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Events for the 3rd week of March

  • ECE Seminar: The Role of Machine Learning in Electronic Design Automation

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

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Vidya A. Chhabria, Ph.D. Candidate, Electrical and Computer Engineering Department, University of Minnesota

    Talk Title: The Role of Machine Learning in Electronic Design Automation

    Abstract: For several decades, advances in hardware, accelerated by Moore\'s law and enabled by electronic design automation (EDA) tools, have sustainably met the demands for high computation at low energy and cost. However, emerging applications demand computing power far beyond today\'s system capabilities. Rapid advances in high-performance computing address the problem by using accelerators for specialized tasks such as machine learning (ML), increasing design diversity and system complexity. With Moore\'s law running out of steam, EDA tools now play a crucial role in meeting these computational demands. EDA tools are challenged to build chips that not only compensate for slow down in scaling, but also provide high performance for both ML and non-ML applications, which use a variety of new architectural techniques and operate under stringent performance constraints. Conventional EDA tools involve computationally expensive analysis and optimizations and are suboptimal as they often tradeoff speed for accuracy. ML promises to address these challenges as it has found tremendous success in solving these problems in classification, detection, and design space exploration problems in several different applications.

    In this talk, I will show how leveraging ML techniques can revolutionize EDA tools by addressing the existing challenges. In particular, the talk will focus on tools that aid designers in (i) delivering power inside the chip without significant losses to meet power demands and (ii) sending the heat outside the chip to avoid high temperatures. The first section of the talk will show how a fast ML inference brings down several hours of runtime to a few milliseconds on industry-scale designs for these tasks. The second section will demonstrate how ML enables high-quality solutions through rapid optimizations. A key challenge with the proposed ML-based methods is the limited availability of open-source data and benchmarks for training and evaluation. The third section will show how ML can generate synthetic training sets and benchmarks for evaluating novel EDA solutions to these tasks. I will conclude by presenting avenues for future research in ML and EDA.

    Biography: Vidya A. Chhabria is a Ph.D. candidate in the Electrical and Computer Engineering department at the University of Minnesota. She received her B.E. in Electronics and Communication from M. S. Ramaiah Institute of Technology, India, in 2016, and her M.S. in Electrical Engineering from the University of Minnesota in 2018. Her research interests are in the areas of electronic design automation, IC design, and machine learning. She has interned at Qualcomm Technologies, Inc. in the summer of 2017 and NVIDIA Corporation during the summers of 2020 and 2021. She received the ICCAD Best Paper Award in 2021, the University of Minnesota Doctoral Dissertation Fellowship in 2021, Louise Dosdall Fellowship in 2020, and Cadence Women in Technology Scholarship in 2020.


    Host: Dr. Pierluigi Nuzzo, nuzzo@usc.edu

    Webcast: https://usc.zoom.us/j/91321182725?pwd=ZDl0Qzc0b0F3cVRlZE1ORE11VHdCQT09

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

    WebCast Link: https://usc.zoom.us/j/91321182725?pwd=ZDl0Qzc0b0F3cVRlZE1ORE11VHdCQT09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • ***NO ISE 651, Epstein Seminar - Spring Recess***

    Tue, Mar 15, 2022

    Daniel J. Epstein Department of Industrial and Systems Engineering

    Conferences, Lectures, & Seminars


    Audiences: Everyone Is Invited

    Contact: Grace Owh

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  • Repeating EventVirtual First-Year Admission Information Session

    Tue, Mar 15, 2022 @ 11:00 AM - 12:00 PM

    Viterbi School of Engineering Undergraduate Admission

    Workshops & Infosessions


    Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for high school students and their family members to learn more about the USC Viterbi undergraduate experience. Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life. Guests will be able to ask questions and engage in further discussion toward the end of the session.

    Register Here!



    Audiences: Everyone Is Invited

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    Contact: Viterbi Admission

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  • DEN@Viterbi - Online Graduate Engineering Virtual Information Session

    Tue, Mar 15, 2022 @ 12:00 PM - 01:00 PM

    DEN@Viterbi, Viterbi School of Engineering Graduate Admission

    Workshops & Infosessions


    Join USC Viterbi School of Engineering for a virtual information session via WebEx, providing an introduction to DEN@Viterbi, our top ranked online delivery system. Discover the 40+ graduate engineering and computer science programs available entirely online.

