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Events for March 17, 2022
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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=U1N4emFRaDBnc0pTd2VXUHMwSkVidz09Location: 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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Virtual First-Year Admission Information SessionThu, 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
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
