Name: Abiodun Sumonu
Pronouns: he/him/his
Institution: The University of Alabama
Department: Department of Mathematics
Biography:
Abiodun, a first-generation fifth-year Ph.D. student at the University of Alabama, is passionate about advancing Mathematics through research. He explores combinatorial optimization problems, particularly convex relaxations for machine learning applications like clustering and classification. His interdisciplinary interests span theoretical mathematics, statistics, and computer science, focusing on leveraging heterogeneous datasets for innovative AI and ML algorithms. His skills extend to Convex Optimization, Mathematical Modeling, and Statistical Inference, complemented by strong quantitative abilities and problem-solving skills. Outside academia, he enjoys travel, quality time with loved ones, and indulging in favorite TV shows and movies while fostering connections.
Academic Status: PhD Student
Year in program: 5th
Research Area/Department: Applied Mathematics; Machine Learning/AI; Mathematics
Other, specify:
Major/Specialty: Mathematics
Degrees Earned or in Progress: Ph.D. Mathematics, Mathematics, Expected Spring 2025 M.A. Mathematics, Mathematics, Spring 2022 B.S. Industrial Mathematics, Spring 2016 Diploma, Computer Science, Spring 2012
What courses or academic preparation have you completed to prepare for a summer internship experience?
Scientific Computing in Python, Biostatistics, Machine Learning, Stochastic Processes and Applications, Probability and Statistics, Data Structures and Algorithms, Linear Optimization, Nonlinear Optimization, Partial Differential Equations, Numerical Analysis, and Linear Algebra.
Have you published any research or worked on research/technical projects? No
Where has your research been published or where have you conducted research/technical projects?
Please describe your research/academic interests:
My research focuses on the application of semidefinite programming (SDP) to the clustering and biclustering of weighted graphs and bipartite graphs. We explore two relaxation techniques to address the k-densest clique and biclique problems, which involve partitioning complete weighted graphs or bipartite graphs into k-disjoint subgraphs. Our primary objective is to assess, using the stochastic block model, whether it is possible to identify clusters of a size that scales logarithmically with respect to the total number of nodes in the graph. My dissertation research aims to establish both necessary and sufficient conditions for clusterability within any given dataset. We also compare our findings and the outcomes achieved by various widely used clustering algorithms for validation purposes.
Computational and Data Science Areas:
Applied Mathematics; Computational Science Applications, i.e., Bioscience, Cosmology, Chemistry, Environmental Science, Nanotechnology, Climate, etc.; Data Analytics and Visualization; High-Performance Computing; Machine Learning and AI; Quantum Computing and Information Science
Research Synergy:
I am passionate about computational, AI/machine learning, and data sciences research due to their potential to address real-world challenges. My research in convex relaxations for machine learning demonstrates my commitment to these fields. Proficient in Python, MATLAB, and data science tools. My experiences, during my first sustainable research pathway internship, have provided me with more insight on how the DOE Labs work and better prepared me for DOE lab work next summer. My background, technical skills, and practical experience make me well-suited to contribute effectively to research in these areas. I am eager to apply myself to drive innovation and tackle complex research problems within the DOE labs.
Motivation:
I am motivated to join this program again because I aspire to broaden my horizons in the field of Machine Learning and Artificial Intelligence beyond what my doctoral studies have offered. I am enthusiastic about seizing the abundant opportunities that the Sustainable Research Pathways (SRP) program provides, such as workshops, seminars, and internships, which facilitate meaningful ML/AI research with practical, real-world applications. Furthermore, I look forward to the prospect of building valuable connections with fellow researchers and seasoned professionals in this field. As I near the completion of my doctoral degree, I envision leveraging the skills and experiences I acquire through this program to propel me toward a fulfilling and impactful career in the realm of Machine Learning and Artificial Intelligence research.
Lightning Talk Title: Two-Way Clustering?