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UID:ai1ec-20115@www.clsp.jhu.edu
DTSTAMP:20240329T002543Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:
Abstract
\nData science in small medi
cal datasets usually means doing precision guesswork on unreliable data pr
ovided by those with high expectations. The first part of this talk will f
ocus on issues that data scientists and engineers have to address when wor
king with this kind of data (e.g. unreliable labels\, the effect of confou
nding factors\, necessity of clinical interpretability\, difficulties with
fusing more data sets). The second part of the talk will include some rea
l examples of this kind of data science in the field of neurology (predict
ion of motor deficits in Parkinson’s disease based on acoustic analysis of
speech\, diagnosis of Parkinson’s disease dysgraphia utilising online han
dwriting\, exploring the Mozart effect in epilepsy based on the music info
rmation retrieval) and psychology (assessment of graphomotor disabilities
in children with developmental dysgraphia).
\nBiography
\nJiri Mekyska is the head of the BDALab (Brain Diseases Analysis Labor
atory) at the Brno University of Technology\, where he leads a multidiscip
linary team of researchers (signal processing engineers\, data scientists\
, neurologists\, psychologists) with a special focus on the development of
new digital endpoints and digital biomarkers enabling to better understan
d\, diagnose and monitor neurodegenerative (e.g. Parkinson’s disease) and
neurodevelopmental (e.g. dysgraphia) diseases.
\n
DTSTART;TZID=America/New_York:20210329T120000
DTEND;TZID=America/New_York:20210329T131500
LOCATION:via Zoom
SEQUENCE:0
SUMMARY:Jiri Mekyska (Brno University of Technology) “Data Science in Small
Medical Data Sets: From Logistic Regression Towards Logistic Regression”
URL:https://www.clsp.jhu.edu/events/jiri-mekyska-brno-university-of-technol
ogy/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2021\,March\,Mekyska
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-22408@www.clsp.jhu.edu
DTSTAMP:20240329T002543Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract
\nAI-powered appl
ications increasingly adopt Deep Neural Networks (DNNs) for solving many p
rediction tasks\, leading to more than one DNNs running on resource-constr
ained devices. Supporting many models simultaneously on a device is challe
nging due to the linearly increased computation\, energy\, and storage cos
ts. An effective approach to address the problem is multi-task learning (M
TL) where a set of tasks are learned jointly to allow some parameter shari
ng among tasks. MTL creates multi-task models based on common DNN architec
tures and has shown significantly reduced inference costs and improved gen
eralization performance in many machine learning applications. In this tal
k\, we will introduce our recent efforts on leveraging MTL to improve accu
racy and efficiency for edge computing. The talk will introduce multi-task
architecture design systems that can automatically identify resource-effi
cient multi-task models with low inference costs and high task accuracy.
div>\n
\n
Biography
\n
\n
\nHui Guan is an Assistant Professor in the
College
of Information and Computer Sciences (CICS) at the University o
f Massachusetts Amherst\, the flagship campus of the UMass system. She rec
eived her Ph.D. in Electrical Engineering from
North Carolina State Univer
sity in 2020. Her research lies in the intersection between mac
hine learning and systems\, with an emphasis on improving the speed\, scal
ability\, and reliability of machine learning through innovations in algor
ithms and programming systems. Her current research focuses on both algori
thm and system optimizations of deep multi-task learning and graph machine
learning.
\n\n \n
DTSTART;TZID=America/New_York:20221111T120000
DTEND;TZID=America/New_York:20221111T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Hui Guan (University of Massachusetts Amherst) “Towards Accurate an
d Efficient Edge Computing Via Multi-Task Learning”
URL:https://www.clsp.jhu.edu/events/hui-guan-university-of-massachusetts-am
herst/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2022\,Guan\,November
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