SUNMONTUEWEDTHUFRISAT
Events for March 17, 2022
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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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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
