College of Arts and Sciences
Mathematics + Computer Science Seminar

Speaker: Yelena Vaynberg
3D printing technology has transformed the manufacturing landscape, making it possible to create complex shapes that were once challenging or unachievable with traditional methods. In this presentation, we will explore the mathematical underpinnings that are fundamental to 3D printing technologies, with a particular emphasis on the algorithms that translate digital models into executable printing instructions. We'll examine the role of mathematics in the slicing process, the significance of topology in enhancing structural integrity, and discuss how mathematical optimization can be utilized to minimize material use and reduce printing duration. Furthermore, this presentation will showcase how these principles are applied in practice by producing complex mathematical figures, including fractals and geometric sculptures.

Speaker: Eric Martinson
Repeated visual observations of an environment are common in big data: people capturing temporally separate video streams with phones; facility security combining fixed cameras with human patrols; robots cleaning or monitoring a home. The challenge, however, is effectively processing these large highly repetitive data to extract useful results. Event-based methods like object detection struggle with a lack of application specific training data, while anomaly-based methods have high false positive rates requiring significant human review. Indoor spaces further complicate the matter as they are often co-occupied by people, changing constantly, and have highly individual detection requirements. What is needed are new ways for incorporating context into the search, discarding that which a human observer would otherwise ignore. To address this challenge, we have developed a novel system for Meaningful Change Detection, integrating two recent advances in machine learning: Neural Radiance Fields (NeRF’s) and Contrastive Language-Image Pre-Training (CLIP). Combining these approaches allows us to generate before and after images from the same viewpoint with a NeRF model, then apply semantically meaningful queries to search for changes useful to the application. This talk will present early results from the first prototype system and discuss future directions for investigation.

Speaker: Bashkim Zendeli
In this presentation, I will discuss trivial, yet pivotal obstacles encountered in basic algebra and calculus, which frequently lead to contradictions. Furthermore, I will introduce my approach to solving an applied physics problem. I will introduce known but not often used in calculus classes the FM techniques for evaluating non elementary integrals. I will provide an illustrative example from number theory and examine the integration of complex variable functions, emphasizing their connection and significance to Zeta functions and their implications for the Riemann Hypothesis.

Speaker: Wisam Bukaita
The fundamental connection between data science and research is evident in that data plays with the research process. As researchers and students, our first step towards conducting impactful research is obtaining and utilizing data to support our hypotheses. Without data, research is merely based on ideas and thoughts, lacking real-world insights. In this sense, data forms the foundation of any meaningful research. However, simply having data is not enough; it needs to be managed, organized, and analyzed to derive meaningful conclusions. This is where the toolbox of data science comes into play. These powerful tools and techniques allow us to efficiently manage and manipulate data, enabling us to draw relevant insights and draw strong conclusions. The data science toolbox encompasses a range of essential components, from data extraction and organization to predictive modeling and visualization, all of which are vital for successful research. Whether conducting a literature review, hypothesis testing, or building predictive models, the data science toolbox is a valuable resource for researchers and students alike.

Speaker: Haissam Badih
Recent advancements in malware necessitate a robust security solution, as traditional and hybrid malware detection approaches fall short of modern cyber-attack strategies. This paper introduces a novel method to detect malware targeting webcam protocols and addresses crypto-jacking threats within blockchain technology. Our approach enhances blockchain security by injecting a specialized application into each node, enabling the identification and protection of miners and nodes against crypto-jacking. The application monitors CPU usage to detect abnormal processes, utilizing a cuckoo process for further analysis without alerting the attacker, thereby preventing compromised miners from conducting transactions. This innovative method not only secures nodes but also contributes to creating a denylist of infected internet protocols and blockchain addresses, enhancing overall network safety. Tested against threat actors and regular users, our approach is an effective defense mechanism against malware, particularly crypto-jacking.

Speaker: Christopher Cartwright
The intertwining of mathematics, physics, and music has a long and convoluted history. We will trace some of the significant developments in music with input from math and physics related to musical scales and tonality, string theory ancient and modern, overtones and frequencies. The journey will see contributions from Pythagorus to Fourier via Euler, Galileo to Einstein via Helmholtz, and Bach to Stravinsky via Beethoven.

Speaker: Bruce Pell
In this talk, I’ll present an overview of my past, present and future research projects related to modeling the spread of infectious diseases. Along the way, we’ll discuss reasons why such a task is essential and what types of mathematical tools can be used to understand the dynamic spread of diseases. Specific case studies will be presented from previous research projects (Ebola, Zika and Plague) and current and future projects (COVID-19, wastewater-based epidemiological surveillance, pathogen fitness, and thermal mismatch curves).

Speaker:  Matthew D. Johnston
We investigate how a population's natural and vaccine immunity affects the competitive balance between two strains of an infectious disease with different epidemiological characteristics. Specifically, we consider the case where one strain is more transmissible and the other strain is more immune-resistant. Our analysis shows that vaccination has a significant effect on the competitive balance between two strains, potentially leading to dramatic flips from one strain dominating in the population to the other. It also shows that which strain gains an advantage as a population's immunity level increases depends upon the integration between the mechanisms of natural and vaccine immunity.