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Conferences, Lectures, & Seminars
Events for April

  • ECE Seminar: Joint Wireless Communication and Sensing in mmWave and Terahertz Spectrum

    Wed, Apr 01, 2020 @ 01:15 PM - 02:15 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Yasaman Ghasempour, Ph.D. Candidate, ECE, Rice University

    Talk Title: Joint Wireless Communication and Sensing in mmWave and Terahertz Spectrum

    Abstract: Millimeter-wave and terahertz bands are emerging as the most promising spectrum to meet the data-rate and latency demands of future wireless applications, including virtual reality and autonomous cars. Moreover, large spectral availability and mm-scale wavelength provide the possibility for ubiquitous and high-resolution sensing. My research builds a foundation for joint communication and sensing in such high-frequency regimes. This perspective yields a paradigm shift in the design and development of future wireless systems. In this talk, I will present the world's first single-shot and single-antenna motion sensing system in THz bands. We demonstrate a novel node architecture exploiting a single leaky wave antenna, which is primarily used for beam steering in THz networks. I will show how we leverage this device's spatial-spectral characteristics in new ways to enable motion sensing functionalities with a single THz pulse transmission. I will then discuss the opportunities offered by this platform to enhance next-generation communication in unprecedented ways. In particular, we tackle the mobility, blockage, and scalability challenges of highly directional THz networks by efficiently adapting steering direction for mobile users. Finally, I will share several research directions that I would like to pursue in the future.

    Biography: Yasaman Ghasempour is currently a Ph.D. Candidate in Electrical and Computer Engineering at Rice University. She received her Master's degree in Electrical and Computer Engineering from Rice University and her Bachelor's degree in Electrical Engineering from Sharif University of Technology in Iran. Her research interests include wireless communication and sensing, with a focus on emerging millimeter-wave and terahertz spectrum. She has published in top-tier IEEE and ACM conferences and journals and has been named an EECS rising star in 2019. She is also the recipient of Texas Instruments Distinguished Fellowship among multiple IEEE/ACM societies awards.

    Host: Urbashi Mitra (ubli@usc.edu) and Konstantinos Psounis (kpsounis@usc.edu)

    Webcast: https://usc.zoom.us/j/873150824

    WebCast Link: https://usc.zoom.us/j/873150824

    Audiences: Everyone Is Invited

    Posted By: Mayumi Thrasher

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  • ECE Seminar: Safe and Data-efficient Learning for Robotics

    Thu, Apr 02, 2020 @ 11:00 AM - 12:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Somil Bansal, PhD Candidate, Department of EECS, University of California, Berkeley

    Talk Title: Safe and Data-efficient Learning for Robotics

    Abstract: Machine learning has led to tremendous progress in domains such as computer vision, speech recognition, and natural language processing. Fueled by these advances, machine learning approaches are now being explored to develop intelligent physical systems that can operate reliably in unpredictable environments. These include not only robotic systems such as autonomous cars and drones, but also large-scale cyberphysical systems such as transportation and energy systems. However, learning techniques widely used today are extremely data inefficient, making it challenging to apply them to real-world physical systems. Moreover, they lack the necessary mathematical framework to provide guarantees on correctness, causing safety concerns as data-driven physical systems are integrated in our society. We combine tools from robust optimal control theory with machine learning and computer vision to develop data-efficient and provably safe learning-based control algorithms for physical robotic systems. In particular, we design modular architectures that combine system dynamics models with modern learning-based perception approaches to solve challenging perception and control problems in a priori unknown environments in a data-efficient fashion. Moreover, due to their modularity, these architectures are amenable to simulation-to-real transfer, and can be used for different robotic systems without any retraining. Crucially, we use models not only for faster learning, but also to monitor and recognize the learning system's failures, and to provide online corrective safe actions when necessary. This allows us to provide safety assurances for learning-enabled systems in unknown and human-centric environments, which has remained a challenge to date.

    Biography: Somil Bansal completed his B.Tech. in Electrical Engineering from Indian Institute of Technology, Kanpur in 2012, and an M.S. in Electrical Engineering and Computer Sciences from UC Berkeley in 2014. Since 2015, he is pursuing a PhD degree in Electrical Engineering and Computer Sciences at UC Berkeley, under the supervision of Prof. Claire Tomlin in the Hybrid Systems Laboratory. His research interests are in exploring how machine learning tools can be combined with the control theoretic frameworks to develop data-efficient and safe learning-based control algorithms for physical robotic systems, especially when the system is operating in an uncertain environment. During his PhD, he has also worked closely with companies like Skydio, Google, Boeing, as well as NASA Ames. Somil has received several awards, most notably the outstanding graduate student instructor award at UC Berkeley and the academic excellence award at IIT Kanpur.


