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Conferences, Lectures, & Seminars
Events for February

  • CS Colloquium: Ankur Mehta (UCLA) - Towards $1 robots

    Tue, Feb 01, 2022 @ 02:30 PM - 03:50 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Ankur Mehta, University of California, Los Angeles

    Talk Title: Towards $1 robots

    Series: Computer Science Colloquium

    Abstract: Note: *New time: 2:30PM-3:50PM PT*

    Robots are pretty great -- they can make some hard tasks easy, some dangerous tasks safe, or some unthinkable tasks possible. And they're just plain fun to boot. But how many robots have you interacted with recently? And where do you think that puts you compared to the rest of the world's people?

    In contrast to computation, automating physical interactions continues to be limited in scope and breadth. I'd like to change that. But in particular, I'd like to do so in a way that's accessible to everyone, everywhere. In our lab, we work to lower barriers to robotics design, creation, and operation through
    material and mechanism design, computational tools, and mathematical analysis. We hope that with our efforts, everyone will be soon able to enjoy the benefits of robotics to work, to learn, and to play.

    **Prof. Ankur Mehta will give his talk in person at SGM 124 and we will also host the talk over Zoom.**

    Register in advance for this webinar at:

    https://usc.zoom.us/webinar/register/WN_dUyqi3ZTQiWuseUgfp4fYw

    After registering, attendees will receive a confirmation email containing information about joining the webinar.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Now, this is a story all about how
    My life took me to where I am now
    And I'd like to take a minute, just sit right there
    I'll tell you how I became a prof at UCLA.

    From East Pennsylvania born and raised
    MIT is where I spent the next of my days
    Getting my Masters and Bachelor's too
    ECE is the field I did then pursue.

    Then a couple of years until I finally would
    From California, Berkeley get my doctor hood
    I got in one lil' postdoc at MIT CSAIL
    And then I moved to LA just south of Bel Air.

    Prof. Ankur Mehta is an assistant professor of Electrical and computer Engineering at UCLA, and directs the Laboratory for Embedded Machines and Ubiquitous Robots (LEMUR). Pushing towards his visions of a future filled with robots, his research interests involve printable robotics, rapid design and
    fabrication, control systems, and multi-agent networks. He has received the NSF CAREER award and a Samueli fellowship, and has received best paper awards in the IEEE Robotics & Automation Magazine and the International Conference on Intelligent Robots and Systems (IROS).

    Prior to joining the UCLA faculty, Prof. Mehta was a postdoc at MIT's Computer Science and Artificial Intelligence Laboratories investigating design automation for printable robots. Before to that, he conducted research as a graduate student at UC Berkeley in wireless sensor networks and systems, small autonomous aerial robots and rockets, control systems, and micro-electro-mechanical systems (MEMS).

    When not in the lab, Ankur enjoys puzzles, ultimate frisbee, board games.


    Host: Stefanos Nikolaidis

    Location: Seeley G. Mudd Building (SGM) - 124

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series

    Wed, Feb 02, 2022 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Jie Gao, Department of Computer Science, Rutgers University

    Talk Title: Protecting Data Privacy in an Increasingly Connected World

    Series: Center for Cyber-Physical Systems and Internet of Things

    Abstract: Ubiquitous sensing, wireless communication and distributed computation have transformed the way we interact with the physical world. While we celebrate the convenience and the improved quality of life having smart devices around us, the data collection practices have moved from remote fields to private living environments and work spaces; and data collected have also shifted from non-sensitive scientific data to personal, sensitive data that is closely related to the user's health conditions, emotional states, physical activities and social relationships. In this talk I will review some of our recent work on protecting privacy of sensitive data by minimalist-style sensing and targeted data perturbation, as well as fundamental challenges/impossibility results regarding data privacy and utility.

    Biography: Professor Jie Gao is currently Professor in Department of computer science, Rutgers University. She was on the faculty of Computer Science department at Stony Brook University from 2005-2019. She received B.Eng from the Special Class of the Gifted Young, University of Science and Technology of China in 1999, Ph.D in Computer Science from Stanford University in 2004 and was a postdoc at Caltech from 2004-2005. She received the NSF career award in 2006, IMC best paper award (2009), EWSN best paper award (2021) and multiple Research Excellence Awards in computer science department of Stony Brook. She is currently serving on the editorial board of ACM Transactions on Sensor Networks, International Journal of Computational Geometry and Applications and IEEE Transactions on Network Science and Engineering. She published over 140 referred papers in computer networking and theoretical computer science fields, and has graduated 17 Ph.D students.

    Host: Pierluigi Nuzzo and Bhaskar Krishnamachari

    Webcast: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Location: Online

    WebCast Link: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Audiences: Everyone Is Invited

    Contact: Talyia White

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  • AME Seminar

    Wed, Feb 02, 2022 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Artur Tyliszczak, Częstochowa University of Technology, Częstochowa, Slaskie, Poland

    Talk Title: Passive and Active Control of Turbulent Jets and Flames -” A CFD Research

    Abstract: Interest in flow control techniques is driven by a possible improvement of performance, safety and efficiency of various technical devices. Existing strategies of steering and controlling fluid flows can be divided into two approaches: passive and active. The former is based on shaping the flow domains and is usually optimized for specific flow conditions. The latter requires an external energy input (an excitation, forcing), which can be varying in response to the instantaneous flow behavior. The active methods are thus more costly but also much more flexible. Under a variety of different flow regimes, they result in a better overall response than the passive methods. In this talk, I will focus on the CFD study of passive and active control applications for jets and flames. In the latter case, proper flow control is especially important as the efficiency of combustion processes is directly related to fuel-oxidizer mixing -” a process, which we would like to have under full control. I will discuss to what extent the flow field can be modified and controlled by the selection of shapes of jet nozzles or tuning of excitation parameters.

    Biography: Artur Tyliszczak is a Professor in the Faculty of Mechanical Engineering and Computer Science at Częstochowa University of Technology (CUT) in Poland. He leads the CFD Research Group. He earned an M.S. degree in Mechanical Engineering in 1997 from the CUT and a PhD degree in 2002 from the CUT and von Karman Institute for Fluid Dynamics (Belgium). He worked at Cambridge University (UK) as a Marie-Curie Experienced Researcher (2010-2011) and a visiting professor (2016). His group works on the development of high-order numerical methods for CFD and their applications for open and near-wall non-reacting and reacting flows. Currently, his main research concentrates on passive and active flow control in jet type flows and flows in porous and granular layers. Artur Tyliszczak is a recipient of prestigious individual awards from the Polish scientific community, the Ministry of Science and Education, the Polish Academy of Science, the Polish Association of Theoretical and Applied Mechanics. Recently he received a Senior Award from the Fulbright Commission for his stay at USC.

    Host: AME Department

    More Info: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Webcast: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Location: James H. Zumberge Hall Of Science (ZHS) - 252

    WebCast Link: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

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  • Astani Department of Civil and Environmental Engineering Seminar

    Thu, Feb 03, 2022 @ 12:30 PM - 01:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Katherine Graham, President's Postdoctoral Fellow, Environmental Engineering, Georgia Tech

    Talk Title: Viruses in the urban water cycle: sources of pollution, fate, and public health decision-making

    Abstract: Coastal environments across the globe are treasured for their ability to provide sustenance, recreational activities, job opportunities, and diverse habitats for flora and fauna. They are also important human habitats: in the US, 94.7 million people live in a coastal county and worldwide approximately 44 percent of people live within 150 km of a coastline. However, due to population growth, pollution, and overuse of natural resources, many water resources in coastal areas are polluted. Sources of microbial pollution, including wastewater and urban runoff, can transport pathogens to waters where they pose public health risks. However, current indicators for fecal contamination in the environment, like fecal indicator bacteria (FIB), have been shown to poorly correlate with pathogens in the environment, complicating their use in certain settings. Thus a better understanding of the sources and fate of pathogens in the environment and solutions that sustainably reduce the impact of these sources on water resources is needed. At the same time, there are benefits to be gleaned from characterizing anthropogenic pollution sources, as they can be reflective of the infectious diseases circulating in a community. Therefore, tools to harness the information present in sewage should be developed in tandem with strategies to reduce the impact of pollution sources on the environment.
    In this seminar, I will first detail how viral pathogens in urban runoff are removed in green stormwater infrastructure to prevent impairments to surface waters and groundwater. Second, I will show how information on pathogens in wastewater can be used to inform public health decision-making, particularly during the COVID 19 pandemic. I will then discuss future teaching and research directions at the intersection of water, climate, and health issues, with the ultimate goal of providing safe and accessible water for all.


    Biography: Katherine (Katy) Graham is a President Postdoctoral Fellow at Georgia Tech in Environmental Engineering. Katy received her BSE in Chemical Engineering from the University of Michigan in 2015 and completed her MS and PhD at Stanford University in 2021. She is broadly interested in the characterization of pathogens and microbial communities in the natural and built environments to inform public health risk assessment and decision-making. She collaborates with multidisciplinary scientists and engineers to understand how to sustainably mitigate sources of microbial pollution to the environment, the utilization of human waste to understand public health trends, and the use of genomic information on pathogens in the environment to inform advanced public health risk assessment models.

