BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250408T032437Z UID:7B53CAF2-4B4B-4DFB-8737-F6598C3930D8 DTSTART;TZID=America/New_York:20250328T110000 DTEND;TZID=America/New_York:20250328T120000 DESCRIPTION:Abstract – There is a significant range of physical phenomena —from nonlinear elasticity\, to symmetry\, noise\, topology\, and disord er — that are rarely utilized in traditional computing paradigms. Yet th ese phenomena can unlock new efficiencies\, by directly processing signals in their natural domain\, and by bypassing the traditional abstraction st ack associated with digital CMOS technology. However\, building physical c omputers is challenging. Information processing tasks generally involve co mplex input-output relations\, thus requiring designs that are highly expr essive\; and for these designs\, the relation between function and structu re is nontrivial\, complicating the simulation\, design\, and fabrication of devices. In my talk\, I will illustrate our journey towards using metam aterials for physical computing\, with two recent examples. First\, I will talk about our results in passive speech recognition\, where we leverage a phononic metamaterial to implement wake-up-word detection with zero stan dby power consumption. Second\, I will discuss our ongoing work in self-le arning materials\, that autonomously adapt to improve their performance— driven by their ability to form long-term memories in response to examples and external feedback.\n\nCo-sponsored by: Advanced Science Research Cent er - the Graduate Center - City University of New York\n\nSpeaker(s): Marc Serra-Garcia\n\nRoom: Auditorium\, Bldg: Advanced Science Research Center CUNY\, 85 St. Nicholas Terrace 2.325\, New York\, New York\, United State s\, NY 10031\, Virtual: https://events.vtools.ieee.org/m/473401 LOCATION:Room: Auditorium\, Bldg: Advanced Science Research Center CUNY\, 8 5 St. Nicholas Terrace 2.325\, New York\, New York\, United States\, NY 10 031\, Virtual: https://events.vtools.ieee.org/m/473401 ORGANIZER:viktoriia.rutckaia47@gc.cuny.edu SEQUENCE:26 SUMMARY:IEEE NY JOINT MTT AP PHO & NANO CHAPTER - SEMINAR: Physical computi ng in metamaterials URL;VALUE=URI:https://events.vtools.ieee.org/m/473401 X-ALT-DESC:Description: <br /><p><strong><span data-olk-copy-source="Messag eBody">Abstract</span></strong> &ndash\; There is a significant range of p hysical phenomena&mdash\;from nonlinear elasticity\, to symmetry\, noise\, topology\, and disorder &mdash\; that are rarely utilized in traditional computing paradigms. Yet these phenomena can unlock new efficiencies\, by directly processing signals in their natural domain\, and by bypassing the traditional abstraction stack associated with digital CMOS technology. Ho wever\, building physical computers is challenging. Information processing tasks generally involve complex input-output relations\, thus requiring d esigns that are highly expressive\; and for these designs\, the relation b etween function and structure is nontrivial\, complicating the simulation\ , design\, and fabrication of devices. In my talk\, I will illustrate our journey towards using metamaterials for physical computing\, with two rece nt examples. First\, I will talk about our results in passive speech recog nition\, where we leverage a phononic metamaterial to implement wake-up-wo rd detection with zero standby power consumption. Second\, I will discuss our ongoing work in self-learning materials\, that autonomously adapt to i mprove their performance&mdash\;driven by their ability to form long-term memories in response to examples and external feedback.</p> END:VEVENT END:VCALENDAR