BEGIN:VCALENDAR
VERSION:2.0
PRODID:ILLC Website
X-WR-TIMEZONE:Europe/Amsterdam
BEGIN:VTIMEZONE
TZID:Europe/Amsterdam
X-LIC-LOCATION:Europe/Amsterdam
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700329T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701025T030000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:/NewsandEvents/Archives/2022/newsitem/13298/21
---27-May-2022-2022-Program-for-Women-and-Mathemat
ics-The-Mathematics-of-Machine-Learning-
DTSTAMP:20220113T150450
SUMMARY:2022 Program for Women and Mathematics "Th
e Mathematics of Machine Learning"
DTSTART;VALUE=DATE:20220521
DTEND;VALUE=DATE:20220527
LOCATION:New Jersey, U.S.A.
DESCRIPTION:Co-sponsored by the National Science F
oundation, Lisa Simonyi, the Institute for Advance
d Study (IAS), and Princeton University Department
of Mathematics, Women and Mathematics (WAM) is an
annual program that aims to recruit and retain mo
re women in mathematics. WAM aims to counter the i
nitial imbalance in the numbers of men and women e
ntering mathematics training as well as the higher
attrition rate of female mathematicians compared
to their male counterparts at every critical trans
ition stage in mathematical careers. WAM encourage
s female mathematicians to form collaborative rese
arch relationships and to become active in a verti
cal mentoring network spanning a continuum from un
dergraduates to emerita professors, which provides
support and reduces the sense of isolation experi
enced by many women in mathematics. While there ar
e a number of women's programs targeted solely at
undergraduates, or graduate students, or postdocs,
very few programs provide the depth and breadth t
hat come from simultaneously including features ta
ilored for undergraduate students, graduate studen
ts, and researchers from a broad spectrum of US in
stitutions, all in one united community of scholar
s, as WAM does. Terng Lecture Series: Cynthia Rud
in (Duke University), Introduction to Interpretabl
e Machine Learning. Uhlenbeck Lecture Series: Mar
ia Florina Balcan (Carnegie Mellon University), Fo
undations for Learning in the Age of Big Data
X-ALT-DESC;FMTTYPE=text/html:\n Co-sponsored b
y the National Science Foundation, Lisa Simonyi, t
he Institute for Advanced Study (IAS), and Princet
on University Department of Mathematics, Women and
Mathematics (WAM) is an annual program that aims
to recruit and retain more women in mathematics. W
AM aims to counter the initial imbalance in the nu
mbers of men and women entering mathematics traini
ng as well as the higher attrition rate of female
mathematicians compared to their male counterparts
at every critical transition stage in mathematica
l careers. WAM encourages female mathematicians to
form collaborative research relationships and to
become active in a vertical mentoring network span
ning a continuum from undergraduates to emerita pr
ofessors, which provides support and reduces the s
ense of isolation experienced by many women in mat
hematics. While there are a number of women's prog
rams targeted solely at undergraduates, or graduat
e students, or postdocs, very few programs provide
the depth and breadth that come from simultaneous
ly including features tailored for undergraduate s
tudents, graduate students, and researchers from a
broad spectrum of US institutions, all in one uni
ted community of scholars, as WAM does.

\n\n <
p>Terng Lecture Series: Cynthia Rudin (Duke Univer
sity), Introduction to Interpretable Machine Learn
ing.

\n Uhlenbeck Lecture Series: Maria Florin
a Balcan (Carnegie Mellon University), Foundations
for Learning in the Age of Big Data
URL:https://www.ias.edu/math/wam/program-years/202
2-program-women-and-mathematics
END:VEVENT
END:VCALENDAR