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SUNMONTUEWEDTHUFRISAT
Conferences, Lectures, & Seminars
Events for August
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AI Seminar- Composable Interventions for Language Models
Fri, Aug 09, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Arinbjorn Kolbeinsson, University of Virginia
Talk Title: Composable Interventions for Language Models
Abstract: Virtual zoom link: https://usc.zoom.us/j/7042850182?pwd=OTQ3aW9LUjErTC9iWGRFQUg0LzlOdz09&omn=96405030645Meeting ID: 704 285 0182Meeting password: 832239
Abstract: Test-time interventions for language models can enhance factual accuracy, mitigate harmful outputs, and improve model efficiency without costly retraining. But despite a flood of new methods, different types of interventions are largely developing independently. In practice, multiple interventions must be applied sequentially to the same model, yet we lack standardized ways to study how interventions interact. We fill this gap by introducing composable interventions, a framework to study the effects of using multiple interventions on the same language models, featuring new metrics and a unified codebase. Using our framework, we conduct extensive experiments and compose popular methods from three emerging intervention categories — Knowledge Editing, Model Compression, and Machine Unlearning. Our results from 310 different compositions uncover meaningful interactions: compression hinders editing and unlearning, composing interventions hinges on their order of application, and popular general-purpose metrics are inadequate for assessing composability. Taken together, our findings showcase clear gaps in composability, suggesting a need for new multi-objective interventions. All of our code is public: https://github.com/hartvigsen-group/composable-interventions
Biography: Arinbjörn Kolbeinsson is currently serving as a visiting scholar at the University of Virginia, focusing on responsible and accurate models for health and biomedicine. His recent research explores the editing and efficiency of language models, along with the development of composable intervention techniques. Previously, Arinbjörn was a machine learning scientist at Evidation Health Inc., where he developed innovative methods to predict health outcomes using high-frequency multi-modal data. His work was pivotal in advancing differential privacy, disease modeling, and reinforcement learning for health applications. Arinbjörn completed his Ph.D. in Biostatistics at Imperial College London in 2020, specializing in deep learning for health outcome prediction.
Host: Abel Salinas and Maura Covaci
More Info: https://www.isi.edu/events/5056/composable-interventions-for-language-models/
Webcast: https://usc.zoom.us/j/7042850182?pwd=OTQ3aW9LUjErTC9iWGRFQUg0LzlOdz09&omn=96405030645Location: Information Science Institute (ISI) - Virtual Only
WebCast Link: https://usc.zoom.us/j/7042850182?pwd=OTQ3aW9LUjErTC9iWGRFQUg0LzlOdz09&omn=96405030645
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://www.isi.edu/events/5056/composable-interventions-for-language-models/
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Quantum Science & Technology Seminar - Xun Gao, Thursday, August 15th at 11am in EEB 248
Thu, Aug 15, 2024 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Xun Gao, University of Colorado Boulder
Talk Title: Interpretable Quantum Advantage in Neural Sequence Learning
Series: Quantum Science & Technology Seminar Series
Abstract: Quantum neural networks have been widely studied in recent years due to their potential practical utility and recent results showing their ability to efficiently express certain classical data. However, analytic results to date rely on assumptions and arguments from complexity theory. As a result, there is little intuition regarding the source of the expressive power of quantum neural networks or for which classes of classical data any advantage can be reasonably expected to hold. In this study, we examine the relative expressive power between a broad class of neural network sequence models and a class of recurrent models based on quantum mechanics. We demonstrate that quantum contextuality is the source of an unconditional memory separation in the expressivity of the two model classes. Using this intuition, we study the relative performance of our introduced model on a standard translation dataset exhibiting linguistic contextuality. Our quantum models outperform state-of-the-art classical models, even in practice. Finally, I will briefly discuss future directions of quantum neural networks and their potential connections to concepts in condensed matter physics, such as Berry phase and spin glass.
Biography: Xun Gao is an assistant professor at University of Colorado Boulder and an associate fellow at JILA. He got his PhD from Tsinghua University under the supervision of Luming Duan. Then he was a postdoc at Harvard University from Mikhail Lilian's group. His research interests are quantum computational advantage and quantum machine learning.
Host: Quntao Zhang, Wade Hsu, Mengjie Yu, Jonathan Habif & Eli Levenson-Falk
More Information: Xun Gao Flyer.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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CAIS seminar: How to make optimal decisions (that are unfair, biased and non-objective)
Fri, Aug 23, 2024 @ 10:30 AM - 11:30 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Prof. Guido Tack, Monash University
Talk Title: How to make optimal decisions (that are unfair, biased and non-objective)
Abstract: Optimisation technology promises to help us make better decisions: plan the best route on a map, deliver goods quickly and with low emissions, construct efficient staff rosters, or design complex industrial plants. But most of these optimal decisions are in fact compromises. For example, there may be many “optimal” staff rosters that enable an organisation to function effectively and at the lowest possible cost. But some of those “optimal” rosters may be very unfair for certain staff. What if one of your staff asks you why they always get the graveyard shift, and after you’ve analysed the problem, you have to tell them it’s because their name starts with an “A”? This talk is about how optimisation technology can introduce bias and unfairness in subtle ways, and what needs to be done to fix this problem.
Biography: Guido Tack is an Associate Professor in the Department of Data Science and Artificial Intelligence at Monash University, Australia. His research focuses on combinatorial optimisation, in particular architecture and implementation techniques for constraint solvers, translation of constraint modelling languages, and industrial applications. Guido leads the development of the MiniZinc constraint modelling language and toolchain, and he is one of the leading developers of Gecode, a state-of-the-art constraint programming library. Guido’s broader research interests include programming languages and computational logic.
Host: Bistra Dilkina
More Info: https://cais.usc.edu/events/how-to-make-optimal-decisions-that-are-unfair-biased-and-non-objective/
Location: Ethel Percy Andrus Gerontology Center (GER) - 206
Audiences: Everyone Is Invited
Contact: Bistra Dilkina
Event Link: https://cais.usc.edu/events/how-to-make-optimal-decisions-that-are-unfair-biased-and-non-objective/
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Alfred E. Mann Department of Biomedical Engineering
Fri, Aug 23, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Alyssa Panitch, Ph.D., Emory University and Georgia Tech.
Talk Title: Promoting tissue healing and regeneration using proteoglycan mimetics
Abstract: Proteoglycans play an important role in extracellular matrix remodeling, homeostasis, and signaling. Due to their negatively charged glycosaminoglycan chains as well as distinct core protein structures, they interact with a variety of molecules, including matrix proteins, growth factors, cytokines and chemokines, pathogens, and enzymes. Here we focus on two bioconjugates that were designed to mimic features of existing proteoglycans. The first models the biological activity of the small leucine-rich proteoglycan, decorin. Like native decorin, our decorin mimetic plays a key role in collagen organization and wound healing. Recently, we have augmented the decorin mimetic with peptide ligands to the avb3 integrin receptor found on endothelial and endothelial progenitor cells. The avb3 ligand confers angiogenic activity to the decorin mimetic and supports wound healing in an ischemic environment in diabetic animals. The second mimetic is designed to target inflamed endothelium to restore physical barrier function of the endothelial glycocalyx. By restoring barrier function, the glycocalyx mimetic reduces intravascular thrombosis and inflammation thereby improving healing outcomes following ischemia reperfusion injury in animal models.
Biography: Dr. Panitch received bachelor’s degrees from Smith College in Biochemistry and from the University of Massachusetts-Amherst in Chemical Engineering. She completed her Ph.D. in Polymer Science and Engineering from the University of Massachusetts. After a postdoctoral fellowship at the Swiss Federal Institute of Technology (ETH) and University of Zurich. She started her first faculty position at Arizona State University in 1999 where she was awarded an NSF CAREER award. She is currently the chair of the Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Tech. She is a member and Fellow of the Biomedical Engineering Society, the American Institute for Medical and Biological Engineers (AIMBE)and the National Academy of Inventors. She also serves as an Editor for the Journal of Colloids and Surfaces B: Biointerfaces.
