BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Central BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20251016T165952Z UID:ACABDEAE-0DFE-4329-92BD-478E017941A1 DTSTART;TZID=US/Central:20251001T084500 DTEND;TZID=US/Central:20251001T100000 DESCRIPTION:2025 Fall IEEE OKC Webinar Series\n\nIEEE OKC Computer and Sect ion invites you to join the event below\, co-organized by Oklahoma Interna tional Publishing\, as part of its 2025 Fall OkIP Conferences on Wednesday \, October 01\, 2025.\n\nThis virtual talk\, sponsored by the prestigious IEEE Computer Society Distinguished Visitor Program (DVP)\, is free of cha rge to all attendees. Please register ahead of time to receive the proper instructions for remote participation:\n\nSpeaker:\n\nDr. Houbing Song\, P hD\, FIEEE\nAssociate Professor\nDirector of the NSF Center for Aviation B ig Data Analytics\nDirector of Security and Optimization for Networked Glo be Laboratory\nUniversity of Maryland\, Baltimore County (UMBC)\, Baltimor e\, MD.\n\nAbstract:\n\nMost research on machine learning has focused on l earning from massive amounts of data resulting in large advancements in ma chine learning capabilities and applications. However\, many domains lack access to the large\, high-quality\, supervised data that is required and therefore are unable to fully take advantage of these data-intense learnin g techniques. This necessitates new data-efficient learning techniques tha t can learn in complex domains without the need for large quantities of su pervised data. In this lecture\, I will provide a comprehensive survey of existing literature in the area of data-efficient machine learning\, ident ify the challenges\, and evaluate the trends. I will also introduce our re search findings in this area.\n\nShort Bio:\n\nHoubing Herbert Song (M’1 2–SM’14-F’23) received the Ph.D. degree in electrical engineering fr om the University of Virginia\, Charlottesville\, VA\, in August 2012.\n\n He is currently an Associate Professor\, the Director of the NSF Center fo r Aviation Big Data Analytics (Planning)\, the Associate Director for Lead ership of the DOT Transportation Cybersecurity Center for Advanced Researc h and Education (Tier 1 Center)\, and the Director of the Security and Opt imization for Networked Globe Laboratory (SONG Lab\, www.SONGLab.us)\, Uni versity of Maryland\, Baltimore County (UMBC)\, Baltimore\, MD. Prior to j oining UMBC\, he was a Tenured Associate Professor of Electrical Engineeri ng and Computer Science at Embry-Riddle Aeronautical University\, Daytona Beach\, FL. He serves as an Associate Editor for IEEE Transactions on Arti ficial Intelligence (TAI) (2023-present)\, IEEE Internet of Things Journal (2020-present)\, IEEE Transactions on Intelligent Transportation Systems (2021-present)\, and IEEE Journal on Miniaturization for Air and Space Sys tems (J-MASS) (2020-present). He was an Associate Technical Editor for IEE E Communications Magazine (2017-2020). He is the editor of ten books\, the author of more than 100 articles and the inventor of 2 patents. His resea rch interests include cyber-physical systems/internet of things\, cybersec urity and privacy\, and AI/machine learning/big data analytics. His resear ch has been sponsored by federal agencies (including National Science Foun dation\, National Aeronautics and Space Administration\, US Department of Transportation\, and Federal Aviation Administration\, among others) and i ndustry. His research has been featured by popular news media outlets\, in cluding IEEE GlobalSpec’s Engineering360\, Association for Uncrewed Vehi cle Systems International (AUVSI)\, Security Magazine\, CXOTech Magazine\, Fox News\, U.S. News & World Report\, The Washington Times\, and New Atla s.\n\nDr. Song is an IEEE Fellow (for contributions to big data analytics and integration of AI with Internet of Things)\, an Asia-Pacific Artificia l Intelligence Association (AAIA) Fellow\, and an ACM Distinguished Member (for outstanding scientific contributions to computing). He is an ACM Dis tinguished Speaker (2020-present)\, an IEEE Vehicular Technology Society ( VTS) Distinguished Lecturer (2023-present) and an IEEE Systems Council Dis tinguished Lecturer (2023-present). Dr. Song has been a Highly Cited Resea rcher identified by Clarivate™ (2021\, 2022). Dr. Song received Research .com Rising Star of Science Award in 2022\, 2021 Harry Rowe Mimno Award be stowed by IEEE Aerospace and Electronic Systems Society\, and 10+ Best Pap er Awards from major international conferences\, including IEEE CPSCom-201 9\, IEEE ICII 2019\, IEEE/AIAA ICNS 2019\, IEEE CBDCom 2020\, WASA 2020\, AIAA/ IEEE DASC 2021\, IEEE GLOBECOM 2021 and IEEE INFOCOM 2022.\n\nCo-spo nsored by: Pierre Tiako\n\nAgenda: \n08:55am - 09:00am Virtual Meeting Spe aker Introduction\n\n09:00am - 09:45am Virtual Meeting Keynote\n\n09:45pm - 10:00am Virtual Meeting Q &A\n\nVirtual: https://events.vtools.ieee.org/ m/495316 LOCATION:Virtual: https://events.vtools.ieee.org/m/495316 ORGANIZER:Pierretiako@yahoo.com SEQUENCE:78 SUMMARY:Data-Efficient Machine Learning URL;VALUE=URI:https://events.vtools.ieee.org/m/495316 X-ALT-DESC:Description: <br /><p>2025 Fall IEEE OKC Webinar Series</p>\n<p> IEEE OKC Computer and Section invites you to join the event below\, co-org anized by Oklahoma International Publishing\, as part of its 2025 Fall OkI P Conferences on Wednesday\, October 01\, 2025.