Research
Research
My research focuses on the application of mathematical modeling and machine learning to systems analysis, optimization, and data-driven decision-making. My work bridges the gap between theoretical frameworks and real-world challenges in finance, insurance, and structural analysis.
My interests extend to advanced probabilistic modeling, particularly in leveraging machine learning algorithms to enhance predictive accuracy in risk assessment and decision-making. With a background in applied mathematics and actuarial science, I explore novel methodologies to improve efficiency and reliability in areas such as claims processing, underwriting, and portfolio optimization.
I am particularly drawn to the intersection of optimization and machine learning, seeking innovative ways to develop adaptable models that can address diverse challenges in dynamic systems. My research also includes a focus on data analysis and statistical modeling, key tools for understanding complex patterns and behaviors in systems.
Published Papers:
Antimicrobial Resistance and Tuberculosis Prevalence in Africa: A Public Health Concern (Download Paper)
News
I Presented My Research at the INFORMS 2025 Annual Meeting
I presented my poster, “Microbiome-Aided Classification of Forensic Body Fluids via Conjecturing and Machine Learning,” at the INFORMS 2025 Minority Issues Forum (MIF) Poster Competition during the Annual Meeting.
The research integrates interpretable machine learning with microbial signatures to advance forensic analysis. I'm grateful to my advisor, Dr. Paul Brooks, and collaborators Sahil Chindal, Parastoo B., Mauli Pant, Rekik Ziku, and Godfred Ahenkroa Kesse for their support.
I also gave a talk on Reinforcement Learning alongside colleagues, sharing new findings and future directions.
Photos from the INFORMS 2025 Annual Meeting
On April 29, 2025, I delivered a graduate seminar at Virginia Commonwealth University (VCU) on my research titled “Heuristics to Learning: A Reinforcement Approach to Mathematical Conjecturing.”
My presentation introduced a novel reinforcement learning (RL) method for automating symbolic reasoning tasks by integrating RL into mathematical conjecturing frameworks. Building on the deterministic Dalmatian heuristic used in the CONJECTURING tool by Brooks et al., I proposed a dynamic RL-based approach that filters and evaluates symbolic expressions through sequential decision-making.
The seminar was well received by faculty and graduate students, highlighting new directions for automated reasoning in mathematics.
Me with Member in a Minute | 2024 INFORMS Annual Meeting
The 2024 INFORMS Annual Meeting was held in Seattle, Washington, from October 20–23, 2024, at the Seattle Convention Center.
It brought together 6,000+ professionals in operations research, analytics, data science, and management science to exchange ideas, present research, and network with peers from academia, industry, and government. The meeting featured keynote sessions, technical tracks, tutorials, and opportunities for collaboration across disciplines.
Photos from the INFORMS 2024 Annual Meeting