The panel embarks on an interdisciplinary exploration, converging applied mathematics and Machine Learning. It begins by examining re-exposure effects on individuals previously infected during disease outbreaks, utilizing advanced mathematical models to unravel post-infection dynamics crucial for shaping effective public health strategies. Shifting focus to cybersecurity, the discussion delves into analysis and predictions of persistent vulnerabilities, employing a fusion of Machine Learning and Data Analysis. It proactively identifies and mitigates digital susceptibilities, enhancing cybersecurity measures. Further, epidemiology takes the spotlight, with a study on the impact of COVID-19 variant transitions using meta-population modeling, facilitated by mathematical foundations to comprehend variant-specific transmission patterns and inform targeted interventions. Lastly, the panel explores the mathematics behind reconstructing limited data, particularly in SONAR technology, emphasizing the transformative role of mathematical algorithms in enhancing data accuracy and utility. Collectively, these interdisciplinary endeavors underscore the diverse and impactful roles of computer science, data science, and mathematics in addressing multifaceted real-world challenges.

Moderator:

• Christopher Cox, Visiting Lecturer in Mathematics

Speakers:

• Ruby Sapkota '25, Computer Science and Economics double major
• Anqi Wei '24, Mathmatics and Psychology double major
• Mariam Fatima '25, Mathmatics and Computer Science double major
• Sophie Su '24, Mathmatics and Data Science double major