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Events for the 1st week of April
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ECE Seminar: Label-free Optical Imaging of Living Biological Systems
Mon, Mar 30, 2020 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sixian You, PhD, Bioengineering, UIUC
Talk Title: Label-free Optical Imaging of Living Biological Systems
Abstract: Label-free optical imaging of living biological systems offers rich information that can be of immense value in biomedical tasks such as diagnosing cancer or assessing the tumor microenvironment. Despite the exceptional theoretical potential, current label-free nonlinear microscopy platforms are challenging for real-world clinical and biological applications. The major obstacles include the lack of flexible laser sources, limited contrast, and lack of molecular specificity for diseases.
In this talk, I will present a new optical imaging platform and methodology that will address these challenges. By generating and tailoring coherent supercontinuum from photonic crystal fibers, single-source single-shot metabolic and structural imaging can be achieved, enabling Simultaneous Label-free Auto-fluorescence Multi-harmonic (SLAM) contrast in living cells and tissues. These capabilities further motivate development of analytical tools for tissue assessment and diagnosis, showing broad potential of this label-free imaging technology in discovering new metabolic biomarkers and enabling real-time point-of-procedure applications.
Biography: Sixian You received her Ph.D. in 2019 from the University of Illinois, Urbana-Champaign (UIUC), under the guidance of Prof. Stephen A. Boppart. Her primary research interest is in developing innovative optical imaging solutions for biomedicine. She is particularly interested in developing next-generation label-free multiphoton imaging technologies to study the tumor microenvironment. Sixian was awarded the Microscopy Innovation Award by the Microscopy Society of America and McGinnis Medical Innovation Graduate Fellowship by UIUC.
Host: Justin Haldar, jhaldar@usc.edu
Webcast: https://usc.zoom.us/j/402440976WebCast Link: https://usc.zoom.us/j/402440976
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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ECE Seminar: Joint Wireless Communication and Sensing in mmWave and Terahertz Spectrum
Wed, Apr 01, 2020 @ 01:15 PM - 02:15 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yasaman Ghasempour, Ph.D. Candidate, ECE, Rice University
Talk Title: Joint Wireless Communication and Sensing in mmWave and Terahertz Spectrum
Abstract: Millimeter-wave and terahertz bands are emerging as the most promising spectrum to meet the data-rate and latency demands of future wireless applications, including virtual reality and autonomous cars. Moreover, large spectral availability and mm-scale wavelength provide the possibility for ubiquitous and high-resolution sensing. My research builds a foundation for joint communication and sensing in such high-frequency regimes. This perspective yields a paradigm shift in the design and development of future wireless systems. In this talk, I will present the world's first single-shot and single-antenna motion sensing system in THz bands. We demonstrate a novel node architecture exploiting a single leaky wave antenna, which is primarily used for beam steering in THz networks. I will show how we leverage this device's spatial-spectral characteristics in new ways to enable motion sensing functionalities with a single THz pulse transmission. I will then discuss the opportunities offered by this platform to enhance next-generation communication in unprecedented ways. In particular, we tackle the mobility, blockage, and scalability challenges of highly directional THz networks by efficiently adapting steering direction for mobile users. Finally, I will share several research directions that I would like to pursue in the future.
Biography: Yasaman Ghasempour is currently a Ph.D. Candidate in Electrical and Computer Engineering at Rice University. She received her Master's degree in Electrical and Computer Engineering from Rice University and her Bachelor's degree in Electrical Engineering from Sharif University of Technology in Iran. Her research interests include wireless communication and sensing, with a focus on emerging millimeter-wave and terahertz spectrum. She has published in top-tier IEEE and ACM conferences and journals and has been named an EECS rising star in 2019. She is also the recipient of Texas Instruments Distinguished Fellowship among multiple IEEE/ACM societies awards.
Host: Urbashi Mitra (ubli@usc.edu) and Konstantinos Psounis (kpsounis@usc.edu)
Webcast: https://usc.zoom.us/j/873150824WebCast Link: https://usc.zoom.us/j/873150824
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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ECE Seminar: Safe and Data-efficient Learning for Robotics
Thu, Apr 02, 2020 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Somil Bansal, PhD Candidate, Department of EECS, University of California, Berkeley
Talk Title: Safe and Data-efficient Learning for Robotics
Abstract: Machine learning has led to tremendous progress in domains such as computer vision, speech recognition, and natural language processing. Fueled by these advances, machine learning approaches are now being explored to develop intelligent physical systems that can operate reliably in unpredictable environments. These include not only robotic systems such as autonomous cars and drones, but also large-scale cyberphysical systems such as transportation and energy systems. However, learning techniques widely used today are extremely data inefficient, making it challenging to apply them to real-world physical systems. Moreover, they lack the necessary mathematical framework to provide guarantees on correctness, causing safety concerns as data-driven physical systems are integrated in our society. We combine tools from robust optimal control theory with machine learning and computer vision to develop data-efficient and provably safe learning-based control algorithms for physical robotic systems. In particular, we design modular architectures that combine system dynamics models with modern learning-based perception approaches to solve challenging perception and control problems in a priori unknown environments in a data-efficient fashion. Moreover, due to their modularity, these architectures are amenable to simulation-to-real transfer, and can be used for different robotic systems without any retraining. Crucially, we use models not only for faster learning, but also to monitor and recognize the learning system's failures, and to provide online corrective safe actions when necessary. This allows us to provide safety assurances for learning-enabled systems in unknown and human-centric environments, which has remained a challenge to date.
Biography: Somil Bansal completed his B.Tech. in Electrical Engineering from Indian Institute of Technology, Kanpur in 2012, and an M.S. in Electrical Engineering and Computer Sciences from UC Berkeley in 2014. Since 2015, he is pursuing a PhD degree in Electrical Engineering and Computer Sciences at UC Berkeley, under the supervision of Prof. Claire Tomlin in the Hybrid Systems Laboratory. His research interests are in exploring how machine learning tools can be combined with the control theoretic frameworks to develop data-efficient and safe learning-based control algorithms for physical robotic systems, especially when the system is operating in an uncertain environment. During his PhD, he has also worked closely with companies like Skydio, Google, Boeing, as well as NASA Ames. Somil has received several awards, most notably the outstanding graduate student instructor award at UC Berkeley and the academic excellence award at IIT Kanpur.
Host: Ashutosh Nayyar, ashutosn@usc.edu
Webcast: https://usc.zoom.us/j/811254572WebCast Link: https://usc.zoom.us/j/811254572
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher