Vassar Math & Stats Events Page

(LAVA) Carl Gutowski, VC '85
2023-12-01 15:00 - 16:00 (America/New_York) | Rockefeller Hall 312
LAVA Talk on Software Development with Carl Gutowski (VC ’85), SS&C | Innovest Systems.
Friday, December 1, 2023 at 3PM in Rocky 312.

About Carl:
Come hear about Carl’s path from Vassar to SS&C | Innovest Systems, where he is currently a software architect. (Hint: It involves majoring in math at Vassar, teaching high school math, obtaining a master’s degree in Computer Science) Over the course of his career, Carl has developed software for air traffic control, publishing, education, e-commerce and finance. He’s also a concert flutist who has won the National Flute Association competition and performed at such venues as Carnegie Hall.

(Colloquium) Robin Belton, Smith College
2023-12-14 13:00 - 14:00
Colloquium Talk
Robin Belton, Smith College
Thursday, December 14, 2023 at 1PM
Rocky 312
Title: Adaptive Algorithms in Geometric and Topological Visualization
Abstract: Geometry and Topology have been increasingly used to visualize and summarize the “shape” of data. Mapper is a popular topological data visualization tool that takes as input a set of point cloud data and produces as output a graph of vertices and edges reflecting the structure of the underlying data. To use Mapper, the user must specify many parameters to get the output graph. Optimizing these parameters is an essential part of obtaining a nice Mapper graph but are often challenging to find. We focus on the open cover parameter and propose a new algorithm for learning an open cover that is based on clustering algorithms, statistical tests, and an iterative splitting procedure. At the end, we discuss how to extend these ideas to different settings.

(DSS Colloquium) Brenden Lake, New York University
2024-02-07 17:00 - 18:00 | Rockefeller Hall 300
Data Science & Society (DSS) Colloquium Talk with Brenden Lake, Assistant Professor of Psychology and Data Science at New York University, on Wednesday February 7 at 5PM in Rocky 300.
Title: Addressing two classic debates in cognitive science with deep learning
Abstract: How can advances in machine learning advance our understanding of human development? In this talk, I’ll use deep neural networks to address two classic debates: (1) How much language is learnable from sensory input? Using head-mounted video recordings from a single child, we show how deep neural networks can acquire many word-referent mappings, generalize to novel visual referents, and achieve multi-modal alignment. (2) Can neural networks capture human-like systematic generalization? We address a 35-year-old argument that neural networks are not viable cognitive models because they lack systematic compositionality—the algebraic ability to understand and produce novel combinations from known components. Neural networks can achieve human-like systematic generalization when trained through meta-learning for compositionality, a new method for optimizing compositional skills through practice. These findings emphasize the power of neural networks and their increasing capability for addressing long standing issues in cognitive science.

(Asprey Lecture I) Laura DeMarco, Harvard University
2024-02-08 17:00 - 18:00 | Rockefeller Hall 300
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.
Title: From the solar system to the Mandelbrot set
Abstract: 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.

(Asprey Lecture II), Laura DeMarco, Harvard University
2024-02-09 15:00 - 16:00 | Rockefeller Hall 300
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.
Title: The Mandelbrot set today: what we know and what we don’t know
Abstract: 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.

Integration Bee
2024-02-21 15:15 - 17:00 | Rockefeller Hall 300
Join us for an integration bee on Friday February 21 at 3:15PM in Rocky 300. Both students and faculty are welcome to sign up to compete or simply come to watch. It’ll be a fun, low-stakes event aimed at building rapport within the department. Your participation would be greatly appreciated—please sign up here!

(LAVA) Elijah Appelson, VC '23
2024-03-22 15:00 - 16:00 | Rockefeller Hall 310
LAVA Talk on Data Activism with Elijah Appelson (VC ’23), ACLU.
Friday, March 22, 2024 at 3PM in Rocky 310.

