from 2017-2018. She worked previously as a Forensic scientist for the Philadelphia policedepartment and as a Refinery chemist at Sunoco Chemicals in Philadelphia. She currently directs the Sustainable Lab,which primarily focuses on developing novel materials for sustainable catalytic processes for low-carbon chemicalproduction. Her primary research focus is novel materials development for CO2 sequestration and utilization using aninterdisciplinary toolset from bioengineering, chemistry, material science, chemical, and environmental engineering. Dr.Gilliard-AbdulAziz is the recipient of several awards and recognitions, including the Material Science of ExtremeEnvironments Young Investigator Award (2022), the National Science Foundation Career Award (2022), the Departmentof Energy Early Career Award (2023), and the Sloan Research Fellowship (2024).
Host: Mork Family Department of Chemical Engineering and Materials Science
More Information: 9_3 Leslie Abdul- Aziz Abstract.pdf
Location: James H. Zumberge Hall Of Science (ZHS) - 352
Audiences: Everyone Is Invited
Contact: William Wences
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ECE Seminar: From Single-agent to Federated Reinforcement Learning
Wed, Sep 04, 2024 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Yuejie Chi, Sense of Wonder Group Endowed Professor of Electrical and Computer Engineering in AI Systems, Carnegie Mellon University
Talk Title: From Single-agent to Federated Reinforcement Learning
Abstract: Reinforcement learning (RL) has garnered significant interest in recent years due to its success in a wide variety of modern applications. Q-learning, which seeks to learn the optimal Q-function of a Markov decision process (MDP) in a model-free fashion, lies at the heart of RL practices. However, theoretical understandings on its non-asymptotic sample complexity remain unsatisfactory, despite significant recent efforts. In this talk, we first show a tight sample complexity bound of Q-learning in the single-agent setting, together with a matching lower bound to establish its minimax sub-optimality. We then show how federated versions of Q-learning allow collaborative learning using data collected by multiple agents without central sharing, where an importance averaging scheme is introduced to unveil the blessing of heterogeneity.
Biography: Dr. Yuejie Chi is the Sense of Wonder Group Endowed Professor of Electrical and Computer Engineering in AI Systems at Carnegie Mellon University, with courtesy appointments in the Machine Learning department and CyLab. She received her Ph.D. and M.A. from Princeton University, and B. Eng. (Hon.) from Tsinghua University, all in Electrical Engineering. Her research interests lie in the theoretical and algorithmic foundations of data science, signal processing, machine learning and inverse problems, with applications in sensing, imaging, decision making, and generative AI. Among others, Dr. Chi is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), NSF CAREER Award, ONR YIP Award, AFOSR YIP Award, the inaugural IEEE Signal Processing Society Early Career Technical Achievement Award for contributions to high-dimensional structured signal processing, and multiple paper awards including the SIAM Activity Group on Imaging Science Best Paper Prize and IEEE Signal Processing Society Young Author Best Paper Award. She is an IEEE Fellow (Class of 2023) for contributions to statistical signal processing with low-dimensional structures.
Host: Drs. Richard M. Leahy (leahy@usc.edu) and Mahdi Soltanolkotabi (soltanol@usc.edu)
Webcast: https://usc.zoom.us/j/91569704176?pwd=zHQIlJ6vFqFmWPQYbARB8J3pXRbRiV.1Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
WebCast Link: https://usc.zoom.us/j/91569704176?pwd=zHQIlJ6vFqFmWPQYbARB8J3pXRbRiV.1
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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AME Seminar
Wed, Sep 04, 2024 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Daniel Chung, University of Melbourne
Talk Title: Modeling drag and heat transfer on riblets and roughness
Abstract: Riblets are a surface texture that reduce skin-friction drag in turbulent flow, and can now be found on in-service aircraft. Riblet features are smaller than the smallest vortices of turbulence. On the fuselage of a passenger aircraft, riblet spacing is about 100 microns. Riblet performance is notoriously sensitive to the fine details of their micro-structure, with optimal performance thought to require sharp tips, which are impossible to manufacture and maintain in practice. Thus, their successful application requires careful lifetime management of performance benefits, balanced against manufacturing, installation and maintenance costs. Key to this balancing act is our ability to accurately predict riblet performance given the inevitable micro-structure imperfections. To this end, I will discuss our group’s flow-physical modeling of the interaction between detailed riblet shapes and the near-wall vortices of turbulence; the outcome is a consistent improvement in accuracy of performance predictions across diverse riblet shapes.
Predicting rough-wall heat transfer has been a longstanding challenge, especially when new surface topographies are encountered. The heat-transfer coefficient of accreted ice on aircraft is different from that of engineered heat-exchanger surface textures. The best we can do are empirical correlations, which are not reliable. It is widely known that rough-wall heat transfer is not analogous to skin friction, i.e. not Reynolds analogy, but, then, what is it? With access now to the detailed temperature and flow fields near roughness features, I will show that heat transfer peaks at regions of the surface that are exposed to the oncoming flow, and, at these regions, a local version of Reynolds analogy survives. These insights allow us to develop a simple physics-based model of heat transfer that accounts for topography and working-fluid variations.
Biography: Daniel is an associate professor in the Department of Mechanical Engineering at the University of Melbourne. He obtained his bachelor's degree in engineering and computer science from the University of Melbourne in 2003, and his PhD in aeronautics from Caltech in 2009. He was a postdoc at the Jet Propulsion Laboratory before joining the University of Melbourne in 2012. Daniel's research is in computational fluid mechanics, where he tries to distil turbulent flows into simplified problems and to build physics-based models for prediction. Recently, he has been interested in turbulent flow and thermal convection over rough surfaces, riblets and sea waves, including control. Daniel is currently on a sabbatical at USC until the end of November, hosted by Prof Mitul Luhar, and is keen to explore collaborations.
More Info: https://ame.usc.edu/seminars/
Location: Seaver Science Library (SSL) - 202
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Fri, Sep 06, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Dennis Discher, Ph.D., Robert D. Bent Professor, and Director, Physical Sciences Oncology Center/Project University of Pennsylvania, Philadelphia, PA
Talk Title: From Anti-Tumor Macrophages to Nuclear Mechanobiology
Abstract: Acquired immunity against tumors can in principle exploit the genetic differences that always drive cancers. Myeloid-type innate immune cells typically initiate immunity, but the cohesiveness and microenvironment of solid tumors tends to oppose such functions. We engineer tumoricidal macrophages that engulf cancer cells to initiate acquired immunity, and have discovered a cooperative mechanism for overcoming tumor cohesion. Nucleus mechanosensing has a role in model systems and helps clarify a broader landscape mechano-regulation that extends to trends for mutations across different liquid and solid tumors.
Biography: The Discher lab at Penn has contributed across cell and molecular bioengineering, biophysics, and materials biology. The lab discovered matrix elasticity effects on stem cell differentiation (Cell 2006) and nucleus mechanosensing (Science 2013). Recent efforts have focused on the mechanbiology of genetic changes in cancer and engineering of macrophages against solid tumors (Nat BME 2023). The latter followed molecular studies of ‘foreign’ versus ‘self’ recognition (Science 2013) and were motivated by delivery studies of block copolymer nano-assemblies (Science 2002). Discher is an elected member of the US National Academy of Medicine, the US National Academy of Engineering, and the American Association for the Advancement of Science, and he serves on Editorial Boards of Science, Molecular Biology of the Cell, and PNAS Nexus, among other journals.
