MAA  Mathematical Association of America

Seaway Section

Location: Room 205, Hubbard Hall

1. Time:

   1:20 pm – 1:40 pm

   Title:

   The Pythagorean Theorem from Trigonometry

   Speaker:

   David Clark (SUNY New Paltz)

   Abstract

   This talk will give an application of the speaker's Friday evening banquet talk, sharing a brilliant and surprising discovery made by two hardworking and determined young women while still in high school.

2. Time:

   1:50 pm – 2:10 pm

   Title:

   Lost in Translation: Cardano’s Ars Magna

   Speaker:

   Olympia Nicodemi (SUNY Geneseo)

   Abstract

   Cardano’s pivotal work of 1545, his Ars Magna, was written in Latin. The primary English translation by T. Richard Widmer is readily available. Widmer was a good Latinist who understood the mathematics. Like all translators, Widmer made decisions, most significantly, to express all of Cardano’s mathematical expressions in modern notation. It is a gift to the first-time reader of the work but for historical context, a reader may want a second look. In this talk, we will look at what a more faithful translation can tell us.

3. Time:

   2:20 pm – 2:40 pm

   Title:

   How Did Cardano Solve the Cubic Equation?

   Speaker:

   Gary Towsley (SUNY Geneseo)

   Abstract

   Girolamo Cardano published the first algebraic solution of a general form of the cubic equation in his book, Ars Magna, in 1545. Exactly what does it mean to say that he solved a form of the cubic equation. We will look at what he did and what he might have done (without telling us.)

4. Time:

   2:50 pm – 3:10 pm

   Title:

   Math on the Hill: a Brief History of the Mathematics Department at Syracuse University

   Speaker:

   Lawrence D'Antonio (Ramapo College)

   Abstract

   The Syracuse University Mathematics Department has a long and distinguished history. In this talk we will look at some of the high points of that history. From the creation of the mathematics department in 1871 by John Raymond French (one year after the birth of the university) up to the current day, we will look at the faculty, graduate students and curriculum in mathematics. Some of the figures we will discuss are Edward Roe Jr. (who founded Pi Mu Epsilon), Julia Robinson Roe (the wife of Edward Roe and the first woman Math Ph.D. from Syracuse), Howard Eves, Lipman Bers, Paul Erdös, Paul Halmos, Hans Samelson, Atle Selberg, George Mostow, Wolfgang Jurkat, and David Eugene Smith. Several of these celebrated mathematicians only spent a few years at Syracuse, but there presence there helped define the mathematics department.

Location: Room 207, Hubbard Hall

1. Time:

   1:20 pm – 1:40 pm

   Title:

   Using Storytelling to Teach Mathematics

   Speaker:

   Robert Rogers (SUNY Fredonia)

   Abstract

   Storytelling could be considered the first pedagogical tool as knowledge was often passed down by relaying stories around a campfire. This experience is embedded in the human psyche. It is typically easier for people to remember stories than individual facts. Many mathematics texts have dispensed with the storytelling experience by focusing on the content. This is efficient but ignores what math historian Ivor Grattan-Guinness described as mathematical heritage. Including a narrative behind the mathematics helps to explain how we got from there to here. Providing a context for the mathematics we teach helps students make sense of what are often non-intuitive ideas. This talk will provide examples and access to materials to show how storytelling can be included in the classroom without sacrificing content.

2. Time:

   1:50 pm – 2:10 pm

   Title:

   Psychological Techniques for the Math Classroom

   Speaker:

   Dawn Jones (SUNY Brockport)

   Abstract

   Most teachers are taught the very basic tenets of psychology but often lack time to go into more advanced techniques to actually use in the classroom. In this talk I've give a broad overview of some of these including the Interrogation Protocol. Participants will be asked to practice using the techniques during the session.