    Host: Ashutosh Nayyar, ashutosn@usc.edu

    Webcast: https://usc.zoom.us/j/811254572

    WebCast Link: https://usc.zoom.us/j/811254572

    Audiences: Everyone Is Invited

    Posted By: Mayumi Thrasher

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  • Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Tegan Brennan (University of California, Santa Barbara) - Software Side Channels

    Thu, Apr 02, 2020 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Tegan Brennan, University of California, Santa Barbara

    Talk Title: Software Side Channels

    Series: CS Colloquium

    Abstract: Side channels in software are a class of information leaks where non-functional side effects of software systems (such as execution time, memory usage or power consumption) can leak information about sensitive data. In this talk, I present my research on a new class of side-channel vulnerabilities: JIT-induced side channels. In contrast to side channels introduced at the source code level, JIT-induced side channels arise at runtime due to the behavior of just-in-time (JIT) compilation. I show the existence of this class of side channels across multiple runtimes, and I demonstrate JIT-induced timing channels in large, open source projects large enough in magnitude to be detected over the public internet. I also present an automated approach to inducing this type of side channel in programs. In evaluating my automated technique, I show that programs classified as side-channel free by four state-of-the-art side channel analysis tools are, in fact, vulnerable to JIT-induced side channels. Finally, I discuss my contributions towards scalable quantification of side-channel vulnerabilities through a caching framework for model-counting queries.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Tegan Brennan is a PhD candidate in Computer Science at the University of California, Santa Barbara. Her research is in software engineering, formal verification and computer security. She has worked extensively on side-channel vulnerabilities in software. Tegan is a recipient of an IGERT Fellowship in Network Science, an NCWIT Collegiate Award Honorable Mention in 2018 and an invited participant of the 2019 Rising Stars workshop. She has also interned twice with Amazon's Automated Reasoning Group.

    Host: Chao Wang

    Location: Seminar will be exclusively online (no in-room presentation)

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Vatsal Sharan (Stanford) - Modern Perspectives on Classical Learning Problems: Role of Memory and Data Amplification

    Mon, Apr 06, 2020 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Vatsal Sharan, Stanford University

    Talk Title: Modern Perspectives on Classical Learning Problems: Role of Memory and Data Amplification

    Series: CS Colloquium

    Abstract: This talk will discuss statistical and computation requirements---and how they interact---for three learning setups. In the first part, we inspect the role of memory in learning. We study how the total memory available to a learning algorithm affects the amount of data needed for learning (or optimization), beginning by considering the fundamental problem of linear regression. Next, we examine the role of long-term memory vs. short-term memory for the task of predicting the next observation in a sequence given the past observations. Finally, we explore the statistical requirements for the task of manufacturing more data---namely how to generate a larger set of samples from an unknown distribution. Can "amplifying" a dataset be easier than learning?

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Vatsal Sharan is a Ph.D. student at Stanford, advised by Greg Valiant. He is a part of the Theory group and the Statistical Machine Learning group, and his primary interests are in the theory and practice of machine learning.

    Host: Shaddin Dughmi

    Location: Seminar will be exclusively online (no in-room presentation)

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Astani Civil and Environmental Engineering RA/TA Awards

    Mon, Apr 06, 2020 @ 12:00 PM - 03:30 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Mark Sarkisian, TBA

    Talk Title: TBA

    Abstract: TBA

    Host: Dr. Burcin Becerik-Gerber

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

    Audiences: Everyone Is Invited

    Posted By: Evangeline Reyes

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  • Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Zhiting Hu (Carnegie Mellon University) - Towards Training AI Agents with All Types of Experiences via a Single Algorithm

    Tue, Apr 07, 2020 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Zhiting Hu, Carnegie Mellon University

    Talk Title: Towards Training AI Agents with All Types of Experiences via a Single Algorithm