Host: Peter Wang
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Carla Stanard
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Epstein Institute, ISE 651 Seminar Class
Tue, Sep 10, 2024 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Brad Sturt Ph.D., Department of Information & Decision Sciences, University of Illinois Chicago
Talk Title: Improving the Security of United States Elections with Robust Optimization
Host: Dr. Johannes Royset
More Information: Brad Sturt 651 Flyer 9.10.24.png
Location: Social Sciences Building (SOS) - B2
Audiences: Everyone Is Invited
Contact: Casi Jones/ ISE
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AME Seminar
Wed, Sep 11, 2024 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Jian Lin, University of Missouri
Talk Title: Laser Induced Graphene: 2D-to-3D Transformation
Abstract: Since disclosed in 2014, laser induced graphene (LIG) has been explored for applications in various fields, ranging from materials science, environment to sensor and electronics. Despite much progress, due to limitation of the technology advances, the reports are quite restricted to planar (2D) device fabrication capability. To tackle this challenge, in this talk, we will discuss new strides in advancing the capability from 2D to 3D to unlock LIG potential in multifunctional 3D devices. The first technological advance is to develop a 5-axis laser processing platform in 2023. With the two additional two degrees of freedom, the laser beam can be focused on any arbitrary surfaces so that freeform laser induction (FLI) of representative LIG, metals, and metal oxides as high-performance sensing and electrode materials for 3D conformable electronics was realized. Based on this success, in 2024, we made a new progress in developing a freeform multimaterial assembly platform (FMAP) by integrating 3D printing (fused filament fabrication (FFM), direct ink writing (DIW)) with the FLI technique. 3D printing performs the 3D polymer material assembly, while the FLI in-situ synthesizes functional materials (LIG, metals, and semiconductors) on or within any predesigned locations of the 3D structures by synergistical, programmed control system actuation. By this robotic fabrication platform, a crossbar LED circuit, touchpad for human-machine interactions, multiple sensors, sensor-enveloped springs, 3D micro electromagnets, force feedback manipulators, and microfluidic reactors with embedded heating elements were demonstrated to show versatility and effectiveness of the methodology. Finally, we will discuss how artificial intelligence, generative models can be applied to such a robotic system to push it toward a fully autonomous fabrication system. References: Nat. Commun., 5, 5714, 2014; Adv. Funct. Mater. 33 (1), 2210084, 2023; (Nat. Commun., 15 (1), 4541, 2024.
Biography: Dr. Jian “Javen” Lin is an Associate Professor of Mechanical and Aerospace Engineering and the William R. Kimel Faculty Fellow in Engineering at University of Missouri (MU), where he was an Assistant Professor from 2014 to 2020. Prior to MU, he was a postdoctoral research associate in the Department of Mechanical Engineering & Materials Science at Rice University under guidance of Dr. James M. Tour from 2011 to 2014. He got his B.S. in Mechanical and Automation Engineering from Zhejiang University in 2007. He then studied at University of California-Riverside and received his M.S. in Electrical Engineering and Ph.D. in Mechanical Engineering in 2010 and 2011, respectively. Dr. Lin was awarded the ORAU Ralph E. Powe Junior Faculty Enhancement Award In 2015, received an Emerging Young Investigator award from Journal of Material Chemistry in 2016 and Sony Faculty Innovation Award in 2020. Since 2019, he has been continuously listed in Top 2% Scientists in the World by Stanford Advanced Study Institute. Dr. Lin’s research group dedicates research in materials and advanced manufacturing to promote biomedical, energy, and robotics fields. His research lies in two main clusters: 1) autonomous manufacturing powered by artificial intelligence and robotics; 2) 3D/4D printing. He has published ~ 120 journal papers and 6 issued patents with Google Scholar citations of > 13,000. (https://scholar.google.com/citations?hl=en&user=N9QA8vEAAAAJ&view_op=list_works)
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1Location: Seaver Science Library (SSL) - 202
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/
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Towards Trustworthy Physical AI Generalists
Thu, Sep 12, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ding Zhao , Associate Professor & Dean's Early Career Fellow - Carnegie Mellon University
Talk Title: Towards Trustworthy Physical AI Generalists
Abstract: Large language models like ChatGPT have shown that generalist foundation models can effectively tackle long-horizon tasks by training on extensive text data from the internet. It is anticipated that larger-scale data from the physical world, such as those generated by autonomous vehicles and the healthcare industry, could drive the next wave of AI development. A common challenge in deploying highly intelligent agents at scale in the physical world is ensuring their safety. In this talk, I will present our efforts to establish Trustworthy Physical AI Generalists to support this crucial transformation. I will explore the challenges of ensuring safety and generalization in the development of trustworthy AI, and discuss potential solutions, including rare event analysis, safe reinforcement learning, hierarchical generative models for task identification and transferability, and causal reasoning to improve generalizability. Additionally, I will discuss the advantages and challenges of using LLMs to develop physical AI generalists. I will introduce applications of our work in heart attack detection and acute care, self-driving technology, and robotic autonomy for assisting seniors and conducting safety-critical tasks related to climate change resilience.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
https://usc.zoom.us/j/99488778795?pwd=oXg76V89VYG9b5I0CIEcn2E2Fz7d6z.1
Meeting ID: 994 8877 8795
Passcode: 868727
Biography: Ding Zhao is an Associate Professor and Dean's Early Career Fellow at Carnegie Mellon University, where he leads the Safe AI Lab. His research focuses on developing Trustworthy Physical AI Generalists for high-stakes applications at scale. Prof Zhao was invited by Uber ATG to enhance fleet safety following the world’s first fatal autonomous vehicle collision. Zhao collaborates with leading industry partners, including Google, Nvidia, Amazon, Apple, Microsoft, IBM, Ford, Uber, Bosch, Toyota, and Rolls-Royce. He has grants from NSF, DOT, DOE, and DARPA and published over 120 papers in top venues such as ICML, NeurIPS, ICLR, AISTATS, CoRL, ICRA, IROS, and Nature Communications. Zhao has mentored 20 Ph.D. students and 7 postdocs, with 7 of them becoming faculty members in academia. Zhao has received numerous awards, including CMU Dean's Early Career Fellow Professorship, Provost's Inclusive Teaching Fellows Award, National Science Foundation CAREER Award, MIT Technology Review 35 Under 35 Award in China, George N. Saridis Best IEEE Transactions Paper Award, George Tallman Ladd Research Award, Struminger Teaching Award, Ford University Collaboration Award, Qualcomm Innovation Award, Carnegie-Bosch Research Award, and various industrial fellowship awards from Google DeepMind, Adobe, Toyota, and Bosch. His work has garnered attention from media outlets such as the New York Times, Forbes, TIME, IEEE Spectrum, Popular Science, Telegraph, and Wired.
Host: Assistant Prof. Yue Wang
Webcast: https://usc.zoom.us/j/99488778795?pwd=oXg76V89VYG9b5I0CIEcn2E2Fz7d6z.1Location: Olin Hall of Engineering (OHE) - 132
WebCast Link: https://usc.zoom.us/j/99488778795?pwd=oXg76V89VYG9b5I0CIEcn2E2Fz7d6z.1
Audiences: Everyone Is Invited
Contact: Thomas Lord Department of Computer Science
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Seminar: The USC School of Advanced Computing
Thu, Sep 12, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Gaurav S. Sukhatme, Professor, Computer Science and Electrical Computer Engineering at USC
Talk Title: The USC School of Advanced Computing
Abstract: Dr. Sukhatme will describe plans on the newly announced USC School of Advanced Computing and its place within the wider USC initiative on Frontiers of Computing.
Biography: Gaurav S. Sukhatme is Professor of Computer Science and Electrical and Computer Engineering at the University of Southern California (USC) and an Amazon Scholar. He is the Director of the USC School of Advanced Computing and the Executive Vice Dean of the USC Viterbi School of Engineering. He holds the Donald M. Aldstadt Chair in Advanced Computing at USC. He was the Chairman of the USC Computer Science department from 2012-17. He received his undergraduate education in computer science and engineering at IIT Bombay and his M.S. and Ph.D. degrees in computer science from USC. Sukhatme is the co-director of the USC Robotics Research Laboratory and the USC Robotic Embedded Systems Laboratory director. His research interests are in networked robots, learning robots, and field robotics. He has published extensively in these and related areas. Sukhatme has served as PI on numerous NSF, DARPA, and NASA grants. He was a Co-PI on the Center for Embedded Networked Sensing (CENS), an NSF Science and Technology Center. He is a Fellow of the AAAS, AAAI, and the IEEE, a recipient of the NSF CAREER award, the Okawa Foundation research award, and an Amazon research award. He is one of the founders of the Robotics: Science and Systems conference. He was the program chair of the 2008 IEEE International Conference on Robotics and Automation and the 2011 IEEE/RSJ International Conference on Robots and Systems. He is currently the Editor-in-Chief of Autonomous Robots (Springer Nature) and has served in the past as Associate Editor of the IEEE Transactions on Robotics and Automation, the IEEE Transactions on Mobile Computing, and on the editorial board of IEEE Pervasive Computing.