    Attendees will have the opportunity to connect directly with USC Viterbi representatives during the session to discuss the admission process, program details and the benefits of online delivery.

    Register Today!

    WebCast Link: https://uscviterbi.webex.com/uscviterbi/onstage/g.php?MTID=e2f2e0daa08d3f9af40baf6cb922d0ac9

    Audiences: Everyone Is Invited

    Contact: Corporate & Professional Programs

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  • Mork Family Department Seminar - Juan Restrepo-Florez

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

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Juan Restrepo-Florez, University of Wisconsin-Madison

    Talk Title: A road toward sustainability -from materials to processes-

    Host: Professor A.Hodge

    Location: Social Sciences Building (SOS) - B46

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • ECE Seminar: Data efficient high-dimensional machine learning

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

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Kamyar Azizzadenesheli, Assistant Professor, Department of Computer Science, Purdue University

    Talk Title: Data efficient high-dimensional machine learning

    Abstract: Traditional deep neural networks are maps between finite dimension spaces, and hence, are not suitable for modeling phenomena such as those arising from the solution of partial differential equations (PDE). In the first part of the talk, I introduce a new deep learning paradigm, called neural operators, that learns operators which are maps between infinite dimension spaces. I show that neural operators are universal approximators of operators and demonstrate a series of empirical successes of neural operators in natural sciences.

    In the second part, I talk about the intersection of control theory and reinforcement learning and establish data-efficient learning and decision-making methods for generic dynamical systems. I conclude the talk by presenting empirical successes of these principled methods.

    Biography: Kamyar Azizzadenesheli is an assistant professor at Purdue University, department of computer science, since Fall 2020. Prior to his faculty position, he was at the California Institute of Technology (Caltech) as a Postdoctoral Scholar at the Department of Computing + Mathematical Sciences. Before his postdoctoral position, he was appointed as a special student researcher at Caltech, working with ML and Control researchers at the CMS department and the Center for Autonomous Systems and Technologies. He is also a former visiting student researcher at Caltech. Kamyar Azizzadenesheli is a former visiting student researcher at Stanford University, and researcher at Simons Institute, UC. Berkeley. In addition, he is a former guest researcher at INRIA France (SequeL team), as well as a visitor at Microsoft Research Lab, New England, and New York. He received his Ph.D. at the University of California, Irvine.

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

    Webcast: https://usc.zoom.us/j/93153496285?pwd=SmE3clJMSm9OVmVoNWdhMW1SVlk4QT09

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

    WebCast Link: https://usc.zoom.us/j/93153496285?pwd=SmE3clJMSm9OVmVoNWdhMW1SVlk4QT09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • Technology for Engaged Learning and Collaboration- Microsoft Teams

    Wed, Mar 16, 2022 @ 11:00 AM - 03:00 PM

    USC Viterbi School of Engineering

    Workshops & Infosessions


    This workshop is designed to provide faculty and staff with the tools and the training to enable productive and frictionless collaboration across teaching and workplace environments. This workshop will be accessible to all levels of proficiency and is designed to make the USC provisioned Microsoft Teams software actionable and applicable to a range of work and education needs. Participants will receive direct instruction and support from Microsoft expert, become familiar with the MS Teams environment, learn the software capabilities and features, and experience usage demos.

    This training will consist of three, 1-hour trainings sessions that take place at 11am (MS Teams Basic), 1pm (MS Teams in Education), and 2pm (MS Teams for Encouraging Student Engagement in Collaborative Learning). Participants can choose sessions that they wish to attend. See invite for more session details and register here

    Workshop sessions will be taught by Andrea Tseng-Rioux, Microsoft Education Customer Success Manager, and facilitated by Patrick Barrientes, Microsoft Senior Education Specialist.

    This workshop is organized and hosted by the Viterbi iPodia Program and the Distance Education Network (DEN@Viterbi).

