AI Researcher, Medical Imaging
Building vision-language models that help clinicians read medical images, with a focus on low-resource settings.
MS Engineering Artificial Intelligence, Carnegie Mellon University
About
I am a medical imaging AI researcher working on vision-language models for 3D radiology. My current research designs token-efficient architectures that let language models reason over CT volumes, alongside multi-scale models for tumor classification on MRI.
I am an MS student in Engineering Artificial Intelligence at Carnegie Mellon University, concentrating on AI in healthcare. I build research systems end to end, from data pipeline through model architecture to evaluation, and I care most about making diagnostic imaging AI work in low-resource and African clinical settings, which I pursue through the SPARK and CAMERA MRI Africa initiative.
My first degree was in Mechatronics Engineering at the Federal University of Technology, Minna. That background still shapes how I approach systems, though my work now sits firmly in research.
Vision-Language Models
Medical Image Analysis
PyTorch
3D CT & MRI
Projects
A dual-stage token-compression architecture for 3D chest-CT vision-language models. A frozen ViT slice encoder feeds a task-agnostic Perceiver-style intra-slice stage, then a FiLM-conditioned inter-slice stage produces instruction-aware visual tokens for a LoRA-adapted language model. This cuts the token cost of volumetric input for radiology report generation.
A Feature Pyramid Network combined with a bidirectional Mamba block for pediatric medulloblastoma subtype classification on brain MRI, reaching AUC 99.35% and F1 94.54% on a 761-image dataset. Includes augmentation and class-reweighting pipelines built to address severe subtype imbalance.
Controlled YOLOv8 detection experiments test whether white blood cells systematically drive false-positive parasite detections in Giemsa-stained thick blood smears. DBSCAN spatial clustering and a Mann-Whitney U test quantify how much WBC supervision affects diagnostic reliability.
A disease-conditioned early-warning framework over MIMIC-IV ICU time-series, combining vital signs, lab abnormalities, diagnosis embeddings and treatment exposures to reduce false-positive alerting. Deployed as a Dockerized API with WebSocket streaming for real-time bedside-style inference.
A Raspberry Pi assistive device pairing an HD camera with OCR and speech synthesis, achieving 98% reading accuracy and 40% lower latency. Awarded Best Assistive Project by the Faculty of Engineering, FUT Minna.
Research
My research asks how AI systems generalize across domains, especially in real-world noisy environments.
Architectures that let language models reason over volumetric medical images, with a focus on token efficiency and how visual representations should be conditioned on the clinical question being asked.
Multi-scale and state-space architectures for CT and MRI, particularly for tumor classification under small, imbalanced datasets where minority-class recall matters clinically.
Model compression and efficiency techniques have real costs in clinical accuracy, calibration, and robustness, and those costs may not fall evenly across patient groups.
Diagnostic imaging AI has to work within the constraints of African and low-resource health systems, including affordable acquisition hardware and limited annotated data.
Publications
Convergence of Blockchain, Smart Microgrid and Energy Market
K. E. Jack, S. A. Adeniji · Electrica, 24(2), 367-374 · 2024
K. E. Jack, S. A. Adeniji · Nigerian Journal of Engineering and Applied Sciences · 2024
Design and Implementation of a Secure IoT-Based Remote Monitoring System for an Autonomous Nanny Robot
S. A. Adeniji, K. E. Jack, E. Ani · Submitted, under review
Experience
Language and Spatial Laboratories
2026 - PresentResearch Intern
Vision-language model research for 3D medical imaging: designing ICTC and building its full training pipeline and multi-budget evaluation harness.
IEEE SkillUp Hub (Region 8) / Centre for Future Technologies, University of Chichester
Mar 2026 - PresentResearch Assistant
Research on AI-based diagnostic solutions for low- and middle-income country health systems.
AI for Healthcare Research Lab, Carnegie Mellon University Africa
Jan 2026 - May 2026Research Intern (Volunteer)
Developed machine learning methods for cardiovascular disease detection from ECG signals. The work supported studies on aortic stenosis and regurgitation.
Artificial Intelligence for Clean Energy Ltd., Nigeria
Oct 2023 - Jan 2025Research Assistant (Intern)
Built a Flask and Random Forest energy-monitoring platform on Google Cloud (99% uptime, 30% accuracy improvement) and piloted drones for field data acquisition.
Contact
Open to research collaborations and roles in AI engineering, particularly in medical imaging or vision-language models. Feel free to reach out.