Host: Craig Knoblock
More Info: https://www.isi.edu/events/5111/Seminar:-The-USC-School-of-Advanced-Computing/
Location: Information Science Institute (ISI) - 1135/1137
Audiences: Everyone Is Invited
Contact: Tricia Olmedo
Event Link: https://www.isi.edu/events/5111/Seminar:-The-USC-School-of-Advanced-Computing/
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Semiconductors & Microelectronics Seminar - Yuanwei Li, Thursday, 9/12 at 2:30pm in EEB 132
Thu, Sep 12, 2024 @ 02:30 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yuanwei Li, Stanford University
Talk Title: Towards Designing Functionality: Nano-Architected Materials for Next-Generation Sustainability and Health Monitoring
Series: Semiconductors & Microelectronics Technology
Abstract: A central aim in materials science is the ability to dictate functionality through deliberate design rules, leading to the synthesis and characterization of targeted structures. Inspired by natural materials' assembly and optical properties, my research develops nano-architected materials from nanoscale to macroscale, each tailored with specific chemical and optical properties. This talk will delve into the intersection of chemistry, nanomaterials, and optical physics to innovate materials for enhanced sustainability and health monitoring applications.My research focuses on designing functional colloidal crystals using principles inspired by the geometric intricacies observed in natural systems. Employing DNA-functionalized inorganic nanoparticles as the building blocks, I have developed multicomponent and porous colloidal crystals through programmable assemblies, advancing the complexity achievable in crystalline structures. These crystals are engineered to possess unique functionalities such as negative refraction, broadband absorption, and significant mechanical robustness. Moreover, I address synthetic challenges in creating porous crystals with tunable pore sizes ranging from 10 to 1000 nm, which can be employed in applications from advanced catalysis to optical devices like invisibility cloaks and miniaturized mechanical components.I extend my expertise to designing intricate metamaterials that synergize bottom-up assemblies with top- down lithography for health monitoring by developing optical biosensors. Focusing on the continuous, multiplexed monitoring of key metabolites associated with chronic stress, my approach integrates high-quality- factor dielectric metasurfaces with plasmonic spherical nucleic acids composed of modular DNA aptamer probes. Demonstrating sub-picomolar sensitivity, this optical sensor enables real-time, multiplexed detection across dense arrays of resonators, potentially revolutionizing portable health monitoring systems.
Biography: Yuanwei Li is a postdoctoral fellow in Materials Science and Engineering at Stanford University, as a Stanford Science Fellow under the guidance of Prof. Jennifer Dionne. She focuses on developing new optical nanomaterials for biosensing and photocatalysis. She received her PhD in Chemical and Biological Engineering at Northwestern University as a Ryan Fellow, working with Prof. Chad Mirkin. Her graduate research focused on the programmable assembly of nanoparticles into colloidal crystals with tailored chemical, optical, and mechanical properties by design. Her work has been published in Nature, Science, Nature Materials, and Science Advances. She received the MRS Graduate Student Award, Outstanding Research Award by the International Institute for Nanotechnology, the SPIE Optics and Photonics Education Scholarship, and has been named a Rising Star in Chemical Engineering by MIT.
Host: Jayakanth Ravichandran, Joshua Yang, Chongwu Zhou, Stephen Cronin, Wei Wu
More Information: Yuanwei Li Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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An Undergraduate and Graduate Research Project
Thu, Sep 12, 2024 @ 03:00 PM - 04:00 PM
Astronautical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. James Wertz, Adjunct Professor
Talk Title: Creating an International Lunar University and Living and Working on the Moon in the Near Term
Abstract: The NASA RASC-AL (Revolutionary Aerospace Systems Concepts – Academic Linkage) student competitionhas a topic this year in “Sustained Lunar Evolution” that fits very well with the spring, 2025 ASTE 523course in “Near-Term Lunar Colonies.” The competition is open to a university group with undergraduateand graduate students. Unfortunately, there is no monetary award, but we will use the project as asubstitute for the course final exam. (You don’t have to take the course to join the competition group.) Recent research suggests that we should be able to create a profitable, income-generating lunarsettlement and an international lunar university that would allow graduate students, faculty,entrepreneurs, and tourists to live, work, and vacation on the Moon in the next 5 to 10 years at moderatecost. There is a catch, however. This needs to be a commercial activity – selling products, vacations, realestate, sponsored research, and other commercial elements and activities and making a rather largeprofit. Of course, that profit comes in part from advertising on the Earth for the product or informationthat you are developing on the Moon. Unfortunately, traditional astronautics professionals know zero (orless) about marketing and commercial activity. We’re offering this seminar (and introduction to the spring semester course at USC) for undergraduateand graduate students and faculty in any area (even astronautics) to get your input, ideas, and wisdom onhow to do this and to see if you would be interested in working on USC’s NASA RASC-AL StudentCompetition for Sustained Lunar Colonization. It will take a range of skills to achieve this – marketing, business, legal, science, architecture, engineeringand quite a few more. Come join us to discuss how this could work for your students, your colleagues, oryou. There is literally a new world available to us. Any questions? Send them to jwertz@smad.com.
Biography: Dr. Wertz is an Adjunct Professor at USC and the President of Microcosm. Hisexpertise ranges from topics such as space mission engineering, low-cost spaceand launch systems, autonomous navigation and orbit control, satellite orbit andattitude systems, and low-cost lunar missions.
Host: Dr. James Wertz
Webcast: https://urldefense.com/v3/__https:/usc.zoom.us/j/95576853605?pwd=kYw0tmdC73IaHlqWz4aaZkk1vfC4rD.1__;!!LIr3w8kk_Xxm!p47bYWU0GvqWj_jVloaECYBw-5nM45NiS3hO97qdHfGQ9P4wLvCtAGxEzlAp6FZdvJwqfU8N-RezPA$More Information: USC Lunar Seminar V4 9-12-24.pdf
Location: Ronald Tutor Hall of Engineering (RTH) - 306
Audiences: Everyone Is Invited
Contact: Shanya Olivares
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AI Seminar-How Far Are We from Achieving AI Automation in the Digital World?
Fri, Sep 13, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Shuyan Zhou, Duke University
Talk Title: How Far Are We from Achieving AI Automation in the Digital World?
Abstract: For years, my dream has been to create autonomous AI agents capable of carrying out tedious procedural tasks (e.g., arranging conference travel), allowing me to focus on more creative and exciting tasks. Modern AI models, especially large language models (LLMs) like ChatGPT, have suddenly brought us much closer to achieving such AI agents. But, has my dream already come true? In this talk, I will answer this question by delving into our systematic evaluation of AI agents in realistic tasks. The evaluation uncovers many critical limitations of AI agents, such as tool use, abstract reasoning, and knowledge cutoff. It suggests that LLMs are crucial yet early steps towards AI autonomy. To address these challenges, I will introduce our research of a more suitable “language” for AIs, which overcomes the inherent limitations of using natural language for task solving. Finally, I will discuss how to leverage the vast human-authored knowledge available on the Internet more effectively to better equip agents to perform complex tasks autonomously. Zoom Meeting ID: 930 3528 9629Meeting Password: 911619
Biography: Shuyan Zhou is an incoming Assistant Professor at the Computer Science Department at Duke University in Fall 2025. She is currently a researcher at Meta GenAI. She received her Ph.D. from Carnegie Mellon University, where she was advised by Graham Neubig. Her research in AI focuses on creating AI agents for real-world tasks, such as using computers and generating code. Her work has been recognized at top natural language processing and machine learning conferences and journals such as ICLR, ICML, ACL, EMNLP, and TACL. You can find more about her at https://shuyanzhou.com This AI Seminar presentation recorded and posted on our USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI. Subscribe here to learn more about upcoming seminars: https://www.isi.edu/events/
Host: Abel Salinas and Karen Lake
More Info: https://www.isi.edu/events/5093/how-far-are-we-from-achieving-ai-automation-in-the-digital-world/
Webcast: https://usc.zoom.us/j/93035289629?pwd=FHXpqO3SHcKEppeDseLS1Y2d3blmry.1Location: Information Science Institute (ISI) - Virtual Only
WebCast Link: https://usc.zoom.us/j/93035289629?pwd=FHXpqO3SHcKEppeDseLS1Y2d3blmry.1
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://www.isi.edu/events/5093/how-far-are-we-from-achieving-ai-automation-in-the-digital-world/
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Fri, Sep 13, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Gerald E. Loeb, M.D., Professor, Alfred E. Mann Department of Biomedical Engineering, USC
Talk Title: Bayesian Exploration for Intelligent Haptics and Medical Diagnosis
Abstract: In research, one thing leads to another, often surprisingly. After being asked by DARPA to model control systems for prosthetic arms, we realized that their hands needed tactile sensing technology that didn’t exist, so we invented that and started a company to build the sensors. Then we needed a new form of artificial intelligence to decide what exploratory movements to use with those sensors, so we invented that. Then we realized that the problem of haptic identification of objects was similar to the problem of clinical differential diagnosis. At each step, the clinician must figure out what observation or test will be most useful in arriving at a final diagnosis. So we’re developing that application of Bayesian Exploration now and testing it in a clinic in the USC Ostrow School of Dentistry.