    Host: Dr. Amy Childress

    Webcast: https://usc.zoom.us/j/91873923659? Meeting ID: 918 7392 3659 Passcode: 975701

    Location: Ronald Tutor Hall of Engineering (RTH) - 526

    WebCast Link: https://usc.zoom.us/j/91873923659? Meeting ID: 918 7392 3659 Passcode: 975701

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

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  • CS Colloquium: Sanjiban Choudhury (Cornell University) - Interactive Imitation Learning: Planning Alongside Humans

    Tue, Feb 08, 2022 @ 04:00 PM - 05:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Sanjiban Choudhury, Cornell University

    Talk Title: Interactive Imitation Learning: Planning Alongside Humans

    Series: Computer Science Colloquium

    Abstract: Advances in machine learning have fueled progress towards deploying real-world robots from assembly lines to self-driving. However, if robots are to truly work alongside humans in the wild, they need to solve fundamental challenges that go beyond collecting large-scale datasets. Robots must continually improve and learn online to adapt to individual human preferences. How do we design robots that both understand and learn from natural human interactions?

    In this talk, I will dive into two core challenges. First, I will discuss learning from natural human interactions where we look at the recurring problem of feedback-driven covariate shift. We will tackle this problem from a unified framework of distribution matching. Second, I will discuss learning to predict human intent where we look at the chicken-or-egg problem of planning with learned forecasts. I will present a graph neural network approach that tractably reasons over latent intents of multiple actors in the scene. Finally, we will demonstrate how these methods come together to result in a self-driving product deployed at scale.

    Register in advance for this webinar at:

    https://usc.zoom.us/webinar/register/WN_R-AyYtIjSlG4acgjxUOK9w

    After registering, attendees will receive a confirmation email containing information about joining the webinar.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Sanjiban Choudhury is a Research Scientist at Aurora Innovation and soon-to-be Assistant Professor at Cornell University. His research goal is to enable robots to work seamlessly alongside human partners in the wild. To this end, his work focuses on imitation learning, decision making and human-robot interaction. He obtained his Ph.D. in Robotics from Carnegie Mellon University and was a Postdoctoral fellow at the University of Washington. His research has received best paper awards at ICAPS 2019, finalist for IJRR 2018, and AHS 2014, and winner of the 2018 Howard Hughes award. He is a Siebel Scholar, class of 2013.


    Host: Stefanos Nikolaidis

    Webcast: https://usc.zoom.us/webinar/register/WN_R-AyYtIjSlG4acgjxUOK9w

    Location: Online - Zoom Webinar

    WebCast Link: https://usc.zoom.us/webinar/register/WN_R-AyYtIjSlG4acgjxUOK9w

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • Mork Family Department Spring Seminars - Prof. Zhenglu Li

    Tue, Feb 08, 2022 @ 04:00 PM - 05:00 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Prof. Zhenglu Li, University of California at Berkeley

    Talk Title: Mork Family Department Spring Seminars

    Host: Heather Alexander

    Location: Social Sciences Building (SOS) - B46

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series

    Wed, Feb 09, 2022 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Shahriar Nirjon, Department of Computer Science at the University of North Carolina at Chapel Hill

    Talk Title: Intermittent Learning on Harvested Energy

    Series: Center for Cyber-Physical Systems and Internet of Things

    Abstract: Years of technological advancements have made it possible for small, portable, electronic devices of today to last for years on battery power, and last forever - when powered by harvesting energy from their surrounding environment. Unfortunately, the prolonged life of these ultra-low-power systems poses a fundamentally new problem. While the devices last for years, programs that run on them become obsolete when the nature of sensory input or the operating conditions change. The effect of continued execution of such an obsolete program can be catastrophic. For example, if a cardiac pacemaker fails to recognize an impending cardiac arrest because the patient has aged or their physiology has changed, these devices will cause more harm than any good. Hence, being able to react, adapt, and evolve is necessary for these systems to guarantee their accuracy and response time. We aimed at devising algorithms, tools, systems, and applications that will enable ultra-low-power, sensor-enabled, computing devices capable of executing complex machine learning algorithms while being powered solely by harvesting energy. Unlike common practices where a fixed classifier runs on a device, we take a fundamentally different approach where a classifier is constructed in a manner that it can adapt and evolve as the sensory input to the system, or the application-specific requirements, such as the time, energy, and memory constraints of the system, change during the extended lifetime of the system.


    Biography: Dr. Shahriar Nirjon is an Assistant Professor of Computer Science at the University of North Carolina at Chapel Hill, NC. He is interested in Embedded Intelligence -“ the general idea of which is to make resource constrained real-time and embedded sensing systems capable of learning, adapting, and evolving. Dr. Nirjon builds practical cyber-physical systems that involve embedded sensors and mobile devices, mobility and connectivity, and mobile data analytics. His work has applications in the area of remote health and wellness monitoring, and mobile health. Dr. Nirjon received his Ph.D. from the University of Virginia, Charlottesville, and has won a number of awards including four Best Paper Awards at Mobile Systems, Applications and Services (MOBISYS 2014), the Real-Time and Embedded Technology and Applications Symposium (RTAS 2012), Distributed Computing in Sensor Systems (DCOSS '19), and Challenges in AI and Machine Learning for IoT (AIChallengeIoT '20). Dr. Nirjon is a recipient of NSF CAREER Award in 2021. Prior to UNC, Dr. Nirjon has worked as a Research Scientist in the Networking and Mobility Lab at the Hewlett-Packard Labs in Palo Alto, CA.

    Host: Pierluigi Nuzzo and Bhaskar Krishnamachari

    Webcast: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Location: Online

    WebCast Link: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Audiences: Everyone Is Invited

    Contact: Talyia White

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  • AME Seminar

    Wed, Feb 09, 2022 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Kristi Morgansen, University of Washington (AAE Chair)

    Talk Title: Integrated Sensing and Actuation for Robust Flight Systems

    Abstract: A fundamental element of effective operation of autonomous systems is the need for appropriate sensing and processing of measurements to enable desired system actions. Model-based methods provide a clear framework for careful proof of system capabilities but suffer from mathematical complexity and lack of scaling as probabilistic structure is incorporated. Conversely, learning methods provide viable results in probabilistic and stochastic structures, but they are not generally amenable to rigorous proof of performance. A key point about learning systems is that the results are based on use of a set of training data, and those results effectively lie in the convex hull of the training data. This presentation will focus on use of model-based nonlinear empirical observability criteria to assess and improving and bounding performance of learning pose (position and orientation) of rigid bodies from computer vision. A particular question to be addressed is what sensing data should be captured to best improve the existing training data. The particular tools to be leveraged here focus on the use of empirical observability gramian techniques being developed for nonlinear systems where sensing and actuation are coupled in such a way that the separation principle of linear methods does not hold. These ideas will be discussed relative to both engineering applications in the form of motion planning for range and bearing only navigation in autonomous vehicles, vortex position and strength estimation from pressure measurements on airfoils, and effective strain sensor placement on insect wings for inertial measurements.

    Biography: Kristi Morgansen received a BS and a MS in Mechanical Engineering from Boston University, respectively in 1993 and 1994, an S.M. in Applied Mathematics in 1996 from Harvard University and a PhD in Engineering Sciences in 1999 from Harvard University. Until joining the University of Washington, she was first a postdoctoral scholar then a senior research fellow in Control and Dynamical Systems at the California Institute of Technology. She joined the William E. Boeing Department of Aeronautics and Astronautics in the summer of 2002 as an assistant professor and is currently Professor and Chair of the department. She is also co-Director of the UW Space Policy and Research Center (UW SPARC) and is the Director of the Washington NASA Space Grant Consortium. She has received a number of awards, most recently Fellow of AIAA and member of the Washington State Academy of Sciences.

    Professor Morgansens research interests focus on nonlinear systems where sensing and actuation are integrated, stability in switched systems with delay, and incorporation of operational constraints such as communication delays in control of multi-vehicle systems. Applications include both traditional autonomous vehicle systems such as fixed-wing aircraft and underwater gliders as well as novel systems such as bio-inspired underwater propulsion, bio-inspired agile flight, human decision making, and neural engineering. The results of this work have been demonstrated in estimation and path planning in unmanned aerial vehicles with limited sensing, vorticity sensing and sensor placement on fixed wing aircraft, landing maneuvers in fruit flies, joint optimization of control and sensing in dynamical systems, and deconfliction and obstacle avoidance in autonomous systems and in biological systems including fish, insects, birds, and bats.

    Prof. Morgansens research focuses on guidance, navigation, control for autonomous underwater, surface, air and space systems. She is an advocate for project-based learning, inclusive engineering, multidisciplinary collaboration, and STEAM.

    More Info: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Webcast: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Location: James H. Zumberge Hall Of Science (ZHS) - 252

    WebCast Link: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

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  • ECE-S Seminar - Domain Specialized Architectures and Systems for AI/ML

    Thu, Feb 10, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Divya Mahajan, Senior Researcher, Cloud Accelerated Systems & Technologies, Microsoft

    Talk Title: Domain Specialized Architectures and Systems for AI/ML

    Abstract: Advances in Artificial Intelligence (AI) and Machine Learning (ML) are beginning to revolutionize medicine, manufacturing, commerce, transportation, and other key aspects of our lives. However, such transformative effects are predicated on providing high-performance compute capabilities to enable these learning algorithms. Domain specific accelerators are an efficient and performant means to meet the compute requirements of these large-scale AI/ML. As the new age data-centers become heterogeneous with these emerging domain specific hardware, we must rethink both the architecture and the corresponding system stack.

