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Events for May

  • PhD Thesis Defense - Ehsan Qasemi

    Mon, May 01, 2023 @ 10:30 AM - 12:30 PM

    Thomas Lord Department of Computer Science

    University Calendar


    PhD Thesis Defense - Ehsan Qasemi

    Title: Multi-Modal Preconditioned Inference of Commonsense Knowledge

    Committee Members: Muhao Chen, Aiichiro Nakano, Daniel O Leary, Fred Morstatter, Luis Garcia

    Abstract: Humans can seamlessly reason with circumstantial preconditions of commonsense knowledge. We understand that a glass is used for drinking water, unless the glass is broken or the water is toxic. Despite state-of-the-art (SOTA) models impressive performance in inferring commonsense knowledge, it is unclear whether they understand the circumstantial preconditions. In this dissertation, I initially propose a novel challenge of reasoning with preconditions attributed to commonsense knowledge, design three tasks based on the challenge in text-only setup, and show there is a significant gap between SOTA language models performance and humans on our tasks. I then use weak supervision in a combination of targeted fine-tuning strategies to improve the language models performance on the preconditioned inference task. Finally, I go beyond the text-only setup and investigate the problem of preconditioned inference in a multi-modal setup when the model is challenged to infer the preconditions from an image.

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://usc.zoom.us/j/98769460750

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  • MoBI Seminar: Dr Daniel Toker

    Mon, May 01, 2023 @ 11:00 AM - 12:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr Daniel Toker, Post-doctoral Fellow, Department of Psychology | Department of Neurology, University of California, Los Angeles

    Talk Title: Criticality supports thalamocortical information processing during conscious states

    Series: MoBI Seminar Series

    Abstract: Mounting evidence suggests that during conscious states, neural electrodynamics are poised near a critical point or phase transition, and that this near-critical behavior supports the vast flow of information through thalamocortical networks during waking states. We identify a mathematically specific critical point near which waking neural electrodynamics operate, which is known as the edge-of-chaos critical point, or the boundary between stability and chaos. Our evidence suggests that both the information-richness of cortical activity and the transfer of information between the cortex and thalamus is disrupted during diverse states of unconsciousness because of a transition of low-frequency thalamocortical electric oscillations away from this critical point. Conversely, we show that psychedelics may increase the information-richness of cortical activity and enhance communication between the thalamus and cortex by tuning low-frequency thalamocortical electrodynamics closer to this critical point.


    Biography: Daniel Toker, PhD is a post-doctoral fellow in UCLA's Departments of Psychology and Neurology. He uses human and animal electrophysiology, mathematical modeling, and human brain organoids to study mechanisms underlying the loss and recovery of consciousness from anesthesia, generalized seizures, and coma.


    Host: Dr Richard Leahy, leahy@sipi.usc.edu | Dr Karim Jerbi, karim.jerbi.udem@gmail.com

    Webcast: https://usc.zoom.us/j/99475118848?pwd=ekJELzlxR1FPamwxRFp4cEgrNktRZz09

    More Information: MoBI Seminar Flyer - 05.01.2023 Daniel Toker.pdf

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

    WebCast Link: https://usc.zoom.us/j/99475118848?pwd=ekJELzlxR1FPamwxRFp4cEgrNktRZz09

    Audiences: Everyone Is Invited

    Contact: Miki Arlen

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  • Atoms Bits & Cells Finals

    Mon, May 01, 2023 @ 02:00 PM - 04:00 PM

    Viterbi Technology Innovation and Entrepreneurship

    Receptions & Special Events


    Atoms Bit & Cells is an undergrad competition and program to innovation and develop solutions in three areas:

    Atoms â“ engineering hardware products
    Bits â“ digital projects, such as mobile and web apps, including AI, ML applications
    Cells â“ biomedical or bioengineering projects

    Come and hear from the ABC teams as they present their technology and business. While the compete for a 1k prize towards their business.

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

    Audiences: Everyone Is Invited

    Contact: Viterbi TIE

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  • PhD Dissertation Defense - Wenxuan Zhou

    Mon, May 01, 2023 @ 04:00 PM - 05:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    PhD Dissertation Defense - Wenxuan Zhou

    Title: Robust and Generalizable Knowledge Acquisition from Text

    Committee members: Muhao Chen (chair), Laurent Itti, Tianshun Sun, Robin Jia, Jonathan May

    Abstract: With large amounts of digital text generated every day, it is important to acquire structured knowledge automatically from the text. In my thesis, I will present my work during my Ph.D. from two perspectives: (1) Improving the robustness of knowledge acquisition, especially against bias from training corpus; and (2) building data-efficient knowledge acquisition models in low-resource scenarios, which is important for tasks in high-stake domains. After these, I will discuss challenges and future directions for both (1) and (2).

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://usc.zoom.us/j/6915039300

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  • DEI: AME Town Hall

    Tue, May 02, 2023 @ 11:30 AM - 01:30 PM

    Aerospace and Mechanical Engineering

    Receptions & Special Events


    The AME department wants to hear from YOU! Take a break from studying and join us for lunch to brainstorm and construct initiatives the AME department can support to promote student wellness. This is a community approach to student advocacy, which will facilitate open communication between students, staff, and faculty so that barriers to wellness can be identified and overcome.

    Location: Seeley G. Mudd Building (SGM) - 123

    Audiences: Department Only

    Contact: Victoria Sevilla

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  • 2023 Gordon Prize Special Lecture by Azad M. Madni

    Tue, May 02, 2023 @ 04:00 PM - 05:00 PM

    USC Viterbi School of Engineering

    Conferences, Lectures, & Seminars


    Speaker: Azad M. Madni, University Professor of Astronautics, Aerospace and Mechanical Engineering, and Education

    Talk Title: Road to TRASEE: A Transdisciplinary Systems Engineering Education Paradigm

    Abstract:
    Attend a special lecture on Dr. Azad M. Madni's 2023 Bernard M. Gordon Prize for Innovation in Engineering & Technology Education Award-winning research.

    Online only. Please use event password 954094 for access to the live stream.



    Biography:
    Azad M. Madni is a member of the National Academy of Engineering and Professor of Astronautical Engineering. He is the holder of the Northrop Grumman Foundation Fred O'Green Chair in Engineering. he has a joint appointment in Astani Department of Civil and Environmental Engineering. He is the Executive Director of USC's Systems Architecting and Engineering Program in the Viterbi School of Engineering. He is also the Director of the Distributed Autonomy and Intelligent Systems Laboratory. He is the chair and co-founder of the IEEE SMC Society's Systems Science and Engineering award-winning Technical Committee for Model Based Systems Engineering. He has served as General Chair of the Conference on Systems Engineering Research since 2008. He is a Life Fellow/Fellow of AAAS, AIAA, IEEE, INCOSE, IETE, SDPS, and Washington Academy of Science. He has received prestigious awards and honors from nine different societies. His research has been sponsored by several government agencies including DARPA, NSF, DHS S&T, DoD-SERC, NASA, DTRA, OSD, MDA, ONR, AFOSR, AFRL, ARI, ARL, RDECOM, CECOM, ERDC, NAVAIR, NAVSEA, SPAWAR, MARCOR, DOE, and NIST. His research has also been sponsored by major aerospace and automotive companies including Boeing, General Motors, Raytheon, Northrop Grumman Corporation, SAIC, and Lockheed Martin ORINCON.

