Michael Mahoney  Teaching
Classes
Summer Schools and Workshops
"Foundations of Data Science":
Fall 2018 program at the Simons Institute at UC Berkeley.
MMDS 2016, 2014, ...:
for more information, see the
main MMDS web page,
or the MMDS Video Library.
"Mathematics of Data":
2016 Summer School at the PCMI (Park City Mathematics Institute);
for more information, see the
flyer and the (upcoming) edited volume of lectures.
"RandNLA: Randomization in Numerical Linear Algebra":
2015 Gene Golub SIAM Summer School, June 2015, in Delphi, Greece;
for more information, see the
flyer, or
the SIAM News article about it.
"Theoretical Foundations of Big Data Analysis":
Fall 2013 program at the Simons Institute at UC Berkeley.
MMDS Workshops
We started the
MMDS Workshops on "Algorithms for Modern Massive Data Sets"
to address
algorithmic and statistical challenges in modern largescale statistical data analysis.
MMDS 2016
took place on the campus of UC Berkeley on June 2124, 2016.
See the
main MMDS web page
for more information.
MMDS 2014
took place on the campus of UC Berkeley on June 1720, 2014.
See the
main MMDS web page
for more information.
Click
here for the entire
video collection.
MMDS 2012
took place on the campus of Stanford University on July 1013, 2012.
For pdfs and videos of the presentations, go to the main MMDS web
page; or click
here for the entire
video collection.
MMDS 2010
took place on the campus of Stanford University on June 1518, 2010.
MMDS 2010 addressed computation in largescale scientific and internet data
applications more generally.
See
the MMDS web page
for details, including articles and all the speaker overheads!
MMDS 2008
took place on June 2528, 2008.
MMDS 2008 grew out of our expectation for what
the algorithmic and statistical
foundations of
largescale
data analysis
should look like a generation from now.
Click
here
for an article that appeared in SIGKDD Explorations and
SIAM News about the meeting.
MMDS 2006
took place on June 2124, 2006.
MMDS 2006 was originally motivated by the complementary perspectives
brought by numerical linear algebra and theoretical computer science to
matrix algorithms in largescale data applications.
Click
here
for an article in SIAM News about the meeting.
These MMDS meetings generated intense interdisciplinary interest and were a
big success.