    In this talk, I will provide an overview of my contributions to design, deploy, and utilize accelerators for a wide class of AI/ML applications. I will first discuss pioneering works TABLA and DaNA, which are comprehensive full-stack solutions for machine learning accelerators that integrate with data management systems. These solutions expose a high-level programming interface to programmers that have limited knowledge about hardware design, nevertheless, can benefit from performance and efficiency gains through acceleration. Then, I will describe FAE, a novel framework that leverages statistical properties of data to best utilize the heterogeneous compute and memory resources for recommender model training. Finally, I will conclude with my future research vision towards devising architectures and systems for sustainable massive-scale distributed AI/ML by exploring the challenges which arise from the cross-pollination of different components in the data processing pipeline.

    Biography: Divya Mahajan is a Senior Researcher in the Cloud Accelerated Systems & Technologies group at Microsoft. She leads the research, design, and deployment of communication primitives for massive-scale distributed deep learning. She obtained her PhD in Computer Science from Georgia Institute of Technology. She obtained her Masters from The University of Texas Austin, Texas and Bachelors from Indian Institute of Technology Ropar. Her research interests lie in designing novel architectures and building robust systems to address the needs of new and emerging applications. She is passionate about continuing innovative research to have a broad impact on computing and society in general.

    Divya is the recipient of National Council for Women and Information Technology Collegiate Award, President of India Gold Medal at IIT, and has been a Finalist in the Qualcomm Innovation Fellowships. Her work has been recognized with the College of Computing Dissertation Award, HPCA Distinguished Paper Award, and has appeared in top architecture, database, systems, and machine learning venues like ISCA, MICRO, HPCA, ASPLOS, VLDB, NeurIPS and high impact journals like IEEE Micro.

    https://www.microsoft.com/en-us/research/people/divyam/


    Host: Dr. Murali Annavaram, annavara@usc.edu

    Webcast: https://usc.zoom.us/j/96503892197?pwd=Nk13S1RZb25tMlN1QnUxRWZXN2lNZz09

    More Information: ECE Seminar-Mahajan-021022.pdf

    WebCast Link: https://usc.zoom.us/j/96503892197?pwd=Nk13S1RZb25tMlN1QnUxRWZXN2lNZz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Mork Family Department Spring Seminars - Prof. Kayla Nguyen

    Thu, Feb 10, 2022 @ 10:00 AM - 10:00 AM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Prof. Kayla Nguyen, University of Illinois Urbana-Champaign

    Talk Title: Mork Family Department Spring Seminars

    Host: TBA

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    Audiences: Everyone Is Invited

    Contact: Greta Harrison

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  • Astani Department of Civil and Environmental Engineering Seminar

    Thu, Feb 10, 2022 @ 12:30 PM - 01:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Ryan Kingsbury, Ph.D., Postdoctoral Researcher, Lawrence Berkeley National Laboratory

    Talk Title: Accelerated discovery of precise ion separation materials

    Abstract: Climate change, water scarcity, and food security are among the greatest public health and environmental challenges of our time. Emerging electrochemical technologies have the potential to help address all three by offering clean, efficient means of producing clean energy, purifying nontraditional water supplies, or recovering valuable resources from waste. All such technologies incorporate engineered materials that promote the selective movement of charged species (ions) in aqueous solutions, and their technical and economic feasibility is often constrained by how well those materials perform. Thus, advancing the state of the art of ion separation materials, such as polymer ion exchange membranes, is crucial to the development of sustainable technologies across the water-energy nexus. However, we lack the molecular-level understanding of ion separations that we need to rationally design new materials with higher performance. This seminar will describe our current understanding of ion transport phenomena in polymer membranes, identify crucial knowledge gaps, and highlight opportunities to advance the state of the art by combining experiments and atomistic simulations. Insights from this research will accelerate development of materials with more precise ion selectivity, leading to powerful new technologies for addressing sustainability challenges.




    Biography: Dr. Ryan Kingsbury has been a postdoctoral researcher in the Energy Storage and Distributed Resources division at Lawrence Berkeley National Laboratory since 2019. Prior to LBNL, he obtained a PhD in Environmental Sciences and Engineering from the University of North Carolina at Chapel Hill, where he was named a National Science Foundation Graduate Research Fellow and recognized with a Student Fellowship Award by the North American Membrane Society. Before graduate school he worked as a consulting engineer in the drinking water treatment field, earning a Professional Engineering license in 2013, and founded a startup company to develop a novel energy storage process. Ryan studies electrochemical technologies for clean water and clean energy production, with a particular focus on developing ion-selective materials.

    Host: Dr. Amy Childress

    Webcast: https://usc.zoom.us/j/91873923659 Mtg ID: 918 7392 3659 Pass: 975701

    Location: Ronald Tutor Hall of Engineering (RTH) - 526

    WebCast Link: https://usc.zoom.us/j/91873923659 Mtg ID: 918 7392 3659 Pass: 975701

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

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  • Advanced Manufacturing Seminar

    Fri, Feb 11, 2022 @ 10:00 AM - 11:30 AM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Vijay Srinivasan, Engineering Laboratory, National Institute of Standards and Technology

    Talk Title: Emergence of Biotechnology Platforms During COVID-19: A Lesson in Modern Biology

    Abstract: The COVID-19 pandemic has accelerated the development and manufacturing of vaccines at an unprecedented rate. This has been enabled by the emergence of biotechnology platforms such as mRNA and Viral Vectors. In this seminar, I will outline the engineering aspects of such platforms and the modern biology behind their evolution.

    Biography: Dr. Vijay Srinivasan is a Senior Advisor in the Engineering Laboratory at the National Institute of Standards and Technology, Gaithersburg, Maryland. He joined NIST in 2009, after 26 years at IBM Research during which he was also an Adjunct Professor at the Columbia University, New York. Dr. Srinivasan has published widely and is a Fellow of ASME and AAAS.

    Host: Center for Advanced Manufacturing

    More Info: Registration link: https://usc.zoom.us/webinar/register/WN_ldAXMSAaT2iL638ngk5IZw

    Webcast: Registration link: https://usc.zoom.us/webinar/register/WN_ldAXMSAaT2iL638ngk5IZw

    WebCast Link: Registration link: https://usc.zoom.us/webinar/register/WN_ldAXMSAaT2iL638ngk5IZw

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

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  • ECE Seminar - Dealing with Data Deluge in the Edge Systems

    Mon, Feb 14, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Ramyad Hadidi, Machine Learning Researcher, SK hynix America

    Talk Title: Dealing with Data Deluge in the Edge Systems

    Abstract: Each day, a huge amount of data is generated. Edge systems--any computing agent but large-scale datacenter machines--not only fuel this data deluge but also play an increasingly vital role in processing them. Unlike conventional systems that are engineered with abundant resources, edge systems operate in real-world conditions facing several unknown design-space trade-offs with limited resources restricting their full-scale autonomy. This leads to isolated, time-consuming, and costly approaches to each challenge that result in ad-hoc edge systems but not the optimal one. To effectively maneuver the constraints and unique multi-dimensional design space of edge systems, my research develops novel machine learning techniques and exploits hardware-software synergy by setting roadmaps across the hardware-software stack for the next generation of edge systems.

    In my talk, first, with an example of quadcopter drones, I show how my research is a pioneer that reveals the unique multi-dimensional design space of edge systems and suggests optimal points within this space depending on the use case. By formulating the fundamental drone subsystems and introducing our open-source customizable drone, I explain how computations impact this design space. As an example of optimized computations, by exploring implementations of simultaneous localization and mapping (SLAM) on various hardware platforms (CPU, GPU, FPGA, and ASIC), I demonstrate which implementation is more reasonable for drones. The second part of my talk emphasizes the necessity of modern machine learning techniques, such as those utilizing heavy neural networks, in comprehending complex raw data in edge systems and acting upon the outcomes. I show how my research empowers edge devices to break their individual resource constraints by distributing the computation on collaborating peer devices and proposes edge-aware neural networks by exploring hardware-software co-designs, algorithmic modifications, and system-level optimizations. In the end, I propose my plans for effectively handling data in exotic frontiers of edge systems with unique constraints to stimulate thought-provoking applications for our future.

    Biography: Ramyad Hadidi is currently a machine learning researcher at SK hynix working at the intersection of hardware, software, and edge devices, focusing on the efficient execution of deep learning algorithms. Ramyad Hadidi received his Ph.D. in computer science from Georgia Institute of Technology in May 2021 under the supervision of Professor Hyesoon Kim with his thesis titled "Deploying Deep Neural Networks in Edge with Distribution." Ramyad's research interests include but are not limited to computer architecture, robotics, edge computing, and machine learning. Besides his dissertation research, Ramyad has contributed to research on processing-in-memory, GPU systems, and hardware accelerators for sparse problems, believing a balance between depth and breadth leads to genuine research problems.

