📜 = Patent 📝 = Paper 💻 = Code 🔗 = Link
AI in Medical Diagnostics
Applications of artificial intelligence in healthcare and more specifically medical diagnostics. For example: developing smartphone-based applications for automated interpretation of rapid diagnostic tests, particularly for HIV, syphilis, and COVID-19.
📜 Adaptable Automated Interpretation of Rapid Diagnostic Tests Using Self-Supervised Learning and Few-Shot Learning📝 SMARTtest: A Smartphone App to Facilitate HIV and Syphilis Self- and Partner-Testing, Interpretation of Results, and Linkage to Care📝 Rapidly Adaptable Automated Interpretation of Point-of-Care COVID-19 Diagnostics💻 Public repository for the machine learning pipelines of SMARTtest🔗 Example production machine learning pipeline for the INSTI test kitMedical Image Segmentation
Applying deep learning techniques for medical imaging. For example: longitudinal multiple sclerosis (MS) lesion segmentation and contrast-agnostic spinal cord segmentation.
📝 Contrast-agnostic Segmentation of the Spinal Cord Using Deep Learning📝 Team Neuropoly: Description of the Pipelines for the MICCAI 2021 MS New Lesions Segmentation ChallengeComputational Neuroscience and Brain-Machine Interfaces
My interest in neuroscience broadly includes formulating computational models for understanding the brain and developing brain-machine interfaces. For example: autonomous optimization of neuroprosthetic stimulation parameters for motor cortex and spinal cord outputs.
📝 Autonomous Optimization of Neuroprosthetic Stimulation Parameters That Drive the Motor Cortex and Spinal Cord Outputs in Rats and MonkeysAI for Educational Technologies
My research in educational technologies broadly includes intelligent tutoring systems and informal artificial intelligence education for K-12.
📝 Hierarchical Multi-Armed Bandits for the Concurrent Intelligent Tutoring of Concepts and Problems of Varying Difficulty Levels📝 Teenagers and Artificial Intelligence: Bootcamp Experience and Lessons LearnedRepresentation Learning
Investigating representation learning techniques for various application domains, e.g., natural language processing.
💻 Implementations for a family of attention mechanisms for applications in NLP💻 Comparatively finetuning BERT for various downstream NLP tasks💻 Implementations of several self-supervised pretext tasks for language and vision modalities💻 Simple, straight-forward extraction of acoustic and prosodic features from sound waves