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UID:ai1ec-20115@www.clsp.jhu.edu
DTSTAMP:20240329T000752Z
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-22395@www.clsp.jhu.edu
DTSTAMP:20240329T000752Z
CATEGORIES;LANGUAGE=en-US:Seminars
CONTACT:
DESCRIPTION:Abstract
\nRecursive call
s over recursive data are widely useful for generating probability distrib
utions\, and probabilistic programming allows computations over these dist
ributions to be expressed in a modular and intuitive way. Exact inference
is also useful\, but unfortunately\, existing probabilistic programming la
nguages do not perform exact inference on recursive calls over recursive d
ata\, forcing programmers to code many applications manually. We introduce
a probabilistic language in which a wide variety of recursion can be expr
essed naturally\, and inference carried out exactly. For instance\, probab
ilistic pushdown automata and their generalizations are easy to express\,
and polynomial-time parsing algorithms for them are derived automatically.
We eliminate recursive data types using program transformations related t
o defunctionalization and refunctionalization. These transformations are a
ssured correct by a linear type system\, and a successful choice of transf
ormations\, if there is one\, is guaranteed to be found by a greedy algori
thm. I will also describe the implementation of this language in two phase
s: first\, compilation to a factor graph grammar\, and second\, computing
the sum-product of the factor graph grammar.
\n
\nBiography
\nDavid Chiang (PhD\, University of Pennsylvania\, 2004) is an assoc
iate professor in the Department of Computer Science and Engineering at th
e University of Notre Dame. His research is on computational models for le
arning human languages\, particularly how to translate from one language t
o another. His work on applying formal grammars and machine learning to tr
anslation has been recognized with two best paper awards (at ACL 2005 and
NAACL HLT 2009). He has received research grants from DARPA\, NSF\, Google
\, and Amazon\, has served on the executive board of NAACL and the editori
al board of Computational Linguistics and JAIR\, and is currently on the e
ditorial board of Transactions of the ACL.
DTSTART;TZID=America/New_York:20221017T120000
DTEND;TZID=America/New_York:20221017T131500
LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218
SEQUENCE:0
SUMMARY:David Chiang (University of Notre Dame) “Exact Recursive Probabilis
tic Programming with Colin McDonald\, Darcey Riley\, Kenneth Sible (Notre
Dame) and Chung-chieh Shan (Indiana)”
URL:https://www.clsp.jhu.edu/events/david-chiang-university-of-notre-dame/
X-COST-TYPE:free
X-TAGS;LANGUAGE=en-US:2022\,Chiang\,October
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