    Host: Dr. Peter Beerel, pabeerel@usc.edu

    Webcast: https://usc.zoom.us/j/93601839133?pwd=QW51RjRSVSsyZStDOVk2RUZ3Q0ZUdz09

    WebCast Link: https://usc.zoom.us/j/93601839133?pwd=QW51RjRSVSsyZStDOVk2RUZ3Q0ZUdz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • ISE 651 Epstein Seminar

    Tue, Feb 15, 2022 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Edward H. Kaplan , Professor of Operations Research Yale School of Management

    Talk Title: COVID 19 Scratch Models to Support Local Decisions

    Host: Dr. Sze-chuan Suen

    More Information: February 15, 2022.pdf

    Location: online via zoom

    Audiences: Everyone Is Invited

    Contact: Grace Owh

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  • CS Colloquium: Julian Togelius (NYU) - Generating content for fun, games, and intelligence

    Tue, Feb 15, 2022 @ 04:00 PM - 05:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Julian Togelius, New York University

    Talk Title: Generating content for fun, games, and intelligence

    Series: Computer Science Colloquium

    Abstract: Designing and developing video games is hard work. Much of that work goes into designing the environments, levels, characters, items, and graphical assets that the player interacts with. For decades, some games have featured procedural content generation, where parts of the game was generated algorithmically as it was being played. Recently, we have seen an explosion of interest in this field, with many new techniques being applied to content generation problems. As video games are increasingly shaping culture and society, including visions of the so called "metaverse", these problems and their solutions are becoming increasingly important. But could procedural content generation also play a role outside of what we think of as games, in fields such as architecture, interior design, and robotics? And can we further the development of artificial general intelligence by generating new tasks and environments to optimally challenge our developing artificial intelligences? I will lay out some visions for this and show some recent approaches to generating game content, based on methods as diverse as evolutionary computation, reinforcement learning, self-supervised learning, and constraint solving.

    ***Prof. Julian Togelius will give his talk in person at SGM 124 and we will also host the talk over Zoom.***

    Register in advance for this webinar at:
    https://usc.zoom.us/webinar/register/WN_ORwP_CgyTLqVQodYr9ny1w

    After registering, attendees will receive a confirmation email containing information about joining the webinar.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Julian Togelius is an Associate Professor in the Department of Computer Science and Engineering, New York University, and a co-founder of modl.ai. He works on artificial intelligence for games and on games for artificial intelligence. His current main research directions involve procedural content generation in games, general video game playing, player modeling, and fair and relevant benchmarking of AI through game-based competitions. Additionally, he works on topics in evolutionary computation, quality-diversity algorithms, and reinforcement learning. From 2018 to 2021, he was the Editor-in-Chief of the IEEE Transactions on Games. Togelius holds a BA from Lund University, an MSc from the University of Sussex, and a PhD from the University of Essex. He has previously worked at IDSIA in Lugano and at the IT University of Copenhagen.


    Host: Stefanos Nikolaidis

    Webcast: https://usc.zoom.us/webinar/register/WN_ORwP_CgyTLqVQodYr9ny1w

    Location: Seeley G. Mudd Building (SGM) - 124

    WebCast Link: https://usc.zoom.us/webinar/register/WN_ORwP_CgyTLqVQodYr9ny1w

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • Mork Family Department Seminar - Yu-Tsun Shao

    Tue, Feb 15, 2022 @ 04:00 PM - 05:15 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Yu-Tsun Shao, Cornell University

    Talk Title: Imaging of Topological Textures and Their Order Parameters

    Host: Professor A.Hodge

    Location: Social Sciences Building (SOS) - B46

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • ECE Seminar - Scalable and Trustworthy Learning for Distributed Intelligence

    Wed, Feb 16, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Tianyi Chen, Assistant Professor, Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute

    Talk Title: Scalable and Trustworthy Learning for Distributed Intelligence

    Abstract: The past decade has witnessed the revival of artificial intelligence (AI) and machine learning (ML) in almost every branch of science and technology. The "fuel" to AI and ML is supplied by the surge of data and computing power. Today, data and computing power are distributed among wireless devices and companies that we term data owners. Due to the pressing need for data in AI/ML tasks and the increasing concerns on data privacy, a sizeable number of AI/ML tasks will be executed across networked data owners with the vision of distributed intelligence.

    In this talk, I will use federated learning (FL) as an example of distributed intelligence. I will highlight its key challenges when it interacts with wireless networks such as efficiency, privacy, security, and robustness. I will focus on two aspects of scalable and trustworthy FL - efficiency and privacy. From a unified view of information correlation among iterative FL updates, I will elaborate i) how we can leverage such correlation to improve the efficiency of FL; and ii) how such correlation may be leveraged by a malicious third party to risk data privacy. Our methods are simple to implement, and they come with rigorous performance guarantees. I will conclude this talk by highlighting a few directions that I will pursue towards distributed intelligence beyond FL.

    Biography: Tianyi Chen is an Assistant Professor in the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI), where he is jointly supported by the RPI - IBM Artificial Intelligence Research Partnership. Dr. Chen received his B. Eng. degree in Electrical Engineering from Fudan University in 2014, and the Ph.D. degree in Electrical and Computer Engineering from the University of Minnesota in 2019. During 2017-2018, he has been a visiting scholar at Harvard University, University of California, Los Angeles, and University of Illinois Urbana-Champaign. Dr. Chen's research focuses on theoretical and algorithmic foundations of optimization, machine learning, and statistical signal processing, with applications in networked computing systems such as wireless and IoT systems.

    Dr. Chen is the inaugural recipient of IEEE Signal Processing Society Best PhD Dissertation Award in 2020 and a recipient of NSF CAREER Award in 2021. He is also the co-author of several best paper awards such as the Best Student Paper Award at the NeurIPS Federated Learning Workshop in 2020 and at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) in 2021.

    Host: Dr. Salman Avestimehr, avestime@usc.edu

    Webcast: https://usc.zoom.us/j/98177654001?pwd=R1h6bUJZUXcxZENZYWtVYmorRVNFQT09

    WebCast Link: https://usc.zoom.us/j/98177654001?pwd=R1h6bUJZUXcxZENZYWtVYmorRVNFQT09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series

    Wed, Feb 16, 2022 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Changliu Liu, Robotics Institute, Carnegie Mellon University

    Talk Title: Safe Control and Learning for Effective Human-Robot Collaboration

    Series: Center for Cyber-Physical Systems and Internet of Things

    Abstract: In this talk, I will discuss our recent work on safe control and learning for effective human-robot collaboration. I will first introduce safe control methods using energy-function-based methods, then discuss how to combine them with learning controllers where an explicit analytical dynamic model of the system is usually not available (especially in human-robot systems). These safe control methods will enable safe reinforcement learning with zero training time violation. Then I will discuss about methods to robustly learn models to predict human behaviors. The key challenge we need to address is the distribution shift between the offline collected human behavioral data and the online measured human behaviors. To mitigate the distribution shift, we introduce two methods: online model adaptation, and offline verification-guided data augmentation. These methods have been applied to facilitate human-robot collaboration in industrial assembly tasks. I will conclude the talk with future visions on how to effectively deploy human-robot systems on factory floors.

    Biography: Dr. Changliu Liu is an assistant professor in the Robotics Institute, School of Computer Science, Carnegie Mellon University (CMU), where she leads the Intelligent Control Lab. Prior to joining CMU, Dr. Liu was a postdoc at Stanford Intelligent Systems Laboratory. She received her Ph.D. from University of California at Berkeley and her bachelor's degrees in engineering and economics from Tsinghua University. Her research interests lie in the design and verification of intelligent systems with applications to manufacturing and transportation. She published the book "Designing robot behavior in human-robot interactions" with CRC Press in 2019. She received many best paper awards, Rising Star in EECS, NSF Career Award, Amazon Research Award, and Ford URP Award.

    Host: Pierluigi Nuzzo and Somil Bansal

    Webcast: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Location: Online

    WebCast Link: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Audiences: Everyone Is Invited

    Contact: Talyia White

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  • AME Seminar

    Wed, Feb 16, 2022 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Gustaaf Jacobs, San Diego State University

    Talk Title: Where do flows separate and how does that affect the optimal control location?

    Abstract: Flow separation can degrade performance in many engineering systems, through reduced lift, increased drag, and decreased efficiency. To alleviate the effects of flow separation on aerodynamic performance, active flow control has been considered since the inception of the field of aerodynamics. Open-loop flow control strategies based on various actuator technologies -” such as plasma actuators, fluidic oscillators, and synthetic jets -” have been shown to effectively alter separated flows, and in some cases to even yield complete reattachment. Most analyses start from the placement of an actuator at an intuitively optimal location near the separation point and/or near the Kutta condition. Optimal placement, however, requires a detailed understanding of non-linear flow separation and wake feedback that is often counterintuitive. In this talk, I will discuss recent developments in Lagrangian analysis of flow separation. This kinematic analysis promises the objective identification of separation lines as zero-mass flux "material"' lines whose footprint is analytically defined from first-principle. The separation profiles start with a subtle upwelling of Lagrangian fluid tracers upstream of the separation point. Using a data-driven technique (using DNS data) I will show that these upwelling locations may well point to optimal actuator locations that require minimal control effort.

    Biography: Professor Jacobs received a M.Sc. in Aerospace Engineering from the Delft University of Technology in 1998, where after graduation, he was appointed to a Research Associate. He received a Ph.D. in Mechanical Engineering from the University of Illinois at Chicago. Following graduation in 2003, he was appointed Visiting Assistant Professor in the Division of Applied Mathematics at Brown University. He later combined this position with a Postdoctoral Fellowship at the Department of Mechanical Engineering at the Massachusetts Institute of Technology. As of 2006 he was appointed Assistant Professor of Aerospace Engineering at San Diego State University and was promoted to Associate Professor in 2010 and Full Professor in 2014. In 2001 he received the Provosts Award for Graduate Research at the University of Illinois at Chicago. In 2002, he was awarded a University Fellowship at the University of Illinois. He received an AFOSR Young Investigator Award in 2009. He became an Associate Fellow of AIAA in 2013. The research interests of Professor Jacobs can broadly be defined in the area of computational multiphase, and multiscale flow physics modeling and simulation using high-order methods. Emphasis is on simulation and analysis of particle-laden flows and flow separation in complex geometries, to aid flow control relating to combustion optimization and drag reduction.

