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DTSTART;VALUE=DATE:20260417
DTEND;VALUE=DATE:20260420
DTSTAMP:20260526T092334
CREATED:20260324T134645Z
LAST-MODIFIED:20260324T134645Z
UID:961-1776384000-1776643199@pages.vassar.edu
SUMMARY:DataFest 2026 @ Vassar
DESCRIPTION:DataFest 2026 @ Vassar is happening April 17-19\, 2026!  All students are invited to join us for this weekend of fun and friend competition around data.  It is free to participate\, but you must register before Friday March 27\, 2026.   \nFor more details\, visit https://pages.vassar.edu/datafest/
URL:https://pages.vassar.edu/mathstats/event/datafest-2026-vassar/
CATEGORIES:Annual,Student
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250213T140000
DTEND;TZID=America/New_York:20250213T150000
DTSTAMP:20260526T092334
CREATED:20250121T161614Z
LAST-MODIFIED:20250121T161614Z
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SUMMARY:(Henry Seely White Lecture II)\, Xiao-Li Meng\, Harvard University
DESCRIPTION:Join us on Thursday February 13 at 2PM in Rocky 300 for the second of two Henry Seely White Lectures delivered by Professor Xiao-Li Meng\, Harvard University. \nNote:  This is the second of two lectures in the Henry Seely White Lecture Series. The first lecture is on Wednesday February 12 at 4PM. \nTitle: From a Cauchy Surprise to the Half-Cauchy Miracle \nAbstract: This talk follows the path from Pillai and Meng (2016\, Annals of Statistics)\, “An Unexpected Encounter with Cauchy and Lévy\,” to Liu\, Meng\, and Pillai (2025\, arXiv:2501.01065)\, “A Heavily Right Strategy for Integrating Dependent Studies in Any Dimension\,” inviting the audience to join a journey to explore an emerging and mystic force in statistical inference: heavy-tail approximations.
URL:https://pages.vassar.edu/mathstats/event/henry-seely-white-lecture-ii-xiao-li-meng-harvard-university/
LOCATION:Rockefeller Hall 300
CATEGORIES:Annual,HSW
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250212T160000
DTEND;TZID=America/New_York:20250212T170000
DTSTAMP:20260526T092334
CREATED:20250121T161343Z
LAST-MODIFIED:20250121T161712Z
UID:738-1739376000-1739379600@pages.vassar.edu
SUMMARY:(Henry Seely White Lecture I)\, Xiao-Li Meng\, Harvard University
DESCRIPTION:Join us on Wednesday February 12 at 4PM in Rocky 300 for the first of two Henry Seely White Lectures delivered by Professor Xiao-Li Meng\, Harvard University. \nNote: This is the first of two lectures in the Henry Seely White Lecture Series. The second lecture is on Thursday February 13 at 2PM. \nTitle: Learning\, Teaching\, and Communication in the Age of AI: Wisdom and Warnings from Harvard Data Science Review (Or: Are you smarter than GPT-4?)
URL:https://pages.vassar.edu/mathstats/event/henry-seely-white-lecture-i-xiao-li-meng-harvard-university/
LOCATION:Rockefeller Hall 300
CATEGORIES:Annual,HSW
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240423T150000
DTEND;TZID=America/New_York:20240423T160000
DTSTAMP:20260526T092334
CREATED:20240321T162134Z
LAST-MODIFIED:20240411T022047Z
UID:537-1713884400-1713888000@pages.vassar.edu
SUMMARY:(Henry Seely White Lecture II)\, Bhramar Mukherjee\, University of Michigan
DESCRIPTION:Join us on Tuesday April 23 at 3PM in Rocky 300 for the second of two Henry Seely White Lectures delivered by Professor Bhramar Mukherjee\, University of Michigan. \nNote:  This is the second of two lectures in the Henry Seely White Lecture Series. The first lecture is on Monday April 22 at 5PM. \nTitle: Analysis of “Big” Real-World Health Care Data: Promises and Perils \nAbstract: Using administrative patient-care data such as Electronic Health Records and medical/Pharmaceutical claims for population-based scientific research have become increasingly common. With vast sample sizes leading to very small standard errors\, researchers need to pay more attention to potential biases in the estimates of association parameters of interest\, specifically to biases that do not diminish with increasing sample size. Of these multiple sources of biases\, in this talk\, we primarily focus on understanding selection bias. We present an analytical framework for understanding selection bias and arriving at bias-reduced inference using external data from a target population. We illustrate our methods via case-studies in cancer and COVID-19. We try to highlight that sampling and study design are at the heart of analysis of big data. This is joint work with many students and colleagues at the University of Michigan School of Public Health.
