## Spring 2024

**Monday, January 22nd**

Matthew Jones, Yale Institute for Network Science

Graph Colorings: How to Make Maps and Work Together

What does a completed Sudoku look like? How many colors do you need to color a map of the United States? How many radio stations can fit in Pennsylvania? Graph colorings are mathematical objects that are all around us, hiding just underneath the surface. In this talk, I will explain what graph colorings are and why they matter to puzzlers, cartographers, radio DJs, and mathematicians. After learning about some famous results like the 4-Color Theorem, we will see what graph colorings can teach us about how groups of people work together to solve problems.

11:30am

Tome 117

Pizza provided

**Tuesday, January 23rd**

Melissa Innerst, Juniata College

The Bayesian Paradigm: A New Way of Thinking About Statistics

Bayesian statistics, named for Reverend Thomas Bayes, is based on the Bayesian interpretation of probability where probability expresses a degree of belief in an event. This is an alternative to the frequentist paradigm of statistics taught in most undergraduate statistics courses. This talk will discuss the major differences between the two paradigms, demonstrate the mechanics of basic Bayesian inference through simple examples, and highlight one application of Bayesian statistics in the field of turtle phenology.

Noon

Tome 115

Pizza provided

**Tuesday, March 19th**

Dr. Johnna Goble, Shippensburg University

How Mathematical Models Can Help Us Understand Biology

Mathematical models can be used to gain a better understanding of various biological systems and mechanisms. In this talk we will begin by looking at the predator-prey model, a seminal model from biomathematics. We will then use techniques from this first model to explore the mechanisms involved in the progression of Prostate Cancer and its response to treatment.

Noon

Tome 115

Pizza provided

**Monday, April 15th**

Mathematics & Computer Science Majors Dinner

Trivia Contest

Pi Mu Epsilon and Upsilon Pi Epsilon inductions and departmental prizes and awards

6:00pm

HUB Social Hall

Catered Meal by Dining Services

**Thursday, April 18th**

All Science Symposium Poster Session

*Abstract deadline is Monday, April 8th*

4:30-6:00pm

HUB Social Hall

Refreshments provided

**Tuesday, April 23rd**

Departmental Honors Presentation

Dzung Dinh '24 - Application of Neural Radiance Field Single-object 3D Reconstruction Algorithms for Volume Estimation

This study explores Neural Radiance Fields (NeRF) for accurate volume estimation from photographs in uncontrolled settings, employing PixelNeRF for depth maps and space carving techniques for computing volume. Testing on synthetic and Stanford Cars datasets reveals robustness and precision, showcasing NeRF's potential to advance nutritional management and other applications.

Noon

Tome 115

Lunch provided

**Friday, April 26th**

Departmental Honors Presentation

Hailie Mitchell '24 - Efficient Bug Finding in Robotic Deep Learning: Adversarial Rendering for GQCNNs

Convolutional neural networks (CNNs) are a type of machine learning model used for image recognition tasks, including in robotic systems, like Grasp Quality CNNs (GQCNNs) that predict the success of a robotic grasp on a 3D object. Adversarial attacks expose vulnerabilities in models by changing input data, so the model outputs an incorrect prediction. Most adversarial attacks perturb 2D images, but we extend attacks to 3D objects. We attack a GQCNN by changing the 3D shape of an object such that the GQCNN makes an incorrect prediction about the quality of a particular grasp on the object in comparison to a physics-based oracle. We show this novel technique, adversarial rendering, can successfully find 3D adversarial examples, exposing vulnerabilities in a GQCNN.

4:30pm

Tome 115

Refreshments provided

**Tuesday, April 30th**

Departmental Honors Presentation

Emily Shambaugh '24 - Factorization Patterns of Polynomials and Partitions

A partition of a natural number n is a non-increasing sequence of natural numbers that sum to n. For example, the partitions of four are: 4, 3+1, 2+2, 2+1+1, and 1+1+1+1. In this talk, we explain how certain types of partitions can be used to describe the ways a polynomial of degree n may factor. We provide a recursive formula for the number of these partitions and generalize these ideas to the case of two polynomials whose degrees sum to n.

Noon

Tome 115

Lunch provided

**Thursday, May 2nd**

Mathematics Senior Research Presentation

(William) Xuyan Cheng '24 - Greedy Heuristics Evaluation for Cubic Multidimensional Knapsack Problem

The 0-1 cubic knapsack problem (CKP) is a challenging optimization problem where the objective is to select a subset of items, each with distinct values and weights, to maximize total value within a knapsack of limited capacity. Quadratic and cubic terms introduce complexity by modeling synergistic value increases when specific combinations of items are selected. This research develops new heuristic algorithms designed to find high-quality solutions for the CKP and its multi-dimensional variant. Thorough computational evaluation was used to identify the most promising heuristic approach.

Noon

Tome 117

Lunch provided

**Friday, May 3rd**

Tilings and Tessellations Poster Session

Math 301 (Prof. Richeson)

1:30-2:45pm

Rector Atrium

Light refreshments provided

**Wednesday, May 8th**

Mathematics & Computer Science End of Year Picnic

Join us as we celebrate the end of the academic year

Noon

Rector Courtyard (Rain Location: Rector Atrium)

BBQ lunch provided