    He is also the 2023 Bernard M. Gordon Prize for Innovation in Engineering & Technology Education Awardee.

    Host: USC Viterbi School of Engineering

    More Info: https://usc.zoom.us/j/93216913796?pwd=Tm1kVEptS1puUVdOSnZVSW56UCt6dz09

    Location: Online Live Stream

    Audiences: Everyone Is Invited

    Contact: Sheriden Smith

    Event Link: https://usc.zoom.us/j/93216913796?pwd=Tm1kVEptS1puUVdOSnZVSW56UCt6dz09

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  • PhD Thesis Defense - Yu-Chuan Yen

    Wed, May 03, 2023 @ 08:30 AM - 10:30 AM

    Thomas Lord Department of Computer Science

    University Calendar


    PhD Thesis Defense - Yu-Chuan Yen

    Title: Constructing an unambiguous user-and-machine-friendly, natural-language protocol specification system

    Committee Members: Barath Raghavan, Ramesh Govindan, Murali Annavaram

    Abstract: Protocol specification has existed for decades to deliver the design and implementation of numerous protocols.
    As the guideline and foundation of diverse advanced systems, the methods to process and compose protocol specification have not changed much despite emerging advanced techniques.

    The production of specifications remains labor-intensive and involves rigorous discussion to avoid miscommunication via natural language media. A key reason behind these facts is the existence of ambiguities in natural language articles. Ambiguities could represent an unreasonable sentence, a multiple-meaning sentence, or any under-specified behaviors. However, identification of ambiguities is challenging to be applied in domain specific context. In addition, lack of studies applying advanced natural language processing techniques limits our understanding and practices of improving specification production. Motivated by the above observations, this thesis makes the first steps in introducing and building a prototype system that is user-and-machine-friendly and able to process natural language protocol specification while guaranteeing the ambiguous level of the specification. The contributions are four-fold. Firstly, it applies advanced natural language processing techniques called Combinatory Categorial Grammar to analyze protocol specification texts and identifies ambiguous sentences that could result in buggy implementations. Secondly, it parses unambiguous English specification and generates corresponding executable protocol codes that can interoperate with well-known third party code. Thirdly, it defines protocol behaviors with a math definition and introduces unambiguous configurations. The specification configuration is easy for authors to design and easy to automatically generate corresponding English specification and executable code. Lastly, it categorizes a set of verification rules that are able to assist in filtering unreasonable configurations which could not be turned into pieces of English paragraphs or code blocks

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://usc.zoom.us/j/2553045376

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  • Mangan Power Distribution Trojan Talk (Virtual)

    Wed, May 03, 2023 @ 12:00 PM - 02:00 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions


    Mangan Power Distribution Group

    Date: Wednesday, May 3rd, 2023
    Time: 12:00 p.m. - 2:00 p.m.
    Location: Zoom RSVP HERE


    HIRING NOW!

    Mangan Power Distribution is a division of Mangan Inc., a nationally recognized Specialty Engineering, Automation, and Integration company. Our engineers excel in providing state-of-the-art electrical engineering services to clients throughout the United States.

    Mangan PDG is looking to hire Graduate and Undergraduate Electrical Engineers with Power Systems background. Students on CPT/OPT are welcome.

    Location: Zoom, please see below for details on how to RSVP

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

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  • PhD Thesis Proposal - Arvin Hekmati

    Wed, May 03, 2023 @ 03:00 PM - 04:30 PM

    Thomas Lord Department of Computer Science

    University Calendar


    PhD Thesis Proposal - Arvin Hekmati

    Title: Correlation-Aware Neural Networks for DDoS Attack Detection In IoT Systems

    Committee Members: Bhaskar Krishnamachari (Chair), Cyrus Shahabi, Aiichiro Nakano, Mohammad Rostami, Cauligi Raghavendra

    Abstract: We present a comprehensive study on applying machine learning to detect distributed Denial of service (DDoS) attacks using large-scale Internet of Things (IoT) systems. While prior works and existing DDoS attacks have largely focused on individual nodes transmitting packets at a high volume, we investigate more sophisticated futuristic attacks that use large numbers of IoT devices and camouflage their attack by having each node transmit at a volume typical of benign traffic. We introduce new correlation-aware architectures that take into account the correlation of traffic across IoT nodes, and we also compare the effectiveness of centralized and distributed detection models. We extensively analyze the proposed architectures by evaluating five different neural network models trained on a dataset derived from a 4060-node real-world IoT system. We observe that long short-term memory (LSTM) and a transformer-based model, in conjunction with the architectures that use correlation information of the IoT nodes, provide higher performance (in terms of F1 score and binary accuracy) than the other models and architectures, especially when the attacker camouflages itself by following benign traffic distribution on each transmitting node. For instance, by using the LSTM model, the distributed correlation-aware architecture gives 81 percent F1 score for the attacker that camouflages their attack with benign traffic as compared to 35 percent for the architecture that does not use correlation information. We also investigate the performance of heuristics for selecting a subset of nodes to share their data for correlation-aware architectures to meet resource constraints.

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://usc.zoom.us/j/92583528716?pwd=S01uOUlYQXU5Z0xudXZXbzgwOE0wQT09

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  • PhD Thesis Defense - Leili Tavabi

    Wed, May 03, 2023 @ 03:00 PM - 05:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    PhD Thesis Defense - Leili Tavabi

    Committee Members: Mohammad Soleymani (Chair), Maja Mataric, Shrikanth Narayanan, Stefan Scherer

    Title: Computational Modeling of Mental Health Therapy Sessions

    Abstract: Despite the growing prevalence of mental health disorders, there is a large gap between the needs and available resources for diagnosis and treatment. The recent advancements in machine learning and deep learning provide an opportunity for developing AI assisted assessment of therapy sessions through automated behavior analysis. In this dissertation, I present multiple approaches for modeling and analyzing client therapist dialogue from real world Motivational Interviews toward efficient and systematic assessment of the sessions. I present models for automatic recognition of client intent on a local utterance level, and quality metrics like therapist empathy at the global session level. I further explore the association of in session behaviors with subsequent outcomes, and provide interpretable insights on psychologically relevant features associated with the modeled constructs

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://usc.zoom.us/j/96609451060?pwd=YnhUOWxjY0ZCaWFadkR4S2srNmZKZz09

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  • PHD Defense - Su Lei

    Thu, May 04, 2023 @ 01:00 PM - 03:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    PHD Defense: Su Lei