    Series: CS Colloquium

    Abstract: Training AI agents for complex problems, such as controllable content generation, requires integrating all sources of experiences (e.g. data, constraints, information from relevant tasks) in learning. Past decades of research has led to a multitude of learning algorithms for dealing with distinct experiences. However, the conventional approach to creating solutions based on such a bewildering marketplace of algorithms demands strong ML expertise and bespoke innovations. This talk will present an alternative approach from a unifying perspective. I will show that many of the popular algorithms in supervised learning, constraint-driven learning, reinforcement learning, etc, indeed share a common succinct formulation and can be reduced to a single algorithm that enables learning with different experiences in the same way. This allows us to create solutions by simply plugging arbitrary experiences in learning, and to systematically enable new learning capabilities by repurposing off-the-shelf algorithms.

    Biography: Zhiting Hu is a Ph.D. student in the Machine Learning Department at CMU. He received his B.S. from Peking University. His research interests lie in the broad area of machine learning. His research was recognized with best demo nomination at ACL2019, best paper award at ICLR 2019 DRL workshop, outstanding paper award at ACL2016, and IBM Fellowship.

    Host: Yan Liu

    Location: Seminar will be exclusively online (no in-room presentation)

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • **CANCELED** ISE 651 - Epstein Seminar

    Tue, Apr 07, 2020 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Wotao Yin, Professor, UCLA

    Talk Title: TBD

    Host: Dr. Meisam Razaviyayn

    Location: Ethel Percy Andrus Gerontology Center (GER) - 206

    Audiences: Everyone Is Invited

    Posted By: Grace Owh

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  • Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Yuxiong Wang (Carnegie Mellon University) - Learning to Learn More with Less

    Thu, Apr 09, 2020 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Yuxiong Wang, Carnegie Mellon University

    Talk Title: Learning to Learn More with Less

    Series: CS Colloquium

    Abstract: Understanding how humans and machines learn from few examples remains a fundamental challenge. Humans are remarkably able to grasp a new concept from just few examples, or learn a new skill from just few trials. By contrast, state-of-the-art machine learning techniques typically require thousands of training examples and often break down if the training sample set is too small.

    In this talk, I will discuss our efforts towards endowing visual learning systems with few-shot learning ability. Our key insight is that the visual world is well structured and highly predictable in feature, data, and model spaces. Such structures and regularities enable the systems to learn how to learn new tasks rapidly by reusing previous experience. I will focus on two topics to demonstrate how to leverage this idea of learning to learn, or meta-learning, to address a broad range of few-shot learning tasks: task-oriented generative modeling and meta-learning in model space. I will also discuss some ongoing work towards building machines that are able to operate in highly dynamic and open environments, making intelligent and independent decisions based on limited information.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Yuxiong Wang is a postdoctoral fellow in the Robotics Institute at Carnegie Mellon University. He received a Ph.D. in robotics from Carnegie Mellon University under the supervision of Martial Hebert in 2018. His research interests lie in computer vision, machine learning, and robotics, with a particular focus on few-shot learning and meta-learning. He has spent time at Facebook AI Research (FAIR), and has collaborated with researchers in other institutions, including NYU, UIUC, UC Berkeley, Cornell University, INRIA (France), and CSIC-UPC (Spain).

    Host: Ramakant Nevatia

    Location: Seminar will be exclusively online (no in-room presentation)

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Astani Civil and Environmental Engineering Seminar

    Thu, Apr 09, 2020 @ 04:00 PM - 05:00 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Shihong Lin, Vanderbilt University

    Talk Title: Energy Efficiency of Desalination

    Abstract: TBA

    Host: Dr. Amy Childress

    Location: Kaprielian Hall (KAP) - 209

    Audiences: Everyone Is Invited

    Posted By: Evangeline Reyes

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  • Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Charith Mendis (MIT) - Modernizing Compiler Technology using Machine Learning

    Mon, Apr 13, 2020 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Charith Mendis, MIT

    Talk Title: Modernizing Compiler Technology using Machine Learning

    Series: CS Colloquium

    Abstract: Compilers are the workhorse that bridge the gap between human readable and machine executable code. The diversity of modern programs, along with the advent of new and complex hardware architectures, has strained the capabilities of current compilers, making development and maintenance of automatic program optimizations in compilers exceedingly challenging. In spite of this, modern compiler optimizations are still hand-crafted using technology that existed decades ago and usually make optimization decisions considering an abstract machine model. It is high time that we modernize our compiler toolchains using more automated decision procedures to make better optimization decisions while reducing the expertise required to build and maintain compiler optimizations.

