Illinois ECE Seminars
Monday, June 27, 2022
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Final Examination: Amish Goel, June 27, 2022 @ 9:30 AM, ECEB 2015, Exam Topic: Locally optimal decision rules and randomization techniques for detecting universal adversarial perturbation based attacks against image classifiers
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Final Examination: Bowen Cheng, June 27, 2022 @ 5:00 PM, Online, Exam Topic: From Pixels to Regions: Toward Universal Image Segmentation
Tuesday, June 28, 2022
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Final Examination: Yuan Ting Hu, June 28, 2022 @ 10:00 AM, Online, Exam Topic: Towards Holistic Scene Understanding from Monocular Video
Wednesday, June 29, 2022
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Final Examination: Moitreya Chatterjee, June 29, 2022 @ 8:30 AM, https://illinois.zoom.us/j/88237874318?pwd=TEg2TlNVaWxWaVU0ZytMQVFYVU1Zdz09, Exam Topic: Efficient Audio-Visual Representations for Synthesis and Reasoning Tasks
Friday, July 1, 2022
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Final Examination: Siddhartha Nigam, July 1, 2022 @ 1:00 PM, Zoom, Exam Topic: Verification and Validation of Microgrid Control Architectures on a Controller Hardware in the Loop Testbed
Wednesday, July 6, 2022
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Final Examination: Teck Yian Lim, July 6, 2022 @ 8:30 AM, Online, Exam Topic: DATA-DRIVEN TECHNIQUES IN SIGNAL RESTORATION AND DETECTION PROBLEMS
Friday, July 15, 2022
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Final Examination: Jose Sanchez Vicarte, July 15, 2022 @ 2:00 PM, TDA, Exam Topic: RIDING THE WAVE: THE UPS AND DOWNS OF MOORE'S LAW
Friday, July 29, 2022
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Final Examination: Patrick Su, July 29, 2022 @ 12:00 PM, HMNTL 2003, Exam Topic: Strain-Controlled Impurity-Induced Disordering Apertures for Enhanced Optical Mode Control in Vertical-Cavity Surface-Emitting Lasers
Sunday, July 31, 2022
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Prelim Examination: Surya Bakshi, July 31, 2022 @ 12:00 PM, CSL 141, Exam Topic: Formalizing Smart-Contract Based Blockchain Protocols
Friday, August 12, 2022
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Final Examination: Konik Kothari, August 12, 2022 @ 10:00 AM, CSL/Zoom, Exam Topic: Learning for inverse problems
Tuesday, October 11, 2022
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Prelim Examination: Theodoros Mamalis, October 11, 2022 @ 11:00 AM, Online, Exam Topic: Improving algorithmic performance using stochastic learning rates: in-expectation and almost-surely stochastic approximation, and online learning, results and applications