    Committee: Jonathan Gratch (Chair), Laurent Itti, Shri Narayanan

    Abstract: In this dissertation, I innovate automatic facial analysis methods and use them to yield fundamental insights into the source and function of facial expressions in face-to-face social interaction. Facial expressions play an essential role in shaping human social behavior. The ability to accurately recognize, interpret and respond to emotional expressions is a hallmark of human social intelligence, and automating this ability is a key focus of computer science research. Machines that possess this skill could enhance the capabilities of human-machine interfaces, help diagnose social disorders, improve predictive models of human behavior, or serve as methodological tools in social science research. My dissertation focuses on this last application. Specifically, I examine two competing perspectives on the social meaning of facial expressions and show that automated methods can yield novel insights. In terms of technical innovation, I develop novel methods to interpret the meaning of facial expressions in terms of facial expressivity. Within computer science, facial expression analysis has been heavily influenced by the "basic emotion theory" which claims that expressions reflect the activation of a small number of discrete emotions (e.g., joy, hope, or fear). Thus, automatic emotion recognition methods seek to classify facial displays into these discrete categories to form insights into how an individual is interpreting a situation and what they will do next. However, more recent psychological findings have largely discredited this theory, highlighting that people show a wide range of idiosyncratic expressions in response to the same event. Motivated by this more recent research, I develop supervised machine learning models to automatically measure perceived expressivity from video data. In terms of theoretical innovation, I demonstrate how automatic expressivity recognition yields insight into alternative psychological theories on the nature of emotional expressions in social tasks by analyzing a large corpus of people engaged in the iterated prisoner's dilemma task. This is a canonical task used to test theories of social cognition and the function of facial expressions. First, I explore the appraisal perspective which claims that expressions reflect an individual's appraisal of how actions within a social task relate to their goals. I find that by analyzing facial expressions produced by participants, a computer can reliably predict how actions in the task impact participants' appraisals (specifically, we predict if the action was unexpected). Further, we show that automatic expressivity recognition dramatically improves the accuracy of these predictions over traditional emotion recognition. This lends support to the theory that expressions are, in a sense, directly caused by the social task. Second, I explore a contrasting perspective, interpersonal-dynamics theory, which argues that expressions are, in a sense, directly caused by the partner's expressions. This perspective emphasizes processes such as synchrony, mimicry, and contagion to explain moment-to-moment expressions. The appraisal perspective counters that any observed synchrony simply reflects a shared appraisal of social actions. I use automatic expressivity recognition to contrast these perspectives. Specifically, I analyze synchrony in two experimental conditions: a "still" condition where dyads see only a still image of their partner, and a "video" condition with real-time visual access to their partner's facial reactions. Using Dynamic Time Warping, I evaluate synchrony in both real and randomly paired dyads. Results reveal that synchrony exists even without visual cues, suggesting that shared appraisals contribute to synchrony, but that synchrony significantly increases when the partner is visible. This suggests that both perspectives must be integrated to best explain facial displays. In conclusion, both appraisal and interpersonal-dynamics perspectives reinforce the significance of emotional expressivity in interpreting facial displays and fostering social coordination in cooperative and competitive contexts. These insights offer valuable contributions to affective computing and the understanding of social interaction mechanisms. I also discuss potential limitations and future research directions for further exploring the complexities of social interactions.

    Location: https://usc.zoom.us/j/6448851979?pwd=TThsRC96Vk9KZnVLV0RIc1g5NGVuQT09

    Audiences: Everyone Is Invited

    Contact: Asiroh Cham

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  • Ming Hsieh ECE Seminar - Prof. Jacob Nagel, Technion

    Fri, May 05, 2023 @ 10:00 AM - 11:30 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Jacob Nagel, Professor, Technion

    Talk Title: Israel National Security Challenges and its Impact on Force Buildup and Technological Developments, focusing on the Technion Center for Science Security and Technology

    Abstract: This seminar (60-90 minutes) will combine topics from my past positions at the Israeli Ministry of Defense and as Israeli National Security Advisor, and in my current position as head of the Technion Center for Science Security and Technology (CSST).
    I am continually changing and updating my presentation according to worldwide events and technological advances. This presentation includes current and past events, as well as some anecdotes during some interesting meetings in Israel and around the world. As part of the seminar, I will show movies, photos, and technological demonstrations. Moreover, I will discuss the US-Israel MOU agreement (worth $38B, led and signed by me in 2016) and the special relationship between Israel and the US. Additionally, I will highlight my Technion Center activities and options for cooperation.

    Biography: Professor Jacob Nagel (Israel Brigadier General, Res.) is a visiting professor in Aerospace Engineering at the Technion, Haifa, Israel. At the Technion, he is head of the CSST and the Peter Munk Research Institute. He is a senior fellow at the Foundation for Defense of Democracies. With 40 years of experience in Israeli government and intelligence, he teaches defense R&D strategy and policy as well as systems engineering. His areas of interest include: robotics and micro vehicles, aeronautics, rocket engine, space (e.g., satellites and payloads), cyber security, data science, image analysis, energy, and lasers. He served as the Head of the Israel National Security Council (NSC) and as Israel's National Security Advisor (Acting) to the Prime Minister. He also led the negotiations with the US on the most recent ten-year MOU and signed the agreement worth $38B. Prof. Nagel served in various technological positions at the Israel Ministry of Defense - Directorate of Defense R&D. These include the Scientific Deputy and the Acting Head of the military R&D, which is sometimes called "Israel's DARPA".

    Host: Alan Willner

    More Information: Nagel_Willner_Seminar_5.5.23.pdf

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

    Audiences: Everyone Is Invited

    Contact: Corine Wong

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  • Alfred E.Mann Department of Biomedical Engineering - Seminar series

    Fri, May 05, 2023 @ 11:00 AM - 12:00 PM

    Alfred E. Mann Department of Biomedical Engineering

    Conferences, Lectures, & Seminars


    Speaker: Matthew Borzage, Ph.D., Assistant Professor, Department of Pediatrics, KSOM, USC

    Talk Title: "Approaches for Neurovascular Magnetic Resonance Imaging-Going Beyond BOLD"

    Abstract:


    Biography:



    Host: Brent Liu

    More Info: zoom link available upon request

    More Information: Flyer Matthew Borzage.pdf

    Location: Corwin D. Denney Research Center (DRB) - 145

    Audiences: Everyone Is Invited

    Contact: Carla Stanard

    Event Link: zoom link available upon request

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  • ECE Seminar: Robust Classification under Sparse Adversarial Attacks

    Mon, May 08, 2023 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Payam Delgosha, Research Assistant Professor, Computer Science Department, University of Illinois at Urbana Champaign

    Talk Title: Robust Classification under Sparse Adversarial Attacks

    Abstract: It is well-known that machine learning models are vulnerable to small but cleverly-designed adversarial perturbations that can cause misclassification. While there has been major progress in designing attacks and defenses for various adversarial settings, many fundamental and theoretical problems are yet to be resolved. In this talk, we consider classification in the presence of L0-bounded adversarial perturbations, a.k.a. sparse attacks. This setting is significantly different from other Lp-adversarial settings, with p >= 1, as the L0-ball is non-convex and highly non-smooth. In this talk, we discuss the fundamental limits of robustness in the presence of sparse attacks. In order to find an upper bound on the robust error, we introduce novel classification methods that are based on truncation. Furthermore, in order to find a lower bound on the robust error, we design a specific adversarial strategy which tries to remove the information about the true label given the adversary's budget. We discuss scenarios where the bounds match asymptotically. Motivated by the theoretical success of the proposed algorithm, we discuss how to incorporate truncation as a new component into a neural network architecture, and verify the robustness of the proposed architecture against sparse attacks through several experiments. Finally, we investigate the generalization properties and sample complexity of adversarial training in this setting.