Biography: Gerald Loeb received his medical degree from Johns Hopkins University and has led research in fundamental neurophysiology and applied neural prosthetics at the US NIH, Queen’s University (Canada) and now University of Southern California. He is Fellow of the US National Academy of Inventors and has been a founder or principal in several companies that have commercialized his inventions. He now directs the USC BME Innovation Space and teaches students about the development and regulation of medical devices.
Host: Jennifer Treweek
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Carla Stanard
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Geometric Regularizations for 3D Shape Generation
Mon, Sep 16, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Qixing Huang , Associate Professor, Computer Science Department - University of Texas at Austin
Talk Title: Geometric Regularizations for 3D Shape Generation
Abstract: Generative models, which map a latent parameter space to instances in an ambient space, enjoy various applications in 3D Vision and related domains. A standard scheme of these models is probabilistic, which aligns the induced ambient distribution of a generative model from a prior distribution of the latent space with the empirical ambient distribution of training instances. While this paradigm has proven to be quite successful on images, its current applications in 3D generation encounter fundamental challenges in the limited training data and generalization behavior. The key difference between image generation and shape generation is that 3D shapes possess various priors in geometry, topology, and physical properties. Existing probabilistic 3D generative approaches do not preserve these desired properties, resulting in synthesized shapes with various types of distortions. In this talk, I will discuss recent work that seeks to establish a novel geometric framework for learning shape generators. The key idea is to model various geometric, physical, and topological priors of 3D shapes as suitable regularization losses by developing computational tools in differential geometry and computational topology. We will discuss the applications in deformable shape generation, latent space design, joint shape matching, and 3D man-made shape generation. This research is supported by NSF IIS 2413161.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
https://usc.zoom.us/j/93012253116?pwd=4bJJFbbbfblFHRjabBBvvCuavDml6J.1
Meeting ID: 930 1225 3116
Passcode: 570060
Biography: Qixing Huang is an associate professor with tenure at the computer science department of the University of Texas at Austin. His research sits at the intersection of graphics, geometry, optimization, vision, and machine learning. He has published more than 100 papers at leading venues across these areas. His research has received several awards, including multiple best paper awards, the best dataset award at Symposium on Geometry Processing 2018, IJCAI 2019 early career spotlight, multiple industrial and NSF awards, and 2021 NSF Career award. He has also served as area chairs of CVPR, ECCV, ICCV and technical papers committees of SIGGRAPH and SIGGRAPH Asia, and co-chaired Symposium on Geometry Processing 2020.
Host: Assistant Prof. Yue Wang
Webcast: https://usc.zoom.us/j/93012253116?pwd=4bJJFbbbfblFHRjabBBvvCuavDml6J.1Location: Olin Hall of Engineering (OHE) - 136
WebCast Link: https://usc.zoom.us/j/93012253116?pwd=4bJJFbbbfblFHRjabBBvvCuavDml6J.1
Audiences: Everyone Is Invited
Contact: Thomas Lord Department of Computer Science
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CSC/CommNetS-MHI Seminar: Thomas Zhang
Mon, Sep 16, 2024 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Thomas Zhang, University of Pennsylvania
Talk Title: Guarantees for Representation Learning: Distribution Shift, Optimization, Fewer Samples
Abstract: In many areas of machine learning, it is the general understanding that broadly useful features can be extracted from data across different tasks or domains. This forms the key intuition behind the "pre-train then fine-tune" paradigm, and more generally representation learning (learning the feature mappings) and transfer learning (downstream performance on unseen tasks). Naturally, there has been significant effort to document the benefit of using diverse multi-task data both empirically and theoretically. However, prior works impose various assumptions that greatly affect their applicability, especially in settings involving data generated by dynamical systems, e.g. robotics and control.
In this talk, I will introduce the multi-task representation learning problem, and walk through the pathologies arising from sequential settings, previewed in the talk title. I will then present our recent results addressing many of these issues. In particular, we provide generalization guarantees which illustrate the benefit of learning a shared representation across domains, remaining valid even when there are too few samples to solve each task individually. We then show that optimizing for the representation is surprisingly hard, requiring critical algorithmic modifications to ensure convergence. Lastly, I will show how these results, descended from iid learning, can be lifted to dynamical systems to ensure closed-loop performance.
Biography: Thomas Zhang is a 5th-year PhD student at the University of Pennsylvania advised by Prof. Nikolai Matni. His research interests involve a combination of dynamical systems, statistical learning, and control theory. Prior to Penn, Thomas received BSc’s in Mathematics and Statistics & Data Science from Yale University, where he then spent a year as a research scientist in the Applied Mathematics Program.
Host: Dr. Stephen Tu, stephen.tu@usc.edu
More Information: 2024.09.16 CSC Seminar - Thomas Zhang.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
Audiences: Everyone Is Invited
Contact: Miki Arlen
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Epstein Institute, ISE 651 Seminar Class
Tue, Sep 17, 2024 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Damek Shea Davis, University of Pennsylvania, Cornell University
Talk Title: Nonconvex optimization for statistical estimation and learning: beyond smoothness and convexity
Host: Dr. Johannes Royset
Location: Social Sciences Building (SOS) - B2
Audiences: Everyone Is Invited
Contact: Casi Jones/ ISE
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MFD Distinguished Lecture Series: Dr. Gennady Gor
Tue, Sep 17, 2024 @ 04:00 PM - 05:20 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Dr. Gennady Gor, Associate Professor, Chemical and Materials Engineering. New Jersey Institute of Technology
Talk Title: Elasticity of Fluids in Nanopores: Molecular Modeling and Ultrasonic Experiments
Series: Mork Family Department Distinguished Lecture Series
Abstract: Fluids confined in nanopores are ubiquitous in nature and technology. In recent years, the interest in confined fluids hasgrown, driven by research on unconventional hydrocarbon resources -- shale gas and shale oil, much of which areconfined in nanopores. When fluids are confined in nanopores, many of their properties differ from those of the samefluid in the bulk. These properties include density, freezing point, transport coefficients, thermal expansion coefficient,and, as it was shown recently, elastic properties.
The elastic modulus of a fluid confined in the pores contribute to the overall elasticity of the fluid-saturated porousmedium and determine the speed at which elastic waves traverse through the medium. In this talk I will show howelastic modulus of a confined fluid in a nanopore can be calculated based on Monte Carlo and molecular dynamicssimulations and illustrate it with calculations for various fluids. Additionally, I will present our recent experimentalmeasurements of elastic properties of water confined in nanoporous glass samples. Our results suggest that some of themodels widely used for describing elasticity of fluid-saturated porous solids need to be revised.
Biography: Dr. Gennady Gor is an associate professor at NJIT. He received a PhD in theoretical physics from St. PetersburgUniversity, Russia, in 2009. He continued his research in the United States, first at Rutgers University, and then atPrinceton University and Naval Research Laboratory. In 2016, he joined the faculty of NJIT.
