Biography
Dr. Hongmei Chi is the Google Endowed Professor in the Department of Computer and Information Sciences at Florida A&M University. From 2016 to 2021, she directed the FAMU Center for Cybersecurity. She is director of CIS graduate program since 2021. With extensive research funding in machine learning, quantum computing, digital forensics, HPC, and data science, Dr. Chi teaches undergraduate and graduate courses, including network security, Blockchain, cryptography, AI, and deep learning.
Degrees Earned
Ph.D in Computer Science, 2004 Master's degree in statistics, 1998
Research Areas
Applied Mathematics; Computer Science; Data Science; Machine Learning/AI
Research Interests
My research and academic interests lie at the intersection of Quantum Computing, High Performance Computing (HPC), and XAI, with applications in genetic data analysis and digital forensics. I am particularly focused on leveraging these advanced computational paradigms to develop innovative, interpretable, and scalable solutions that enhance both biomedical discovery and cybersecurity.
Topical Areas
Applied Computer Science; Applied Mathematics; Artificial Intelligence and Intelligent Systems; Computer Science
Research Synergy
High Performance Computing (HPC) has become an indispensable tool in solving complex scientific and biomedical challenges, particularly in the rapidly evolving field of precision medicine. With the increasing availability of high-dimensional genetic data, the integration of XAI and HPC offers a transformative opportunity to uncover meaningful biological insights, identify actionable biomarkers, and accelerate the discovery of targeted therapies. The Department of Energy (DOE) Laboratories possess cutting-edge HPC infrastructure and world-class expertise that are uniquely positioned to support and advance this type of computationally intensive research. To fully harness the potential of HPC in biomedical applications, it is vital to foster strong, sustained collaborative efforts between HBCU faculty-student research teams and DOE Lab scientists. The primary objective of this initiative is to promote interdisciplinary research collaborations that leverage the strengths of Florida A&M University (FAMU) faculty and students alongside DOE Lab personnel. By working together on projects focused on XAI-driven exploration of genetic data, we can develop robust, interpretable models that not only advance scientific understanding but also enhance the transparency and trustworthiness of AI systems used in healthcare
Motivation
Collaboration fosters strong professional relationships and networks. For FAMU team, these connections extend beyond the immediate project and can lead to future research opportunities, collaborations, and career advancements. Each of us brings a unique set of skills and knowledge to the table. When I and my students, and DOE Lab staff join forces, we create a powerhouse of expertise that can address complex challenges in XAI and HPC. Our combined knowledge is a valuable resource for tackling real-world problems, like genetic data. For my students, this collaborative effort is an invaluable opportunity for hands-on learning. Working alongside experienced faculty and lab staff not only enhances theoretical knowledge but also provides practical exposure. These experiences are transformative and empower next-generation students to become future leaders in the field.
Supervising Students Plan
Clearly articulate the objectives and goals of the collaboration between faculty/student teams and DOE Lab staff in the HPC and computing project. Make sure my students understand the purpose and expected outcomes. (1) Assign specific roles and responsibilities to each student in my team. Ensure that each student understands their role in the project and how it contributes to the overall success. (2) Create a detailed project timeline that includes milestones and deadlines. This timeline should be shared with all students in my team to keep everyone on track. (3) I will meet with my student daily/weekly. Encourage collaboration between faculty/student teams and DOE Lab staff by organizing regular meetings,
Student Merit
I mentored 20 graduate students and more than 30 undergraduates. 90% of them are African Americans. I work with students for highly motivated and like to learn and interested in XAI and HPC.
Lightning Talk Title
XAI for Secure Interpretable Analysis of Genomic Data
Keywords
XAI; Precision Medicine; Genomic Data, privacy-preserving model, federated machine learning, scRNA-seq, data privacy, patients’ data privacy