    Host: AME Department

    More Info: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Webcast: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Location: James H. Zumberge Hall Of Science (ZHS) - 252

    WebCast Link: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

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  • ECE Seminar - Human-centered machine intelligence: From robust signal analytics to trustworthy human-technology partnership

    Thu, Feb 17, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Theodora Chaspari, Assistant Professor, Computer Science & Engineering Department, Texas A&M University

    Talk Title: Human-centered machine intelligence: From robust signal analytics to trustworthy human-technology partnership

    Abstract: Recent converging advances in sensing and computing allow the ambulatory long-term tracking of individuals yielding a rich set of real-life multimodal bio-behavioral signals, such as speech, physiology, and facial expressions. While such measurements coupled with artificial intelligence (AI) and machine learning (ML) algorithms have been heralded as promising solutions to addressing pressing societal challenges, public and expert determination of whether this integration is a good prospect is widely debated. At the same time, interactions between humans and AI are increasingly moving away from simple diagnosis of human outcomes to collaborative relationships, in which humans work side-by-side with AI systems for carrying out a set of common goals. This talk will describe new signal analytics and ML algorithms for trustworthy human-centered machine intelligence focusing on four main pillars of trustworthiness, namely robustness, privacy preservation, explainability, and fairness. We will first present our work on personalized ML models of human outcomes, generalizable learning of human states via the formulation of weakly supervised learning algorithms, and context-aware signal representations for reliably modeling interpersonal interaction. Following that, we will discuss a privacy-preserving mood recognition framework through user anonymization and examine factors of socio-demographic bias in signals and ML systems that may perpetuate social disparities in human-centered analytics. Finally, we will present our recent work on human-AI collaboration that examines how human stakeholders (e.g., clinicians) interact with AI/ML along dimensions of trust formation, maintenance, and repair. We will demonstrate the effectiveness of the proposed approaches through examples in mental health, public health, workforce training and re-skilling, and team science.

    Biography: Theodora Chaspari is an Assistant Professor in the Computer Science & Engineering Department at Texas A&M University. She has received her Bachelor of Science (2010) in Electrical & Computer Engineering from the National Technical University of Athens, Greece and her Master of Science (2012) and Ph.D. (2017) in Electrical Engineering from the University of Southern California. Between 2010 and 2017 she worked as a Research Assistant at the Signal Analysis and Interpretation Laboratory at USC. She has also been a Lab Associate Intern at Disney Research (2015). Theodora's research interests lie in the areas of signal processing, machine learning, data science, and affective computing. She is a recipient of the NSF CAREER Award (2021), TAMU Montague Teaching Award (2021), USC Women in Science and Engineering Merit Fellowship (2015), and USC Annenberg Graduate Fellowship (2010). Papers co-authored with her students have been nominated and won awards at the ASC 2021, ACM BuildSys 2019, IEEE ACII 2019, ASCE i3CE 2019, and IEEE BSN 2018 conferences. She is serving as an Editor of the Elsevier Computer Speech & Language, and in various conference organization committees (ACM ICMI 2023/2020/2018, ACM IUI 2021, ACM KDD 2022, IEEE ACII 2022/2021/2019/2017, IEEE BSN 2018). She has further developed and taught several graduate and undergraduate courses in signal analytics and ML. Her work is supported by federal and private funding sources, including the NSF, NIH, NASA, IARPA, AFRL, AFOSR, General Motors, Keck Foundation, and the Engineering Information Foundation.

    Host: Dr. Justin Haldar, jhaldar@usc.edu

    Webcast: https://usc.zoom.us/j/91654081503?pwd=aTFTN293KzFMTWFIUlQ4MkJHOFQxdz09

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

    WebCast Link: https://usc.zoom.us/j/91654081503?pwd=aTFTN293KzFMTWFIUlQ4MkJHOFQxdz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Astani Civil and Environmental Engineering Seminar

    Thu, Feb 17, 2022 @ 12:30 PM - 01:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Kandis Leslie Abdul-Aziz , Chemical and Environmental Engineering, University of California, Riverside

    Talk Title: Creating Value from Waste and Pollution: Sustainable chemical processes to promote a circular economy

    Abstract: A circular economy aims to redefine growth, focusing on society-wide benefits beyond the current take-make-waste industrial model. It entails an economic outlook that goes beyond the consumption of finite resources and develops strategies for utilizing waste. This presentation presents two strategies for the utilization of waste. The project will discuss the advent of a circular agriculture economy and how one of the top waste agriculture crops in the U.S., corn stover, can be engineered into valuable products. The first part of the presentation will discuss our efforts to convert corn stover into a source for sugar fatty acid ester pesticides to control the population of Asian citrus psyllid. Asian citrus psyllid poses a risk to citrus crops, and there are limited organic pesticide choices for growers and farmers. Sugar fatty acid ester pesticides are renewable and environmentally benign alternatives. The second part of the presentation will demonstrate the influence of hydrothermal carbonization (HTC) or slow pyrolysis (SP) process conditions on the physicochemical properties of precursor biochars and activated carbon (AC). The AC is achieved through a direct or two-step method with subsequent chemical activation using KOH. A theory is developed on the biochar propensity to be chemically activated based on the composition of the lignocellulosic structure. The activated carbon was utilized for wastewater treatment applications. The presentation will conclude with a discussion on a circular economy and the role of engineers.




    Biography: Dr. Kandis Leslie Abdul-Aziz is an Assistant Professor in the Chemical and Environmental Engineering department at the University of California, Riverside. She joined the University in 2018 after receiving her Ph.D. in Chemistry from the University of Illinois at Urbana-Champaign. Before joining UC Riverside, she was a Provost postdoctoral fellow at the University of Pennsylvania, where she developed procedures for synthesizing heterogeneous catalysts using atomic layer deposition. She has also worked previously as a Forensic scientist for the Philadelphia police department and as a Refinery chemist at Sunoco Chemicals in Philadelphia after receiving a B.S. in Chemistry from Temple University. Her research group develops sustainable catalytic processes using an interdisciplinary toolset from materials and chemical engineering and physical chemistry for sustainable applications. She was awarded a 2022 National Science Foundation Career Award and is a Scialog Negative Emissions Fellow.

    Host: Dr. Amy Childress

    Webcast: https://usc.zoom.us/j/91873923659 Meeting ID: 918 7392 3659 Passcode: 975701

    Location: Michelson Center for Convergent Bioscience (MCB) - 101

    WebCast Link: https://usc.zoom.us/j/91873923659 Meeting ID: 918 7392 3659 Passcode: 975701

    Audiences: Everyone Is Invited

    Contact: Evangeline Reyes

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  • AME Special Seminar

    Thu, Feb 17, 2022 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Neda Maghsoodi, California Institute of Technology

    Talk Title: Nonlinear Dynamics of Soft Filaments: From Biology to Engineering

    Abstract: Filaments of soft materials such as rubber and biopolymers can easily undergo large complex deformations under external stimuli. Soft filaments offer exciting applications in engineering, life science, and medicine since their nonlinear conformational changes can be harnessed for robust functional mechanisms. Understanding the dynamics of soft filaments is fundamental to advancing future engineering and medical applications. In this talk, I present analytical models for two fascinating soft filaments that function in the nonlinear regime; 1) protein filaments forming the contractile injection machinery of the virus bacteriophage T4, and 2) photoactivated liquid crystal elastomeric (LCEs) filaments proposed for future soft robotics. I present dynamic models for both filaments based on continuum rod theory to predict their dynamical nonlinear behaviors. For context, bacteriophage T4 is an intriguing nano-scale injection machine that infects the bacterium E. coli (its host) by rupturing the cell membrane and then injecting its genome into the host cell. The injection process is initiated when the injection machine, composed of protein filaments, undergoes a nonlinear conformational change to drive the T4 needle into the host. Photomechanical LCEs are engineered, light-sensitive polymers that undergo large reversible deformations under illumination. For these systems, I show how phage T4 exploits large deformations of the protein filaments for efficient DNA injection, and how one can harness nonlinear deformations of LCE filament to generate periodic motions under steady illumination. These findings have significant implications for designing future biomedical devices and bio-inspired soft robotics.



    Biography: Neda Maghsoodi is a postdoctoral scholar at the California Institute of Technology (CalTech). Neda received her Ph.D. in Mechanical Engineering in 2019 and her M.Sc. in Biomedical Engineering in 2017 from the University of Michigan-Ann Arbor. Prior to joining the University of Michigan, she earned her B.Sc. and M.Sc. both in Mechanical Engineering from Iran. Her research lies at the interface of applied mechanics, materials science, and biology and develops novel theoretical models to elucidate nonlinear dynamics of soft filaments at a broad range of time- and length-scales. In her academic career, Neda was named a Rising Star in Mechanical Engineering by UC-Berkeley (2020), received the Best Paper award in the ASME International Conference on Micro- and Nanosystems, and received the Best B.Sc. Thesis award of the year from the Iranian Society of Acoustics and Vibration. The novelty and significance of her Ph.D. research have been featured in Biophysical Journal as the New and Notable Article and in several news outlets.