    In this talk, I will show how we can leverage the changes in the computing environment to modernize compiler optimizations, using auto-vectorization (automatic conversion of scalar code into vector code) as an example.
    First, I will demonstrate how we can take advantage of modern solvers and computing platforms to perform vectorization. Modern compilers perform vectorization using hand-crafted algorithms, which typically only find local solutions under linear performance models. I present goSLP, which uses integer linear programming to find a globally optimal instruction packing strategy to achieve superior vectorization performance.

    Next, I will discuss how to modernize the construction of compiler optimizations by automatically learning the optimization algorithm. I present Vemal, the first end-to-end learned vectorizer which eliminates the need for hand-writing an algorithm. The key is to formulate the optimization problem as a sequential decision making process in which all steps guarantee correctness of the resultant generated code. Not only does Vemal reduce the need for expert design and heuristics, but also it outperforms hand-crafted algorithms, reducing developer effort while increasing performance.

    Finally, I will show how we can use data to learn better non-linear performance models, rather than the complex and incorrect hand-crafted models designed by experts, to enhance the decision procedure used in Vemal. I present Ithemal, the first learned cost model for predicting throughput of x86 code. Ithemal more than halves the error-rate of complex analytical models such as Intel's IACA.
    Both Vemal and Ithemal achieve state-of-the-art results and pave the way towards developing more automated and modern compiler optimizations with minimal human burden.

    This lecture satisfies requirements for CSCI 591: Research Colloquium.

    Biography: Charith Mendis is a final year PhD student in Computer Science and Artificial Intelligence Laboratory at Massachusetts Institute of Technology. His research interests include Compilers, Machine Learning and Program Analysis. He completed his Master's degree at MIT for which he received the William A. Martin Thesis Prize and his bachelor's degree at University of Moratuwa, Sri Lanka for which he received the institute Gold Medal. Charith was the recipient of the best student paper award at IEEE Big Data conference and the best paper award at ML for Systems workshop at ISCA. He has published work at both top programming language venues such as PLDI and OOPSLA as well as at top machine learning venues such as ICML and NeurIPS. Charit's recent work on performance prediction is used at Google as part of their CPU modeling effort.

    Host: Mukund Raghothaman

    Location: Seminar will be exclusively online (no in-room presentation)

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Innovative DIY PPE Disinfectant Methods Featuring Andrea Armani

    Tue, Apr 14, 2020 @ 12:00 PM - 12:45 PM

    Mork Family Department of Chemical Engineering and Materials Science, USC Viterbi School of Engineering

    Conferences, Lectures, & Seminars


    Speaker: Andrea Armani, Ray Irani Chair in Engineering and Materials Science

    Talk Title: Innovative DIY PPE Disinfectant Methods

    Abstract: Join us for a live session followed by Q and A. Please register via the Eventbrite link. A webinar link will be sent to all registrants before the event through email.

    Host: Andrea Armani

    More Info: https://www.eventbrite.com/e/viterbi-live-innovative-diy-ppe-disinfectant-methods-feat-andrea-armani-tickets-102213924600

    Audiences: Everyone Is Invited

    Posted By: Greta Harrison

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  • Seminar will be exclusively online (no in-room presentation) - CS Colloquium: TBA

    Tue, Apr 14, 2020 @ 04:00 PM - 05:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: TBA, TBA

    Talk Title: TBA

    Series: CS Colloquium

    Abstract: TBA

    Biography: TBA

    Host: Ramesh Govindan

    Location: Seminar will be exclusively online (no in-room presentation)

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Hoda Heidari (Cornell University) - Distributive Justice for Machine Learning: An Interdisciplinary Perspective on Defining, Measuring, and Mitigating Algorithmic Unfairness

    Thu, Apr 16, 2020 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Hoda Heidari, Cornell Universtiy

    Talk Title: Distributive Justice for Machine Learning: An Interdisciplinary Perspective on Defining, Measuring, and Mitigating Algorithmic Unfairness

    Series: CS Colloquium

    Abstract: Automated decision-making tools are increasingly in charge of making high-stakes decisions for people-”in areas such as education, credit lending, criminal justice, and beyond. These tools can exhibit and exacerbate certain undesirable biases and disparately harm already disadvantaged and marginalized groups and individuals. In this talk, I will illustrate how we can bring together tools and methods from computer science, economics, and political philosophy to define, measure, and mitigate algorithmic unfairness in a principled manner. In particular, I will address two key questions:

    - Given the appropriate notion of harm/benefit, how should we measure and bound unfairness? Existing notions of fairness focus on defining conditions of fairness, but they do not offer a proper measure of unfairness. In practice, however, designers often need to select the least unfair model among a feasible set of unfair alternatives. I present (income) inequality indices from economics as a unifying framework for measuring unfairness--both at the individual- and group-level. I propose the use of cardinal social welfare functions as an alternative measure of fairness behind a veil of ignorance and a computationally tractable method for bounding inequality.

    - Given a specific decision-making context, how should we define fairness as the equality of some notion of harm/benefit across socially salient groups? First, I will offer a framework to think about this question normatively. I map the recently proposed notions of group-fairness to models of equality of opportunity. This mapping provides a unifying framework for understanding these notions, and importantly, allows us to spell out the moral assumptions underlying each one of them. Second, I give a descriptive answer to the question of "fairness as equality of what?". I mention a series of adaptive human-subject experiments we recently conducted to understand which existing notion best captures laypeople's perception of fairness.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Hoda Heidari is currently a Postdoctoral Associate at the Department of Computer Science at Cornell University, where she collaborates with Professors Jon Kleinberg, Karen Levy, and Solon Barocas through the AIPP (Artificial Intelligence, Policy, and Practice) initiative. Hoda's research is broadly concerned with the societal aspects of Artificial Intelligence, and in particular, the issues of unfairness and discrimination for Machine Learning. She utilizes tools and methods from Computer Science (Algorithms, AI, and ML) and Social Sciences (Economics and Political Philosophy) to quantify and mitigate the inequalities that arise when socially consequential decisions are automated.

    Host: Aleksandra Korolova and Bistra Dilkina

    Location: Seminar will be exclusively online (no in-room presentation)

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Astani Civil and Environmental Engineering Seminar

    Thu, Apr 16, 2020 @ 02:00 PM - 03:00 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Oscar Lopez, University of Illinois, Urbana Champaign

    Talk Title: TBA

    Abstract: TBA

    Host: Dr. Qiming Wang

    Location: Kaprielian Hall (KAP) - 209

    Audiences: Everyone Is Invited

    Posted By: Evangeline Reyes

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  • Seminar will be exclusively online (no in-room presentation) - CS Colloquium: Mathew Monfort (MIT) - Towards Understanding Moments in Time

    Mon, Apr 20, 2020 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Mathew Monfort, MIT

    Talk Title: Towards Understanding Moments in Time

    Series: CS Colloquium

    Abstract: When people observe events they are able to abstract key information and build concise summaries of what is happening. These summaries include the important contextual and semantic information (what, where, who and how) necessary for the observer to understand the event and how it relates to their current state. With this in mind, the descriptions people generate for videos of different dynamic events can greatly improve our understanding of the key information of interest for each event and help us learn rich representations that we can apply to a number of different tasks. Going a step further, taking sequences of events into consideration allows us to build an understanding of how observations can be abstracted into contextually meaningful descriptions useful for understanding the relationships between each event and higher-level goals. In this talk I will provide an overview of recent work in the area of video understanding and highlight details of how we can learn, and utilize, detailed video representations for improving our understanding of moments in time.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Mathew Monfort is a Research Scientist at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). He received a PhD. in computer science from the University of Illinois at Chicago in 2016, a M.S. in Computer Science from Florida State University in 2011 and a B.A. in Mathematics from Franklin and Marshall College in 2009. His research has included approached on applying machine learning methods to autonomous driving, inverse planning, video understanding and areas related to learning from human behavior.

    Host: Ramakant Nevatia

    Location: Seminar will be exclusively online (no in-room presentation)

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Viterbi Live

    Tue, Apr 21, 2020 @ 01:00 PM - 02:00 PM

    USC Viterbi School of Engineering

    Conferences, Lectures, & Seminars


    Speaker: Darryl Hwang, Daniel Stemen, and Shawn Chapman, USC

    Talk Title: Handling the PPE Shortage: Update on Mask Design & Fabrication

    Abstract: Since our virtual workshop on April 3rd, the USC team has made significant progress in developing reusable filtered masks with the help of the local manufacturing community. This forum will share details of the current design and plans for fabricating these masks. This will enable the LA community to help produce usable masks based on a safe design concept. Register for this community update if you want to help mitigate the PPE shortage. Space will be limited to promote conversation and idea sharing.

