# MSCS Seminar Calendar

Monday November 11, 2024

**Combinatorics and Discrete Probability Seminar**

Notes on two-point concentration of the independence number of the random graph

Tom Bohman (Carnegie Mellon University)

3:00 PM in 1227 SEO

It is well known that for any constant
probability p there exists a function k(n) such that the independence number of the binomial random graph G(n,p) is concentrated on two values (i.e.the independence number of G(n,p) is k(n) or
k(n)+1 with high probability). In this
talk we discuss the extension of this
result to p(n) that tends to 0 with n. In particular, we determine the probability at which two point concentration of the independence number of G(n,p) breaks down. We also discuss the independence number of G(n,m), and show that there is a range of values for m in which the independence number of G(n,m) is concentrated on two values while the independence number of the corresponding
G(n,p) is not concentrated on two values.
Joint work with Jakob Hofstad.

**Algebraic Geometry Seminar**

Elliptic surfaces over an elliptic base

Francois Greer (Michigan State)

3:00 PM in 636 SEO

Elliptic surfaces are a fairly well understood class of complex
projective surfaces. They come with two discrete invariants,
$g$ and $d$, both nonnegative integers. I will discuss some
new results (joint with P. Engel, A. Ward, and Y. Zhang) about
the moduli space and Hodge theory of elliptic surfaces with
$(g,d)=(1,1)$. While they have Kodaira dimension one, they behave
like K3 surfaces in many respects, and they provide an interesting
test case for the Hodge Conjecture in dimension 4.

Wednesday November 13, 2024

**Logic Seminar**

Internality of autonomous systems of differential equations

Leo Jimenez (Ohio State University)

10:00 AM in 304 Taft

When solving a differential equation, one sometimes finds that solutions can be expressed using a finite number of fixed, particular solutions, and some complex numbers. As an example, the set of solutions of a linear differential equation is a finite-dimensional complex vector space. A model-theoretic incarnation of this phenomenon is internality to the constants in a differentially closed field of characteristic zero. In this talk, I will define what this means, and discuss some recent progress, joint with Christine Eagles, on finding methods to determine whether the solution set of a differential equation is internal. As a corollary, we obtain a criterion for solutions to be orthogonal to the constants, and in particular not Liouvillian. I will show a concrete application to Lotka-Volterra systems.

**Statistics and Data Science Seminar**

Taking Mobile Consumer’s Pulse--An Integrated Analysis of Mobile Application Usage and In-App Advertising Response

Dr. Yingda Lu (UIC, Department of Information and Decision Sciences)

4:00 PM in 636 SEO

Consumers have increasingly spent more time on mobile applications, and companies have also allocated more resources to advertisement in mobile applications and are actively seeking ways to improve the click-through rate of in-app ads. However, there is a lack of research leveraging consumers’ mobile application usage to understand in-app advertisement. In this study, we develop an integrated model of mobile application usage and in-app advertising response. We use a hidden-Markov model (HMM), which allows consumer involvement in mobile activities to drive temporal changes in both consumer mobile application usage and in-app advertising response. Our framework captures three components that are understudied in previous research on in-app advertising responses: 1) contextual mobile app in which consumers are targeted; 2) long-range correlation in preceding periods and 3) multitasking across mobile apps. To address the challenge of long-range correlation in traditional HMM, we further extend HMM by incorporating a long short-term memory (LSTM) autoencoder into the state transition. Using a unique panel dataset, we find salient temporal patterns and persistence of consumers’ underlying involvement that govern both application usage and advertisement response. Interestingly, consumers’ responses to advertisements follow an inverted-U shape where consumers are most likely to respond to advertisements in a medium state of involvement. Consumers’ advertisement responses are also subject to a contextual effect. For example, consumers are more likely to respond to advertisements when they use Entertainment apps compared with other apps. Our simulation indicates that incorporating mobile usage information, such as a temporal state of involvement and contextual effects at the individual level (viewing history), can significantly improve the effectiveness of targeting strategies. For instance, incorporating contextual effect and multitasking can increase performance by as much as 21.2%. This improvement can be further enhanced with the help of the LSTM autoencoder to address the long-range correlations in HMM. We are the first to connect consumers’ mobile application usage with their in-app ad response.

Friday November 15, 2024

**Departmental Colloquium**

Reflections on a mathematician's journey into the public eye.

Noah Giansiracusa (Bentley University)

3:00 PM in 636 SEO

During the pandemic I felt I needed a break from my usual research program so on a whim decided to try writing a book aimed at the general public, knowing nothing about how to do so. Unexpectedly, this launched a winding professional journey that over the past five years has included meetings with congressional staffers, guest appearances on CNN, a handful of newspaper op-eds, signing a literary agent, a friendship with a Nobel prize-winning economist, and plenty of Twitter feuds along the way. In this talk I'll share what I've learned from this adventure (including the missteps), distilling practical advice for those wishing to explore some of these professional avenues or others like them. I'll emphasize what role a mathematical background has played in a range of activities (like writing and networking) that don't outwardly appear to involve any mathematics.

Please let Laura Schaposnik at schapos@uic.edu know if you'd like to join Noah for dinner, or if you'd like to meet him during the day. He's doing a lot of interesting interdisciplinary maths: https://www.noahgian.com/

Monday November 18, 2024

**Algebraic Geometry Seminar**

Rational normal curves, phylogenetic trees, and tropical geometry

Noah Giansiracusa (Bentley University)

3:00 PM in 636 SEO

I'll discuss joint work with Alessio Caminata, Luca Schaffler, and Han-Bom Moon in which we study equations defining (the closure of) the locus of n points in projective space that lie on a rational normal curve and apply these equations to resolve a question of Lior Pachter and David Speyer from 2004 on the tropical geometry of the space of phylogenetic trees.

Tuesday November 19, 2024

Wednesday November 20, 2024

**Statistics and Data Science Seminar**

Stage-Aware Learning for Dynamic Treatments

Annie Qu (University of California at Irvine)

4:00 PM in 636 SEO

Recent advances in dynamic treatment regimes (DTRs) provide powerful optimal treatment searching algorithms, which are tailored to individuals’ specific needs and able to maximize their expected clinical benefits. However, existing algorithms could suffer from insufficient sample size under optimal treatments, especially for chronic diseases involving long stages of decision-making. To address these challenges, we propose a novel individualized learning method which estimates the DTR with a focus on prioritizing alignment between the observed treatment trajectory and the one obtained by the optimal regime across decision stages. By relaxing the restriction that the observed trajectory must be fully aligned with the optimal treatments, our approach substantially improves the sample efficiency and stability of inverse probability weighted based methods. In particular, the proposed learning scheme builds a more general framework which includes the popular outcome weighted learning framework as a special case of ours. Moreover, we introduce the notion of stage importance scores along with an attention mechanism to explicitly account for heterogeneity among decision stages. We establish the theoretical properties of the proposed approach, including the Fisher consistency and finite-sample performance bound. Empirically, we evaluate the proposed method in extensive simulated environments and a real case study for COVID-19 pandemic.

Monday November 25, 2024

Tuesday November 26, 2024

Monday December 2, 2024

Tuesday December 3, 2024

Wednesday December 4, 2024

Friday December 6, 2024

Monday January 13, 2025

Wednesday January 15, 2025

Friday January 24, 2025

Monday January 27, 2025

Monday February 3, 2025

Friday February 7, 2025

Monday February 10, 2025

Monday February 24, 2025

Friday February 28, 2025

Monday March 10, 2025

Friday March 14, 2025

Monday March 31, 2025

Wednesday April 2, 2025

Friday April 4, 2025