Vassar Math & Stats Events Page

(LAVA) Lisa (Esposito) Cannizzaro, VC '16
2024-09-20 15:00 - 16:00 (America/New_York)
LAVA Talk on Actuarial Science, with Lisa (Esposito) Cannizzaro (VC ’16), Allied World Reinsurance. Friday, September 20, 2024 at 3PM in Rocky 312.

About Lisa:
Lisa is an Assistant Vice President & Reinsurance Pricing Actuary at Allied World Reinsurance. She graduated from Vassar in 2016 with a double major in Mathematics & Economics. After graduating, she spent a year in Philadelphia working as a pricing actuary for Chubb Insurance. She has been with Allied World Reinsurance in New York for the last seven years, where her primary responsibility is to price reinsurance treaties. She became an Associate of the Casualty Actuarial Society in June 2023 and is working towards obtaining her Fellowship.

(Colloquium) Ophelia Adams, University of Rochester
2024-09-20 16:00 - 17:00 | Rockefeller Hall 310

(Colloquium) Jeff Goldsmith, Columbia University
2024-10-04
Wearable Devices in Public Health Research
Friday, October 4th at 4pm in Rocky 310
In the last ten years, technological advances have made many activity- and physiology-monitoring wearable devices available for use in both clinical trials and large-scale epidemiological studies. This trend will continue and even expand as devices become cheaper and more reliable. These developments open up a tremendous opportunity for clinical and public health researchers to collect critical data at an unprecedented level of detail, while posing new challenges for statistical analysis of rich, complex data. This talk will present a collection of examples and analysis approaches that use accelerometer data, including activity classification; identifying and interpreting variability in activity trajectories; building regression models in which activity trajectories are the response; and understanding shifts in the circadian rhythms that underlie the timing of activity. We’ll draw on several applications, including the Baltimore Longitudinal Study of Aging and data collected through the Columbia Center for Children’s Environmental Health.

(Colloquium) Dean Spyropoulos (VC '19), Michigan State University
2024-10-22 16:00 - 17:00

(LAVA) Tony Caletti, VC '18
2024-10-25 15:00 - 16:00
LAVA Talk on Data Science, with Tony Caletti (VC ’18), Doorh Dash. Friday, October 25, 2024 at 3PM in Rocky 312.

About Tony:

Tony Caletti will discuss his career working as a data analyst and data scientist in the tech industry. While working for DraftKings and DoorDash, he has focused on A/B testing, marketing optimization, opportunity analysis, causal inference, and predictive modeling. He’ll share tips for how to develop an analytical mindset, what skills are helpful to get a job, the value of technical vs soft skills, and his opinion on the differences between data engineering, analytics, data science, and machine learning.

(Colloquium) Zheng Bian (VC '17), Clarkson University
2024-10-28 16:00 - 17:00

Pre-Registration Q&A
2024-11-11 15:30 - 16:30
Have questions about which Math/Stats courses to take next semester? Come enjoy some free cider and donuts and chat with faculty and students!
Monday, November 11, 3:30PM in the Rocky 3rd Floor Hallway

(Colloquium) Joshua Snoke, RAND Corporation
2024-11-14 15:30 - 16:30

(LAVA) Robert Ronan, VC '15
2024-11-15 15:00 - 16:00
LAVA Talk on Transitioning From Math To Machine Learning By Mistake (Or Necessity), with Robert Ronan (VC ’15), NYU Langone Health. Friday, November 15, 2024 at 3PM in Rocky 310.

About Robert:

Robert Ronan, VC ’15, is a machine learning engineer at the cardiology department of NYU Langone Health, where he leads the department’s machine learning and data hub efforts. His work involves employing deep learning models to detect patterns in EKGs that clinicians cannot easily identify, enabling improved clinical decision support, and predictive utility of EKGs. Robert holds master’s degrees in computer science and mathematics from NYU Tandon School of Engineering and The CUNY Graduate Center, respectively, and a bachelor’s in mathematics from Vassar College. In his free time, he is an amateur mixologist and an extensive collector of vintage amaro, a style of Italian liqueur.