About Elijah:
Elijah is a data analyst at the ACLU of Louisiana. His work focuses on gathering, analyzing, and visualizing data related to civil liberties (primarily criminal legal reform) throughout the state. He is deeply committed to using data to honor and amplify people’s lived experiences to make data accessible to all stakeholders. Elijah graduated from Vassar College in 2023 with a Degree in Mathematics (core/pure path). While at Vassar, he undertook multiple positions at justice organizations, including the Center for Community Alternatives, Vera Institute of Justice, and Court Watch of Dutchess County.

Math Jam 2024
2024-03-22 18:00 - 20:00 | The Aula
Math Jam on Friday, March 22, 2024 from 6-8PM in the Aula/Ely Hall.
Vassar Students – Sign-up with Dr. Lisa Lowrance to volunteer at this community outreach event!

(Colloquium) Phanuel de Andrade Mariano 3/26
2024-03-26 15:00 - 16:00
The Hot Spots Problem

Phanuel de Andrade Mariano
Union College

Tuesday, March 26th
3pm Rocky 312

Consider a perfectly insulated 1-dimensional rod, or a 2-dimensional plate, or better yet, a 3-dimensional room. Perfectly insulated means that heat cannot escape this room. The Hot Spots Problem asks about what happens to the location of the “hot spots” and “cold spots” of this insulated body over a long period of time. To understand this problem we will introduce the equation that describes the evolution of heat over time. Moreover, we will discuss what is known (and not known) about this problem. We end the talk by discussing the connection between the Hot Spots Problem and Probability theory. In particular, this connection will be through a theory of random particles called Brownian motion.

DataFest 2024
2024-04-05 17:00 - 2024-04-07 17:00
Save the date for DataFest 2024 @ Vassar: April 5-7, 2024. This is our annual data competition open to all Vassar students, as well as neighboring schools by invitation. For more details about DataFest, including past DataFests at Vassar, visit: https://pages.vassar.edu/datafest/

(DSS Colloquium) Thomas Schmickl, University of Graz, Austria
2024-04-08 17:00 - 18:00 | Rockefeller Hall 300
Data Science & Society (DSS) Colloquium Talk with Thomas Schmickl, University of Graz, Austria, on Monday April 8 at 5PM in Rocky 300.
Title: Can Robots Save Nature?
Abstract: Our planet is on the brink of the 6th mass extinction, as our ecosystems are rapidly losing both
diversity and biomass. As intra- and inter-specific interaction networks weaken, ecosystems
become increasingly unstable, setting off on a downward trajectory along a deadly spiral. I
explore how robotic systems can play a crucial role in supporting ecosystems and communities.
I will show three levels of agency and how a “tech for good” approach might be helpful to
fight ecosystem decay: monitoring, intervention and restoration. By mitigating ecosystem
decay, robots may buy us precious time to address the root causes of environmental crises. I
will show innovative systems that we’ve developed over recent years — the initial strides toward going beyond mere animal-interaction systems by establishing eco-effective robotics.
Poster Link: https://pages.vassar.edu/dss/files/2024/03/Schmickl.presentation.8April2024.pdf

(Colloquium) Anna Pun 4/9
2024-04-09 15:00 - 16:00
The Magic of Tableaux: Exploring the Wonders of Algebraic Combinatorics
Anna Pun
CUNY Baruch College
Tuesday, April 9th
3pm Rocky 312
Tableaux are one of the most fundamental and versatile objects in algebraic combinatorics, as they can encode and connect various concepts and structures in the field. In this talk, we will start with the definition and properties of Young tableaux, which are graphical representations of partitions of integers. We will then see how tableaux can be used in algebra: their connection to symmetric functions and partition algebras; how they can be related to various combinatorial operations, such as the RSK-algorithm and the Jeu-de-taquin procedure; and how they can give rise to various combinatorial structures, such as lattice paths, vacillating tableaux, and parking functions. We will also explore some variations of tableaux, such as composition tableaux and set-valued tableaux, and discuss some interesting problems and conjectures that arise from them. We will conclude with some open questions and directions for future research on tableaux and their applications in algebraic combinatorics.