3. Time:

   2:20 pm – 2:40 pm

   Title:

   Diseases & Dice: All You Need Is a Handful of Dice (and a Few Susceptible Students)

   Speaker:

   Daniel Look (St. Lawrence University)

   Abstract

   Sometimes the best way to get students interested in mathematics is to start with something that doesn’t look much like mathematics at all. Diseases & Dice began as a workshop for Math & Everything, an annual outreach event we host at St. Lawrence University to show secondary school students how mathematics is connected to, well, everything.\[\newline\] The activity is pretty simple: students get into groups, roll some dice, and see how quickly an infectious disease can spread through a classroom. Along the way, we collect some data and use it to build a simple discrete-time model of an epidemic. I now use it as an icebreaker in my differential equations course, returning to the game when we discuss the classic SIR model later in the semester. In this talk, I’ll briefly introduce Math & Everything, walk you through a round of Diseases & Dice, do a little math modeling, and show some interactive visualizations that help us explore what’s going on mathematically. \[\newline\] And yes, some of you will be rolling dice.

Location: Room 208, Hubbard Hall

1. Time:

   1:20 pm – 1:40 pm

   Title:

   Understanding Seasonal Affective Disorder Content on Instagram

   Speaker:

   Shandeepa Wickramasinghe (Utica University)

   Abstract

   Seasonal Affective Disorder (SAD) is a form of depression related to seasonal changes, but many people now learn about SAD through social media. What kind of information do they find? In this talk, we explore SAD-related Instagram posts collected using seven hashtags. We look at when these posts appear, who creates them, what symptoms and coping strategies they discuss, and how people engage with the content. Using mathematical and statistical methods, we compare monthly patterns across hashtags and examine how similar their seasonal patterns are. We also look at engagement across different types of content creators while taking follower count into account. Finally, we study the language used to describe SAD symptoms and coping strategies by examining how frequently different words appear and how those frequencies change with rank. This project combines mathematics, psychology, and social media data to provide a clearer picture of the SAD-related information people may encounter when searching Instagram.

2. Time:

   1:50 pm – 2:10 pm

   Title:

   Using Machine Learning to Accelerate Root-Finding in a Biological Model

   Speaker:

   Garrett Otto (SUNY Cortland)

   Abstract

   In a certain spatial population model, determining the population density in each new generation requires finding the locations where the density is at a value associated with high reproduction. A coarse grid search coupled with standard root-finding methods works quickly and accurately most of the time, but occasionally misses roots. In this talk, we will see how I trained a decision-tree classifier to identify when the faster coarse-grid method can be safely used and when a slower, fine-grid search is needed.

3. Time:

   2:20 pm – 2:40 pm

   Title:

   Growth and Decay Functions of Finite Graphs

   Speaker:

   Robert Sulman (SUNY Oneonta)

   Abstract

   For a finite undirected graph $G$, let $\mu^{+}(G)$ be the graph obtained from $G$ by adding an edge between any two non-adjacent vertices of $G$ having the same degree, and let $\mu^{-}(G)$ be the graph obtained from $G$ by removing an edge between any two adjacent vertices having the same degree. When either function is applied repeatedly to a given $G$, we must reach a “stable” graph. A graph $G$ is “$\mu^{+}$ complete” if the stable graph reached after progressive $\mu^{+}$ applications from $G$ is the complete graph on $n$ vertices, where $o(G) = n$. The graph is “$\mu$-collapsible” if the stable terminal graph of the $\mu^{-}$ progression for $G$ is the empty graph consisting of $n$ isolated vertices (no edges). We recognize certain graphs not to be $\mu^{+}$ complete or $\mu^{-}$ collapsible based on how specific vertices or distributed in the graph $G$. The “dual” of $G$ is defined to be the graph obtained from $G$ by removing all edges and connecting all pairs of vertices that were not adjacent in $G$. The dual enables us to bridge both $\mu^{+}$ and $\mu^{-}$ related notions and sometimes simplify calculations. We also introduce the sets $(\mu^{+})^{-1}(G)$ and $(\mu^{-})^{-1}(G)$ as well as how the dual helps us find inverse images.