    Biography: Payam Delgosha received his B.Sc. in Electrical Engineering and Pure Mathematics in 2012, and his M.Sc. in Electrical Engineering in 2014, both from Sharif University of Technology, Tehran, Iran. He received his Ph.D. in Electrical Engineering and Computer Sciences from the University of California at Berkeley in 2020. He joined the computer science department at the University of Illinois at Urbana Champaign as a research assistant professor in 2020. He received the 2020 IEEE Jack Keil Wolf ISIT best student paper award.

    Host: Dr. Richard M. Leahy, leahy@sipi.usc.edu

    Webcast: https://usc.zoom.us/j/97124212376?pwd=NTd0QzRzSXk3OGlzL0dIdFdXMmZYZz09

    More Information: ECE Seminar-Delgosha-050823.pdf

    WebCast Link: https://usc.zoom.us/j/97124212376?pwd=NTd0QzRzSXk3OGlzL0dIdFdXMmZYZz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • MoBI Seminar: Dr Bradley Voytek

    Mon, May 08, 2023 @ 11:00 AM - 12:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr Bradley Voytek, Department of Cognitive Science, Halıcıoğlu Data Science Institute, UC San Diego

    Talk Title: The physiology and function of aperiodic neural activity

    Series: MoBI Seminar Series

    Abstract: Perception, action, and cognition depend upon coordinated neural activity. This coordination operates within noisy, distributed neural networks, which themselves change with development, aging, and disease. Extensive field potential and EEG research shows that neural oscillations interact with neuronal spiking. This interaction has been proposed to be a mechanism for implementing dynamic coordination between brain regions, placing oscillations at the forefront of neuroscience research. Our work challenges our conception of what an oscillation even is. Beginning from basic theory and modeling, we show that traditional analyses conflate non-oscillatory, aperiodic activity with oscillations. To do this, we leverage neural modeling and a breadth of empirical data-”spanning human iPSC-derived cortical organoids, animal electrophysiology, invasive human EEG, and large-scale data mining. We show that, while not all things that appear oscillatory are so, the physiological information we can extract from the local field potential and EEG may nevertheless be far richer than previously thought, including nonsinusoidality of oscillation waveform shape and the aperiodic signal.


    Biography: Bradley Voytek is a Professor in the Department of Cognitive Science, the Halıcıoğlu Data Science Institute, and the Neurosciences Graduate Program at UC San Diego. He's an Alfred P. Sloan Neuroscience Research Fellow and a Kavli Fellow of the National Academies of Sciences, as well as a founding faculty member of the UC San Diego Halıcıoğlu Data Science Institute and the Undergraduate Data Science program. After his PhD at UC Berkeley, he joined Uber as their first data scientist-”when it was a 10-person startup-”where he helped build their data science strategy and team. His research lab combines large-scale data science and machine learning to study how brain regions communicate with one another, and how that communication changes with aging and disease. He is an advocate for promoting science to the public and speaks extensively with students at all grade levels about the joys of scientific research and discovery. In addition to his academic publications, his outreach work has appeared in outlets ranging from Scientific American and NPR to the San Diego Comic-Con. He is currently writing a book with neuroscientist Ashley Juavinett regarding the powerful future of data science in neuroscience discovery, though his most important contribution to science is his book with fellow neuroscientist Tim Verstynen, "Do Zombies Dream of Undead Sheep?", by Princeton University Press.


    Host: Dr Richard Leahy, leahy@sipi.usc.edu | Dr Karim Jerbi, karim.jerbi.udem@gmail.com

    Webcast: https://usc.zoom.us/j/97647013783?pwd=d1h2N3hxYUpJVU9CWlduYTZzMWNGQT09

    More Information: MoBI Seminar Flyer - 05.08.2023 Bradley Voytek.pdf

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

    WebCast Link: https://usc.zoom.us/j/97647013783?pwd=d1h2N3hxYUpJVU9CWlduYTZzMWNGQT09

    Audiences: Everyone Is Invited

    Contact: Miki Arlen

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  • PhD Thesis Proposal - Mehrnoosh Mirtaheri

    Mon, May 08, 2023 @ 02:00 PM - 03:30 PM

    Thomas Lord Department of Computer Science

    University Calendar


    PhD Thesis Proposal - Mehrnoosh Mirtaheri

    Committee members: Aram Galstyan, Mohammad Rostami, Fred Morstatter, Cyrus Shahabi, Antonio Ortega

    Title: Scalable Graph-Based Models for Temporal Knowledge Graphs: Learning, Applications

    Abstract: Temporal knowledge graphs (TKGs) have emerged as a powerful tool for modeling relationships between entities in large raw text datasets. By capturing and representing these relationships in a structured, interpretable format, TKGs enable the extraction of valuable insights from vast amounts of unstructured information. Knowledge graphs allow for the identification of patterns and trends over time, enhancing our understanding of evolving connections and interactions between various entities. Moreover, they facilitate complex reasoning tasks, question answering, and data driven decision making by offering a more comprehensive view of the relationships found within the text.
    This thesis focuses on developing various models to address different challenges associated with TKG completion, such as data scarcity, scalability, and continuously evolving data. By tackling these challenges, the proposed models aim to enhance the capabilities of TKGs for analyzing and processing complex relationships within large scale text data. As a result, they enable more accurate and effective knowledge extraction and representation. The advancements presented in this thesis can greatly benefit a wide range of applications that rely on understanding the underlying structure of relationships in massive raw text datasets.


    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://usc.zoom.us/j/99893841028?pwd=RlhVd29VcTltdnFCRW54dHc3ZjhrZz09

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  • PhD Thesis Proposal - Yufeng Yin

    Mon, May 08, 2023 @ 02:00 PM - 04:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    PhD Thesis Proposal - Yufeng Yin

    Committee Members: Mohammad Soleymani (chair), Jonathan Gratch, Mayank Kejriwal, Lynn Miller, Maja Mataric, and Xuezhe Ma

    Title: Towards Generalizable Facial Expression and Emotion Recognition

    Abstract: Facial expression and emotion recognition are critical components of human behavior understanding. However, the performance of automatic recognition methods degrades when evaluated across datasets or subjects, due to variations in humans and environmental factors. The manual coding required by supervised methods also presents significant practical limitations since they are not feasible when working with new datasets or individuals.