(DSS Event) Lens Media Lab, Yale University
2024-04-09 17:30 - 18:30 | Taylor Hall
Data Science & Society (DSS) Event with the Yale University Lens Media Lab on Tuesday April 9 with Thomas Schmickl, University of Graz, Austria, on Monday April 8 (multiple times; see below).
Please join us at 5:30PM, Tuesday, April 9 in Taylor Hall Room 102. Paul Messier, Kappy Mintie, and Damon Crockett from the Lens Media Lab at Yale University will participate in a panel titled
“Characterizing Twentieth-Century Photographic Papers: A Multidisciplinary Approach.”
The panel will describe how the Paperbase project was conceived, the interdisciplinary work required to develop it, and how it can be used by scholars interested in the history of photography. Each of the panelists, who come from different disciplinary backgrounds, will also describe how they came to work at the Lens Media Lab and how their specialized knowledge contributed to the production of Paperbase.
The panel will be followed by a hands-on demonstration of some of the Lens Media Lab’s data collection tools at 6:30 PM in the Taylor Hall Jade Room.
Vassar students have the chance to meet with the panelists to discuss data science, art, and photography, among other topics, between 12PM and 2PM on April 9 in College Center 240. Food will be provided. Students are directed to sign up at this link.
This event is sponsored by Vassar’s Data Science and Society initiative, the Department of Art, and the Frances Lehman Loeb Art Center.

(LAVA) Kai Matheson, VC '19
2024-04-12 15:00 - 16:00 | Rockefeller Hall 310
LAVA Talk on Data Science for Social Good: From Academia to Federal Consulting, with Kai Matheson (VC ’19), Accenture.
Friday, April 12, 2024 at 3PM in Rocky 310.

About Kai:
Kai works at Accenture as a data analytics consultant for the USDA. They build interactive data visualizations for national food insecurity programs and spearhead USDA’s Data Science Training Program. Kai is passionate about utilizing quantitative methods for social good. At Vassar, they made an effort to weave together their own “quantitative social science” curriculum: majoring in math, minoring in urban studies, and housing their thesis in the economics department along with a computer science advisor. Among their favorite experiences at Vassar was working as a Q-Tutor and SI. After graduation, Kai worked as a predoctoral research fellow at Harvard, contributing to academic papers on economic mobility and inequality. However, as life happens, they made a major pivot by going into consulting next. In the process, they learned how to prioritize mental health and balance. They aspire to return to academia later in their career.

(Henry Seely White Lecture I), Bhramar Mukherjee, University of Michigan
2024-04-22 17:00 - 18:00 | Rockefeller Hall 300
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.
Note: This is the first of two lectures in the Henry Seely White Lecture Series. The second lecture is on Tuesday April 23 at 3PM.
Title: The Data Struggle of the Unseen
Abstract: 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.

(Henry Seely White Lecture II), Bhramar Mukherjee, University of Michigan
2024-04-23 15:00 - 16:00 | Rockefeller Hall 300
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.
Note: This is the second of two lectures in the Henry Seely White Lecture Series. The first lecture is on Monday April 22 at 5PM.
Title: Analysis of “Big” Real-World Health Care Data: Promises and Perils
Abstract: 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.

(Colloquium) Leo Goldmakher 4/26
2024-04-26 15:00 - 16:00
Some Fascinating Characters in Number Theory
Are there infinitely many primes of the form n^2+1, where n is an integer? No one knows. In fact, there’s no example of any (single variable) polynomial of degree 2 or greater that’s been proved to output infinitely many primes. By contrast, the linear polynomial n+1 outputs infinitely many primes, a fact that’s been known for over 2000 years. Rather less trivially, Dirichlet proved in 1837 that any linear polynomial of the form an+b with a, b coprime must output infinitely many primes. To make his proof work, Dirichlet introduced certain nice functions called characters, which evolved (over the course of the next hundred years) into fundamental objects of study in algebra and number theory. I will discuss some of the history and mathematics of Dirichlet’s characters, including a very recent and simple characterization of them that seems to have been previously overlooked.

(LAVA) Lisa (Esposito) Cannizzaro, VC '16
2024-09-20 15:00 - 16:00
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