    Host: AME Department

    More Info: https://usc.zoom.us/j/95277955610

    Webcast: https://usc.zoom.us/j/95277955610

    WebCast Link: https://usc.zoom.us/j/95277955610

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

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  • Mork Family Department Seminar - Malik Wagih

    Thu, Feb 17, 2022 @ 04:00 PM - 05:15 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Malik Wagih , Massachusetts Institute of Technology

    Talk Title: Grain Boundary Segregation for Alloy Design: From Atoms to Polycrystals

    Host: Professor A.Hodge

    Location: Kaprielian Hall (KAP) - 147

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • Grand Challenges Scholars Program 2.0 with Dean Yortsos

    Thu, Feb 17, 2022 @ 05:30 PM - 07:00 PM

    Viterbi School of Engineering Student Affairs

    Conferences, Lectures, & Seminars


    Speaker: Dean Yannis Yortsos , Dean Yannis Yortsos

    Talk Title: Grand Challenges Scholars Program 2.0 with Dean Yortsos

    Abstract: Come join us at our upcoming event, the Grand Challenges Scholars Program 2.0 with Dean Yortsos on Thursday, February 17th, 2022 from 5:30-7:00 p.m. in RTH 105 and Zoom. Dean Yortsos presents on his vision for the new/updated GCSP and its required mindsets.


    Host: Grand Challenges Scholars Program

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

    Audiences: Undergrad

    Contact: Viterbi Undergraduate Programs

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  • ECE Seminar

    Fri, Feb 18, 2022 @ 10:00 AM - 11:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Yingyan (Celine) Lin, Department of Electrical and Computer Engineering, Rice University

    Talk Title: Towards Network-Accelerator Co-Search for Promoting Ubiquitous on-Device Intelligence and Green AI

    Abstract: Deep learning (DL)-powered intelligence embedded into numerous daily-life devices promises to transform the quality of human life. Despite this great promise, there is a vast and increasing gap between the prohibitive complexity of powerful DL algorithms and the constrained resources in daily-life devices. While DL accelerators have the potential to close the aforementioned immense gap and push forward green AI, their power has yet to be unleashed due to the following fundamental challenges: (1) fast DL algorithm advances vs. slow DL accelerator development, and (2) the promise of algorithm and accelerator co-search vs. the Lack of such co-search. Therefore, it is imperative to develop innovative techniques that can expedite the development of optimal DL accelerators and unlock the promise of co-searching for optimal DL algorithms and accelerators for maximizing their achievable hardware efficiency.

    In this talk, I will present our recently developed techniques towards DL network-accelerator co-search, serving as a timely holistic effort toward addressing the aforementioned challenges. Specifically, I will start by introducing our techniques for designing hardware-aware DL algorithms (i.e., top-down efforts) and algorithm-award DL accelerators (i.e., bottom-up efforts), which helps us to gain important insights for understanding their design space and optimization. Then, I will share our first-of-their-kind techniques that are among the very first generic efforts to enable simultaneous searching for optimal DL algorithms and accelerators (i.e., bridging efforts) to maximize both task accuracy and hardware efficiency. Finally, I will conclude my talk with exciting (1) applications of our co-search framework and (2) pointers to future directions.

    Biography: Yingyan (Celine) Lin is an Assistant Professor in the Department of Electrical and Computer Engineering at Rice University. She leads the Efficient and Intelligent Computing (EIC) Lab at Rice, which focuses on developing efficient machine learning techniques towards green AI and ubiquitous machine learning powered intelligence. She received a Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2017.

    Prof. Lin is a NSF CAREER Award, IBM Faculty Award, and Facebook Research Award recipient. She was selected as a Rising Star in EECS by the 2017 Academic Career Workshop for Women at Stanford University. She received a Best Student Paper Award at the 2016 IEEE International Workshop on Signal Processing Systems (SiPS 2016), and the 2016 Robert T. Chien Memorial Award for Excellence in Research at UIUC. Prof. Lin is currently the lead PI on multiple multi-university projects (e.g., RTML and 3DML) and her group has been funded by NSF, NIH, DARPA, ONR, Qualcomm, Intel, IBM, and Facebook.

    Host: Dr. Murali Annavaram, annavara@usc.edu

    Webcast: https://usc.zoom.us/j/95976355759?pwd=cTkwMlpoVFRuOEkxQ2JHNy9MYzJ4dz09

    WebCast Link: https://usc.zoom.us/j/95976355759?pwd=cTkwMlpoVFRuOEkxQ2JHNy9MYzJ4dz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • ECE Seminar: Next-Generation Wireless Networks for Billions of IoT Devices

    Tue, Feb 22, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Ali Abedi, Research Lecturer, Cheriton School of Computer Science, University of Waterloo

    Talk Title: Next-Generation Wireless Networks for Billions of IoT Devices

    Abstract: It is estimated that the total number of Internet of Things (IoT) devices will grow to 80 billion devices in a few years. Unfortunately, existing wireless networks cannot satisfy the diverse requirements of IoT applications in terms of power consumption, data rate, and privacy. Some IoT devices, such as contact sensors used in intrusion detection systems, transmit only a few bytes of data occasionally, while other devices such as virtual reality headsets require a continuous stream of data with a high data rate. Moreover, many IoT devices run on a battery; therefore, they have very strict power consumption requirements. The battery in many IoT devices has to be changed every few months, which is time consuming and costly. Furthermore, old batteries have adverse environmental effects, if not disposed of properly. In addition to these problems, bringing many IoT devices to our smart homes and offices creates many privacy concerns. How can users be confident that their privacy is not violated in smart environments?

    In this talk, I present the design of next generation wireless networks that satisfy the diverse requirements of IoT applications. To enable low-power wireless networking for IoT applications that require low data rates, I present a system that enables a battery-free IoT device to transmit its data to nearby WiFi devices. Next, I describe the design of a low-power and low-cost millimeter wave network for IoT devices that require up to 100 Mbps of bandwidth. Finally, I discuss privacy issues caused by wireless signals transmitted by many IoT devices in a smart environment.

    Biography: Ali Abedi is currently a research lecturer at the University of Waterloo. His research interests are in the areas of wireless networks and mobile systems with a special focus on the Internet of Things (IoT) and smart environments. He received his Ph.D. in computer science from the University of Waterloo. His work has been published in top systems and networking venues such as SIGCOMM, MobiCom, and HotNets. He was awarded the gold medal in the Student Research Competition (SRC) competition at Mobicom 2018. His research projects have been featured in ACM GetMobile, ACM Tech News, and Science Daily. He has received multiple grants from the Natural Sciences and Engineering Research Council of Canada (NSERC). His research has resulted in multiple patents, and has attracted interests from companies such as Google, Qualcomm, and ecobee.

    Host: Dr. Konstantinos Psounis, kpsounis@usc.edu

    Webcast: https://usc.zoom.us/j/96468306783?pwd=cmJGWE91d1M0VDM1aGhaaXJNdDFPZz09

    WebCast Link: https://usc.zoom.us/j/96468306783?pwd=cmJGWE91d1M0VDM1aGhaaXJNdDFPZz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • ECE Seminar: Solving the Cloud Efficiency Crisis with Fast and Accessible Scheduling

    Tue, Feb 22, 2022 @ 12:00 PM - 01:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Kostis Kaffes, Electrical Engineering Department, Stanford University

    Talk Title: Solving the Cloud Efficiency Crisis with Fast and Accessible Scheduling

    Abstract: Operating systems (OS) specialization is necessary as the one-size-fits-all approach of fundamental OS operations such as scheduling is incompatible with today's diverse application landscape. Such specialization can improve application performance and cloud platform efficiency by an order of magnitude or more. Towards this goal, I will first discuss Shinjuku, a specialized OS that supports an order of magnitude higher load and lower tail latency than state-of-the-art systems by enabling better scheduling. Shinjuku leverages hardware support for virtualization to preempt as often as every 5 microseconds and disproves the conventional wisdom that interrupts are incompatible with microsecond timescales. Then, I will present Syrup, a framework that enables everyday application developers to specify custom scheduling policies easily and safely deploy them across different layers of the stack over existing operating systems like Linux, bringing the benefits of specialized scheduling to everyone. For example, Syrup allowed us to implement policies that previously required specialized dataplanes in less than 20 lines of code and improve the performance of an in-memory database by 8x without needing any application modification.

    Biography: Kostis Kaffes is a final-year Ph.D. candidate in Electrical Engineering at Stanford University, advised by Christos Kozyrakis. He is broadly interested in computer systems, cloud computing, and scheduling. His thesis focuses on end-host, rack-scale, and cluster-scale scheduling for microsecond-scale tail latency with the goal of improving efficiency in the cloud. Recently, he has been looking for ways to make it easier to implement and deploy custom scheduling policies across different layers of the stack. Kostis's research has been supported by a Facebook Research Award and various scholarships and fellowships from Stanford, A.G. Leventis Foundation, and Gerondelis Foundation. Prior to Stanford, he received his undergraduate degree in Electrical and Computer Engineering from the National Technical University of Athens in Greece.