    More Information: Technology for Engaged Learning and Collaboration Workshop invitation.pdf

    Location: Ronald Tutor Hall of Engineering (RTH) - 217 or virtually (MS Teams)

    Audiences: Everyone Is Invited

    Contact: Elisabeth Weiss

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  • DEN@Viterbi: How to Apply Virtual Info Session

    Wed, Mar 16, 2022 @ 05:00 PM - 06:00 PM

    DEN@Viterbi, Viterbi School of Engineering Graduate Admission

    Workshops & Infosessions


    Join USC Viterbi representatives for a step-by-step guide and tips for how to apply for formal admission into a Master\'s degree or Graduate Certificate program. The session is intended for individuals who wish to pursue a graduate degree program completely online via USC Viterbi\'s flexible online DEN@Viterbi delivery method.

    Attendees will have the opportunity to connect directly with USC Viterbi representatives and ask questions about the admission process throughout the session.

    Register Now!

    WebCast Link: https://uscviterbi.webex.com/uscviterbi/onstage/g.php?MTID=ea0410f8021a0d20ed13626cbc8bca81d

    Audiences: Everyone Is Invited

    Contact: Corporate & Professional Programs

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  • A Study Break w/ Tesla: Weekly Series Feb 9 - April 13 (Virtual)

    Wed, Mar 16, 2022 @ 06:00 PM - 06:45 PM

    Viterbi School of Engineering Career Connections

    Workshops & Infosessions


    A Study Break w/ Tesla- is a series of professional workshops presented by the Hardware + Cell Engineering Internship Recruiting Team that will be offered on Wednesday evenings from February through April, 6:00 pm -6:45 pm.
    Each event will offer a 25-minute presentation on a specific topic, followed by a 20-minute opportunity for participants to ask questions and network with the Tesla team.

    Event: An Intro to Cell Engineering | March 16 - RSVP HERE

    Description: This session will provide an introduction to co-op and internship opportunities within the Cell Engineering team at Tesla. Prior to attending, visit the Tesla career page for a base understanding of the intern opportunities available.
    External employer-hosted events and activities are not affiliated with the USC Career Center. They are posted on Viterbi Career Connections because they may be of interest to members of the Viterbi community. Inclusion of any activity does not indicate USC sponsorship or endorsement of that activity or event. It is the participant\'s responsibility to apply due diligence, exercise caution when participating, and report concerns to vcareers@usc.edu

    Location: RSVP in Viterbi Career Gateway

    Audiences: Everyone Is Invited

    Contact: RTH 218 Viterbi Career Connections

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  • Mork Family Department Seminar - Vida Jamali

    Thu, Mar 17, 2022 @ 10:00 AM - 11:20 AM

    Mork Family Department of Chemical Engineering and Materials Science

    Conferences, Lectures, & Seminars


    Speaker: Vida Jamali, University of California-Berkeley

    Talk Title: Imaging, Learning, and Engineering of Soft Matter Systems at the Nanoscale

    Host: Professor A.Hodge

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

    Audiences: Everyone Is Invited

    Contact: Heather Alexander

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  • ECE Seminar: Machine Learning for Precision Health: A Holistic Approach

    Thu, Mar 17, 2022 @ 10:00 AM - 11:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dr. Ahmed Alaa, Postdoctoral Associate, Broad Institute of MIT & Harvard, MIT CSAIL

    Talk Title: Machine Learning for Precision Health: A Holistic Approach

    Abstract: Machine learning (ML) methods, combined with large-scale electronic health databases, could enable a personalized approach to healthcare by improving patient-specific diagnosis, prognostic predictions, and treatment decisions. If successful, this approach would be transformative for clinical research and practice. In this talk, I will describe a holistic approach to ML for precision health that comprises a three-step procedure: (1) data characterization and understanding, (2) model development and (3) model deployment. Next, I will demonstrate one instantiation of this approach in the context of developing ML models for predicting patient-level response to therapies using observational data. I will focus on a multi-task learning model that uses Gaussian processes to estimate the causal effects of a treatment on individual patients and discuss its application in various disease areas. Finally, I will discuss exciting avenues for future work, including ML methods for learning from unannotated clinical data, generating synthetic data and integrating clinical knowledge into data-driven modeling.