URL:https://pages.vassar.edu/mathstats/event/henry-seely-white-lecture2/
LOCATION:Rockefeller Hall 300
CATEGORIES:Annual,HSW
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240422T170000
DTEND;TZID=America/New_York:20240422T180000
DTSTAMP:20260526T092334
CREATED:20230926T031022Z
LAST-MODIFIED:20240411T022158Z
UID:148-1713805200-1713808800@pages.vassar.edu
SUMMARY:(Henry Seely White Lecture I)\, Bhramar Mukherjee\, University of Michigan
DESCRIPTION:Join us on Monday April 22 at 5PM in Rocky 300 for the first of two Henry Seely White Lectures delivered by Professor Bhramar Mukherjee\, University of Michigan. \nNote: This is the first of two lectures in the Henry Seely White Lecture Series. The second lecture is on Tuesday April 23 at 3PM. \nTitle: The Data Struggle of the Unseen \nAbstract: Despite several proposed roadmaps to increase diversity in scientific research\, most of the world’s research data are collected on people of European ancestry. We rely on summary statistics from historically privileged populations and then devise clever statistical methods to transfer/transport them for cross-ancestry use. In this talk\, I would first argue the obvious: for building fair algorithms we need fair training datasets. However\, till we have reached the dream of equitable big data at a global scale\, statisticians have an important role to play. In fact we have the perfect tools to study the “unobserved” through modeling of missing data\, selection bias and alike. I will share examples from my personal journey as a statistician where doing good and timely statistical work with imperfect data quantified important disparity in health outcomes and led to policy impact. I will conclude the talk with a call to arms for statisticians to lead efforts for creating\, curating\, collecting data and pioneering new scientific studies\, not just remain on the design and analytic fringes. As public health statisticians\, our job is not just to predict\, but to prevent. The talk is based on years of work with my students and colleagues at the Department of Biostatistics\, University of Michigan and inspired by the transformative experience we shared as a statistical team working on the COVID-19 pandemic.
URL:https://pages.vassar.edu/mathstats/event/henry-seely-white-lecture1/
LOCATION:Rockefeller Hall 300
CATEGORIES:Annual,HSW
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240209T150000
DTEND;TZID=America/New_York:20240209T160000
DTSTAMP:20260526T092334
CREATED:20240205T191054Z
LAST-MODIFIED:20240206T150038Z
UID:484-1707490800-1707494400@pages.vassar.edu
SUMMARY:(Asprey Lecture II)\, Laura DeMarco\, Harvard University
DESCRIPTION:Join us on Friday February 9 at 3PM in Rocky 300 for the second of two Asprey Lecturers delivered by Professor Laura DeMarco. Harvard University. \nTitle: The Mandelbrot set today: what we know and what we don’t know \nAbstract: One of the most famous–and still not fully understood–objects in mathematics is the Mandelbrot set.  By definition\, it is the set of complex numbers c for which the recursive sequence {c\, c^2+c\, (c^2+c)^2+c\, …}\, defined by x_1 = c and x_{n+1} = (x_n)^2+c\, is bounded.  But this set turns out to be rich and complicated and related to many different areas of mathematics.  I will present an overview of what’s known and what’s not known about the Mandelbrot set\, and I’ll describe recent work that (perhaps surprisingly) employs tools from arithmetic geometry to study these systems.  The new work is a collaboration with Myrto Mavraki.
URL:https://pages.vassar.edu/mathstats/event/asprey-lecture-ii-laura-demarco-harvard-university/
LOCATION:Rockefeller Hall 300
CATEGORIES:Annual,Asprey
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240208T170000
DTEND;TZID=America/New_York:20240208T180000
DTSTAMP:20260526T092334
CREATED:20231130T153902Z
LAST-MODIFIED:20240206T145959Z
UID:318-1707411600-1707415200@pages.vassar.edu
SUMMARY:(Asprey Lecture I) Laura DeMarco\, Harvard University
DESCRIPTION:Join us on Thursday February 8 at 5PM in Rocky 300 for the first of two Asprey Lecturers delivered by Professor Laura DeMarco. Harvard University. \nTitle: From the solar system to the Mandelbrot set \nAbstract: The field of dynamical systems has a long and fascinating history:  it originated with the study of planetary motion and has become a central part of mathematics today\, with many connections to algebra\, geometry\, and analysis.  In this talk\, I will present some of its historical development\, with emphasis on the subtle question of linearization and how that leads to deep and difficult problems that remain unsolved today.
URL:https://pages.vassar.edu/mathstats/event/asprey-lectures/
LOCATION:Rockefeller Hall 300
CATEGORIES:Annual,Asprey
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