Programs - Summer Research Institute in Math & Computing (SRIMAC)

Choose from an exciting array of academic experiences in the School of Computing and Data Science:

Promoting Robust and Optimized Techniques for Effective Cybersecurity Tools (P.R.O.T.E.C.T.): Software Development

This research experience will examine the current state of secure software development and ways to improve the process. Software vulnerabilities represent some of the most challenging cybersecurity threats. As a society, we need new solutions to develop robust software applications without the plethora of vulnerabilities in today's systems. Together, we will discuss vulnerabilities in requirements gathering, modeling, implementation, testing, and opportunities for improvement. Each student will pick a specific problem and use-case to improve. Finally, students will hypothesize solutions, gather evidence to test their hypothesis, and disseminate their results to fellow student researchers.

Unraveling the Mysteries of the Quantum Mixmaster Universe

In this project, we will construct numerical solutions to the Wheeler-DeWitt equation describing wavefunctions for the quantum Mixmaster universe, a cosmological model where the spatial universe is topologically a sphere with anisotropic geometry.  Although recent interest in the quantum Mixmaster and related Bianchi cosmological models has focused on evidence that such Bianchi wavefunctions may allow for quantum avoidance of the classical Big Bang singularity, the effects of varying the ordering assigned to noncommuting operators when constructing the Wheeler-DeWitt equation has not been extensively considered.  Based on a parallel with varying the diffusivity coefficient in the telegraph equation, we suggest that the chosen factor ordering in the Mixmaster Wheeler-DeWitt equation may have relevance to the issue of singularity avoidance, and we propose numerically evolving Gaussian initial data to probe the nature of the quantum Mixmaster propagator.  Various numerical schemes will be explored and coded in MATLAB.  The investigation will include stability analysis to select a reliable numerical scheme and will aim to provide evidence on which to base subsequent analytic descriptions of the propagator for the quantum Mixmaster model

PySpark & LLMs: The New Age of Data Analytics

In today's rapidly evolving technological landscape, Big Data has become integral to numerous applications. Traditional methods often struggle to manage the sheer volume and complexity of this data. This program introduces students to Big Data analysis techniques using PySpark, a powerful cluster-computing system, in tandem with the extensive capabilities of Large Language Models like OpenAI's GPT variants.

During the course, students will:
1. Gain foundational knowledge of Big Data concepts.
2. Engage with PySpark to experience its potent distributed data processing capabilities.
3. Explore the application of Large Language Models in extracting, understanding, and generating insights from massive datasets.
4. Work on real-world datasets, facing and overcoming challenges typical of high-volume data processing tasks.