The central focus of Dr. Gor's research is in interactions of fluids with porous materials. He is an expert in molecularmodeling of fluid adsorption, known for his contributions to modern methods of adsorption porosimetry and thedevelopment of the theory of adsorption-induced deformation. His current research interests include confined liquidsand electrolytes, atmospheric aerosols, lithium-ion batteries, and ultrasound propagation in porous media. Dr. Gorauthored more than 70 peer-reviewed publications and is the recipient of the National Research Council Associateship(2014) and the NSF CAREER Award (2020)
Host: Mork Family Department of Chemical Engineering and Materials Science
More Information: 9_17 Gennady Gor Abstract.pdf
Location: James H. Zumberge Hall Of Science (ZHS) - 352
Audiences: Everyone Is Invited
Contact: William Wences
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Equalizing Hollywood: Using AI to Make Professional Grade Art & Content That Sells & Impacts
Wed, Sep 18, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Stephen Gibler, Adjunct Professor, USC School of Cinematic Arts
Talk Title: Equalizing Hollywood: Using AI to Make Professional Grade Art & Content That Sells & Impacts
Abstract: In “Equalizing Hollywood,” discover how AI is democratizing the creation of professional-grade art and content. This talk explores innovative AI tools that empower creators to produce high-quality work that not only sells but also makes a significant impact. Learn about the transformative potential of AI in leveling the playing field in the entertainment industry. Join us to see how technology is shaping the future of Hollywood and beyond.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Stephen Gibler has been a producer in Los Angeles for over a decade, running production for over 50 projects, including eight feature films, over a dozen commercials, 30 new media projects, 5 reality TV shows, and one of the largest immersive art museums in China ’The Silos’. He has collaborated with renowned figures in the film industry, such as James Ivory, Jackie Earle Haley, Haley Joel Osment, Molly Ringwald, Drake Doremus, and many others while also working with brands such as Amazon, Lancome, Head & Shoulders, Fiverr, AMC, and so forth. Stephen now serves as an adjunct professor at USC’s School of Cinematic Arts and is the founder of AI Tech Startup, Logline AI, aiming to use AI to accelerate the creative process in filmmaking.
Host: CAIS
More Info: https://cais.usc.edu/events/usc-cais-seminar-with-stephen-gibler/
Location: Montgomery Ross Fisher Building (school Of Social Work) (MRF) - 102
Audiences: Everyone Is Invited
Contact: Thomas Lord Department of Computer Science
Event Link: https://cais.usc.edu/events/usc-cais-seminar-with-stephen-gibler/
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AAI-CCI-MHI Seminar on CPS
Wed, Sep 18, 2024 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Roy Fox, Assistant Professor of Computer Science Department, University of California Irvine
Talk Title: World Models -” Driven by Vision and Language; Driving Transfer
Series: EE598 Seminar Series
Abstract: An agent learning to control its environment would often do well to first model it. Compared to control policies, world models use a richer training signal and tend to generalize and transfer better. However, for a model to induce good behavior, it must be highly accurate in all reachable states, which may require too much data. Because efforts to leverage web-scale data for control are yet to succeed as they famously have for vision and language, we ask: can information encoded in vision and language foundation models help guide world modeling? In the first part of this talk, we will see two such methods: one that uses a segmentation foundation model to block visual distractions and keep state representations task-relevant; and one that queries a language model to hypothesize about abstract world models that guide exploration and planning. In the second part of the talk, we will revisit the transfer power of world models in two settings: simulation-to-reality and delayed perception. We will see how a model of a simulator can be adapted to reality with a tiny amount of data; and how a world model can transfer across varying delays of the agent's observations.
Biography: Roy Fox is an Assistant Professor of Computer Science at the University of California, Irvine. His research interests include theory and applications of control learning: reinforcement learning (RL), control theory, information theory, and robotics. His current research focuses on structured and model-based RL, language for RL and RL for language, and optimization in deep control learning of virtual and physical agents.
Host: Stephen Tu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Ariana Perez
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MFD Seminar: Dr. Marina Filip | Understanding Excitons in Chemically and Structurally Heterogeneous Semiconductors
Wed, Sep 18, 2024 @ 02:00 PM - 03:00 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Dr. Marina Filip, Oxford University
Talk Title: Understanding Excitons in Chemically and Structurally Heterogeneous Semiconductors
Abstract: In the first part of the talk, studies of exciton delocalization in several heterogeneous semiconductors within the broader family of halide perovskites will be presented. The discussion will include a recent analysis of optical excitations in quasi-two-dimensional (quasi-2D) organic-inorganic halide perovskites and demonstrate how subtle structural features can significantly impact exciton delocalization in these systems. In the second part of the talk (time permitting), a new methodological development will be introduced that generalizes the Bethe-Salpeter equation (BSE) to account for the influence of ionic vibrations on the dielectric screening of excitons. This approach allows for the computation of the rate of exciton dissociation upon scattering with phonons.
Biography: Marina Filip is an Associate Professor of Condensed Matter Physics at the University of Oxford and a Tutorial Fellow in Physics at University College, Oxford. Before joining the Oxford Physics faculty in February 2020, Marina was a postdoctoral scholar in the Physics Department at UC Berkeley and Lawrence Berkeley National Laboratory (2018-2020) and the Materials Department at the University of Oxford (2015-2018). Marina received her doctorate in Materials Science from the University of Oxford in 2016, and completed her undergraduate studies in Physics, at the University of Bucharest, Romania. Marina was recently awarded the 2024 IUPAP Early Career Scientist Prize in Semiconductor Physics, and is currently visiting Berkeley as a Somorjai Miller Visiting Professor.
Host: Dr. Zhenglu Li
More Information: Seminar_Marina-Filip.pdf
Location: Kaprielian Hall (KAP) - 145
Audiences: Everyone Is Invited
Contact: Candy Escobedo
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AME Seminar
Wed, Sep 18, 2024 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Agnimitra Dasgupta, USC
Talk Title: Solution of physics-constrained inverse problems using conditional diffusion models
Abstract: Inverse problems involve deducing the cause from observed effects and are ubiquitous across several science and engineering disciplines. Generally ill-posed, an inverse problem often has multiple solutions. The Bayesian paradigm remains popular for the statistical treatment of inverse problems because it is useful for characterizing the relative plausibility of different solutions. However, Bayesian inference is computationally intractable in most practical scenarios. Some recurring challenges include summarizing available data into informative priors, sampling high-dimensional posteriors, and the need for multiple evaluations of a compute-intensive numerical model, likely black-box and mis-specified, for the forward physics. This talk will introduce conditional score-based diffusion models for solving inverse elasticity problems. A conditional score-based diffusion model uses a neural network to approximate the target posterior distribution’s ‘score function’, defined as the gradient of the logarithm of the density. Subsequently, Langevin dynamics enables the generation of new realizations from the target posterior. Training the diffusion model requires a supervised dataset, and forward model simulations can easily construct it. Therefore, the proposed approach is simulation-based and likelihood-free, and there is no need for gradient computations through the forward physics model. Moreover, the diffusion model is reusable for different measurement instances, unlike conventional MCMC-based inference, which amortizes the cost of inference. This talk will demonstrate the efficacy of conditional score-based diffusion model-driven inference on several physics-constrained inverse problems, primarily concerning inverse elasticity problems, that involve synthetic and real experimental data.
Biography: Agnimitra Dasgupta is a Postdoctoral Research Associate in the Aerospace and Mechanical Engineering Department at the University of Southern California (USC). He obtained his Ph.D. in Civil Engineering from USC and a Master's in Civil Engineering from the Indian Institute of Science. Agnimitra's research interest lies at the intersection of uncertainty quantification and scientific machine learning with applications ranging from the health to infrastructure sectors. Agnimitra received the Provost’s Fellowship from USC between 2017 and 2021.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1Location: Seaver Science Library (SSL) - 202
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/
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Civil and Environmental Engineering Department Seminar Series
Thu, Sep 19, 2024 @ 02:00 PM - 03:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Robert Otani, Thornton Tomasetti Inc
Talk Title: AI and the future of Structural Engineering
Host: Dr Lucio Soibelman and Dr David Gerber
More Information: Robert Otani Announcement.docx
Location: Kaprielian Hall (KAP) - 209
Audiences: Everyone Is Invited
Contact: Salina Palacios
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Fri, Sep 20, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Weixin Tang, Neubauer Family Assistant Professor of Chemistry- University of Chicago
Talk Title: Directed evolution of adenosine deaminases for epigenetic profiling and gene editing
Abstract: N6-methyladenosine (m6A), the most prevalent internal mRNA modification in higher eukaryotes, depicts a regulatory network extensively involved in the mRNA life cycle. To elucidate the multitude of functions served by m6A, we developed evolved TadA-assisted N6-methyladenine sequencing (eTAM-seq), an enzyme-assisted sequencing technology that detects and quantifies m6A by global adenosine deamination. With eTAM-seq, we profiled m6A in the transcriptomes of cell lines and mouse tissues. I will discuss development, applications, and current limitations of eTAM-seq. For the second half of my talk, I will present our recent progress on directed evolution of an adenine base editor (ABE) with increased context compatibility. Existing ABEs function most effectively when the target A is in a TA context. We report directed evolution of TadA8r, a new TadA variant that extends potent deoxyadenosine deamination to RA (R = A or G). ABE8r outperforms existing editors in correcting 41.9% of 9,407 disease-associated G:C-to-A:T transitions in the human genome, and shows a controlled off-target profile. I will present the development and applications of TadA8r in gene editing. I will also discuss how directed evolution may be harnessed to shape context compatibility and specificity.
