Highly accomplished Ph.D. Candidate in Computer Science & Engineering with a strong foundation in Artificial Intelligence, Machine Learning, and Electrical Engineering. Specializes in Multimodal AI, Generative Models, and Data Curation, with a proven track record of leading impactful research in medical imaging, egocentric data, and financial analytics. Leverages deep expertise in neural networks, large language models, and advanced data processing to develop innovative solutions and drive significant advancements in AI research and applications. Published extensively in top-tier conferences and journals, demonstrating a commitment to advancing the field.
Summary
Contributed to the development of foundational AI models for histopathology.
Highlights
Led research efforts to develop a foundational model for histopathology, training on millions of gigapixel-sized histology images.
Scaled-up computational resources on the Argonne National Lab computing cluster to process and analyze massive datasets.
Clinically evaluated developed models, ensuring their efficacy and relevance in real-world medical applications.
Summary
Developed and deployed machine learning models for financial data analysis.
Highlights
Developed robust models for extracting critical customer financial information from unstructured banking data.
Identified and extracted key customer earning and spending data to feed into downstream lending systems.
Implemented predictive models for income, spending patterns, and financial reconciliations, enhancing lending decision accuracy.
Summary
Designed and implemented production-ready recommendation systems.
Highlights
Developed production recommendation systems leveraging advanced techniques such as attention mechanisms and epsilon-greedy bandit strategies.
Improved user engagement and system efficiency through data-driven insights and model optimization.
Summary
Conducted research on biosignal processing and neural network applications.
Highlights
Integrated disparate multivariate time series data, characterized spectral components for dynamic dimensionality reduction.
Trained neural networks for robust classification of brain EEG signals, enhancing diagnostic capabilities.
Performed detailed characterization of EEG spectral components to understand brain activity patterns.
Summary
Driving cutting-edge research in multimodal AI, focusing on medical imaging and advanced generative models.
Highlights
Led research in Multimodal Large Language Models (LLMs) for medical imaging, developing foundational medical multimodal datasets (Quilt-1M, MedNarratives) with over 1 million image-text pairs.
Engineered state-of-the-art multimodal AI models (QuiltNet, Quilt-LLaVA) for comprehensive medical image analysis, achieving superior performance.
Pioneered a multi-agent AI framework, PathFinder, for clinical diagnosis, outperforming human experts in diagnostic accuracy.
Developed robust benchmarks, MedBlink, to evaluate the performance of multimodal medical AI models.
Leading initiatives to enhance the 'physics' in image and video generative models, focusing on Newtonian physics principles to generate large-scale video scene graph datasets and pipelines.
Summary
Conducted advanced research on efficient multimodal representations for egocentric data.
Highlights
Led research to develop efficient multimodal representations for egocentric data, integrating video, text, audio, IMU, and hand data.
Developed 'Perceive-Predict' framework, leveraging predictive coding between co-occurring modalities to reconstruct missing data.
Applied research to significantly reduce the capture cost of expensive modalities like video, demonstrating practical impact on data collection efficiency.
Awarded By
University of Washington
Awarded a grant of $100,000 for innovative AI research in population health.
Awarded By
Microsoft
Received a $20,000 grant to advance research in foundational AI models.
Awarded By
International Brain Research Organization (IBRO) & Simons Foundation
Awarded a 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 Systems specialization.
Awarded By
Obafemi Awolowo University
Awarded for outstanding academic achievement as the best male graduating student.
Awarded By
Federal Government of Nigeria
Recipient of a national scholarship with a cumulative value of $1500 for academic excellence.
Awarded By
Total/NNPC
Recipient of a national merit scholarship with a cumulative value of $1500.
Awarded By
Etisalat Nigeria
Awarded a merit scholarship valued at $250.
Published by
IEEE/CVF Conference on Computer Vision and Pattern Recognition
Summary
Introduced a novel approach for visual instruction tuning using localized narratives from histopathology videos, enhancing multimodal AI understanding in medical contexts.
Published by
Machine Learning for Health (ML4H)
Summary
Explored the effectiveness of multi-modal masked autoencoders in learning compositional representations for histopathological images, contributing to robust medical image analysis.
Published by
LABORATORY INVESTIGATION, ELSEVIER SCIENCE INC
Summary
Developed a method for risk stratification of solitary fibrous tumors using whole slide image analysis, providing a valuable tool for pathological assessment.
Published by
ML4H Symposium
Summary
Contributed to a comprehensive reflection on recent advancements, applications, and challenges in machine learning for health, summarizing key discussions from research roundtables.
Published by
Computers in Biology and Medicine
Summary
Developed a supervised domain generalization approach to integrate diverse scalp EEG datasets, improving automated epileptic seizure detection across varied data sources.
Published by
International Journal of Online and Biomedical Engineering (IJOE)
Summary
Conducted an empirical study to characterize the temporal dynamics of EEG spectral components, providing insights into brain signal patterns.
Native
Deep Learning, Generative Models, Multimodal AI, Computer Vision, Natural Language Processing, Neural Networks, Recommendation Systems, Predictive Modeling, Domain Generalization.
PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers, OpenCV.
Data Curation, Data Analysis, Big Data, Statistical Modeling, Data Visualization, Feature Engineering.
Python, MATLAB, C++.
Medical Imaging, Histopathology, EEG Signal Processing, Clinical Diagnosis, Biosignal Processing.
Scientific Writing, Experiment Design, Algorithm Development, Prototyping, Academic Publishing.
Argonne National Lab Computing Cluster (ANL), Cloud Platforms (conceptual understanding).
Generative Modeling, Multimodal Representation Learning, Data Curation, Medical AI, Computer Vision, Natural Language Processing.
Summary
A project focused on bridging medical vision and language understanding through procedural and localized narratives to create a unified framework for medical imaging analysis.