    Host: Dr. Murali Annavaram, annavara@usc.edu

    Webcast: https://usc.zoom.us/j/96988520485?pwd=aHRIY1BBWW5PVEtCeDlWSnAwUUxsUT09

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

    WebCast Link: https://usc.zoom.us/j/96988520485?pwd=aHRIY1BBWW5PVEtCeDlWSnAwUUxsUT09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • CS Colloquium: Siddharth Srivastava (Arizona State University) - Principles and Algorithms for Data-Efficient Assistive Sequential Decision Making

    Tue, Feb 22, 2022 @ 01:00 PM - 02:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Siddharth Srivastava, Arizona State University

    Talk Title: Principles and Algorithms for Data-Efficient Assistive Sequential Decision Making

    Series: Computer Science Colloquium

    Abstract: Can we balance efficiency and reliability while designing assistive AI systems? What would such AI systems need to provide? In this talk I will present some of our recent work addressing these questions. In particular, I will show that a few fundamental principles of abstraction are surprisingly effective in designing efficient and reliable AI systems that can plan and act over multiple timesteps. Our results show that abstraction mechanisms are invaluable not only in improving the efficiency of sequential decision making, but also in developing AI systems that can explain their own behavior to non-experts, and in computing user-interpretable assessments of the limits and capabilities of Black-Box AI systems. I will also present some of our work on learning the requisite abstractions in a bottom-up fashion. Throughout the talk I will highlight the theoretical guarantees that our methods provide along with results from empirical evaluations featuring decision-support/digital AI systems and physical robots.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.

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    https://usc.zoom.us/j/99395482251

    Meeting ID: 993 9548 2251

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    Host: Sven Koenig

    Webcast: https://usc.zoom.us/j/99395482251

    Location: Online - Zoom

    WebCast Link: https://usc.zoom.us/j/99395482251

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • ISE 651 Epstein Seminar

    Tue, Feb 22, 2022 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. M. Ravi Shankar, Professor, Dept. of Industrial Engineering, University of Pittsburgh

    Talk Title: Molecular Patterning Directs Morphing and Motility in Liquid Crystalline Polymers

    Host: Prof. Yong Chen

    More Information: February 22, 2022.pdf

    Location: Online/Zoom

    Audiences: Everyone Is Invited

    Contact: Grace Owh

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  • CS Distinguished Lecture: Hod Lipson (Columbia University) - Automating discovery: From cognitive robotics to particle physics

    Tue, Feb 22, 2022 @ 04:00 PM - 05:20 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Hod Lipson, Columbia University

    Talk Title: Automating discovery: From cognitive robotics to particle physics

    Series: Computer Science Distinguished Lecture Series

    Abstract: Can machines discover scientific laws automatically? Despite the prevalence of big data, the process of distilling data into scientific laws has resisted automation. Particularly challenging are situations with small amounts of data that is difficult or expensive to collect. This talk will outline a series of recent research projects, starting with self-reflecting robotic systems, and ending with machines that can formulate hypotheses, design experiments, and interpret the results, to discover new scientific laws. We will see examples from psychology to cosmology, from classical physics to modern physics, from big science to small science.

    Register in advance for this webinar at:
    https://usc.zoom.us/webinar/register/WN_kYlG0b5QS3OShAvhHwF1hg

    After registering, attendees will receive a confirmation email containing information about joining the webinar.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.


    Biography: Hod Lipson is a professor of Engineering at Columbia University in New York, and a co-author of the award winning book "Fabricated: The New World of 3D printing", and "Driverless: Intelligent cars and the road ahead". His work on self-aware and self-replicating robots challenges conventional views of robotics, and his TED talk on self-aware machines is one of the most viewed presentations on AI. Lipson directs the Creative Machines Lab, which pioneers new ways to make machines that create, and machines that are creative. For more information visit http://hodlipson.com


    Host: Stefanos Nikolaidis

    Webcast: https://usc.zoom.us/webinar/register/WN_kYlG0b5QS3OShAvhHwF1hg

    Location: Online - Zoom Webinar

    WebCast Link: https://usc.zoom.us/webinar/register/WN_kYlG0b5QS3OShAvhHwF1hg

    Audiences: Everyone Is Invited

    Contact: Computer Science Department

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  • Mork Family Department Seminar - Jonathan Paras

    Tue, Feb 22, 2022 @ 04:00 PM - 05:15 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Jonathan Paras, Massachusetts Institute of Technology

    Talk Title: Quantifying Electronic Contributions to Phase Transitions

    Host: Prof. Andrea Hodge

    Location: SOS B46

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • ECE Seminar: Human/System Co-design to Protect Data Privacy

    Wed, Feb 23, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Haojian Jin, PhD Candidate, Human-Computer Interaction Institute, Carnegie Mellon University

    Talk Title: Human/System Co-design to Protect Data Privacy

    Abstract: Privacy is changing how we build computing systems. Recent regulations, such as General Data Protection Regulation, California Consumer Privacy Act, Children's Online Privacy Protection Act, require developers to offer greater privacy protections. However, developers struggle to turn these high-level privacy principles into low-level code implementation.

    The primary cause of this difficulty is that privacy is a multi-stakeholder issue: developers want to achieve more functionality and productivity; users want more control with lower effort; regulators wish to audit systems with limited resources and do not want to stifle innovation; finally, system deployments need to remain proprietary and efficient.

    In this talk, I will present two systems to illustrate that these Human/System requirements can jointly inform system design up-front and not be afterthoughts. I will describe (1) applying human/system co-design for data minimization, a foundational privacy principle in modern privacy regulation, and (2) how user and other stakeholder experience is transformed in co-designed systems. I will conclude with plans to create a virtuous cycle ecosystem where building trustworthy systems is rewarded, and developers compete to guarantee greater user protection, not less.

    Biography: Haojian Jin is a final-year Ph.D. candidate in the Human-Computer Interaction Institute at Carnegie Mellon University, advised by Jason Hong and Swarun Kumar. His research lies at the intersection of human-computer interaction, privacy, and mobile systems. His work has been recognized with a UbiComp Gaetano Borriello Outstanding Student Award, Research Highlights at Communications of the ACM and GetMobile, and best paper awards at Ubicomp and ACM Computing Reviews. See more at: http://haojianj.in/.

    Host: Dr. Bhaskar Krishnamachari, bkrishna@usc.edu

    Webcast: https://usc.zoom.us/j/92527250101?pwd=dlQ1YzV1enJTYnRaQmFBbFpnZS9ZQT09

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

    WebCast Link: https://usc.zoom.us/j/92527250101?pwd=dlQ1YzV1enJTYnRaQmFBbFpnZS9ZQT09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series

    Wed, Feb 23, 2022 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Gaurav Gupta, Amazon Web Services (AWS) AI lab

    Talk Title: Operator Learning for Partial Differential Equations

    Series: Center for Cyber-Physical Systems and Internet of Things

    Abstract: The partial differential equations (PDEs) model several real-world setups of Physics, Engineering, biology, Epidemiology. The solution can be formulated as an operator map problem. We show that learning the operator kernels can be efficiently performed by exploiting the fundamental properties. We will discuss a novel multiwavelets-based neural operator approach to achieve a compressed representation and show applications on several benchmarks PDE datasets. Next, we also discuss a class of PDEs called 'Initial Value Problems,' which has applications in predictions and forecasting. We develop a compact non-linear neural operator which maps initial conditions to activities at a later time. The proposed approach yields data efficiency which is necessary to deal with scarce real-world datasets, and as a case study we formulate and solve urgent real-world problems like Epidemic forecasting (e.g., COVID19).

    Biography: Gaurav Gupta is currently a researcher (Applied Scientist) at Amazon Web Services (AWS) AI labs. He completed his PhD from USC Viterbi. His research interests span the domain of time-series modeling, learning partial differential equations, information theory for machine learning, fractional dynamical models, complex networks, brain EEG signals modeling. He is working on inter-disciplinary mathematical and applied problems on forecasting, PDEs, and has publications in top venues like Neurips, ICLR, Nature, IEEE Control Society, ACM cyber-physical society.


    Host: Pierluigi Nuzzo, nuzzo@usc.edu

    Webcast: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Location: Online

    WebCast Link: https://usc.zoom.us/webinar/register/WN_zyIBh_1gQLmKpMJG0GyLxw

    Audiences: Everyone Is Invited

    Contact: Talyia White

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  • AME Seminar

    Wed, Feb 23, 2022 @ 03:30 PM - 04:30 PM

    Aerospace and Mechanical Engineering

    Conferences, Lectures, & Seminars


    Speaker: James Hanna, UN Reno

    Talk Title: Exterior algebra and the proportional selective modification of dynamical systems, from rotors to nonlinear lattices

    Abstract: This is the story of a seemingly trivial problem, born of quarantine, that surprised me by turning into something more interesting. I will introduce a new technique for adding dissipation or otherwise modifying dynamical systems to selectively change any number of conserved quantities, while only reducing the total number of conserved quantities by one. I will first present a naive approach to a simple example, a textbook problem of a specially damped rotor often used to explain the failure of the Explorer 1 satellite. Then (in joint work with M. Aureli), we generalize the approach to any number of dimensions and conserved quantities. The resulting dynamics drives the modified system to a nontrivial state of the original system.