Biography: Weixin Tang received her B.S. in Chemistry and Biology from Tsinghua University and her Ph.D. in Chemistry from the University of Illinois, Urbana-Champaign. She was a Jane Coffin Childs Memorial postdoctoral fellow at the Broad Institute of MIT and Harvard prior to joining the Chemistry Department at the University of Chicago as a Neubauer Family Assistant Professor in 2019. The Tang Lab works towards a comprehensive toolbox for precise manipulation of the human genome, and for detecting epigenetic and epitranscriptomic modifications at high resolution. Tang was named a Searle Scholar in 2021. She also received the NIBIB Trailblazer Award and the Packard Fellowship for science and engineering.
Host: Peter Wang
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Carla Stanard
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ECE Seminar: Technology development for functional and morphological imaging of the middle and inner ear
Tue, Sep 24, 2024 @ 01:30 PM - 02:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Brian E. Applegate, Professor of Otolaryngology-“Head & Neck Surgery, Ophthalmology, and Biomedical Engineering, University of Southern California
Talk Title: Technology development for functional and morphological imaging of the middle and inner ear
Abstract: Over the past decade we have been developing Optical Coherence Tomography and Vibrometry (OCTV) to measure the detailed morphology and vibratory response of the ear. With micron scale spatial resolution and subnanometer sensitivity to vibration it is well suited to measuring the spatially resolved vibratory response of both the inner and middle ear. In small animals, it is possible to image directly through the bone of the otic capsule for noninvasive spatially resolved vibrometry of the cochlear partition. In humans as well as small animals, it’s possible to image the tympanic membrane and ossicles through the ear canal to reveal the vibratory response of the middle ear. Nominally, this approach allows for the measure of vibratory response along the light path of the instrument, hence 1-D. In recent work we have developed a system that incorporates 3 separate sample arms in a single interferometer. This allows us to reconstruct the full 3-D vector of motion. We have also begun translating OCTV for use in humans with the development of hand-held and surgical microscope mounted devices which can be used in the clinic and OR. The seminar will be split between these two projects, outlining the technical design and discussing recent results for each.
Biography: Dr. Applegate is a Professor of Otolaryngology–Head & Neck Surgery, Ophthalmology, and Biomedical Engineering at the University of Southern California. He received his Ph.D. in physical chemistry from The Ohio State University. He won a National Institutes of Health postdoctoral fellowship grant to continue his training at Duke University in biomedical engineering. Upon completing his fellowship, he joined the faculty of Texas A&M University where he worked for 12 years advancing to the rank of Associate Professor of Biomedical Engineering. He moved to the University of Southern California in 2019 where he joined his longtime collaborator to continue their work on functional imaging of the ear. Throughout his career, his research has been supported by grants from the National Science Foundation, including the NSF Career award, the Department of Defense, and the National Institutes of Health. He has served on numerous study sections at the National Institutes of Health including a term on Imaging Guided Interventions and Surgery [IGIS]. He has served as an Associate Editor for IEEE Transactions on Medical Imaging and Optics Letters. He has been elected a fellow of Optica and SPIE. His research interests are broadly to develop novel biophotonic technologies and apply them to the diagnosis and monitoring of human disease.
Host: Dr. Richard M. Leahy, leahy@usc.edu
More Info: (USC NetID login required to join seminar)
Webcast: https://usc.zoom.us/j/95027937825?pwd=hEzt0iA1hkdnoINOSMiV2wrXGzcIGo.1Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/95027937825?pwd=hEzt0iA1hkdnoINOSMiV2wrXGzcIGo.1
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
Event Link: (USC NetID login required to join seminar)
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Epstein Institute, ISE 651 Seminar Class
Tue, Sep 24, 2024 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Jose Blanchet, Standford University
Talk Title: TBD
Host: Dr. Meisam Razaviyayn
Location: Social Sciences Building (SOS) - B2
Audiences: Everyone Is Invited
Contact: Casi Jones/ ISE
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MFD Distinguished Lecture Series: Dr. Albert Musaelian
Tue, Sep 24, 2024 @ 04:00 PM - 05:20 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Dr. Albert Musaelian, Harvard University
Talk Title: Designing Neural Network Architectures for Effective Scientific Computing
Abstract: Machine learning models hold significant potential to accelerate and enhance scientific computing. A prominent example is the development of machine learning interatomic potentials (MLIPs), which address the trade-offs between accuracy and computational cost in atomic-scale simulations of chemical and material systems. These models have been successfully applied to simulations of systems ranging from batteries to pharmaceuticals—simulations that would have been infeasible without machine-learning techniques.
This talk will cover the background of MLIPs and their machine learning aspects, with a focus on the “E(3)-equivariant” neural network MLIP architectures, NequIP and Allegro, developed to exploit the symmetries inherent in the physical problems. The presentation will explore their architecture, the design process, and the relationship between network architecture, domain science, and practical engineering, which together enable new capabilities for downstream scientific applications.
Biography: Albert Musaelian researches novel neural network architectures that can improve atomic-scale simulations in computational chemistry and materials science, in particular leading significant work on the NequIP and Allegro architectures and the software frameworks underlying them. He completed his PhD at Harvard University in the Materials Intelligence Research (MIR) group under the guidance of Prof. Boris Kozinsky and with the support of the DOE Computational Science Graduate Fellowship (CSGF).
Host: Dr. Paulo Branicio & Dr. Ken-ichi Nomura
Location: James H. Zumberge Hall Of Science (ZHS) - 352
Audiences: Everyone Is Invited
Contact: Candy Escobedo
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AME Seminar
Wed, Sep 25, 2024 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Oliver Schmidt, University of California at San Diego
Talk Title: Modal Decomposition for the Discovery of Nonlinear Flow Physics
Abstract: Modal decomposition techniques are at the forefront of uncovering nonlinear flow physics from large experimental and numerical datasets, particularly in complex engineering and natural flows. Among the most prominent of these techniques are Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD), which extract the energetically and dynamically most relevant flow features, respectively. While both methods yield accurate low-dimensional representations of flow dynamics, neither provides direct, quantitative insight into the nonlinear interactions that govern these dynamics. The common approach remains to rely on power or cross-spectral peaks as heuristic indicators of nonlinear interactions.
In this talk, I will present a novel orthogonal triadic decomposition technique that systematically identifies and quantifies nonlinear flow phenomena. By extracting flow structures linked to triadic nonlinear interactions—the core mechanism of energy transfer in turbulence—this method offers a powerful new tool for physical discovery. I will demonstrate its application in two examples: cylinder flow, a canonical flow example, and large-eddy simulation data of a plasma-actuated twin rectangular jet, a complex engineering flow. These cases illustrate how this decomposition technique not only improves our understanding of nonlinear interactions but also lays the groundwork for future reduced-order models of complex flows.
