BEGIN:VCALENDAR VERSION:2.0 X-WR-CALNAME:EventsCalendar PRODID:-//hacksw/handcal//NONSGML v1.0//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York LAST-MODIFIED:20240422T053451Z TZURL:https://www.tzurl.org/zoneinfo-outlook/America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:College of Engineering,Lectures and Seminars DESCRIPTION:Topic: “Deep Learning for RF and Optical Devices”  Abstrac t: RF and optical devices or systems play important roles in our daily lif e ranging from wireless communication to imaging. Emerging applications su ch as 5G/6G, internet-of-things, and autonomous driving have imposed strin gent requirements to RF and optical devices. The emerging of new RF and op tical components such as metasurfaces has led to new challenges. In this t alk, I discuss our research activities in deep learning-based techniques f or addressing these technical challenges. First, I will discuss our deep l earning modeling approach for predicting the performance of freeform optic al metasurface structures. Our neural network approach overcomes two key c hallenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch and accurate EM-wave phase predic tion. Second, to demonstrate the capability of deep learning techniques fo r complex and non-intuitive metasurface design, I will present a novel con ditional generative network that can achieve meta-atom/metasurface designs based on different performance requirements. Applications of these deep l earning networks will also be discussed. Lastly, the application of deep l earning techniques for RF components design will be explored and showcased .  Biography: Dr. Hualiang Zhang is a Professor at the Electrical and Com puter Engineering Department, University of logo Lowell. He recei ved his B.S. degree in Electrical Engineering from the University of Scien ce and Technology of China in 2003. He received his Ph.D. degree in Electr ical and Computer Engineering from the Hong Kong University of Science and Technology in 2007. From 2007 to 2009 he was a postdoctoral research asso ciate in the Department of Electrical and Computer Engineering at the Univ ersity of Arizona. His current teaching and research interests are on high frequency circuits, components, and systems, enabled by advanced computat ional techniques, new materials, and innovative manufacturing technologies . Applications of his research include wireless communications (e.g. Satco m, 5G and 6G), internet-of-things, radar sensing, wearable electronics, en ergy-efficient electronic systems, and optical imaging and sensing systems , ranging from RF/microwave/millimeter-wave to infrared and even beyond. H e has published over 300 refereed journal and conference papers, as well a s 1 book chapter and 7 patents (4 issued, and 3 pending) in related resear ch topics. He has been actively involved in organizing conferences and wor kshops on microwave, antennas, and wireless devices & systems. He is an as sociate editor of Wiley’s International Journal of Numerical Modelling – Electronic Networks, Devices and Fields. He received the 2018 ECE Depa rtment Teaching Excellence award. Dr. Zhang is a senior member of IEEE.  The Seminars is open to the public free of charge.   *For further informa tion, please contact Dr. Yifei Li via email at yifei.li@umassd.edu.\nEvent page: /events/cms/ece-seminar-speaker-dr-hualiang-z hang-professor-electrical-and-computer-engineering-department-university-o f-massachusetts-lowell.php X-ALT-DESC;FMTTYPE=text/html:

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Topic: “Deep Learning for RF and Optical Devices” 

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Abstract: RF and optical devices or syste ms play important roles in our daily life ranging from wireless communicat ion to imaging. Emerging applications such as 5G/6G\, internet-of-things\, and autonomous driving have imposed stringent requirements to RF and opti cal devices. The emerging of new RF and optical components such as metasur faces has led to new challenges. In this talk\, I discuss our research act ivities in deep learning-based techniques for addressing these technical c hallenges. First\, I will discuss our deep learning modeling approach for predicting the performance of freeform optical metasurface structures. Our neural network approach overcomes two key challenges that have limited pr evious neural-network-based design schemes: input/output vector dimensiona l mismatch and accurate EM-wave phase prediction. Second\, to demonstrate the capability of deep learning techniques for complex and non-intuitive m etasurface design\, I will present a novel conditional generative network that can achieve meta-atom/metasurface designs based on different performa nce requirements. Applications of these deep learning networks will also b e discussed. Lastly\, the application of deep learning techniques for RF c omponents design will be explored and showcased. 

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Biography: Dr. Hualiang Zhang is a Professor at the Electrical and Computer Engineering D epartment\, University of logo Lowell. He received his B.S. degre e in Electrical Engineering from the University of Science and Technology of China in 2003. He received his Ph.D. degree in Electrical and Computer Engineering from the Hong Kong University of Science and Technology in 200 7. From 2007 to 2009 he was a postdoctoral research associate in the Depar tment of Electrical and Computer Engineering at the University of Arizona. His current teaching and research interests are on high frequency circuit s\, components\, and systems\, enabled by advanced computational technique s\, new materials\, and innovative manufacturing technologies. Application s of his research include wireless communications (e.g. Satcom\, 5G and 6G )\, internet-of-things\, radar sensing\, wearable electronics\, energy-eff icient electronic systems\, and optical imaging and sensing systems\, rang ing from RF/microwave/millimeter-wave to infrared and even beyond. He has published over 300 refereed journal and conference papers\, as well as 1 b ook chapter and 7 patents (4 issued\, and 3 pending) in related research t opics. He has been actively involved in organizing conferences and worksho ps on microwave\, antennas\, and wireless devices & systems. He is an asso ciate editor of Wiley’s International Journal of Numerical Modelling – Electronic Networks\, Devices and Fields. He received the 2018 ECE Depart ment Teaching Excellence award. Dr. Zhang is a senior member of IEEE. 

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The Seminars is open to the public free of charge.  

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*For f urther information\, please contact Dr. Yifei Li via email at yifei.li@uma ssd.edu.

Event page: https://www.umass d.edu/events/cms/ece-seminar-speaker-dr-hualiang-zhang-professor-electrica l-and-computer-engineering-department-university-of-massachusetts-lowell.p hp

DTSTAMP:20260404T173919 DTSTART;TZID=America/New_York:20260410T130000 DTEND;TZID=America/New_York:20260410T140000 LOCATION:Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A SUMMARY;LANGUAGE=en-us:ECE Seminar* Speaker: Dr. Hualiang Zhang, Professor, Electrical and Computer Engineering Department, University of Massachuset ts Lowell UID:3738927fdb42c7c14a6c0a4b7c0eb62f@www.umassd.edu END:VEVENT END:VCALENDAR