Host: Peter Wang
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Carla Stanard
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Epstein Institute, ISE 651 Seminar Class
Tue, Aug 27, 2024 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Professor Qiang Huang, Daniel J. Epstein Dept. of Industrial & Systems Engineering, USC
Talk Title: Introduction to first class (enrolled students only)
Location: Social Sciences Building (SOS) - B2
Audiences: Everyone Is Invited
Contact: Casi Jones/ ISE
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AME Seminar - Laufer Lecture
Wed, Aug 28, 2024 @ 12:30 PM - 01:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Thomas J.R. Hughes, University of Texas at Austin
Talk Title: The Finite Element Method and Isogeometric Analysis: Past, Present, Future
Abstract: I will begin by probing into the past to discover the origins of the Finite Element Method (FEM), and then trace the evolution of those early developments to the present day in which the FEM is ubiquitous in science, engineering, mathematics, and medicine, and the most important discretization technology in Computational Mechanics. However, despite its enormous success, there are still problems with contemporary technology, for example, building meshes from Computer Aided Design (CAD) representations is labor intensive, and a significant bottleneck in the design-through-analysis process; the introduction of geometry errors in computational models that arise due to feature removal, geometry clean-up and CAD “healing,” necessary to facilitate mesh generation; the inability of contemporary technology to “close the loop” with design optimization; and the failure of higher-order finite elements to achieve their full promise in industrial applications. These issues are addressed by Isogeometric Analysis (IGA), the vision of which was first presented in a paper published October 1, 2005 [1]. Since then, IGA has become a focus of research within both FEM and CAD and is now a mainstream analysis methodology that has provided a new paradigm for computational model development [2-4]. The key concept utilized in the technical approach is the development of a new foundation for FEA, based on rich geometric descriptions originating in CAD, more tightly integrating design and analysis. Industrial applications and commercial software developments have expanded recently. I will briefly present the motivation leading to IGA, its status, recent progress, areas of current activity, and what it offers for analysis model development and the design-through-analysis process. I will also argue that IGA provides an alternative and more robust approach to higher-order finite element analysis, filling the gap between low-order, geometrically versatile approaches and high-order, geometrically restrictive spectral methods. Finally, I will speculate on the future, the technologies that will prevail, computer developments, and the role of machine learning. [1] T.J.R. Hughes, J.A. Cottrell and Y. Bazilevs, “Isogeometric Analysis: CAD, Finite Elements, NURBS, Exact Geometry and Mesh Refinement,” Computer Methods in Applied Mechanics and Engineering, 194, (2005) 4135-4195. [2] J.A. Cottrell, T.J.R. Hughes and Y. Bazilevs, “Isogeometric Analysis: Toward Integration of CAD and FEA,” Wiley, Chichester, U.K., 2009. [3] Special Issue on Isogeometric Analysis, (eds. T.J.R. Hughes, J.T. Oden and M. Papadrakakis), Computer Methods in Applied Mechanics and Engineering, 284, 1-1182, (1 February 2015). [4] Special Issue on Isogeometric Analysis: Progress and Challenges, (eds. T.J.R. Hughes, J.T. Oden and M. Papadrakakis), Computer Methods in Applied Mechanics and Engineering, 316, 1-1270, (1 April 2017).
