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UID:ai1ec-20115@www.clsp.jhu.edu
DTSTAMP:20240328T170017Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract\nData science in small medical datasets usually means
doing precision guesswork on unreliable data provided by those with high e
xpectations. The first part of this talk will focus on issues that data sc
ientists and engineers have to address when working with this kind of data
(e.g. unreliable labels\, the effect of confounding factors\, necessity o
f clinical interpretability\, difficulties with fusing more data sets). Th
e second part of the talk will include some real examples of this kind of
data science in the field of neurology (prediction of motor deficits in Pa
rkinson’s disease based on acoustic analysis of speech\, diagnosis of Park
inson’s disease dysgraphia utilising online handwriting\, exploring the Mo
zart effect in epilepsy based on the music information retrieval) and psyc
hology (assessment of graphomotor disabilities in children with developmen
tal dysgraphia).\nBiography\nJiri Mekyska is the head of the BDALab (Brain
Diseases Analysis Laboratory) at the Brno University of Technology\, wher
e he leads a multidisciplinary team of researchers (signal processing engi
neers\, data scientists\, neurologists\, psychologists) with a special foc
us on the development of new digital endpoints and digital biomarkers enab
ling to better understand\, diagnose and monitor neurodegenerative (e.g. P
arkinson’s disease) and neurodevelopmental (e.g. dysgraphia) diseases.
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-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\n\\n\\nAbstr
act
\nData science in small medical datasets usually means
doing precision guesswork on unreliable data provided by those with high e
xpectations. The first part of this talk will focus on issues that data sc
ientists and engineers have to address when working with this kind of data
(e.g. unreliable labels\, the effect of confounding factors\, necessity o
f clinical interpretability\, difficulties with fusing more data sets). Th
e second part of the talk will include some real examples of this kind of
data science in the field of neurology (prediction of motor deficits in Pa
rkinson’s disease based on acoustic analysis of speech\, diagnosis of Park
inson’s disease dysgraphia utilising online handwriting\, exploring the Mo
zart effect in epilepsy based on the music information retrieval) and psyc
hology (assessment of graphomotor disabilities in children with developmen
tal dysgraphia).
\nBiography
\nJiri Mekyska is the he
ad of the BDALab (Brain Diseases Analysis Laboratory) at the Brno Universi
ty of Technology\, where he leads a multidisciplinary team of researchers
(signal processing engineers\, data scientists\, neurologists\, psychologi
sts) with a special focus on the development of new digital endpoints and
digital biomarkers enabling to better understand\, diagnose and monitor ne
urodegenerative (e.g. Parkinson’s disease) and neurodevelopmental (e.g. dy
sgraphia) diseases.
\n\n
X-TAGS;LANGUAGE=en-US:2021\,March\,Mekyska
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-21487@www.clsp.jhu.edu
DTSTAMP:20240328T170017Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract\nEnormous amounts of ever-changing knowledge are avai
lable online in diverse textual styles and diverse formats. Recent advance
s in deep learning algorithms and large-scale datasets are spurring progre
ss in many Natural Language Processing (NLP) tasks\, including question an
swering. Nevertheless\, these models cannot scale up when task-annotated t
raining data are scarce. This talk presents my lab’s work toward building
general-purpose models in NLP and how to systematically evaluate them. Fir
st\, I present a general model for two known tasks of question answering i
n English and multiple languages that are robust to small domain shifts.
Then\, I show a meta-training approach that can solve a variety of NLP tas
ks with only using a few examples and introduce a benchmark to evaluate cr
oss-task generalization. Finally\, I discuss neuro-symbolic approaches to
address more complex tasks by eliciting knowledge from structured data and
language models.\n\nBiography\n\nHanna Hajishirzi is an Assistant Profess
or in the Paul G. Allen School of Computer Science & Engineering at the Un
iversity of Washington and a Senior Research Manager at the Allen Institut
e for AI. Her research spans different areas in NLP and AI\, focusing on d
eveloping general-purpose machine learning algorithms that can solve many
NLP tasks. Applications for these algorithms include question answering\,
representation learning\, green AI\, knowledge extraction\, and conversati
onal dialogue. Honors include the NSF CAREER Award\, Sloan Fellowship\, Al
len Distinguished Investigator Award\, Intel rising star award\, best pape
r and honorable mention awards\, and several industry research faculty awa
rds. Hanna received her PhD from University of Illinois and spent a year a
s a postdoc at Disney Research and CMU.
DTSTART;TZID=America/New_York:20220225T120000
DTEND;TZID=America/New_York:20220225T131500
LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j
/96735183473
SEQUENCE:0
SUMMARY:Hanna Hajishirzi (University of Washington & Allen Institute for AI
) “Toward Robust\, Knowledge-Rich NLP”
URL:https://www.clsp.jhu.edu/events/hanna-hajishirzi-university-of-washingt
on-allen-institute-for-ai-toward-robust-knowledge-rich-nlp/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\nAbstr
act
\nEno
rmous amounts of ever-changing knowledge are available online in diverse
textual styles and diverse formats. Recent advances in deep learning algor
ithms and large-scale datasets are spurring progress in many Natural Langu
age Processing (NLP) tasks\, including question answering. Nevertheless\,
these models cannot scale up when task-annotated training data are scarce.
This talk presents my lab’s work toward building general-purpose models i
n NLP and how to systematically evaluate them. First\, I present a general
model for two known tasks of question answering in English and multiple l
anguages that are robust to small domain shifts. Then\, I show a meta-tra
ining approach that can solve a variety of NLP tasks with only using a few
examples and introduce a benchmark to evaluate cross-task generalization.
Finally\, I discuss neuro-symbolic approaches to address more comp
lex tasks by eliciting knowledge from structured data and language models.
\n\nBiography
\n\n<
div>Hanna Hajishirzi is an
Assistant Professor in the Paul G. Allen School of Computer Science & Eng
ineering at the University of Washington and a Senior Research Manager at
the Allen Institute for AI. Her research spans different areas in NLP and
AI\, focusing on developing general-purpose machine learning algorithms th
at can solve many NLP tasks. Applications for these algorithms include que
stion answering\, representation learning\, green AI\, knowledge extractio
n\, and conversational dialogue. Honors include the NSF CAREER Award\, Slo
an Fellowship\, Allen Distinguished Investigator Award\, Intel rising star
award\, best paper and honorable mention awards\, and several industry re
search faculty awards. Hanna received her PhD from University of Illinois
and spent a year as a postdoc at Disney Research and CMU.\n
BODY>
X-TAGS;LANGUAGE=en-US:2022\,February\,Hajishirzi
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