    In this thesis proposal, we investigate how to improve the generalization ability of the perception model through representation learning and synthetic data generation with minimal human efforts. (i) We explore unsupervised domain adaptation (UDA) approaches to obtain domain invariant and discriminative features without any target labels. The experiments show that UDA can effectively reduce the domain gap between datasets or subjects and improve model cross corpus performance for emotion recognition. (ii) We explore approaches for synthetic data generation to address the problems of the scarcity of labeled data and the diversity of subjects. Our results indicate that synthetic data can not only improve action unit (AU) detection performance but also fairness across genders, demonstrating its potential to solve AU detection in the wild. We will also discuss our future work involving unsupervised personalization on unseen speakers for emotion recognition through feature representation learning and label distribution calibration. Our proposed methods enhance model recognition accuracy and generalization ability to unseen subjects and datasets, paving the way for more effective human behavior analysis in a variety of applications.

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://usc.zoom.us/j/94638614965?pwd=c0ozL09VVjVBNmNwRmQ4NTAybWwzdz09

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  • PhD Defense - Isabel Rayas

    Mon, May 08, 2023 @ 02:00 PM - 04:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    PhD Candidate Defense: Isabel Rayas


    In-person: RTH 306

    Zoom: https://usc.zoom.us/j/95235693966?pwd=cE92UC8zejROMi8yYytyT3F5YnY1UT09

    Committee:
    Gaurav Sukhatme (Chair), David Caron, Stefanos Nikolaidis

    Title: Advancing Robot Autonomy for Long-Horizon Tasks

    Abstract:
    Autonomous robots have real-world applications in diverse fields, such as mobile manipulation and environmental exploration, and many such tasks benefit from a hands-off approach in terms of human user involvement over a long task horizon. However, the level of autonomy achievable by a deployment is limited in part by the problem definition or task specification required by the system. Task specifications often require technical, low-level information that is unintuitive to describe and may result in generic solutions, burdening the user technically both before and after task completion. In this thesis, we aim to advance task specification abstraction toward the goal of increasing robot autonomy in real-world scenarios. We do so by tackling problems that address several different angles of this goal. First, we develop a way for the automatic discovery of optimal transition points between subtasks in the context of constrained mobile manipulation, removing the need for the human to hand-specify these in the task specification. We further propose a way to automatically describe constraints on robot motion by using demonstrated data as opposed to manually-defined constraints. Then, within the context of environmental exploration, we propose a flexible task specification framework, requiring just a set of quantiles of interest from the user that allows the robot to directly suggest locations in the environment for the user to study. We next systematically study the effect of including a robot team in the task specification and show that multirobot teams have the ability to improve performance under certain specification conditions, including enabling inter-robot communication. Finally, we propose methods for a communication protocol that autonomously selects useful but limited information to share with the other robots.

    Location: Ronald Tutor Hall of Engineering (RTH) - 306

    Audiences: Everyone Is Invited

    Contact: Asiroh Cham

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  • PhD Defense - Michiel De Jong

    Mon, May 08, 2023 @ 03:00 PM - 05:00 PM

    Thomas Lord Department of Computer Science

    University Calendar



    Title: EXPANDING THE QUALITY-COMPUTE FRONTIER FOR RETRIEVAL-AUGMENTED LANGUAGE MODELS

    Abstract: Retrieval-augmented language models set the state-of-the-art on a broad spectrum of knowledge-intensive tasks, outperforming orders of magnitude larger models. However, such models can also be expensive for training and inference. Model performance and computational cost represent two sides of the coin: we can generally improve performance through scale at the expense of an increased computational burden. Therefore, we are really interested in pushing out the quality-compute frontier, improving performance at any given level of computational resources.

    In this dissertation, I analyze the factors that determine the computational burden of retrieval-augmented language models and propose strategies to extract a better performance-compute trade-off. The dissertation consists of three sections. The first section contains a detailed analysis of components of retrieval-augmented models and introduces methods to improve generation efficiency. The second section explores the use of dense memory to reduce the cost of encoding retrievals. Finally, the third section proposes a hybrid between dense memory and text retrieval, combining lessons from previous chapters.

    Names of the Dissertation defense committee members:
    Chair: Leana Golubchik
    Members:
    Fei Sha
    Dani Yogatama
    Jacob Bien

    Venue: Zoom, https://usc.zoom.us/my/lgzoomeeting



    Location: https://usc.zoom.us/my/lgzoomeeting

    Audiences: Everyone Is Invited

    Contact: Asiroh Cham

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  • Kuldeep Meel (National University of Singapore) - Functional Synthesis: An Ideal Meeting Ground for Formal Methods and Machine Learning

    Tue, May 09, 2023 @ 11:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Kuldeep Meel, National University of Singapore

    Talk Title: Functional Synthesis: An Ideal Meeting Ground for Formal Methods and Machine Learning

    Abstract: Don't we all dream of the perfect assistant whom we can just tell what to do and the assistant can figure out how to accomplish the tasks? Formally, given a specification F(X,Y) over the set of input variables X and output variables Y, we want the assistant, aka functional synthesis engine, to design a function G such that F(X,G(X)) is true. Functional synthesis has been studied for over 150 years, dating back Boole in 1850's and yet scalability remains a core challenge. Motivated by progress in machine learning, we design a new algorithmic framework Manthan, which views functional synthesis as a classification problem, relying on advances in constrained sampling for data generation, and advances in automated reasoning for a novel proof-guided refinement and provable verification. The significant performance improvements call for interesting future work at the intersection of machine learning, constrained sampling, and automated reasoning.

    Biography: Kuldeep Meel holds the NUS Presidential Young Professorship in the School of Computing at the National University of Singapore (NUS). His research interests lie at the intersection of Formal Methods and Artificial Intelligence. He is a recipient of the 2022 ACP Early Career Researcher Award, the 2019 NRF Fellowship for AI and was named AI's 10 to Watch by IEEE Intelligent Systems in 2020. His research program's recent recognitions include the CACM Research Highlight Award, 2022 ACM SIGMOD Research Highlight, IJCAI-22 Early Career Spotlight, best paper award nominations at ICCAD-21 and DATE-23.

    Host: Mukund Raghothaman

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: Everyone Is Invited

    Contact: Angela Inostroza-Hoffman / Computer Science

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  • PhD Dissertation Defense - Zimo Li

    Tue, May 09, 2023 @ 02:00 PM - 04:00 PM

    Thomas Lord Department of Computer Science

    University Calendar


    PhD Dissertation Defense - Zimo Li

    Committee Members: Andrew Nealen, Laurent Itti, Stefanos Nikolaidis, Mike Zyda

    Title: Human Appearance and Performance Synthesis Using Deep Learnin

    Abstract: Synthesis of human performances is a highly sought after technology in the entertainment industry. In this dissertation, we will go over several new deep learning solutions which tackle the problems of human facial and body performance synthesis.