Biography: Thomas J.R. Hughes holds B.E. and M.E. degrees in Mechanical Engineering from Pratt Institute and an M.S. in Mathematics and Ph.D. in Engineering Science from the University of California at Berkeley. He taught at Berkeley, Caltech, and Stanford before joining the University of Texas at Austin. At Stanford he served as Chairman of the Division of Applied Mechanics, Chairman of the Department of Mechanical Engineering, Chairman of the Division of Mechanics and Computation, and held the Crary Chair of Engineering. Dr. Hughes is an elected member of the U.S. National Academy of Sciences, the U.S. National Academy of Engineering, the American Academy of Arts and Sciences, the Royal Society of London, the Austrian Academy of Sciences (Section for Mathematics and the Physical Sciences), the Istituto Lombardo Accademia di Scienze e Lettere (Mathematics Section), and the Academy of Medicine, Engineering and Science of Texas. Dr. Hughes is a Fellow of the AAAS, AIAA, ASCE, ASME, the U.S. Association for Computational Mechanics (USACM), the International Association for Computational Mechanics (IACM), the American Academy of Mechanics (AAM), the Society for Industrial and Applied Mathematics (SIAM), and the Engineering Mechanics Institute of ASCE. Dr. Hughes is a Founder and past President of USACM and IACM, past Chairman of the Applied Mechanics Division of ASME, past Chairman of the US National Committee on Theoretical and Applied Mechanics, and co-editor emeritus of the international journal, Computer Methods in Applied Mechanics and Engineering. He is an Honorary Member of the Japanese Association for Computational Mechanics (JACM). Dr. Hughes is one of the most widely cited authors in Engineering Science. He has been elected to Distinguished Member, ASCE’s highest honor, and has received ASME’s highest honor, the ASME Medal. He has also been awarded the Walter L. Huber Civil Engineering Research Prize and von Karman Medal from ASCE, the Timoshenko, Worcester Reed Warner, and Melville Medals from ASME, the von Neumann Medal from USACM, the Gauss-Newton Medal from IACM, the Computational Mechanics Award from the Japan Society of Mechanical Engineers (JSME), the Grand Prize from the Japan Society of Computational Engineering and Science (JSCES), the Computational Mechanics Award from JACM, the Humboldt Research Award for Senior Scientists from the Alexander von Humboldt Foundation, the Wilhem Exner Medal from the Austrian Association für SME (Öesterreichischer Gewerbeverein, OGV), the International Scientific Career Award from the Argentinian Association for Computational Mechanics (AMCA), the SIAM/ACM (Association for Computing Machinery) Prize in Computational Science and Engineering, the Southeastern Universities Research Association (SURA) Distinguished Scientist Award, the O.C. Zienkiewicz Medal from the Polish Association for Computational Mechanics (PACM), the A.C. Eringen Medal from the Society for Engineering Science (SES), the Ralph E. Kleinman Prize from SIAM, the Monie A. Ferst Award of Sigma Xi, The Scientific Research Honor Society, and the William Benter Prize in Applied Mathematics from the Liu Bie Ju Centre for Mathematical Sciences, City University of Hong Kong.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/94634476349?pwd=5I3aFQUoV4sLjbxKf6PhwhBbyDFcjZ.1Location: Ronald Tutor Campus Center (TCC) - 350
WebCast Link: https://usc.zoom.us/j/94634476349?pwd=5I3aFQUoV4sLjbxKf6PhwhBbyDFcjZ.1
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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AAI-CCI-MHI Seminar on CPS
Wed, Aug 28, 2024 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sonia Roberts, Assistant Professor Wesleyan University
Talk Title: From legged robots to knitted ones
Series: EE598 Seminar Series
Abstract: Traditional robotics assumes rigid bodies interacting with rigid environments. However, the real world is soft. Robots will need to be able to move over materials like sand, snow, and leaf litter, and will need to be able to interact with fruits, fabrics, and of course humans. I will discuss two types of soft interactions between a robot and the world: Robot locomotion on granular media, and the use of knitting as a computational fabrication method to create soft sensors for robots.
Biography: Dr. Sonia Roberts is an Assistant Professor of Computer Science at Wesleyan University working on knitted sensors for soft robot skins and legged robot locomotion on granular media. In 2023, she completed a postdoc with Prof. Kris Dorsey at Northeastern as part of the Institute for Experiential Robotics, where she worked on soft origami sensors. She received her PhD in Electrical and Systems Engineering from the University of Pennsylvania in 2021, where she worked with Prof. Dan Koditschek in the GRASP Lab to develop a reactive controller to reduce the energetic cost of transport for legged robots on sand. Prior to coming to Penn, Sonia worked at Janelia Farm Research Campus on a team building a rough behavioral map of the fruit fly brain, and with John Long using evolutionary robotics tools to answer biological questions at Vassar College.
