Image of LEAP 2023: Panel 17 - Data Synergy: Converging Analysis with Interdisciplinary Models

LEAP 2023: Panel 17 - Data Synergy: Converging Analysis with Interdisciplinary Models

Anqi Wei ’24
Major: Mathmatics and Psychology double major

Analysis of COVID-19 Variant Transition and Spread Dynamics

What impact does the transition from the Delta variant to the Omicron variant have on the spread of COVID-19? I spent eight weeks digging into the world of mathematics, statistics, and COVID research during the UMass Math & Stats REU 2023 program. The focus of my group was on a meta-population model that looked at how different COVID variants move between different groups of people in different places, specifically in London and New York City. We wanted to improve how we spot COVID hotspots and track how the virus spreads. Working closely with another student and my mentor, I gained a deeper understanding of how mathematics can be used to solve real-world problems.


Mariam Fatima ’25
Major: Mathematics and Computer Science double major

Limited Data Tomography

Over the summer, I worked as a research scholar in Tufts University. My project was to reconstruct limited data obtained from Tomography. Tomography is the mathematics behind X-ray and Ultrasound technologies including SONAR, which was my focus. So, how do we generate, reconstruct and analyze data through the lens of applied mathematics? What kind of singularities do we observe and what causes them? Understanding the math behind these is important for scientists to discover what features are present and absent in the objects they’re reconstructing. And hence, Limited Data Tomography is an important part of research fields in Mathematics and Computer Science!


Ruby Sapkota ’25
Major: Computer Science and Economics double major

Fortifying Cybersecurity through Machine Learning in Big Tech

Last summer, following my sophomore year, I joined Microsoft as a Software Engineer and Product Manager Intern in their ‘Digital Security and Resilience’ team. My time was filled with learning, meeting new people, and relishing the natural beauty of Washington State.

My project focused on employing data analysis and machine learning to predict persistent vulnerabilities on internal devices, a critical step in ensuring a secure cyber experience. The process involved cleaning and analyzing raw, confidential company data, followed by constructing three distinct machine-learning models. The most effective model was then selected, refined, and trained. Additionally, I created a Power BI report that serves as a visually intuitive summary of the results, providing enhanced accessibility compared to traditional reports. This early industry exposure significantly deepened my appreciation for my chosen career path. I also had the opportunity to participate in numerous intern programs, which greatly enriched my inaugural internship experience.


Sophie Su ’24
Major: Mathematics and Data Science double major

Immuno-epidemiological Model for Transient Immune Protection

This research investigates what happens when people who have been sick before are exposed to the same disease again. We used a special math model to understand how our natural defenses (like our immune system) react when this happens. We ran computer simulations to guess how likely it is for someone to get sick again and how long it might take. We found that if one is around a lot of sick people or if there's a long time between exposures, the chance of getting sick again goes up. This information could help us make better plans to prevent and control diseases.