(Colloquium) Andrew Ackerman, University of North Carolina at Chapel Hill
2024-11-15 16:30 - 17:30 | Rockefeller Hall 310
Colloquium Talk
Andrew Ackerman, University of North Carolina at Chapel Hill
Friday November 15, 2024 at 4:30PM
Rocky 312
Title: Measures of Fairness and High Dimensional Data Integration
Abstract:The first component of this talk will present a representative discussion from a novel course, entitled Moral Machine Learning, developed at the University of North Carolina at Chapel Hill. In particular, we motivate and introduce statistical measures of fairness used to assess classification algorithms. This discussion will culminate in an Incompleteness Theorem, which demonstrates that these measures are, in some fundamental way, not totally reconcilable. How to assess fairness despite this incompleteness result will motivate open questions discussed at the conclusion of the second component of this talk. This latter component will primarily be focused on original research. We present completed work for high dimensional data integration for human neuroscience. In particular, neuroimaging studies, such as the Human Connectome Project (HCP), often collect multifaceted data to study the complex human brain. However, these data are often analyzed in a pairwise fashion, which can hinder our understanding of how different brain-related measures interact. In this study, we analyze the multi-block HCP data using the Data Integration via Analysis of Subspaces (DIVAS) method. We integrate structural and functional brain connectivity, substance use, cognition, and genetics in an exhaustive five-block analysis. This gives rise to the important finding that genetics is the single data modality most predictive of brain connectivity, outside of brain connectivity itself. Moreover, investigations of shared space loadings provide interpretable associations between particular brain regions and drivers of variability, such as alcohol consumption in the substance-use data block. Novel Jackstraw hypothesis tests are developed for the DIVAS framework to establish statistically significant loadings. We conclude by discussing proposed future work, at both the faculty and undergraduate levels, in each of data integration and algorithmic fairness.

Math Jam - Fall 2024
2024-11-15 18:00 - 20:00 | The Aula
Vassar Students: Run a station at the Math Jam working with 2nd-8th graders. Sign up here to volunteer.
Local community: Sign up here to register your 2nd-8th grader.
November 15, 2024 from 6-8 pm at the Aula/Ely Hall

(Colloquium) Federica Ricci, University of California, Irvine
2024-11-18 16:00 - 17:00 | Rockefeller Hall 312
Colloquium Talk
Federica Ricci, University of California, Irvine
Monday November 18, 2024 at 4:00PM
Rocky 312
Title: Statistical modeling of sparse networks

Abstract: The study of scientific and social phenomena often requires modeling data in the form of networks, i.e. the set of interactions between entities like proteins, neurons or people. Statistical models provide ways to address important questions, including why some entities are connected but not others, and whether there are interactions that have not been observed. In this talk, I will present my work on developing a class of models that can discover an unobserved set of clusters (or communities) among interacting entities and that can learn the number of clusters from data. Unlike previous approaches with those properties, the proposed framework can model sparse networks. Capturing sparsity is especially important when dealing with large networks: for example, in online social networks, someone’s connections grow much slower than linearly with the number of users. I will summarize a posterior-inference method based on Markov Chain Monte Carlo and I will show the advantages of this approach on a set of social and biological networks.

(Colloquium) Ivan Cheltsov, University of Edinburgh
2024-11-22 16:00 - 17:00

(Colloquium) Nikolas Schonsheck (VC '15), Rockefeller University
2024-12-04 16:00 - 17:00

The 85th Annual Putnam Competition
2024-12-07 10:00 - 18:00
Participate in the 85th Annual William Lowell Putnam Mathematical Competition.
Register here.
For more information: https://maa.org/putnam/
Saturday, December 7, 2024 in Rockefeller Hall
The Putnam Competition consists of a session A and a session B.
Session A: 10 am – 1 pm
Session B: 3 pm – 6 pm.