    Facial performance synthesis is a complex multistep graphics problem. First, the target performance to be modified must be tracked and captured accurately. Then, based on the desired modification (whether to change the identity, facial expressions, or both), a modified source performance must be synthesized or captured from a different actor. Finally, the original facial performance must be removed and replaced with the synthesized one. This multistep process poses many unique challenges. Using conventional CG tracking and retargeting of expressions from the source to target using a 3D mesh and static texture will give an undesired rubbery skin effect. Furthermore, inaccuracies in the expression tracking of the source performance using a blendshape model will result in the uncanny valley effect in the output performance. It is often necessary to use costly capture methods, such as a Light Stage, to obtain highly accurate 3D captures and dynamic textures of a source performance in order to avoid these pitfalls. Even then, final modified performances are often uncanny.

    When dealing with human body to motion synthesis, creating new motions often requires manual artist animations, tracking new motions on an actor, or stitching together subsequences of previous animations. These methods are limited by cost, or are not able to generate appreciably novel motions.

    Over the last several years, the advancement of AI based generation techniques have let us address many of these issues. In this thesis, we will go over several novel techniques which reduce the cost (time, money, ease-of-access), and improve the quality of facial reenactment, as well as body motion synthesis, pipelines. The applications of these techniques allow us to tackle new problem settings in an efficient way.

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://us05web.zoom.us/j/86385849747?pwd=V2lwR2FXekI5WVpNMGU0bWF5clJIQT09

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  • Innovation For Defense Applications Showcase

    Tue, May 09, 2023 @ 04:30 PM - 06:30 PM

    Viterbi Technology Innovation and Entrepreneurship

    University Calendar


    You are invited to join us for the Innovation For Defense Applications team presentations showcase. This semester we have teams that have worked on various problems sets for their Department of Defense sponsors.

    The event will be held on the USC campus at the Ronald Tutor Hall (RTH) in room 526. Doors will open at 4:30 pm and will include light refreshments at the event.

    If you can not attend in person, we will also provide a ZOOM link for a virtual option.

    RSVP



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

    Audiences: Everyone Is Invited

    Contact: Johannah Murray

    Event Link: https://forms.gle/EZP7rh2y4uPHMcne7

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  • MoBI Seminar: Quanzheng Li

    Mon, May 15, 2023 @ 11:00 AM - 12:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr Quanzheng Li, Associate Professor of Radiology | Massachusetts General Hospital | Harvard Medical School

    Talk Title: Artificial intelligence for healthcare using multimodality medical data

    Series: MoBI Seminar Series

    Abstract: The development of Artificial Intelligence (AI) in healthcare is currently at a critical moment, with tremendous potential for the future but an uncertain trajectory, given the rapid development over the past five years and the emergence of foundation models with astonishing capabilities. In this talk, I will discuss our recent work on using deep neural networks for PET and MR image reconstruction and denoising. Additionally, I will demonstrate how we can leverage deep learning for clinical applications using various multimodality medical data, including imaging, waveforms, electronic health records (EHRs), video, and pathology. Furthermore, I will present some of our preliminary results on the medical application of foundation models (such as GPT and SAM) and discuss potential opportunities and challenges of AI in healthcare.

    Biography: Quanzheng Li is an Associate Professor of Radiology at Massachusetts General Hospital (MGH), Harvard Medical School. Dr. Li is also the senior director for research and development, data science office, Massachusetts General Brigham, and the director of the Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital. He received his Ph.D degree in Electrical Engineering from the University of Southern California (USC) in 2005 and had his postdoc training also at USC with Richard Leahy. Dr. Li is the recipient of 2015 IEEE NPSS early achievement award. He is an associate editor of IEEE Transaction on Image Processing, IEEE TMI, Medical Physicis and members of editorial boards of Theronostics and Physics in Medicine and Biology. Dr. Li has more than 200 peer reviewed articles and his team has won AAPM-NIH low dose CT challenge and 2018 Camelyon Challenge on digital pthology. His research interests include image reconstruction for PET, SPECT, CT and MRI, and multimodality medical data analysis using artificial intelligence.


    Host: Dr Richard Leahy, leahy@sipi.usc.edu | Dr Karim Jerbi, karim.jerbi.udem@gmail.com

    Webcast: https://usc.zoom.us/j/98341725765?pwd=Zm56d2tJWEhTN2JxM1kzd1lEUUhhdz09

    More Information: MoBI Seminar Flyer - 05.15.2023 Quanzheng Li.pdf

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 349

    WebCast Link: https://usc.zoom.us/j/98341725765?pwd=Zm56d2tJWEhTN2JxM1kzd1lEUUhhdz09

    Audiences: Everyone Is Invited

    Contact: Miki Arlen

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  • Six Sigma Green Belt for Process Improvement

    Tue, May 16, 2023 @ 09:00 AM - 05:00 PM

    USC Viterbi School of Engineering

    Conferences, Lectures, & Seminars


    The Six Sigma Green Belt for Process Improvement course is a short 3 day course where you master the use of Six Sigma to identify problems in your organization, and develop plans to combat them. The Six Sigma Green Belt course is recommended for anyone looking for ways to support your company, no need for an engineering background.

    Delivery options: On Campus and Online (Interactive)

    Location: TBD

    Audiences: Everyone Is Invited

    Contact: Melissa Medeiros

    Event Link: https://engage.usc.edu/viterbi/rsvp?id=387007

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  • Viterbi Startup Garage: Founding & Funding Deep Tech Companies

    Wed, May 17, 2023 @ 12:00 PM - 01:00 PM

    Viterbi Technology Innovation and Entrepreneurship

    University Calendar


    Viterbi Startup Garage: Founding & Funding Deep Tech Companies

    Who: Joe Wilson Managing Partner at Undeterred Capital

    What: Companies face challenges when they are seeking to commercialize technological breakthroughs. How do these companies attract funding and how are they scaled over time?

    When: Wed, May 17, 2023 (12-1 PM PT)

    Where: Zoom
    (Register for Zoom Link)


    Location: Zoom

    Audiences: Everyone Is Invited

    Contact: VSG

    Event Link: https://vsg-events.my.canva.site/vsg-oceanside-chat-may-17-2023

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  • PhD Dissertation Defense - Heramb Nemlekar

    Thu, May 18, 2023 @ 11:30 AM - 01:30 PM

    Thomas Lord Department of Computer Science

    University Calendar


    PhD Dissertation Defense - Heramb Nemlekar

    Committee: Gaurav Sukhatme, Heather Culbertson, Jyotirmoy Deshmukh, Satyandra K. Gupta, Stefanos Nikolaidis (Chair)


    Title: Efficiently Learning Human Preferences for Proactive Robot Assistance in Assembly Tasks

