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UID:calendar.1308451.field_date.0@hlrdm.library.harvard.edu
DTSTAMP:20220816T111951Z
DESCRIPTION:\n \n Data Science Friday at Countway Library Presents: \;The
BD2K Guide to the Fundamentals of Data Science Series Lunch and Learn!\n
\n\n\n\n \n The webinars provided essential training suitable for individual
s at an introductory overview level\, and consist of presentations from ex
perts across the country covering the basics of data management\, represen
tation\, computation\, statistical inference\, data modeling\, and other t
opics relevant to “big data” in biomedicine. This webinar series is a coll
aboration between the TCC\, the NIH Office of the Associate Director for D
ata Science\, and BD2K Centers Coordination Center (BD2KCCC).\n \n\n \n Join
us in the Countway Library Ware Room for Data Science Friday. These Lunch
and Learn sessions will provide a platform for those interested to view th
e webinar and engage in a discussion afterwards. And don't forget to bring
your lunch!\n \n\n \n \;\n \n\n \n Friday May 11: \;Biomedicine and th
e Foundations of Data\n \n\n \n \;\n \n\n \n Speaker: Michael Mahoney\,Univ
ersity of California\, Berkeley \;\n \n\n \n Michael W. Mahoney is at the
University of California at Berkeley in the Department of Statistics and
at the International Computer Science Institute (ICSI). He works on algori
thmic and statistical aspects of modern large-scale data analysis. Much of
his recent research has focused on large-scale machine learning\, includi
ng randomized matrix algorithms and randomized numerical linear algebra\,
geometric network analysis tools for structure extraction in large informa
tics graphs\, scalable implicit regularization methods\, and applications
in genetics\, astronomy\, medical imaging\, social network analysis\, and
internet data analysis. He received his PhD from Yale University with a di
ssertation in computational statistical mechanics\, and he has worked and
taught at Yale University in the mathematics department\, at Yahoo Researc
h\, and at Stanford University in the mathematics department. Among other
things\, he is on the national advisory committee of the Statistical and A
pplied Mathematical Sciences Institute (SAMSI)\, he was on the National Re
search Council's Committee on the Analysis of Massive Data\, he co-organiz
ed the Simons Institute's Fall 2013 program on the Theoretical Foundations
of Big Data Analysis\, and he runs the biennial MMDS Workshops on Algorit
hms for Modern Massive Data Sets. He is currently running the NSF/TRIPODS-
funded FODA (Foundations of Data Analysis) Institute at UC Berkeley.\n \n\n
\n Lecture Abstract \;\n \n\n \n Recent technological advances have permit
ted the generation of enormous quantities of data in a wide range of appli
cation domains\, from the social sciences and social media to electronic a
nd traditional commerce to the physical and biomedical sciences. This has
in turn generated interest in foundational issues. Examples of such issues
are to understand what is common and what is distinct between data in eac
h of these areas and methods applied to data from each of these areas\, to
address theoretical questions underlying machine learning and data analys
is tools\, and to ask what does it even mean to provide a foundation for a
n area as diverse as what is currently called data science. Dr. Mahoney wi
ll address some of these questions\, including how biomedicine may fit wit
hin this area. He will provide a 'test case' example of how work on founda
tional topics has been applied to biomedical problems: the development of
algorithmically and statistically principled and interpretable low-rank ma
trix decompositions and how they can then be implemented and applied to te
rabytes of data to solve very practical genetics and medical imaging probl
ems. He will then conclude by describing some challenges and opportunities
.\n \n\n \n \;\n \n\n \n Attend: \;Optional Registration \;
\n \n\n \n Contact: \;julie_goldman@hms.harvard.edu\n \n\n
DTSTART;TZID=America/New_York:20180511T120000
DTEND;TZID=America/New_York:20180511T130000
LAST-MODIFIED:20180418T143441Z
LOCATION:Countway Library Ware Room 505
SUMMARY:Data Science Friday Webinar
URL;TYPE=URI:https://hlrdm.library.harvard.edu/event/data-science-friday-we
binar
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