(Colloquium) Austin MBaye, Northeastern University
2025-01-31 16:00 - 17:00 | Rockefeller Hall 312

(Henry Seely White Lecture I), Xiao-Li Meng, Harvard University
2025-02-12 16:00 - 17:00 | Rockefeller Hall 300
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.
Note: This is the first of two lectures in the Henry Seely White Lecture Series. The second lecture is on Thursday February 13 at 2PM.
Title: Learning, Teaching, and Communication in the Age of AI: Wisdom and Warnings from Harvard Data Science Review (Or: Are you smarter than GPT-4?)

(Henry Seely White Lecture II), Xiao-Li Meng, Harvard University
2025-02-13 14:00 - 15:00 | Rockefeller Hall 300
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.
Note: This is the second of two lectures in the Henry Seely White Lecture Series. The first lecture is on Wednesday February 12 at 4PM.
Title: From a Cauchy Surprise to the Half-Cauchy Miracle
Abstract: 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.

(Colloquium) Jamie Juul, Colorado State University
2025-02-21 16:00 - 17:00 | Rockefeller Hall 312

(Colloquium) Andrew Schultz, Wellesley College
2025-02-25 16:00 - 17:00 | Rockefeller Hall 310

(Colloquium) Tim Goldberg, Lenoir–Rhyne University
2025-03-24 16:00 - 17:00 | Rockefeller Hall 312

(Colloquium) Katie Ellman-Aspnes, Notre Dame University
2025-04-03 16:00 - 17:00 | Rockefeller Hall 300

(Colloquium) Jinyoung Park, Courant Institute at NYU
2025-04-17 16:00 - 17:00 | Rockefeller Hall 300

(Colloquium) Becca Thomases, Smith College
2025-04-23 16:00 - 17:00 | Rockefeller Hall 300

(Colloquium) Peter Oberly, University of Rochester
2025-08-25 08:00 - 17:00 | Rockefeller Hall 312

(Colloquium) Robyn Brooks, University of Utah
2025-08-25 16:00 - 17:00 | Rockefeller Hall 312

(Asprey) Terence Tao, UCLA
2025-09-11 17:00 - 18:00, 2025-09-12 15:00 - 16:00 | Rockefeller Hall 300

(Colloquium) Ron Buckmire, Marist University
2025-09-24 16:00 - 17:00 | Rockefeller Hall 203
Different Differences: An Introduction to Nonstandard Finite Difference Schemes
From Calculus we know that a derivative of a function can be approximated using a
difference quotient. There are different forms of the difference quotient, such as the
forward difference (most common), backward difference (less common) and centered
difference (more accurate). In this talk I will discuss several different differences,
specifically nonstandard finite differences (NFSD) that can be used to approximate the
derivatives that appear in differential equations as a solution technique. Many NSFD
schemes have been created, discovered, and promoted by Dr. Ronald E. Mickens, an
African American Emeritus Professor of Physics at Clark Atlanta University, who has
written more than 300 research articles and a dozen books. I’ll provide examples of
NSFD schemes that can be used to solve a wide variety of problems drawn from
several different areas, such as first-semester Calculus, elementary ordinary differential
equations, and more advanced differential equations.

(LAVA) Anthony Cheng, VC '23
2025-10-03 15:00 | Rockefeller Hall 310
LAVA Talk on Learning how to learn: my journey from the classroom to doing research, with Anthony Cheng (VC ’23), PhD Student, University of Pennsylvania. Friday, October 3, 2025 at 3PM in Rocky 310.

About Anthony:
Anthony Cheng is a Class of 2023 alum who studied applied math, competed on the varsity swim team, and played trumpet in the symphony orchestra while at Vassar. He enrolled in the engineering dual-degree program with Dartmouth, studying mechanical engineering with a focus in dynamical systems. He continued at Dartmouth for another year of graduate research in fluid mechanics applied to glaciology contexts and defended his master’s thesis in July 2025. Now, Anthony has started a PhD program at UPenn in Mechanical Engineering and Applied Mechanics, with a focus in fluid mechanics applied to urban wind pattern sensing and reconstruction.