    Biography: Dr. Ahmed Alaa is a postdoctoral associate at Massachusetts Institute of Technology (MIT) and the Broad Institute of MIT and Harvard University. Previously, he was a joint postdoctoral scholar at Cambridge University, Cambridge Center for AI in Medicine and the University of California, Los Angeles (UCLA). He obtained his Ph.D. in Electrical and Computer Engineering from UCLA, where he was also a recognized (visiting) Ph.D. student at Oxford University. His research focuses on developing machine learning (ML) methods that can leverage healthcare data to enable a patient-centric approach to medicine, whereby ML models can inform disease diagnosis, prognosis and treatment decisions based on the characteristics of individual patients. He is the recipient of the (school-wide) 2021 Edward K. Rice Outstanding Doctoral Student Award at UCLA.

    Host: Dr. Ashutosh Nayyar, ashutosn@usc.edu

    Webcast: https://usc.zoom.us/j/94383946134?pwd=U1N4emFRaDBnc0pTd2VXUHMwSkVidz09

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

    WebCast Link: https://usc.zoom.us/j/94383946134?pwd=U1N4emFRaDBnc0pTd2VXUHMwSkVidz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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  • CS Colloquium: Amir Houmansadr (UMass Amherst) - Communication Secrecy in the Age of AI

    Thu, Mar 17, 2022 @ 11:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Amir Houmansadr, UMass Amherst

    Talk Title: Communication Secrecy in the Age of AI

    Series: CS Colloquium

    Abstract: Internet users face constant threats to the secrecy of their communications: repressive regimes deprive them of open access to the Internet, corporations and surveillance organizations monitor their online behavior, advertising companies and social networks collect and share their private information, and cybercriminals hurt them financially by stealing their private information. In this talk, I will present the key research challenges facing communication secrecy in a world overtaken by the AI. In particular, I will introduce new ML-specific mechanisms to defeat AI-enabled surveillance. I will also discuss crucial AI trustworthiness research problems that are essential to the secrecy of Internet communications in the age of AI.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Amir Houmansadr is an associate professor of computer science at UMass Amherst. He received his Ph.D. from the University of Illinois at Urbana-Champaign in 2012, and spent two years at the University of Texas at Austin as a postdoctoral scholar. Amir is broadly interested in the security and privacy of networked systems. To that end, he designs and deploys privacy-enhancing technologies, analyzes network protocols and services (e.g., messaging apps and machine learning APIs) for privacy leakage, and performs theoretical analysis to derive bounds on privacy (e.g., using game theory and information theory). Amir has received several awards and recognitions including the 2013 IEEE S&P Best Practical Paper Award, a 2015 Google Faculty Research Award, an NSF CAREER Award in 2016, a CSAW 2019 Applied Research Competition Finalist, an IMC 2020 Best Paper Award Runner-up, and a Facebook 2021 Privacy Enhancing Technologies Award Finalist. He is an Associate Editor of the IEEE TDSC and frequently serves on the program committees of major security conferences.

    Host: Barath Raghavan

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • CS Colloquium: Matthew Mirman (ETH Zürich) - Trustworthy Deep Learning: methods, systems and theory

    Thu, Mar 17, 2022 @ 11:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Matthew Mirman , ETH Zürich

    Talk Title: Trustworthy Deep Learning: methods, systems and theory

    Series: CS Colloquium

    Abstract: Deep learning models are quickly becoming an integral part of a plethora of high stakes applications, including autonomous driving and health care. As the discovery of vulnerabilities and flaws in these models has become frequent, so has the interest in ensuring their safety, robustness and reliability. My research addresses this need by introducing new core methods and systems that can establish desirable mathematical guarantees of deep learning models.

    In the first part of my talk I will describe how we leverage abstract interpretation to scale verification to orders of magnitude larger deep neural networks than prior work, at the same time demonstrating the correctness of significantly more properties. I will then show how these techniques can be extended to ensure, for the first time, formal guarantees of probabilistic semantic specifications using generative models.

    In the second part, I will show how to fuse abstract interpretation with the training phase so as to improve a model\'s amenability to certification, allowing us to guarantee orders of magnitude more properties than possible with prior work. Finally, I will discuss exciting theoretical advances which address fundamental questions on the very existence of certified deep learning.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Matthew Mirman is a final-year PhD student at ETH Zürich, supervised by Martin Vechev. His main research interests sit at the intersection of programming languages, machine learning, and theory with applications to creating safe and reliable artificial intelligence systems. Prior to ETH, he completed his B.Sc. and M.Sc. at Carnegie-Mellon University supervised by Frank Pfenning.