4. Time:

   2:50 pm – 3:10 pm

   Title:

   The Remarkable Random Walk

   Speaker:

   James Marengo (RIT)

   Abstract

   Imagine you are standing on the integer zero and you use a coin toss to decide if your next position will be at +1 or -1. You now repeat this process indefinitely and move one unit to your right or left according as this coin comes up heads or tails. Are you guaranteed to eventually return to zero? We will use Markov chains to explore the mathematics needed to answer this question as well as similar versions of it in more than one dimension. The conclusions may surprise you!

Location: Room 209, Hubbard Hall

1. Time:

   1:20 pm – 1:40 pm

   Title:

   When the Weakest Link Hallucinates: Proving Workflow Safety in Lean 4

   Speaker:

   Angel Rivera (Utica University)

   Abstract

   A complex pipeline is only as strong as its weakest link, and when generative AI is introduced into safety-critical workflows, probabilistic models often become precisely that. In multi-stage processes such as financial underwriting, software compilation, and medical logistics, an undetected hallucination at an upstream node can compound across subsequent tasks, rendering downstream outputs unreliable despite "human-in-the-loop" assurances. In this talk, we show how to reinforce these vulnerable links by formalizing task dependency networks and error-containment semantics in Lean 4. We model workflow Directed Acyclic Graphs (DAGs) as inductive data structures over heterogeneous agents (human, autonomous AI, supervised AI) and define error heredity via an inductive propagation relation. Within this framework, we formally state and prove the Verification Barrier Invariant: when an independent, sound verification gate guards the critical path, upstream hallucinations are mathematically quarantined and cannot contaminate mission-critical sink nodes. Finally, we demonstrate compile-time counterexample extraction on unsafe graph topologies, showing how proof assistants provide applied mathematicians and computer scientists with a practical tool to enforce rigorous safety invariants on probabilistic systems.

2. Time:

   1:50 pm – 2:10 pm

   Title:

   Forensic Mathematics: 9/11 25 Years Later (PART 1)

   Speaker:

   Jonathan Hoyle (Carnegie Mellon University (formerly))

   Abstract

   2026 is the 25th Anniversary of the World Trade Center attack on 9/11. This presentation will cover the events following that day, and detail the forensic mathematics developed to identify the 2,977 victims of the attack. The victim identification project was the largest and most complex forensic project in history up to that time. This presentation will describe the events of 9/11 from a forensic perspective and detail the mathematics of DNA victim identification. These include DNA fingerprinting, kinship analysis, and other genetic applications. Moreover, this discussion will include how the mathematics was adapted to fit the statistical requirements of the medico-legal examiners at the NYC Office of the Chief Medical Examiner. In addition, we will discuss lessons learned from the project, share how the DNA analysis industry has adapted to the various mathematical approaches, and share a view of how this work changed the face of DNA identification over the past quarter century.

3. Time:

   2:20 pm – 2:40 pm

   Title:

   Forensic Mathematics: 9/11 25 Years Later (PART 2)

   Speaker:

   Jonathan Hoyle (Carnegie Mellon University (formerly))

   Abstract

   2026 is the 25th Anniversary of the World Trade Center attack on 9/11. This presentation will cover the events following that day, and detail the forensic mathematics developed to identify the 2,977 victims of the attack. The victim identification project was the largest and most complex forensic project in history up to that time. This presentation will describe the events of 9/11 from a forensic perspective and detail the mathematics of DNA victim identification. These include DNA fingerprinting, kinship analysis, and other genetic applications. Moreover, this discussion will include how the mathematics was adapted to fit the statistical requirements of the medico-legal examiners at the NYC Office of the Chief Medical Examiner. In addition, we will discuss lessons learned from the project, share how the DNA analysis industry has adapted to the various mathematical approaches, and share a view of how this work changed the face of DNA identification over the past quarter century.