Highly accomplished Machine Learning Researcher and Ph.D. Candidate at the University of Washington’s Paul G. Allen School of Computer Science & Engineering, specializing in generative modeling, multimodal representation learning, and large-scale data curation. My research drives state-of-the-art advancements in medical imaging and video generation, backed by prestigious grants from Microsoft and the UW Population Health Initiative. With a First Class B.Sc. in Electronic & Electrical Engineering from Obafemi Awolowo University and impactful research internships at Apple and Mayo Clinic, I bring a proven track record of innovation. My work is published in top-tier venues like CVPR and NeurIPS, spanning critical applications in healthcare, finance, and recommendation systems.
Summary
Leading cutting-edge research in multimodal AI for medical imaging and advanced video generation, driving innovation in state-of-the-art models and dataset creation.
Highlights
Spearheaded research on Multimodal Large Language Models (LLMs) for medical imaging, developing novel medical multimodal datasets (Quilt-1M, MedNarratives) and state-of-the-art models (QuiltNet, Quilt-LLaVA).
Engineered a multi-agent AI framework, PathFinder, for clinical diagnosis, achieving performance superior to human experts and establishing improved benchmarks (MedBlink).
Directing efforts to integrate Newtonian physics into image and video generative models, focusing on generating large-scale, temporally consistent video scene graph datasets and pipelines.
Summary
Provided comprehensive instructional and academic support for undergraduate and graduate courses in Computer Science.
Highlights
Taught Data Programming (CSE 160) for Fall 2021, Winter 2022, and Fall 2022 semesters, guiding students through fundamental concepts and practical applications.
Provided instruction and support for Introduction to Artificial Intelligence (CSE 473) during Spring 2023, Fall 2023, and Winter 2024 academic terms.
Summary
Led research on efficient multimodal representations for egocentric data, focusing on reducing data capture costs.
Highlights
Pioneered research in efficient multimodal representations for egocentric data, including video, text, audio, IMU, and hands.
Developed 'Perceive-Predict,' a novel framework leveraging predictive coding between co-occurring modalities to reconstruct missing data, significantly reducing capture costs for expensive modalities like video.
Summary
Directed research efforts to develop a foundational model for histopathology using large-scale image datasets.
Highlights
Led research towards developing a foundational model for histopathology, training on millions of gigapixel-sized histology images.
Scaled-up compute operations on the Argonne National Lab computing cluster, resulting in clinically evaluated models for improved diagnostic capabilities.
Summary
Developed machine learning models for financial information extraction and integration into lending systems.
Highlights
Engineered models to extract crucial customer financial information from unstructured banking data.
Processed key customer earning and spending data to feed into downstream lending pipelines, including predicting income, analyzing spending patterns, and performing reconciliations.
Summary
Designed and implemented production-grade recommendation systems for enhanced user experience.
Highlights
Developed and deployed production recommendation systems utilizing advanced techniques such as attention mechanisms and epsilon-greedy bandit strategies.
Summary
Conducted research in biosignal processing, focusing on EEG signal analysis and neural network applications.
Highlights
Integrated disparate multivariate time series data, employing spectral component characterization for dynamical dimensionality reduction.
Developed and trained neural networks for accurate classification and characterization of brain EEG signals.
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 a prestigious 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
Received a cumulative scholarship valued at $1500 for academic excellence.
Awarded By
Total/NNPC
Awarded a cumulative scholarship valued at $1500 based on national merit.
Awarded By
Etisalat Nigeria
Received a scholarship valued at $250 for academic merit.
Published by
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Summary
Introduces a novel approach for visual instruction tuning using localized narratives from histopathology videos to enhance multimodal LLMs.
Published by
ML4H Symposium
Summary
A collaborative reflection on the latest advancements, applications, and challenges in machine learning for health, derived from research roundtables.
Published by
In Submission to ICCV
Summary
Explores linking medical vision and language through procedural and localized narratives to create a comprehensive framework for medical imaging analysis.
Published by
United States and Canadian Academy of Pathology's (USCAP) 114th Annual Meeting
Summary
Introduces MsCAMIL network for automated triage of colorectal polyps, enhancing efficiency in pathological diagnosis.
Published by
United States and Canadian Academy of Pathology's (USCAP) 114th Annual Meeting
Summary
Compares the diagnostic performance of MsCAMIL against human pathology trainees in colorectal polyp diagnosis.
Published by
Computers in Biology and Medicine
Summary
Investigates domain generalization techniques to integrate diverse EEG datasets for improved automatic epileptic seizure detection.
Published by
International Journal of Online and Biomedical Engineering (IJOE)
Summary
Provides an empirical characterization of the temporal dynamics of EEG spectral components, contributing to a deeper understanding of brain activity.
Native
Generative Modeling, Multimodal Representation Learning, Deep Learning, Neural Networks, Foundation Models, Predictive Coding, Recommendation Systems, Attention Mechanisms, Epsilon-Greedy Bandit Strategy.
Medical Imaging, Histopathology, Video Generation, Scene Graph Generation, Image Analysis, Whole Slide Imaging, Egocentric Data.
Multimodal LLMs, Text Analysis, Localized Narratives.
Data Curation, Large-Scale Datasets, Time Series Analysis, EEG Signal Processing, Dimensionality Reduction, Statistical Analysis, Data Programming.
Python.
PyTorch, TensorFlow.
Experimental Design, Model Development, Performance Evaluation, Scientific Writing, Peer Review, Grant Writing.
Project Leadership, Team Collaboration, Teaching, Mentoring.
Generative Modeling, Multimodal Representation Learning, Data Curation, Artificial Intelligence in Healthcare.
