Model behavior
- Psychophysics & signal detection
- Bayesian DDM / LBA
- Mixed-effects models
- ML, calibration & SHAP
Open to opportunities · Authorized to work in the U.S. without sponsorship
Research Scientist · Quantitative UXR · Human Factors
Designing better human-machine interaction
I translate behavioral and physiological evidence into product decisions by combining psychophysics, pupillometry, Bayesian modeling, and working prototypes to understand where performance breaks and what an interface should do next.
Selected work
Research that changes what a system does
Each case study shows the question, method, tradeoff, and product implication, not just the final artifact.
01 · XR interaction / remote testbed
Why gaze produced more selection errors
99.2% of gaze errors were slips; hand input reached 5.15 bits/s.
Built a remote testbed, compared hand and simulated gaze with Fitts' law and NASA-TLX, then modeled failure dynamics with a Bayesian LBA. The gaze condition was simulated, not evaluated in a headset.
02 · Human factors / proof of concept
Designing alerts around cognitive risk
Targeted ≤0.6 alerts/min with calibrated state probabilities.
Prototyped a training dashboard that turns pupil, HRV, and grip features into instructional thresholds. All validation used synthetic sessions; clinical effectiveness remains untested.
03 · Research roadmap / in progress
Locating the failure point in dual-task performance
Separating evidence quality, response caution, and motor strain.
A staged dissertation program connecting psychophysics, pupillometry, drift-diffusion modeling, and grip-force signals. Industry applications shown in the case study are concepts, not deployed systems.
04 · Foundational research / NSERC
Testing whether brief meditation changes memory
60+ participants across behavioral and EEG measures.
Led a master's thesis from study design through analysis. The findings come from a controlled research sample and should not be read as a general product-effectiveness claim.
Technical toolkit
Methods selected for the decision
I work across measurement, inference, and implementation so the research question survives contact with the product.
Measure latent state
- Pupillometry & eye tracking
- Grip-force & tremor analysis
- EEG & psychophysiology
- Artifact control & QC
Make it testable
- React / TypeScript systems
- R Shiny dashboards
- Python, R, PyMC & Stan
- Reproducible workflows
Industry experience
Research inside a clinical AI environment
Direct experience supporting the data quality and research operations behind surgical-safety technology.
Research Assistant I
- Supported instance-segmentation workflows for surgical tool detection and tracking.
- Evaluated annotation quality across analyst teams using inter-rater reliability (κ and ICC).
- Synthesized ML-for-surgical-safety research to support internal research operations.
Education
Training across neuroscience and psychology
A research foundation spanning perception, cognition, quantitative methods, and human behavior.
Neurotree profile (academic genealogy)
PhD, Psychology & Cognitive Neuroscience
UC Riverside · Expected 2026
MSc, Psychology
Queen's University · 2020
Hon. BSc, Biological Sciences & Psychology
York University · 2017
Start a conversation
Need rigorous research that ships?
I am open to Research Scientist, Quantitative UXR, and Human Factors opportunities, especially with teams building adaptive, safety-critical, or immersive systems. Authorized to work in the U.S. without sponsorship.