    Host: Mukund Raghothaman

    Location: 115

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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  • Repeating EventVirtual First-Year Admission Information Session

    Thu, Mar 17, 2022 @ 11:00 AM - 12:00 PM

    Viterbi School of Engineering Undergraduate Admission

    Workshops & Infosessions


    Our virtual information session is a live presentation from a USC Viterbi admission counselor designed for high school students and their family members to learn more about the USC Viterbi undergraduate experience. Our session will cover an overview of our undergraduate engineering programs, the application process, and more on student life. Guests will be able to ask questions and engage in further discussion toward the end of the session.

    Register Here!



    Audiences: Everyone Is Invited

    View All Dates

    Contact: Viterbi Admission

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  • ECE-EP Seminar - Mehdi Kiani, Thursday, March 17 at 2pm in EEB 248

    Thu, Mar 17, 2022 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Mehdi Kiani, Pennsylvania State University

    Talk Title: Wireless Hybrid Electrical-Acoustic Systems for Body-Machine Interface

    Abstract: We have already witnessed significant efforts towards the research and development of neurotechnologies to radically enhance our understanding of the extremely complex central and peripheral nervous systems (CNS and PNS) by modulating and imaging their activities. These technologies can eventually be utilized in establishing body-machine interfaces (BMIs) with the CNS and PNS to offer effective, minimally invasive, and long-term solutions for neurological disorders and chronic disabilities such as spinal cord and brain injuries, stroke, Parkinson\'s disease, epilepsy, rheumatoid arthritis, and diabetes, to name a few. Despite all the developments over the past decade, closed-loop BMIs with minimally invasive high-spatiotemporal-resolution recording and stimulation capabilities from the large-scale distributed CNS/PNS circuits is still one of the grand challenges of the neuroscience research in the 21st century. In this talk, I will present our recent efforts (and future work) towards the development of advanced minimally invasive BMIs for modulating and sensing neural and electrophysiological activities with high spatiotemporal resolution at large scale. These BMIs are enabled by innovative integrated circuits, ultrasound, and wireless power/data (with different modalities such as ultrasound and magnetoelectric) technologies. I will particularly present two projects that leverage ultrasound beam focusing and steering with electronic beamforming to enable wireless implantable technologies for high-resolution, large-scale brain neuromodulation and gastric electrical-wave mapping.

    Biography: Dr. Kiani received his Ph.D. degree in Electrical and Computer Engineering from the Georgia Institute of Technology in 2014. He joined the faculty of the School of Electrical Engineering and Computer Science at the Pennsylvania State University in August 2014 where he is currently an Associate Professor. His research interests are in the multidisciplinary areas of analog, mixed-signal, and power-management integrated circuits; ultrasound; and wireless power/data transfer and energy harvesting for wireless implantable medical devices and neural interfaces. He was a recipient of the 2020 NSF CAREER Award. He is currently an Associate Editor of the IEEE Transactions on Biomedical Circuits and Systems and IEEE Transactions on Biomedical Engineering. He also serves as a Technical Program Committee member of the IEEE International Solid-State Circuits Conference (ISSCC) in the IMMD subcommittee.

    Host: ECE-Electrophysics

    More Information: Mehdi Kiani Flyer.pdf

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

    Audiences: Everyone Is Invited

    Contact: Marilyn Poplawski

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  • ECE-EP Seminar - Najme Ebrahimi, Friday, March 18th at 10am in EEB 248

    Fri, Mar 18, 2022 @ 10:00 AM - 11:00 AM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Najme Ebrahimi, University of Florida

    Talk Title: Next Generation Intelligent and Secured Wireless World: From IoT and Sensors to Wideband and Multi-band Scalable Circuit and System

    Abstract: The future intelligent and secured wireless world needs connectivity at any time anywhere and under extreme conditions with over one trillion sensors and Internet-of-Things (IoT) devices connected to the network. To this end, the autonomous, and yet connected, wireless world is envisioned to provide sensing and high-data-rate communications, accurate localization and ranging, and resiliency. The major challenges to attain these goals are latency and energy efficiency requirements, that are largely affected by interference, multi-path, and channel fading. To tackle these challenges, wideband high frequency scalable arrays are desired to provide high data-rate communications and directional beams for interference cancellation. Furthermore, wideband/multiband circuits and systems are needed for accurate localization in the presence of severe multipath and fading in ultra-dense environments in IoT networks.