    Abstract:
    Robots that support humans in collaborative tasks need to adapt to the individual preferences of their human partners efficiently. While prior work has mainly focused on learning human preferences from demonstrations in the actual task, obtaining this data can be expensive in real world settings such as assembly and manufacturing. Thus, this dissertation proposes leveraging prior knowledge of (i) similarities in the preferences of different users in a given task and (ii) similarities in the preferences of a given user in different tasks for efficient robot adaptation. Firstly, to leverage similarities between users, we propose a two stage approach for clustering user demonstrations to identify the dominant models of user preferences in complex assembly tasks. This allows assistive robots to efficiently infer the preferences of new users by matching their actions to a dominant preference model. We evaluate our approach in an IKEA assembly study and show that it can improve the accuracy of predicting user actions by quickly inferring the user preference. Next, to leverage similarities between tasks, we propose learning user preferences as a function of task agnostic features (e.g., the mental and physical effort of user actions) from demonstrations in a short canonical task and transferring the preferences to the actual assembly. Obtaining demonstrations in a canonical task requires less time and human effort, allowing robots to learn user preferences efficiently. In a user study with a manually designed canonical task and an actual task of assembling a model airplane, we observe that our approach can predict user actions in the actual assembly based on the task agnostic preferences learned in the canonical task. We extend our approach to account for users that change their preferences when switching tasks, by updating the transferred user preferences during the actual task. In a human to robot assembly study, we demonstrate how an assistive robot can adapt to the changing preferences of users and proactively support them, thereby reducing their idle time and enhancing their collaborative experience. Lastly, we propose a method to automatically select a canonical task suitable for the transfer learning of human preferences based on the expressiveness of the task. Our experiments show that transferring user preferences from a short but expressive canonical task improves the accuracy of predicting user actions in longer actual tasks. Overall, this dissertation proposes and evaluates novel approaches for efficiently adapting to human preferences, which can enhance the productivity and satisfaction of human workers in real-world assemblies.

    Location: Henry Salvatori Computer Science Center (SAL) - 213

    Audiences: Everyone Is Invited

    Contact: Melissa Ochoa

    Event Link: https://usc.zoom.us/j/91591350584?pwd=a2lRcE9peGFCeFBLa05sRW1vT25UUT09

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  • MoBI Seminar: Dr Patrick Chauvel

    Mon, May 22, 2023 @ 11:00 AM - 12:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr Patrick Chauvel, Professor of Neurology, University of Pittsburgh

    Talk Title: Comprehending epilepsy with SEEG: the interplay of physiology and modeling

    Series: MoBI Seminar Series

    Abstract: Stereoelectroencephalography (SEEG) is the only method allowing direct intracerebral recording of seizure onset and propagation in patients with epilepsy. As such, SEEG data are ground truth for building sound research hypotheses on human focal epilepsies. However, since the origins, interpretation of the SEEG signal for the purpose of surgical treatment as well as for research has been a challenge. This lecture will explain why and how different and evolving types of modeling helped understand the nature and significance of ictal electrical patterns.

    Biography: After achieving his medical and scientific studies in Neurology and in Neuroscience, Patrick Chauvel became an INSERM (Paris) researcher (1975). At that time, he began his work in experimental and later clinical research into the mechanisms of the epilepsies. Under the mentorship of Talairach and Bancaud at Hospital Sainte-Anne/University René Descartes, Paris, he developed SEEG (Stereo-Electro- EncephaloGraphy) as a presurgical method using intracerebral electrodes for epilepsy surgery (1975-1990). His research work has been devoted to the pathophysiology of the epileptogenic zone, emergence of seizure clinical semiology, and human cerebral cortex physiology. He has promoted the concept of epileptogenic network over the classical epileptic focus idea and opened new vistas in markers of the epileptogenic zone and semiology of focal epilepsies. Taking over from Jean Bancaud, Patrick Chauvel served as the Director of the SEEG Unit in Hospital Sainte-Anne in Paris (1986-1990), then Professor and Chairman of Neurology in Rennes (1990-1997) where he configured a new type of Epilepsy Unit including research, then Professor and Chairman of Clinical Neurophysiology and Director of the INSERM Institute of Systems Neuroscience in Marseille (1997-2014). In 2014, he relocated to the Epilepsy Center of the Cleveland Clinic in order to promote the development of presurgical investigation using SEEG in North America. He was appointed as Professor of Neurology at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University. In Brisbane, he guided developing SEEG-based epilepsy surgery and related research program. He is currently Professor of Neurology at the University of Pittsburgh, USA, and Honorary Professor at the University of Queensland, Australia. He is developing new methods for presurgical investigation of drug-resistant epilepsies based on SEEG, and research on biomarkers of the epileptogenic zone and neural networks generating clinical semiology. He is the author of more than 250 original articles in international journals and is a member of several international Scientific and Medical Societies. He is a Member of the Royal Academy of Medicine in Belgium.


    Host: Dr Richard Leahy, leahy@sipi.usc.edu | Dr Karim Jerbi, karim.jerbi.udem@gmail.com

    Webcast: https://usc.zoom.us/j/96993141795?pwd=VjN1L0ZKNkYvU3JQMnkrRFVqOSt6QT09

    More Information: MoBI Seminar - 2023.05.22 Patrick Chauvel Flyer.pdf

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

    WebCast Link: https://usc.zoom.us/j/96993141795?pwd=VjN1L0ZKNkYvU3JQMnkrRFVqOSt6QT09

    Audiences: Everyone Is Invited

    Contact: Miki Arlen

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  • Traffic Flow of Urban Air Mobility: Modeling, Control, and Simulation

    Tue, May 30, 2023 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Jack Haddad, Associate Professor of Transportation Engineering with the Civil and Environmental Engineering faculty, the Technion -“ Israel Institute of Technology

    Talk Title: Traffic Flow of Urban Air Mobility: Modeling, Control, and Simulation

    Abstract: In this talk, we will focus on traffic flow modeling, control, and simulation of urban air mobility. The imminent penetration of low-altitude passenger and delivery aircraft into the urban airspace will give rise to new urban air transport systems, which we call low-altitude air city transport (LAAT) systems. As the urban mobility revolution approaches, we must investigate (i) the individual and collective behavior of LAAT aircraft in cities, and (ii) ways of controlling LAAT systems. Future LAAT systems exemplify a new class of modern large scale engineering systems -” networked control systems. They are spatially distributed, consist of many interconnected elements with control loops through digital communication networks such that the system signals can be exchanged among all components through a common network. Therefore, a decentralized controller design in framework of the unilateral event-driven paradigm is considered. Inspired by controlled urban road networks, in this talk we first establish the concept of Macroscopic Fundamental Diagram (MFD) for LAAT systems and develop a collective and aggregate aircraft traffic flow model. Then, based on that, we design an adaptive boundary feedback flow control which is robust to various anomalies in technical devices and network communication links for LAAT systems.