    This session will be hosted on Zoom. Links and passwords will be sent to all registered participants the morning of April 21.

    For any questions, please email us at engalums@usc.edu.

    To view recordings from the April 3rd workshop, please click here: https://viterbischool.usc.edu/online-events-series/

    Host: Viterbi Advancement

    More Info: https://ppe-shortage-community-update.eventbrite.com

    More Information: VITERBI LIVE EVENTBRITE COVER.jpg

    Audiences: Select Participants

    Posted By: Kristy Ly

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  • **CANCELED** ISE 651 - Epstein Seminar

    Tue, Apr 21, 2020 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Ravi Shankar Meenakshisundaram, Professor, University of Pittsburg

    Talk Title: TBD

    Host: Prof. Yong Chen

    Location: Ethel Percy Andrus Gerontology Center (GER) - 206

    Audiences: Everyone Is Invited

    Posted By: Grace Owh

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  • Repeating EventIntellithon

    Thu, Apr 23, 2020

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Intelligence Community Representatives, Government

    Talk Title: Intellithon

    Abstract: nterested in science and technology for intelligence?

    Students from our consortium schools (USC, San Jose State University, Florida A&M, and Santa Monica College) are invited to apply for participation in our first annual Intellithon (April 23-24).

    The event, to be held on the USC campus, will feature student team contests and networking opportunities with intelligence community professionals.

    Host: USC Intelligence Community Center for Academic Excellence

    More Info: https://sites.usc.edu/iccae/apply/

    Audiences: Everyone Is Invited

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    Posted By: Jennifer Ramos/Electrophysics

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  • Remarkable Trajectory Seminar - Professor John Silvester

    Thu, Apr 23, 2020 @ 11:00 AM - 12:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Professor John Silvester, Professor of Electrical and Computer Engineering / Ming Hsieh Department of Electrical and Computer Engineering

    Talk Title: Perspectives from a 3-Phase Academic Career

    Series: Remarkable Trajectory

    Abstract: Phase I -“ Research and Teaching (1979 -“ 2020) It was 1979 when I joined the Electrical Engineering Department at USC. I was affiliated with the Computer Engineering and the Communication Sciences Groups since my research areas were Computer Networks and Computer Architecture. My Ph.D. work was related to "Packet Radio" -“ an area that developed many of the key concepts we find in current cellular and other mobile network architectures. A few years later, my research interests moved to network service integration (Everything Over the Internet) and my research group started to look at techniques to better understand network design and optimization for this Brave New World.

    Phase II -“ Academic Technology Strategy Development (1994 -“ 2006) In 1994, I wrote a position paper about the decentralization of computing infrastructure (the move away from large mainframes to mini- and micro-computers) which also discussed the potential for service integration to Internet-based communication. I was asked by the Provost to take on a short-term (one-year) 50% time position to make some recommendations regarding a future strategy for Computing (both Administrative and Academic) and Communications (Internet and Telephony) for USC. Twelve years later I returned to my full-time faculty position in EE, after 3 years as (half-time) Vice-Provost for Academic Computing) and 9 years (full-time) as Vice-Provost for Scholarly Technology. Those were "interesting and challenging times" -“ remember that the Internet went "main-stream" around 1994-6. During that time, I became involved in Advanced Research and Education Network development, at the State, National, and International levels (Internet2, CENIC, Pacific Wave, APAN.)

    Phase III -“ Academic Politics (and Service) (2007 -“ 2019) I served on many School and University Committees over the years, culminating in several years on the Engineering Faculty Council, of which I was Chair for 2 terms, and on the Academic Senate for 5 years with 3 years on the Academic Senate Executive Committee and one year as Academic Senate President. This was during a period of growth and change at USC that presented many challenges.


    Host: ECE Department

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

    Audiences: Everyone Is Invited

    Posted By: Benjamin Paul

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  • Repeating EventIntellithon

    Fri, Apr 24, 2020

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Intelligence Community Representatives, Government

    Talk Title: Intellithon

    Abstract: nterested in science and technology for intelligence?