Host: Feifei Qian
More Information: AAI-CCI-MHI Seminar on CPS Sonia Roberts.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Ariana Perez
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Semiconductors & Microelectronics Technology Seminar - John Paul Strachan, Thursday, Aug. 29th at 2pm in EEB 248
Thu, Aug 29, 2024 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: John Paul Strachan, Peter Grünberg Institute (PGI-14), Forschungszentrum Jülich, Jülich, Germany RWTH Aachen University, Aachen, Germany
Talk Title: Engineering memristor-CMOS based neuromorphic architectures for computational acceleration: NP-hard optimization problem solvers and building associative memories
Series: Semiconductors & Microelectronics Technology
Abstract: There is simultaneously an interest for more energy-efficient hardware in challenging applications, as well as a drive to overhaul the von Neumann architecture toward more brain-like architectures. I will describe our in-memory approach that applies to both these two topic areas, especially where emerging memories like memristors can be utilized with traditional CMOS in new circuits and architectures. Such hybrid circuits can yield challenges in variability, offer many benefits. I will discuss our modified Hopfield neural network accelerator for challenging optimization problem classes such as Boolean satisfiability (3-SAT), showing performance comparisons to competing approaches with both mature and emerging technologies. In complementary work, we build new architectures around content addressable memories (CAM), which offer a highly parallel pattern look-up capability. Designs are improved utilizing non-volatile and analog memristive devices for higher data density and lower energy than CMOS-only counterparts. We utilize such circuits in a variety of associative computing applications, including security, genomics, and machine learning. Going further, we are interested in how learning can be incorporated into such memory circuits and we describe a modified "differentiable" CAM circuit that is compatible with gradient-based training algorithms and illustrate some applications of such a circuit.
Biography: John Paul Strachan directs the Peter Grünberg Institute on Neuromorphic Compute Nodes (PGI-14) at Forschungszentrum Jülich and is a Professor at RWTH Aachen. Previously he led the Emerging Accelerators team as a Distinguished Technologist at Hewlett Packard Labs, HPE. His teams explore novel types of hardware accelerators using emerging device technologies, with expertise spanning materials, device physics, circuits, architectures, benchmarking and building prototype systems. Their interests span applications in machine learning, network security, and optimization. John Paul has degrees in physics and electrical engineering from MIT and a PhD in applied physics from Stanford University. He has over 60 patents, has authored or co-authored over 100 peer-reviewed papers, and been the PI in many USG research grants. He has previously worked on nanomagnetic devices for memory for which he was awarded the Falicov Award from the American Vacuum Society, and has developed sensing systems for precision agriculture in a company which he co-founded. He serves in professional societies including IEEE IEDM ExComm, the Nanotechnology Council ExComm, and past program chair and steering member of the International Conference on Rebooting Computing.
Host: J Yang, H Wang, C Zhou, S Cronin, W Wu
More Information: John Paul Strachan_2024-08-29.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Alfred E. Mann Department of Biomedical Engineering
Fri, Aug 30, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Jonathan Wang, Senior Scientist II - AbbVie , Irvine , CA
Talk Title: Navigating an Early Career in Biopharmaceuticals: From Graduate Research to Industry Expertise
Abstract: In this seminar, we will explore an early-stage career journey through various facets of the biopharmaceutical industry. Starting with a brief introduction that highlights graduate work at the University of Southern California in the Chung lab, we focus on a project utilizing polymeric micelle nanoparticles for targeted drug delivery to treat polycystic kidney disease. The talk will then delve into an industrial application, looking at process development of a pipeline vaccine in late-stage clinical development. Following this, we examine the opposite end of the regulatory approval spectrum and investigate the challenges of commercial production of a viral vector biologic. The seminar will conclude with practical tips and case studies on navigating the industry, to offer valuable insights for aspiring professionals.
Biography: Dr. Jonathan Wang obtained his undergraduate degree in Mechanical Engineering from Johns Hopkins University and PhD degree in Biomedical Engineering at the University of Southern California. During his graduate studies with Professor Eun Ji Chung, he worked on developing nanoparticles for drug delivery applications such as kidney disease. During his time at USC, he was awarded the USC Best Research Assistant Award, Viterbi Undergraduate Research Mentoring Awards, and was a recipient of the Alfred E. Mann Innovation in Engineering Doctoral Fellowship. After his PhD studies, he has joined industry as Scientist at Emergent BioSolutions and MilliporeSigma, and is now a Senior Scientist II at AbbVie in Irvine, CA.
Host: Eunji Chung
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: Everyone Is Invited
Contact: Carla Stanard