</p>\n<p><br>This virtual t alk\, sponsored by the prestigious IEEE Computer Society Distinguished Vis itor Program (DVP)\,&nbsp\;is free of charge to all attendees. Please regi ster ahead of time to receive the proper instructions for remote participa tion:</p>\n<header>\n<div class="title">\n<div class="text">\n<div class=" title-with-actions">\n<h2>Speaker:</h2>\n</div>\n</div>\n</div>\n</header> \n<div class="page-content">\n<div class="editor-output">\n<p><strong>Dr. Houbing Song\, PhD\, FIEEE</strong><br>Associate Professor<br>Director of the NSF Center for Aviation Big Data Analytics<br>Director of Security and Optimization for Networked Globe Laboratory<br>University of Maryland\, B altimore County (UMBC)\, Baltimore\, MD.</p>\n<p><strong>Abstract:</strong ></p>\n<p>Most research on machine learning has focused on learning from m assive amounts of data resulting in large advancements in machine learning capabilities and applications. &nbsp\;However\, many domains lack access to the large\, high-quality\, supervised data that is required and therefo re are unable to fully take advantage of these data-intense learning techn iques. &nbsp\;This necessitates new data-efficient learning techniques tha t can learn in complex domains without the need for large quantities of su pervised data. In this lecture\, I will provide a comprehensive survey of existing literature in the area of data-efficient machine learning\, ident ify the challenges\, and evaluate the trends. I will also introduce our re search findings in this area.</p>\n<p><br><strong>Short Bio:</strong></p>\ n<p>Houbing Herbert Song (M&rsquo\;12&ndash\;SM&rsquo\;14-F&rsquo\;23) rec eived the Ph.D. degree in electrical engineering from the University of Vi rginia\, Charlottesville\, VA\, in August 2012.</p>\n<p>He is currently an Associate Professor\, the Director of the NSF Center for Aviation Big Dat a Analytics (Planning)\, the Associate Director for Leadership of the DOT Transportation Cybersecurity Center for Advanced Research and Education (T ier 1 Center)\, and the Director of the Security and Optimization for Netw orked Globe Laboratory (SONG Lab\, www.SONGLab.us)\, University of Marylan d\, Baltimore County (UMBC)\, Baltimore\, MD. Prior to joining UMBC\, he w as a Tenured Associate Professor of Electrical Engineering and Computer Sc ience at Embry-Riddle Aeronautical University\, Daytona Beach\, FL. He ser ves as an Associate Editor for IEEE Transactions on Artificial Intelligenc e (TAI) (2023-present)\, IEEE Internet of Things Journal (2020-present)\, IEEE Transactions on Intelligent Transportation Systems (2021-present)\, a nd IEEE Journal on Miniaturization for Air and Space Systems (J-MASS) (202 0-present). He was an Associate Technical Editor for IEEE Communications M agazine (2017-2020). He is the editor of ten books\, the author of more th an 100 articles and the inventor of 2 patents. His research interests incl ude cyber-physical systems/internet of things\, cybersecurity and privacy\ , and AI/machine learning/big data analytics. His research has been sponso red by federal agencies (including National Science Foundation\, National Aeronautics and Space Administration\, US Department of Transportation\, a nd Federal Aviation Administration\, among others) and industry. His resea rch has been featured by popular news media outlets\, including IEEE Globa lSpec&rsquo\;s Engineering360\, Association for Uncrewed Vehicle Systems I nternational (AUVSI)\, Security Magazine\, CXOTech Magazine\, Fox News\, U .S. News &amp\; World Report\, The Washington Times\, and New Atlas.</p>\n <p>Dr. Song is an IEEE Fellow (for contributions to big data analytics and integration of AI with Internet of Things)\, an Asia-Pacific Artificial I ntelligence Association (AAIA) Fellow\, and an ACM Distinguished Member (f or outstanding scientific contributions to computing). He is an ACM Distin guished Speaker (2020-present)\, an IEEE Vehicular Technology Society (VTS ) Distinguished Lecturer (2023-present) and an IEEE Systems Council Distin guished Lecturer (2023-present). Dr. Song has been a Highly Cited Research er identified by Clarivate&trade\; (2021\, 2022). Dr. Song received Resear ch.com Rising Star of Science Award in 2022\, 2021 Harry Rowe Mimno Award bestowed by IEEE Aerospace and Electronic Systems Society\, and 10+ Best P aper Awards from major international conferences\, including IEEE CPSCom-2 019\, IEEE ICII 2019\, IEEE/AIAA ICNS 2019\, IEEE CBDCom 2020\, WASA 2020\ , AIAA/ IEEE DASC 2021\, IEEE GLOBECOM 2021 and IEEE INFOCOM 2022.</p>\n<p >&nbsp\;</p>\n</div>\n</div>\n<p>&nbsp\;</p><br /><br />Agenda: <br /><p>0 8:55am - 09:00am Virtual Meeting&nbsp\; Speaker Introduction</p>\n<p>09:00 am - 09:45am Virtual Meeting Keynote</p>\n<p>09:45pm - 10:00am Virtual Mee ting Q &amp\;A</p> END:VEVENT END:VCALENDAR