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BEGIN:VEVENT
ORGANIZER;CN="Hunter, Tiffany":mailto:huntert1@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=kzhao27@as
u.edu:mailto:kzhao27@asu.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN='eecs_bscs
@listserv.ohio.edu':mailto:eecs_bscs@listserv.ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN='eecs_bsee
@listserv.ohio.edu':mailto:eecs_bsee@listserv.ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN='eecs_mscs
@listserv.ohio.edu':mailto:eecs_mscs@listserv.ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN='eecs_phd@
listserv.ohio.edu':mailto:eecs_phd@listserv.ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN='eecs_msee
@listserv.ohio.edu':mailto:eecs_msee@listserv.ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Abukamail,
Nasseef":mailto:abukamai@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Allwine, D
aniel":mailto:allwined@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Bartone, C
hris":mailto:bartone@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Chenji, Ha
rsha":mailto:chenji@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Goble, Jam
es":mailto:goble@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Irwin, Den
nis":mailto:irwind@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Jadwisienc
zak, Wojciech":mailto:jadwisie@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Karanth, A
vinash":mailto:karanth@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Kaya, Sava
s":mailto:kaya@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Kelsey, Ra
lph":mailto:kelsey@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Liu, Chang"
:mailto:liuc@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Liu, Jundo
ng":mailto:liuj1@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Mourning,
Chad":mailto:mourning@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Ostermann,
Shawn":mailto:osterman@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Rahman, Fa
iz":mailto:rahmanf@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Schlicher,
Jared":mailto:schliche@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Ugazio, Sa
brina":mailto:ugazio@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Vasiliadis
, Konstantinos":mailto:vassilia@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Welch, Lon
nie":mailto:welch@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Zhu, Jim":m
ailto:zhuj@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Wang, Zhew
ei":mailto:wangz1@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Lindner, P
atricia":mailto:lindnerp@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Ardrey, Gr
egory":mailto:gardrey@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Yadav, Ani
mesh":mailto:yadava@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Plis, Kevi
n":mailto:plis@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Juedes, Da
vid":mailto:juedes@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Fox, Patri
ck":mailto:pfox@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Patterson,
James":mailto:pattersj@ohio.edu
ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Steinberg,
Eric":mailto:steinber@ohio.edu
DESCRIPTION;LANGUAGE=en-US:Title\nRevolutionizing Edge AI: Improved and Aut
omated Model Compression\nBio\nKaiqi Zhao is a final-year Ph.D. candidate
advised by Prof. Ming Zhao in the School of\nComputing and Augmented Intel
ligence (SCAI) at Arizona State University (ASU). Her research\ninterests
are machine learning (ML) and deep learning (DL) model compression as well
as cloud\nand edge computing. Central to her research are innovating solu
tions to optimize large-scale\nML/DL models for Internet-of-Things (IoT) d
ata-driven applications. She has published\nfirst-authored papers at top-t
ier AI conferences\, e.g.\, AISTATS (2023 and 2024)\, ICASSP (Oral)\,\nand
InterSpeech (Oral)\, and top-tier edge computing conferences\, e.g.\, SEC
\, and won the Best\nPoster Award at SEC’24. She did three research inte
rnships at Amazon Web Services as an\nApplied Scientist. One of her intern
ship works about compressing speech recognition models\nhas been integrate
d into the Amazon Alex library for production usage. Additionally\, she wa
s\nawarded the Graduate College Completion Fellowship\, the most prestigio
us award for graduate\nstudents at ASU\, and SCAI Doctoral Fellowships in
2022 and 2023.\nAbstract\nMachine learning models are increasingly employe
d by smart devices on the edge to support\nimportant applications such as
real-time virtual assistants and privacy-preserving healthcare.\nHowever\,
deploying state-of-the-art (SOTA) deep learning models on devices faces m
ultiple\nserious challenges. First\, it is infeasible to deploy large mode
ls on resource-constrained edge\ndevices whereas small models cannot achie
ve the SOTA accuracy. Second\, it is difficult to\ncustomize the models ac
cording to diverse application requirements in accuracy and speed and\ndiv
erse capabilities of edge devices. This talk presents several novel soluti
ons to\ncomprehensively address the above challenges through automated and
improved model\ncompression. First\, it introduces Automatic Attention Pr
uning (AAP)\, an adaptive\,\nattention-based pruning approach to automatic
ally reduce model parameters while meeting\ndiverse user objectives in mod
el size\, speed\, and accuracy. AAP achieves an impressive\n92.72% paramet
er reduction in ResNet-101 on Tiny-ImageNet without causing any accuracy\n
loss. Second\, it presents Self-Supervised Quantization-Aware Knowledge Di
stillation\, a\nframework for reducing model precision without supervision
from labeled training data. For\nexample\, it quantizes VGG-8 to 2 bits o
n CIFAR-10 without any accuracy loss. Finally\, the talk\nexplores two mor
e works\, Contrastive Knowledge Distillation and Module Replacing\, for fu
rther\nimproving the performance of small models. All the works presented
in this talk are designed to\naddress real-world challenges\, with applica
tions extending to production environments such as\nAmazon Alexa\, and hav
e been successfully deployed on diverse hardware platforms\, including\ncl
oud instances and edge devices\, catalyzing AI for the edge.\n____________
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