    In this talk, firstly, I will present novel techniques to overcome the challenges for future wideband/multiband scalable transceiver arrays, including power-efficient local oscillator distribution and phase shifting, image selection architecture, and novel compact antenna-IC integration. I will then discuss our ongoing work towards the wideband/multiband signal generation and modulation for 6G and beyond as well as heterogonous integration of different technologies and modules for extending the Moore\'s law. Secondly, I will present multi-band circuit generation for IoT and sensor nodes to be employed in dense wireless networks. More specifically, I will present the first bidirectional circuitry for IoT transponder that reciprocally generates harmonics and subharmonics, covering two communication frequency bands interchangeably, which makes it a premier tool for localization and sensing protocols. I will also discuss future directions on advanced multi-band reconfigurable architecture for wireless sensors and IoTs compatible with network and physical layer protocols for security, communications, and localization.

    Biography: Najme Ebrahimi is an Assistant Professor of Electrical and Computer Engineering at the University of Florida. Her research focuses on Mm-Wave/THz Scalable Array for high data rate communications and sensing as well as the security and connectivity of the next generation of distributed Internet-of-Things (IoT) networks. She was a post-doctoral research fellow at the University of Michigan- Ann Arbor from 2017 to 2020 under the departmental fellowship and earned her Ph.D. from the University of California, San Diego in June 2017. She was selected as a Rising Star by MIT EECS Rising Star program in 2019 and by ISSCC Rising Star program of the IEEE Solid-State Circuits Society in 2020. She is a member of the Microwave and Mm-Wave Integrated Circuits committee (MTT-14) and serves in the IMS2022 Technical Paper Review Committee (TPRC). She is the recipient of the 2021 DARPA Young Faculty Award (YFA).

    Host: ECE-Electrophysics

    More Information: Najme Ebrahimi Flyer.pdf

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

    Audiences: Everyone Is Invited

    Contact: Marilyn Poplawski

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  • CS Colloquium: Jieyu Zhao (UMD) - Building Accountable NLP Models: on Social Bias Detection and Mitigation

    Fri, Mar 18, 2022 @ 02:00 PM - 03:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Jieyu Zhao , UMD

    Talk Title: Building Accountable NLP Models: on Social Bias Detection and Mitigation

    Series: CS Colloquium

    Abstract: Natural Language Processing (NLP) plays an important role in many applications, including resume filtering, text analysis, and information retrieval. Despite the remarkable accuracy enabled by the advances in machine learning used in many applications, the technique may discover and generalize the societal biases implicit in the data. For example, an automatic resume filtering system may unconsciously select candidates based on their gender and race due to implicit associations between applicant names and job titles, causing the societal disparity discovered by researchers. Various laws and policies have been designed to ensure social equality and diversity. However, there is no such mechanism for a machine learning model for sensitive applications. My research analyzes the potential stereotypes in various machine learning models and develops computational approaches to enhance fairness in a wide range of NLP applications. The broader impact of my research aligns with one the concerns of machine learning community: how can we do AI for social good.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Jieyu Zhao is a postdoctoral research at UMD, working together with Prof. Hal Daumé III. She obtained her PhD from the department of Computer Science at UCLA where she was advised by Prof. Kai-Wei Chang. Her research interest lies in fairness of ML/NLP models. Her paper got the EMNLP Best Long Paper Award (2017). She was one of the recipients of 2020 Microsoft PhD Fellowship and has been selected to participate in 2021 Rising Stars in EECS workshop. Her research has been covered by news media such as Wires, The Daily Mail and South China Morning Post. She was invited by UN-WOMEN Beijing on a panel discussion about gender equality and social responsibility. More detail can be found at https://jyzhao.net/.

    Host: Xiang Ren

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

    Audiences: By invitation only.

    Contact: Assistant to CS chair

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