RESEARCH:

Usman Bello Abdulmalik is an Assistant Professor and Acting Director of the Charles Chidume Mathematics Institute at the African University of Science and Technology in Abuja, Nigeria, specializing in Nonlinear Operator Theory and Optimization. While at the University of Chicago, Abdulmalik will be using deep learning methodologies to research accelerated protective splitting methods to create artificial intelligence algorithms to develop faster solutions for large-scale, nonlinear calculative problems. Ultimately, Abdulmalik’s research will use neural networks to train deviation vectors in protective splitting methods to accelerate the convergence of the methods with applications for radiation therapy treatment planning and tomography (e.g., MRI, sonar, PET scans, X-rays, etc.).

BIO:

Usman Bello Abdulmalik earned his PhD in Functional Analysis and its Application in 2015 from the African University of Science and Technology, (AUST) Abuja, Nigeria. Currently, Abdulmalik is an assistant professor in the Department of Pure and Applied Mathematics at AUST. He works within research areas that include nonlinear operator theory, Hammerstein Integral Equations, Convex and Vector Optimization with Applications in Radiation Therapy Treatment Planning in Clinical Spaces. Recently, Abdulmalik has been the recipient of several grant awards including the National Research Fund (TETFund) Grant where Abdulmalik served as the lead researcher in investigating automated radiotherapy to optimize treatment planning with real-time support. Abdulmalik’s Google Scholar Profile is available here.

RESEARCH:

Suleiman Hamidu is a Neuroscientist and Data Analyst who specializes in research related to dementia and its risk factors. As an Eric and Wendy Schmidt AI in Science Faculty Fellow, Hamidu is going to conduct research that focuses on the genetic, clinical, and environmental risk factors for dementia in African populations so as to better predict and understand dementia risk. His work involves analyzing large-scale clinical, cognitive, and population health datasets from community and hospital cohorts in northern Nigeria to ultimately improve dementia risk stratification and prevention strategies in diverse communities and environments. Hamidu will use artificial intelligence and machine-learning approaches to support the integration and analysis of large and complex datasets including clinical, cognitive, geospatial, and genomic data. These methods will enable risk prediction and pattern discovery beyond the current traditional statistical approaches to understanding dementia risk factors.

BIO:

Suleiman Hamidu earned his PhD in Human Anatomy from Ahmadu Bello University, Zaria, Nigeria, in 2024. Currently, he serves as a lecturer in Human Anatomy (Neuroscience) at Gombe State University and as the Research Deputy Director at the Biomedical Research and Training Centre (BioRTC), Damaturu, Yobe, Nigeria, a state-of-the-art bioscience laboratory. BioRTC works to provide research and training opportunities in biomedical sciences to address local and global problems. Hamidu’s BioRTC research group has secured multiple grants from the Chan Zuckerberg Initiative, Alzheimer’s Association, Rainwater Charitable Foundation, and the Welcome Trust. Hamidu’s work at BioRTC focuses on dementia epidemiology, neuropsychiatric assessment, and the application of data science and AI in low-resource settings. In addition, Hamidu is a visiting research fellow with the highly collaborative and interdisciplinary community of more than 50 neuroscience groups at the University of Sussex. Hamidu’s Google Scholar Profile is accessible here.

RESEARCH:

Omolola Ogbolumani is an Electrical and Electronics Engineer specializing in smart sustainability, with a focus on the intersection of food, energy, and water systems, and on waste management for a circular economy and a sustainable environment. While conducting research at the University of Chicago as an Eric and Wendy Schmidt AI in Science Faculty Fellow, Ogbolumani will develop artificial intelligence (AI) and machine learning (ML) models (e.g., Graph Neural Networks and Long-Short Term Memory networks) to optimize the Food-Energy-Water Nexus (FEW-N) with a focus on human-centered sustainability within the Fifth Industrial Revolution. The Fifth Industrial Revolution refers to an emergent phase of civilization wherein there is an emphasis on complex human-machine collaboration, sustainability, and resilience, blending sophisticated AI and ML with automation and data, centered on human ethics as well as societal and environmental well-being. Ogbolumani’s use of AI and ML models will be able to indicate how to coordinate real-time decisions, constraints, and trade offs across the FEW-N to boost human well-being, ecosystem health, and overall resilience in the Fifth Industrial Revolution. While at UChicago, Ogbolumani will be collaborating with Prof. Junhong Chen, a Crown Family Professor of Molecular Engineering in the UChicago Pritzker School of Molecular Engineering and the Lead Water Strategist at Argonne National Laboratory.

BIO:

Omolola Ogbolumani completed her PhD in Electrical and Electronics Engineering at the University of Johannesburg in 2022, where she was supervised by Prof. Nnamdi Nwulu. Her multidisciplinary research has focused on developing a hybrid decision-making framework for optimal resource allocation in the FEW-N including in agriculture, energy, and water systems, demand response for renewables, and policy guidance for resource security. Currently, Ogbolumani is a faculty member in the Electrical and Electronics Engineering Department at the University of Lagos, Nigeria. Ogbolumani’s Google Scholar Profile is accessible here.

RESEARCH:

Muideen Ogunniran is a teacher in the Department of Mathematical Sciences at Osun State University who specializes in Computational Mathematics and Optimization. While working at the University of Chicago as an Eric and Wendy Schmidt AI in Science Faculty Fellow, Ogunniran will be researching numerical solutions for multi-dimensional partial differential equations (PDEs), including the Lindbald and Fokker Planck equations, using Rational Linear Multi-Step methods and the solutions’ machine-learning (ML) predictions. Solving PDEs provides physics-consistent solutions that serve as reliable training and validation data for ML models. By learning from these solutions, ML models can approximate the PDE’s behavior while respecting the physics-based constraints. Using ML models to compute PDEs provides a fast, real-time evaluative alternative to direct PDE solvers, which would be computationally expensive. For Ogunniran’s research, ML will help to optimize multi-dimensional PDEs using data from explicit numerical solution approaches by uncovering patterns in the equations, accelerating computation, and investigating new theoretical questions. Such fast, physics-constrained predictions can be applied to real-time control, engineering design optimization, and parameter estimation in systems governed by complex dynamics, including energy systems, fluid flows, and autonomous platforms.

BIO:

Muideen Ogunniran earned his PhD in Mathematics from the University of Ilorin, Nigeria, in 2019. His research worked to develop, analyze, and implement numerical schemes in integration singular and stiff problems on differential equations. By combining numerical schemes with ML, we gain accurate physical insights and efficient predictive capabilities beyond what either approach can currently provide alone. Practically, these schemes can be applied as fast surrogate models for real-time simulation, control, optimization, and inverse problems where repeated PDE solves are computationally prohibitive. Ogunniran’s Google Scholar Profile is accessible here.