Biography: Oliver Schmidt is an Associate Professor in the Department of Mechanical and Aerospace Engineering at UC San Diego's Jacobs School of Engineering and a recipient of the NSF CAREER award. Prior to joining UC San Diego, he was a Postdoctoral Scholar in Mechanical and Civil Engineering at the California Institute of Technology. He earned his Ph.D. in Aeronautical Engineering from the University of Stuttgart in 2014. His research centers on physics-based modeling and computational fluid dynamics, with applications spanning aerospace sciences, high-energy laser systems, and physical oceanography. His work is supported by the AFOSR, ONR, DOE, and NSF.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/96060458816?pwd=8LmoG2q6vBCQubqqWpcizd2F1bxqsH.1Location: Seaver Science Library (SSL) - 202
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/
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ECE Seminar: CMOS System-on-Chip Technology for Exploring Earth, the Solar System, and the Space
Thu, Sep 26, 2024 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Adrian Tang, NASA Jet Propulsion Laboratory, California Institute of Technology
Talk Title: CMOS System-on-Chip Technology for Exploring Earth, the Solar System, and the Space
Abstract: In this talk, I will discuss the infusion of CMOS system-on-chip (SoC) technology into NASA-JPL instrumentation for current and future Astronomy, Earth science, and planetary science investigations. It will describe how the adoption of SoC technology has enabled a drastic reduction in the size, mass, and power consumption of space instruments, allowing them to be carried on smaller platforms while also enabling entirely new science investigations through the co-integration of mm-wave/THz and DSP elements into single-chip devices. I will discuss the fundamental design challenges these SoCs face in delivering the fidelity required for NASA’s science investigations, including sensitivity and long-term stability (Allan variance) in radiometers and high dynamic range in radar sensing. Several recently developed SoC based science instruments will be presented including: (1) The ReckTangLE mission, which carried a CMOS 183 GHz emission spectrometer and flew on a 2019 sub-orbital mission investigating stratospheric water vapor on Earth, (2) the WHATSUP 500-600 GHz spectrometer, which measures isotopic ratios of water at Europa, Titan, and Enceladus to better understand the origins of water in our solar system, (3) the Airborne Scanning Microwave Limb Sounder (ASMLS), a 340 GHz limb-sounder mission flown aboard the NASA ER-2 aircraft, (4) the SoC based ground penetrating radar (GPR) for Mars Science Helicopter that explores subsurface deposits of ice at the Martian poles to be better understand the origins of water and ice on the red planet, (5) the NASA CMOS Enhanced MetaSurface Radar mission monitoring the snowpack water content in the southwestern USA to provide the western states accurate water resource planning during periods of prolonged drought, (6) the NASA Spec-Chip instrument which explores comets and asteroids, analyzing the gasses trapped within their icy surfaces when the solar system first formed, giving us a glimpse into our cosmic origins.
Biography: Dr. Adrian Tang has over 20 years of CMOS/SiGe system-on-chip (SoC) development experience and currently directs the space system-on-chip laboratory at NASA’s Jet Propulsion Laboratory, which develops a wide range of CMOS SoCs for exploration of Earth, the solar system, and space. These SoCs are widely deployed across NASA’s spaceborne, airborne, and surface mission portfolios. He has served as a principal or co-principal investigator on over 30 NASA science and technology programs and as a principal investigator and mission manager on 3 NASA sub-orbital missions. He has authored over 160 IEEE articles in radar and mm-wave/THz remote sensing and was recently awarded the 2023 IEEE Region 6 Outstanding Engineer Award and the 2023 NASA Exceptional Achievement Medal.
Host: Dr. Mahta Moghaddam, mahta@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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NL Seminar-Modeling American Sign Language via Linguistic Knowledge Infusion
Thu, Sep 26, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Lee Kezar, USC
Talk Title: Modeling American Sign Language via Linguistic Knowledge Infusion
Series: NL Seminar
Abstract: REMINDER: Meeting hosts only admit on-line guests that they know to the Zoom meeting. Hence, you’re highly encouraged to use your USC account to sign into Zoom. If you’re an outside visitor, please inform us at (nlg-seminar-host(at)isi.edu) to make us aware of your attendance so we can admit you. Specify if you will attend remotely or in person at least one business day prior to the event Provide your: full name, job title and professional affiliation and arrive at least 10 minutes before the seminar begins. If you do not have access to the 6th Floor for in-person attendance, please check in at the 10th floor main reception desk to register as a visitor and someone will escort you to the conference room location. ZOOM INFO: https://usc.zoom.us/j/92497567208?pwd=nWwbWeA3dKwYIjObiFJPxqmwbXb9p9.1 Meeting ID: 924 9756 7208 Passcode: 329410 ABSTRACT: As language technologies rapidly gain popularity and utility, many of the 70 million deaf and hard-of-hearing people who prefer a sign language are left behind. While NLP research into American Sign Language (ASL) is gaining popularity, we continue to face serious challenges like data scarcity and low engagement with ASL users and experts. This presentation will cover how ASL models strongly benefit from neuro-symbolically learning the linguistic structure of signs, yielding gains with respect to their data efficiency, explainability, and generalizability. Concretely, we show that phonological, morphological, and semantic knowledge "infusion" can increase sign recognition accuracy by 30%, enable few- and zero-shot sign understanding, reduce sensitivity to signer demographics, and address longstanding research questions in sign language phonology and language acquisition.
Biography: Lee Kezar (he/they) is fifth-year Ph.D. candidate in the USC Viterbi School of Engineering, advised by Jesse Thomason in the Grounding Language in Actions, Multimodal Observations, and Robotics (GLAMOR) Lab. Their research blends computational, linguistic, and psychological models of ASL to increase access to language technologies and advance theoretical perspectives on signing and co-speech gesture. Read more at https://leekezar.github.io
Host: Jonathan May and Katy Felkner
More Info: https://www.isi.edu/research-groups-nlg/nlg-seminars/
Webcast: https://www.youtube.com/watch?v=INxlzzNtwI4Location: Information Science Institute (ISI) - Conf Rm#689
WebCast Link: https://www.youtube.com/watch?v=INxlzzNtwI4
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://www.isi.edu/research-groups-nlg/nlg-seminars/
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Fri, Sep 27, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Xiaoming (Shawn) He, , Fishcell Department of Bioengineering University of Maryland, College Park
Talk Title: Bioinspired Multiscale Engineering of Cell and Molecule-Based Medicines
Abstract: Over the past decades, tremendous advances have been made in field of medicine. As a result, not only small molecules, peptides/proteins, and nucleic acids (aka, conventional molecule-based medicine) but also cells, tissues, and organs (aka, cell-based medicine), are extensively explored as medicine today. However, the challenges to both medicines in terms of their safety and efficacy from their procurement and fabrication to the clinical uses, is still enormous. The issues range from poor bioavailability to systemic toxicity and low specificity for molecule-based medicine. For cell-based medicine, non-physiological culture in vitro, immune rejection and uncontrolled differentiation of stem cells in vivo, graft-versus-host disease for therapeutic immune cells, and difficulty of long-term banking toward clinical use, are additional hurdles. We have been working on addressing these challenges facing today’s medicine with bioinspired multiscale engineering strategies. In this talk, I will show our recent data on developing novel bioinspired multiscale systems for engineering both cell and molecule-based medicines, particularly stem cells, immune cells, and RNAs, to improve their quality, safety, and efficacy for combating various diseases including heart attack and cancer.
Biography: Xiaoming (Shawn) He is a Professor of Bioengineering at the University of Maryland, College Park, MD. He obtained his Ph.D. degree in Mechanical Engineering in 2004 from the University of Minnesota-Twin Cities and conducted postdoctoral training from 2004-2007 at Harvard Medical School-Massachusetts General Hospital, Boston, MA. He was an Assistant Professor at the University of South Carolina from 2007-2011, and Associate Professor and Full Professor at the Ohio State University from 2011-2017. His current research is focused on developing micro and nanoscale biomaterials and devices to engineer and bank totipotent, pluripotent, and multipotent stem cells for the treatment and early detection of various diseases including but not limited to cancer, infertility, cardiovascular diseases, diabetes, and neurological disorders. His research has been funded by grants with him as the PI from various private foundations like the American Cancer Society (ACS) and government agencies like the NSF and NIH (9 R01s), including the ACS Research Scholar Grant and the NCI Innovative Research in Cancer Nanotechnology (IRCN) Grant. He has published ~150 peer-reviewed journal articles in high-ranking journals including Nature Nanotechnology, Nature Biomedical Engineering, and Nature Communications, in addition to one book and four book chapters. He is an Editor-in-Chief of the Journal of Medical Devices published by the American Society of Mechanical Engineers (ASME). He served as the Chair of the ASME Biotransport Committee, and has been an associate editor or editorial board member of five different journals. He is a fellow of the ASME and the American Institute of Medical and Biological Engineering (AIMBE), and a member of the European Academy of Sciences and Arts.
