AI engineer and researcher in medical artificial intelligence
I develop interpretable machine-learning methods for early detection and monitoring of neurodegenerative diseases, with a focus on Parkinson's disease, Alzheimer's disease, digital biomarkers, and computational neuroscience.
I am an AI engineer and researcher specializing in medical artificial intelligence and computational neuroscience. My research focuses on explainable machine learning for the early detection and monitoring of neurodegenerative diseases.
My work combines brain imaging, online cognitive testing, eye-tracking, movement analysis, facial dynamics, and other digital biomarkers. The central aim is to build tools that are accurate, transparent, and useful in clinical and research settings.
Alongside academic research, I have more than a decade of experience in software engineering and applied AI, building intelligent systems that are reliable, interpretable, and practical.
Machine-learning systems for biomedical data that remain interpretable enough for clinicians, researchers, and patients to understand.
Behavioral, cognitive, imaging, movement, and facial-expression signals that may reveal subtle neurological changes before symptoms become clinically obvious.
Data-driven models that connect measurable behavior and brain structure with disease mechanisms in Parkinson's disease, Alzheimer's disease, and related disorders.
Selected work on interpretable machine learning, digital biomarkers, neuroimaging, cognitive testing, and neurodegenerative disease detection.
Reviews how online cognitive tests, eye tracking, movement data, and other digital biomarkers can support AI-based detection of early Parkinson's and Alzheimer's disease.
Uses T1 MRI scans and interpretable machine-learning features based on brain-region volumes and spatial relationships to distinguish healthy controls, prodromal cases, and Parkinson's disease.
Shows how eye-movement tasks can be analyzed with machine learning to better understand Parkinson's and Alzheimer's disease mechanisms.
Shows that response-time dynamics from online MoCA testing can improve machine-learning classification of Parkinson's disease versus healthy controls.
Uses an online Trail Making Test to show how timing-based measures can capture cognitive and motor changes linked to Parkinson's disease severity.
Compares healthy controls and Parkinson's patients with online neuropsychological tasks to identify subtle cognitive and behavioral changes.
Synthesizes machine-learning approaches for Parkinson's disease datasets, including diffusion tensor imaging, eye tracking, and online cognitive testing.
Argues that Alzheimer's prevention requires finding the beginning of neurodegeneration decades before symptoms become clinically visible.
Combines eye-tracking and neuropsychological testing to predict symptom progression in Parkinson's disease.
Compares rough-set models and other data-mining methods for predicting Parkinson's disease progression across patient groups.
Uses diffusion tensor imaging and data mining to estimate symptom development after deep brain stimulation.
Uses MRI-derived spatial biomarkers in a rule-based model for transparent Parkinson's disease severity classification.
Studies early dementia risk with explainable AI, cognitive test results, and genetic markers.
Studies marker-based gait stability with nonlinear dynamics as a possible screening signal for neurodegenerative disease.
I also explore highly theoretical questions in complex systems, cosmology, and astrophysics, using numerical computation to test ideas against measurable structure. This includes work on cosmic time and the expansion of the universe, as well as gamma-ray bursts as cosmological probes.
Tests cosmological scale-factor assumptions with numerical analysis against supernovae, gamma-ray bursts, cosmic chronometers, and CMB-derived observations.
For publications, academic profiles, software work, and collaboration context, use the links below.