    Students from our consortium schools (USC, San Jose State University, Florida A&M, and Santa Monica College) are invited to apply for participation in our first annual Intellithon (April 23-24).

    The event, to be held on the USC campus, will feature student team contests and networking opportunities with intelligence community professionals.

    Host: USC Intelligence Community Center for Academic Excellence

    More Info: https://sites.usc.edu/iccae/apply/

    Audiences: Everyone Is Invited

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    Posted By: Jennifer Ramos/Electrophysics

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  • **CANCELED** ISE 651 - Epstein Seminar

    Tue, Apr 28, 2020 @ 03:30 PM - 04:50 PM

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Julie Ivy , Professor, NC State University

    Talk Title: TBD

    Host: Dr. Sze-chuan Suen

    Location: Ethel Percy Andrus Gerontology Center (GER) - 206

    Audiences: Everyone Is Invited

    Posted By: Grace Owh

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  • Viterbi Live

    Wed, Apr 29, 2020 @ 11:00 AM - 12:00 PM

    Viterbi School of Engineering Alumni

    Conferences, Lectures, & Seminars


    Speaker: Dr. Ivan Bermejo-Moreno, Professor

    Talk Title: Addressing the Ventilator Shortage and Solutions Discussion

    Abstract: Dr. Ivan Bermejo-Moreno, an assistant professor in the USC Viterbi Department of Aerospace and Mechanical Engineering, will lead a discussion on the current ventilator shortage due to the COVID-19 pandemic. Join us as he shares his findings on ventilator design from the standpoint of fluid mechanics in this 60-minute webinar. He will define the minimal requirements necessary for ventilator functionality, describe several ongoing open-source emergency ventilator efforts worldwide, and cover supply chain, cost, and how to best scale production. This webinar will feature an introduction from Dr. SK Gupta and a live Q&A session.

    This session will be hosted on Zoom. Links and passwords will be sent to all registered participants the morning of April 29.

    For any questions, please email us at engalums@usc.edu.

    Biography: Dr. Bermejo-Moreno received his Ph.D. in aeronautics (2008) from the California Institute of Technology. Afterwards, he held a postdoctoral research fellowship at the Center for Turbulence Research, Stanford University/NASA Ames Research Center (2009-2014). He joined the USC Viterbi Department of Aerospace and Mechanical Engineering as an assistant professor in 2015.

    His research combines numerical methods, physical modeling and high performance computing for the simulation and analysis of turbulent fluidflows involving multi-physics phenomena.

    Host: Viterbi Advancement

    More Info: https://viterbi-live-bermejo-moreno.eventbrite.com

    Audiences: Everyone Is Invited

    Posted By: Kristy Ly

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  • Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar

    Wed, Apr 29, 2020 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Sicun Gao, University of California San Diego

    Talk Title: Automated Reasoning for Reliable Autonomy

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

    Abstract: We face grand challenges as computer systems start engaging us physically with high levels of autonomy. Their tight integration of computational and mechanical components generates behaviors that have not been well-studied in computer science or control engineering. The AI components in these systems complicate software execution flows with nonlinear functions, probabilistic reasoning, and error-prone numerical computation. I will describe a framework for automating the design and implementation of reliable autonomous systems, and the need for powerful algorithmic approaches that combine the full power of combinatorial search, numerical optimization, and statistical learning. I will discuss challenges and opportunities in these directions and how they affect the practicality of autonomy.

    Biography: Sicun Gao is an Assistant Professor in Computer Science and Engineering at UC San Diego. He works on automated reasoning and design automation for autonomous and cyber-physical systems. He received BS from Peking University, PhD from Carnegie Mellon University, and was a postdoctoral researcher at MIT. His awards include the Air Force Young Investigator Award, Silver Medal for the Kurt Godel Research Fellowship Prize, and Honorable Mention for the CMU School of Computer Science Distinguished Doctoral Dissertation.

    Host: Paul Bogdan, pbogdan@usc.edu

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

    Audiences: Everyone Is Invited

    Posted By: Talyia White

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  • Astani Civil and Environmental Engineering Seminar

    Thu, Apr 30, 2020 @ 02:00 PM - 03:00 PM

    Sonny Astani Department of Civil and Environmental Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Rui Huang, University of Texas, Austin

    Talk Title: TBA

    Host: Dr. Qiming Wang

    Audiences: Everyone Is Invited

    Posted By: Evangeline Reyes

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