BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9//
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-FROM-URL:https://www.clsp.jhu.edu
X-WR-TIMEZONE:America/New_York
BEGIN:VTIMEZONE
TZID:America/New_York
X-LIC-LOCATION:America/New_York
BEGIN:STANDARD
DTSTART:20231105T020000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
RDATE:20241103T020000
TZNAME:EST
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20240310T020000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
RDATE:20250309T020000
TZNAME:EDT
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:ai1ec-20115@www.clsp.jhu.edu
DTSTAMP:20240329T084026Z
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-21072@www.clsp.jhu.edu
DTSTAMP:20240329T084026Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract
\nEmotion has intrigued re
searchers for generations. This fascination has permeated the engineering
community\, motivating the development of affective computing methods. How
ever\, human emotion remains notoriously difficult to accurately detect. A
s a result\, emotion classification techniques are not always effective wh
en deployed. This is a problem because we are missing out on the potentia
l that emotion recognition provides: the opportunity to automatically meas
ure an aspect of behavior that provides critical insight into our health a
nd wellbeing\, insight that is not always easily accessible. In this talk
\, I will discuss our efforts in developing emotion recognition approaches
that are effective in natural environments and demonstrate how these appr
oaches can be used to support mental health.
\n\nBiography
\n\nEmily Mower Provost is an
Associate Professor in Computer Science and Engineering and Toyota Faculty
Scholar at the University of Michigan. She received her Ph.D. in Electric
al Engineering from the University of Southern California (USC)\, Los Ange
les\, CA in 2010. She has been awarded a National Science Foundation CAREE
R Award (2017)\, the Oscar Stern Award for Depression Research (2015)\, a
National Science Foundation Graduate Research Fellowship (2004-2007). She
is a co-author on the paper\, “Say Cheese vs. Smile: Reducing Speech-Relat
ed Variability for Facial Emotion Recognition\,” winner of Best Student Pa
per at ACM Multimedia\, 2014\, and a co-author of the winner of the Classi
fier Sub-Challenge event at the Interspeech 2009 emotion challenge. Her re
search interests are in human-centered speech and video processing\, multi
modal interfaces design\, and speech-based assistive technology. The goals
of her research are motivated by the complexities of the perception and e
xpression of human behavior.
DTSTART;TZID=America/New_York:20211206T120000
DTEND;TZID=America/New_York:20211206T131500
LOCATION:Maryland Hall 110 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:Emily Mower-Provost (University of Michigan) “Automatically Measuri
ng Emotion from Speech: New Methods to Move from the Lab to the Real World
”
URL:https://www.clsp.jhu.edu/events/emily-mower-provost-university-of-michi
gan/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2021\,December\,Mower-Provost
END:VEVENT
END:VCALENDAR