    Biography: Hanna is Associate Professor in the department of Mechanical Engineering at the University of Nevada, Reno, which he joined in 2019 as a refugee fleeing the great purge of unapologetic mechanicians from Virginia Tech. A lapsed materials scientist, he spent several years impersonating a postdoctoral physicist at UMass Amherst, and currently performs mechanics without a license. He is interested in applications of geometry to theoretical and experimental classical mechanics, and is currently thinking about shell buckling, cable snapping, pseudomomentum and material symmetry, new formulations of elasticity, and a few other things.

    Host: AME Department

    More Info: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Webcast: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Location: James H. Zumberge Hall Of Science (ZHS) - 252

    WebCast Link: https://usc.zoom.us/j/93987337017?pwd=MWd2dXBSL1FaR1RPaHNscjJ1NW80UT09

    Audiences: Everyone Is Invited

    Contact: Tessa Yao

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  • ECE-S Seminar: Faster, smarter, and greener systems for data-center scale AI

    Thu, Feb 24, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Udit Gupta, PhD Candidate, Harvard University

    Talk Title: Faster, smarter, and greener systems for data-center scale AI

    Abstract: The modern Internet is driven by AI-centric services that determine how we interact with technology and society on a daily basis. The exponential rise in AI is largely fueled by the design, development, and deployment of domain-specific software and hardware that have yielded orders of magnitude improvements for deep learning. Despite these efforts, this talk focuses on an important, yet under-studied area: systems for deep learning-based personalized recommendation. Personalized recommendations form the backbone of our interaction with the Internet including search, e-commerce, streaming, and social media. Systems play a crucial role in enabling accurate, efficient, and sustainable recommendation engines.

    In this talk I show how modern deep learning-based personalized recommendation engines not only consume the majority of AI training and inference cycles in production data centers, but also introduce unique system design challenges to efficient execution. To tackle these challenges, I design solutions across the software and hardware stack to optimize inference efficiency by jointly considering application-level characteristics, unique neural network model architectures, data-center scale implications, and the underlying hardware. Given the rapidly growing infrastructure demands posed by AI and recommendation engines, my work highlights that systems must go beyond performance, power, and energy efficiency to consider environmental footprint as a first order design target to enable sustainable computing. Finally, I chart paths to designing future systems that enable emerging AI-driven applications by balancing performance, efficiency, sustainability, and privacy.

    Biography: Udit Gupta is a PhD student at Harvard University and visiting research scientist at Facebook AI Research. His research interests focus on enabling next-generation responsible AI platforms by designing novel computer systems and hardware. His recent work focuses on the optimization of data center-scale deep learning-based personalized recommendation engines (HPCA 2020, ISCA 2020, MICRO 2021, ASPLOS 2021) and enabling sustainable computing by considering the environmental impact of end-to-end hardware life cycles (HPCA 2021, MLSys 2022). Udit's work has been evaluated at-scale in production data centers and incorporated into standardized benchmarks and infrastructure used by the research community. His research has been recognized as an IEEE MICRO Top Picks honorable mention in 2020 and received an IEEE MICRO Top Picks award in 2021, as well as nominated for best paper at PACT 2019 and DAC 2018. In addition to research, Udit is passionate about building interdisciplinary communities. He has co-founded the PeRSonAl (personalized recommendation systems and algorithms) workshop and CLEAR (computing landscapes with environmental accountability and responsibility) workshops co-located at systems and machine learning conferences like ASPLOS, ISCA, and MLSys. He is also the co-chair of the Computer Architecture Student Association.

    Host: Dr. Murali Annavaram, annavara@usc.edu

    Webcast: https://usc.zoom.us/j/96028058998?pwd=cFFFSm1rdjFBcjdiMURMOWpxMi9tUT09

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

    WebCast Link: https://usc.zoom.us/j/96028058998?pwd=cFFFSm1rdjFBcjdiMURMOWpxMi9tUT09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Mork Family Department Seminar - Rohan Mishra

    Thu, Feb 24, 2022 @ 04:00 PM - 05:15 PM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Prof. Rohan Mishra, Washington University

    Talk Title: Developing real materials for energy applications using a combination of theory and microscopy

    Host: Professor A.Hodge

    Location: Kaprielian Hall (KAP) - 147

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • CILQ Internal Seminar

    Fri, Feb 25, 2022 @ 12:00 PM - 01:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Alan Willner, Professor, USC

    Talk Title: Optical Communications: Innovations and Applications Abound

    Abstract: Optical communications has enjoyed tremendous impact over the past 50 years. Relatively soon after the concrete proposal of optical fiber communications was reported and the low-loss fiber was demonstrated, fiber-based communications dramatically impacted the way society transfers information. However, there are other key areas beyond fiber-based communications that were also envisioned ~50 years ago but are only recently emerging. Such emergence is due to enhanced capacity needs and critical innovations, including advances in photonic integrated circuits (PICs). This talk will highlight various examples of the innovations and emerging applications of optical communications, including:
    1. Free-space optical communications: As opposed to RF, optical links have high directionality and large bandwidth. There is great excitement in the recent emergence of deployed free-space optical links, be they through air or outer-space. Moreover, due to the extremely high losses of RF, even underwater links in the blue-green are gaining significant interest. Also to be discussed is capacity enhancement using multiplexing of multiple orbital-angular-momentum beams.
    2. Non-conventional wavelengths: Fiber systems are overwhelmingly in the near-IR, whereas free-space links can take advantage of a much wider frequency range, from THz to visible. Such systems may utilize: (a) native high-speed components, and/or (b) wavelength-band conversion of near-IR channels to other frequencies.
    3. Optical signal processing (OSP): OSP has long held the promise of high-speed operation and the avoidance of inefficient optical-electrical-optical conversion. Although OSP deployment has been limited, advances in PICs, power efficiency and multi-wavelength operation may soon enable the emergence of OSP for high-performance functions.


    Host: CILQ

    Webcast: https://usc.zoom.us/j/92417517950?pwd=WUkycy90cndVQko5R3RhQ1U3STBDdz09

    Location: via zoom

    WebCast Link: https://usc.zoom.us/j/92417517950?pwd=WUkycy90cndVQko5R3RhQ1U3STBDdz09

    Audiences: Everyone Is Invited

    Contact: Corine Wong

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  • ECE Seminar: Certifiable Outlier-Robust Geometric Perception: Robots that See through the Clutter with Confidence

    Mon, Feb 28, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Heng Yang, Laboratory for Information & Decision Systems, Department of Mechanical Engineering, MIT

    Talk Title: Certifiable Outlier-Robust Geometric Perception: Robots that See through the Clutter with Confidence

    Abstract: Geometric perception is the task of estimating geometric models (e.g., object pose and 3D structure) from sensor measurements and priors (e.g., point clouds and neural network detections). Geometric perception is a fundamental building block for robotics applications ranging from intelligent transportation to space autonomy. The ubiquitous existence of outliers -measurements that tell no or little information about the models to be estimated- makes it theoretically intractable to perform estimation with guaranteed optimality. Despite this theoretical intractability, safety-critical robotics applications still demand trustworthiness and performance guarantees on perception algorithms. In this talk, I present certifiable outlier-robust geometric perception, a new paradigm to design tractable algorithms that enjoy rigorous performance guarantees, i.e., they return an optimal estimate with a certificate of optimality for a majority of problem instances, but declare failure and provide a measure of suboptimality for worst-case instances. Particularly, I present two general-purpose algorithms in the certifiable perception toolbox: (i) an estimator that uses graph theory to prune gross outliers and leverages graduated non-convexity to compute the optimal model estimate with high probability of success, and (ii) a certifier that employs sparse semidefinite programming (SDP) relaxation and a novel SDP solver to endow the estimator with an optimality certificate or escape local minima otherwise. The estimator is fast and robust against up to 99% random outliers in practical perception applications, and the certifier can compute high-accuracy optimality certificates for large-scale problems beyond the reach of existing SDP solvers. I showcase certifiable outlier-robust perception on robotics applications such as scan matching, satellite pose estimation, and vehicle pose and shape estimation. I conclude by remarking three opportunities arising from certifiable perception: to speedup online global optimization by offline learning from data; to enable safe learning-based perception by bridging certifiable estimation with deep representation learning; and to couple and unify perception with action towards trustworthy autonomy.

    Biography: Heng Yang is a final-year Ph.D. candidate in the Laboratory for Information & Decision Systems and the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), working with Prof. Luca Carlone. He holds a B.S. degree from Tsinghua University and an S.M. degree from MIT, both in Mechanical Engineering. His research interests include large-scale convex optimization, semidefinite relaxation, robust estimation, and machine learning, applied to robotics and trustworthy autonomy. His work includes developing certifiable outlier-robust machine perception algorithms, large-scale semidefinite programming solvers, and self-supervised geometric perception frameworks. Heng Yang is a recipient of the Best Paper Award in Robot Vision at the 2020 IEEE International Conference on Robotics and Automation (ICRA), a Best Paper Award Honorable Mention from the 2020 IEEE Robotics and Automation Letters (RA-L), and a Best Paper Award Finalist at the 2021 Robotics: Science and Systems (RSS) conference. He is a Class of 2021 RSS Pioneer.

    Host: Dr. Keith Chugg, chugg@usc.edu

    Webcast: https://usc.zoom.us/j/91553052387?pwd=V0NqTFNJMlBNZkxWVnVIQmYrVWtVQT09

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

    WebCast Link: https://usc.zoom.us/j/91553052387?pwd=V0NqTFNJMlBNZkxWVnVIQmYrVWtVQT09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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