Host: Keyue Shen
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Carla Stanard
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A Code Generation Framework To Replicate Software Design Concepts
Mon, Sep 30, 2024 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Prof. George Heineman, Associate Professor, Computer Science - Worcester Polytechnic Institute
Talk Title: A Code Generation Framework To Replicate Software Design Concepts
Abstract: In 1998, Philip Wadler used the term Expression Problem (EP) to describe a common situation that occurs when devising software that must evolve, specifically with regard to the structure of the data types and the operations over these data types. Over time, software engineers extend systems by adding new data types and/or new operations, and the goal is to avoid changing existing code as part of the extension. Dozens of researchers have investigated approaches to EP using a variety of programming languages. The papers from the research literature often contain only small code fragments and the lack of a common benchmark makes it difficult to compare different approaches with each other. We designed EpCoGen, a code generation framework, to replicate the results of numerous papers using a rich benchmark domain of mathematical expressions. While it was not our intention, in completing this project, we devised a novel CoCo approach to EP based on Covariant Conversions. This result would not have been possible without the meticulous effort in both developing a comprehensive benchmark and trying to replicate existing results from the literature. We generate fully coded solutions in Java, Scala and Haskell, with accompanying test cases, for nine different approaches to EP using a language-independent code generation framework (CoGen) that can be expanded to include additional languages and approaches.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: George Heineman is an Associate Professor of Computer Science at WPI in Worcester, Massachusetts. His research interests include component-based software engineering, modularity, code generation, and algorithms. He is the author of Algorithms in a Nutshell (2ed, O’Reilly Media, 2016), Learning Algorithms (O’Reilly Media, 2021), and an online training video course, Coding Interview Preparation: Learn to Solve Algorithms Problem to Land Your Next Software Role (O’Reilly Media, 2024). George is also an avid puzzler with a lifelong interest in logical and mathematical puzzles. He is the inventor of Sujiken® puzzles (a variation of Sudoku), Trexiken puzzles (a variation of Ken-Ken®) and the online https://wordygame.net, if you are ready to try a more challenging word puzzle than Wordle.
Host: Nenad Medvidovic, Department Chair - USC Thomas Lord Department of Computer Science
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Thomas Lord Department of Computer Science
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CSC/CommNetS-MHI Seminar: Lauren Conger
Mon, Sep 30, 2024 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Lauren Conger, PhD Candidate, Control and Dynamical Systems, Caltech
Talk Title: Wasserstein Gradient Flows for Modeling Strategic Distribution Shift
Series: CSC/CommNetS-MHI Seminar Series
Abstract: We propose a novel framework for analyzing the dynamics of distribution shift in real-world systems that captures the feedback loop between learning algorithms and the distributions on which they are deployed. We propose a coupled partial differential equation model that captures fine-grained changes in the distribution over time by accounting for complex dynamics that arise due to strategic responses to algorithmic decision-making, non-local endogenous population interactions, and other exogenous sources of distribution shift. We prove convergence results in 3 settings for min-max optimization problems over measures, as well as in a cooperative setting, addressing a recent open problem posed in (Wang and Chizat 2024, Convergence of single-timescale mean-field Langevin descent-ascent for two-player zero-sum games).
Biography: Lauren Conger is a PhD candidate in Control and Dynamical Systems at Caltech, advised by Franca Hoffmann, Eric Mazumdar, and John Doyle. Her research is at the intersection of partial differential equations (PDE) analysis, game theory, and control theory. She studies systems of gradient flow PDEs through the lens of game theory with applications in machine learning, and works on system level synthesis, a new control parameterization, in a variety of contexts, including distributed optimization and PDE control.
Host: Dr. Lars Lindemann, llindema@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CSC/CommNetS-MHI Seminar: Lauren Conger
Mon, Sep 30, 2024 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Lauren Conger, PhD Candidate, Control and Dynamical Systems, Caltech
Talk Title: Wasserstein Gradient Flows for Modeling Strategic Distribution Shift
Series: CSC/CommNetS-MHI Seminar Series
Abstract: We propose a novel framework for analyzing the dynamics of distribution shift in real-world systems that captures the feedback loop between learning algorithms and the distributions on which they are deployed. We propose a coupled partial differential equation model that captures fine-grained changes in the distribution over time by accounting for complex dynamics that arise due to strategic responses to algorithmic decision-making, non-local endogenous population interactions, and other exogenous sources of distribution shift. We prove convergence results in 3 settings for min-max optimization problems over measures, as well as in a cooperative setting, addressing a recent open problem posed in (Wang and Chizat 2024, Convergence of single-timescale mean-field Langevin descent-ascent for two-player zero-sum games).
Biography: Lauren Conger is a PhD candidate in Control and Dynamical Systems at Caltech, advised by Franca Hoffmann, Eric Mazumdar, and John Doyle. Her research is at the intersection of partial differential equations (PDE) analysis, game theory, and control theory. She studies systems of gradient flow PDEs through the lens of game theory with applications in machine learning, and works on system level synthesis, a new control parameterization, in a variety of contexts, including distributed optimization and PDE control.
Host: Dr. Lars Lindemann, llindema@usc.edu
More Information: 2024.09.30 CSC Seminar - Lauren Conger.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
Audiences: Everyone Is Invited
Contact: Miki Arlen
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CSC/CommNetS-MHI Seminar: Lauren Conger
Mon, Sep 30, 2024 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Lauren Conger, PhD Candidate, Control and Dynamical Systems, Caltech
Talk Title: Wasserstein Gradient Flows for Modeling Strategic Distribution Shift
Series: CSC/CommNetS-MHI Seminar Series
Abstract: We propose a novel framework for analyzing the dynamics of distribution shift in real-world systems that captures the feedback loop between learning algorithms and the distributions on which they are deployed. We propose a coupled partial differential equation model that captures fine-grained changes in the distribution over time by accounting for complex dynamics that arise due to strategic responses to algorithmic decision-making, non-local endogenous population interactions, and other exogenous sources of distribution shift. We prove convergence results in 3 settings for min-max optimization problems over measures, as well as in a cooperative setting, addressing a recent open problem posed in (Wang and Chizat 2024, Convergence of single-timescale mean-field Langevin descent-ascent for two-player zero-sum games).
Biography: Lauren Conger is a PhD candidate in Control and Dynamical Systems at Caltech, advised by Franca Hoffmann, Eric Mazumdar, and John Doyle. Her research is at the intersection of partial differential equations (PDE) analysis, game theory, and control theory. She studies systems of gradient flow PDEs through the lens of game theory with applications in machine learning, and works on system level synthesis, a new control parameterization, in a variety of contexts, including distributed optimization and PDE control.
Host: Dr. Lars Lindemann, llindema@usc.edu
More Information: 2024.09.30 CSC Seminar - Lauren Conger.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
Audiences: Everyone Is Invited
Contact: Miki Arlen
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Computational Science Distinguished Seminar Series
Mon, Sep 30, 2024 @ 03:45 PM - 05:00 PM
USC School of Advanced Computing
Conferences, Lectures, & Seminars
Speaker: Youssef Marzouk, MIT
Talk Title: Transport methods for Bayesian inference and optimal experimental design
Abstract: Measure transport has emerged as a versatile tool in probabilistic modeling and inference, offering a unifying perspective on various computational challenges. This talk explores the core principles of transport maps and their ability to induce couplings between probability measures, facilitating efficient simulation and analysis. We will survey the diverse landscape of transport representations, from polynomials and invertible neural networks to ODE flow maps, highlighting how these constructions capture different notions of low-dimensional structure in probabilistic models.
The presentation will then focus on recent advancements in two key areas:
1. Nonlinear ensemble filtering: This talk explores novel transport-based algorithms that generalize the ensemble Kalman filter to nonlinear settings, offering improved performance in challenging filtering problems.
2. Simulation-based inference: We will investigate how transport maps can be leveraged to enhance the efficiency and accuracy of inference in scenarios where only forward simulations are available.
Additionally, this talk explores the application of transport-based density estimates in bounding information-theoretic objectives for optimal experimental design, demonstrating the broad utility of this framework in decision-making under uncertainty.
Biography: Youssef Marzouk is the Breene M. Kerr (1951) Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT), and co-director of the Center for Computational Science and Engineering within the MIT Schwarzman College of Computing. He is also a core member of MIT's Statistics and Data Science Center and a PI in the MIT Laboratory for Information and Decision Systems (LIDS).
His research interests lie at the intersection of statistical inference, computational mathematics, and physical modeling. He develops new methodologies for uncertainty quantification, Bayesian computation, and machine learning in complex physical systems, motivated by a broad range of engineering and science applications. His recent work has centered on algorithms for inference, with applications to data assimilation and inverse problems; dimension reduction methodologies for high-dimensional learning and surrogate modeling; optimal experimental design; and transportation of measure as a tool for inference and stochastic modeling.
He received his SB, SM, and PhD degrees from MIT and spent four years at Sandia National Laboratories before joining the MIT faculty in 2009. He is also an avid coffee drinker and an occasional classical pianist.
Host: The School of Advanced Computing
More Info: https://sac.usc.edu/events/
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://sac.usc.edu/events/