    Biography: Jack Haddad is an Associate Professor of Transportation Engineering with the Civil and Environmental Engineering faculty, the Technion -“ Israel Institute of Technology, and the Head of the Technion Sustainable Mobility and Robust Transportation (T-SMART) Laboratory. He received all his degrees B.Sc. (2003), M.Sc. (2006), and Ph.D. (2010) in Transportation Engineering from the Technion. He served as a post-doctoral researcher (2010-2013) at the Urban Transport Systems Laboratory (LUTS), EPFL, Switzerland. His current research interests include urban air mobility, autonomous vehicles, traffic flow modeling
    and control, large-scale complex networks, advanced transportation systems management, and public transportation.
    Dr. Haddad serves as an Associate Editor for two journals: Transportation Research Part C and IEEE Transactions on Intelligent Transportation Systems. He was a recipient of the European Union Marie Curie, Career Integration Grant (CIG), and a recipient of two Israel Science Foundation (ISF) grants. He is currently the head of the Technion Transportation Research Institute (TRI), and the Assistant to the Senior Executive Vice President for Equal Opportunities. He is also a Visiting Faculty Researcher at Google.

    Host: Dr. Petros Ioannou, ioannou@usc.edu

    Webcast: https://usc.zoom.us/j/94759707407?pwd=NGJOMERvNmlyWGtqRkh0dkdDc0dzZz09

    More Information: ECE-Controls_Seminar-2_Announcement.pdf

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

    WebCast Link: https://usc.zoom.us/j/94759707407?pwd=NGJOMERvNmlyWGtqRkh0dkdDc0dzZz09

    Audiences: Everyone Is Invited

    Contact: Miki Arlen

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  • Nonlinear Small-Gain Theory for Networks and Control

    Tue, May 30, 2023 @ 11:00 AM - 12:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Zhong-Ping Jiang, Professor, New York University

    Talk Title: Nonlinear Small-Gain Theory for Networks and Control

    Abstract: The world is nonlinear and linked. Small-gain theory is one of the most important tools to tackle fundamentally challenging control problems for interconnected nonlinear systems. In this talk, I will first review early developments in nonlinear small-gain theorems and associated nonlinear control design and show how it served as a basic tool to unify numerous results in constructive nonlinear control. Then, I will present recent developments in network/cyclic small-gain theorems for complex large-scale nonlinear systems, with applications to networked and event-triggered control under communications and computation constraints. Finally, I will discuss briefly how machine learning techniques can be invoked to relax the conservativeness of small-gain designs, that falls into the emerging area of learning- based control, a new direction in control theory.


    Biography: Zhong-Ping JIANG received the M.Sc. degree in statistics from the University of Paris XI, France, in 1989, and the Ph.D. degree in automatic control and mathematics from ParisTech-Mines (formerly called the Ecole des Mines de Paris), France, in 1993, under the direction of Prof. Laurent Praly.

    Currently, he is a Professor of Electrical and Computer Engineering at the Tandon School of Engineering, New York University. His main research interests include stability theory, robust/adaptive/distributed nonlinear control, robust adaptive dynamic programming, reinforcement learning and their applications to information, mechanical and biological systems. In these fields, he has written six books and is the author/co-author of over 500 peer-reviewed journal and conference papers. Prof. Jiang is a recipient of the prestigious Queen Elizabeth II Fellowship Award from the Australian Research Council, CAREER Award from the U.S. National Science Foundation, JSPS Invitation Fellowship from the Japan Society for the Promotion of Science, Distinguished Overseas Chinese Scholar Award from the NSF of China, and several best paper awards. He has served as Deputy Editor- in-Chief, Senior Editor and Associate Editor for numerous journals. Prof. Jiang is a Fellow of the IEEE, IFAC, CAA and AAIA, a foreign member of the Academia Europaea (Academy of Europe) and is among the Clarivate Analytics Highly Cited Researchers. In 2022, he received the Excellence in Research Award from the NYU Tandon School of Engineering.

    Host: Dr. Petros Ioannou, ioannou@usc.edu

    Webcast: https://usc.zoom.us/j/99411640901?pwd=SjBXZmFjTis3QUZVK3EvOS9ialNWUT09

    More Information: ECE-Controls_Seminar-1_Announcement.pdf

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

    WebCast Link: https://usc.zoom.us/j/99411640901?pwd=SjBXZmFjTis3QUZVK3EvOS9ialNWUT09

    Audiences: Everyone Is Invited

    Contact: Miki Arlen

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  • MoBI Seminar: Dr Annalisa Pascarella

    Tue, May 30, 2023 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr Annalisa Pascarella, Senior Researcher, Institute of Applied Mathematics M. Picone | National Council of Research, Rome, Italy

    Talk Title: New adventures in brain electromagnetism: From EEG source reconstruction to exploring the neural dynamics of meditation with MEG

    Series: MoBI Seminar Series

    Abstract: Electrical source imaging (ESI) is a key component in many EEG analysis pipelines, in both research and clinical settings. Different ESI methods mainly differ by the quality and quantity of a priori information used in the solution of the inverse problem. In this talk I'll present the main result of a recent study in which we compare in-vivo ten different ESI methods from the MNE-python package: wMNE, dSPM, sLORETA, eLORETA, LCMV, dipole fitting, RAP-MUSIC, MxNE, gamma map and Sesame. Exploiting a recently published HD scalp EEG dataset recorded at Niguarda Hospital (Milan, Italy) from Stereo-EEG implanted patients during Single Pulse Electrical Stimulation, the different inverse methods were compared under multiple choices of input parameters to assess the accuracy of the best reconstruction, as well as the impact of the parameters on the localization performance. In the second part of the talk, I'll present some preliminary results on an MEG dataset recorded in a group of expert Buddist monks during resting state (RS) and two different meditation practices: Samatha, a form of focused-attention meditation (FAM) and Vipassana that refers to open-monitoring meditation (OMM). Despite a flourishing body of research investigating the neural correlates of meditation, the underlying neural mechanisms that mediate the distinct processes associated with different forms of meditation are still poorly understood. Exploiting the high temporal resolution of MEG, the key questions we address focus on the characterization of changes in brain dynamics induced by different meditative states as evidenced by criticality and complexity measures.

    Biography: Dr. Annalisa Pascarella is a senior researcher at Institute of Applied Mathematics M. Picone, National Council of Research since October 2011. Her main research interests are centered on the formulation, implementation and validation of computational methods for the solution of the MEG/EEG inverse problems with a focus on Bayesian methods to track neural activity. In the last years she has been involved in the development of Neuropycon, an open-source brain data analysis kit which provides reproducible Python-based pipelines for advanced multi-thread processing of fMRI, MEG and EEG data, with a focus on connectivity and graph analyses. Some of her recent projects include the classification of mental states from MEG measurements during various meditation techniques.

    Host: Dr Richard Leahy, leahy@sipi.usc.edu | Dr Karim Jerbi, karim.jerbi.udem@gmail.com

    Webcast: https://usc.zoom.us/j/91439298406?pwd=bUEvSjlqN1lTZ3lUSVFMbElEV0NVUT09

    More Information: MoBI Seminar - 2023.05.30 Annalisa Pascarella Flyer.pdf

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

    WebCast Link: https://usc.zoom.us/j/91439298406?pwd=bUEvSjlqN1lTZ3lUSVFMbElEV0NVUT09

    Audiences: Everyone Is Invited

    Contact: Miki Arlen

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