Highly accomplished Ph.D. Candidate in Computer Science & Engineering with a strong track record of leading innovative research in Generative AI, Multimodal Representation Learning, and Data Curation. Expert in developing state-of-the-art machine learning models for complex domains including medical imaging, egocentric data, and financial analytics. Proven ability to drive projects from conception to clinical/production evaluation, mentor teams, and publish extensively in top-tier conferences and journals. Seeking to leverage advanced AI/ML expertise to solve challenging real-world problems and contribute to cutting-edge technological advancements.
Summary
Leading advanced research in Multimodal Large Language Models (LLMs) for medical imaging and developing cutting-edge generative models.
Highlights
Spearheaded research in Multimodal Large Language Models (LLMs) for medical imaging, developing novel medical multimodal datasets (Quilt-1M, MedNarratives) and state-of-the-art analytical models (QuiltNet, Quilt-LLaVA).
Created a multi-agent AI framework (PathFinder) for clinical diagnosis that outperformed human experts, and established improved benchmarks (MedBlink).
Directing initiatives to enhance physics-informed image and video generative models, focusing on Newtonian physics for the generation of large-scale video scene graph datasets and pipelines.
Summary
Led research on efficient multimodal representations for egocentric data, optimizing data capture costs.
Highlights
Spearheaded research into efficient multimodal representations for egocentric data (video, text, audio, IMU, hands), developing the "Perceive-Predict" model.
Utilized predictive coding between co-occurring modalities to reconstruct missing modalities, significantly reducing data capture costs for expensive modalities like video.
Summary
Directed research for a foundational AI model in histopathology, scaling computational efforts and validating models clinically.
Highlights
Directed research and development of a foundational AI model for histopathology, leveraging millions of gigapixel histology images.
Scaled computational efforts on the Argonne National Lab computing cluster and conducted rigorous clinical evaluations to validate model efficacy.
Summary
Developed and deployed machine learning models for financial data analysis to enhance lending processes.
Highlights
Engineered and deployed machine learning models to analyze unstructured banking data, extracting critical customer earning and spending insights.
Instrumental in enhancing downstream lending processes, including income prediction, spending pattern analysis, and financial reconciliations.
Summary
Designed and implemented production-grade recommendation systems to optimize user engagement.
Highlights
Designed and implemented production-grade recommendation systems, leveraging advanced techniques such as attention mechanisms and epsilon-greedy bandit strategies.
Optimized system performance and user engagement through data-driven model development and deployment.
Summary
Conducted research on biosignal processing and neural networks for brain EEG signal analysis.
Highlights
Integrated and analyzed disparate multivariate time series data, focusing on characterizing spectral components for dynamic dimensionality reduction.
Developed and trained neural networks for the classification of brain EEG signals and comprehensive characterization of EEG spectral components.
Awarded By
University of Washington
Awarded $100,000 for pioneering AI research in population health.
Awarded By
Microsoft
Received $20,000 grant to advance research in foundational AI models.
Awarded By
IBRO-Simons
Awarded travel grant to attend the Computational Neuroscience Summer School in Cape Town.
Awarded By
Obafemi Awolowo University
Recognized as the top-performing student in the Control Engineering specialization.
Awarded By
Obafemi Awolowo University
Awarded for outstanding academic achievement as the best male graduating student.
Awarded By
Federal Government of Nigeria
National scholarship awarded for academic excellence (cumulative value: $1500).
Awarded By
Total/NNPC
Merit-based scholarship awarded for academic achievement (cumulative value: $1500).
Awarded By
Etisalat Nigeria
Merit-based scholarship awarded for academic achievement (value: $250).
Published by
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Summary
Contributed to research on visual instruction tuning for histopathology videos using localized narratives.
Published by
United States and Canadian Academy of Pathology's (USCAP) 114th Annual Meeting
Summary
Forthcoming comparative study on MsCAMIL performance against pathology trainees in polyp diagnosis.
Native
Generative Modeling, Multimodal Representation Learning, Large Language Models (LLMs), Deep Learning, Neural Networks, Computer Vision, Natural Language Processing (NLP), Reinforcement Learning, Predictive Modeling, Histopathology AI, Biosignal Processing, Time Series Analysis, Recommendation Systems, Attention Mechanisms, Bandit Algorithms.
Python, PyTorch, TensorFlow, Data Curation, Data Analysis, SQL, Cloud Computing (Argonne National Lab Cluster), Git.
Scientific Writing, Experimental Design, Model Evaluation, Statistical Analysis, Data Collection, Peer Review, Algorithm Development, Prototyping.
Teaching Assistant, Mentorship, Curriculum Development, Project Leadership, Team Collaboration, Problem Solving, Critical Thinking, Communication.