Technical advisory
Clarify the scientific and technical path before expensive implementation begins.
- Neurotechnology and BCI strategy
- AI/ML feasibility and architecture reviews
- Study, signal and validation design
Principal Engineer - AI for Human Learning · Babbel Labs
Turning complex brain data into useful systems.
I help research and product teams frame hard questions, build rigorous prototypes, and translate advances in neuroscience and machine learning into decisions they can act on.
- Brain-computer interfaces
- EEG & biosignals
- Health AI
Available for part-time projects under 15 hours per week
30+ Scientific publications
700+ ↗ Google Scholar citations 17 ↗ Google Scholar h-index 20+ Researchers supervised
How I can help
Specialist depth, without the full-time hire.
Focused support for teams working where biological signals, machine learning and real-world constraints meet.
Prototyping & analysis
Move from a research question or messy dataset to an evidence-generating prototype.
- EEG and biosignal analysis pipelines
- Individualized machine-learning models
- Rapid research prototypes
Research translation
Make technically ambitious work legible to decision-makers, collaborators and users.
- Scientific due diligence
- Technical writing and review
- Workshops for interdisciplinary teams
Experience
From neural signals to human learning.
More than a decade building and advising applied AI across neurotechnology, healthcare, research infrastructure and education.
2026 - present
Principal Engineer - AI for Human Learning
Babbel Labs
Building AI-assisted learning systems that combine machine learning, game theory and learning science to personalize how people develop new skills.
- Learning AI
- Research engineering
- Personalization
2020 - 2026
Postdoctoral Fellow - AI in Computational Neuroscience
Charité Universitätsmedizin Berlin
Developed neuroinformatics and brain-simulation methods, including a machine-learning approach that improved classification of Alzheimer's disease, mild cognitive impairment and controls from roughly 55% to 80%. Built EEG-based Unity applications for research and neuroscience education.
- Brain simulation
- Neuroinformatics
- EEG + Unity
2017 - 2020
Postdoctoral Fellow - AI for Healthcare
McMaster University & Vector Institute
Created interpretable AI methods for clinical research across EEG, concussion and medical imaging. Designed government workshops, advised health-AI initiatives and helped interdisciplinary teams move from study design to statistical analysis.
- Clinical AI
- Medical imaging
- Public-sector advisory
2018 - 2020
Research Software Consultant
CANARIE Research Software Team
Acted as domain expert and strategic advisor to software engineers, helping turn exploratory research code into robust, deployable research infrastructure.
- Research software
- Technical strategy
- Team building
2013 - 2014 + consulting
Neuroengineering Researcher
Interaxon
Designed personalized neurofeedback algorithms, signal-quality calibration and machine-learning-based artifact handling for consumer EEG hardware.
- Neurofeedback
- Real-time EEG
- Adaptive algorithms
Selected work
Research that holds up outside the lab.
A selection from peer-reviewed work across adaptive interfaces, neuroinformatics and clinical machine learning.
01 02 03 04
View publications on Google Scholar Neurotechnology
Adaptive neurofeedback that learns with the user
Developed progressive thresholding and co-adaptive BCI methods for training systems that respond to changes in both human learning and brain signals.
- BCI
- Neurofeedback
- Adaptive systems
Neuroinformatics
Individual-level inference from complex brain data
Applied individualized pattern recognition to detect mind wandering from EEG in live lectures, connecting rigorous modelling to a noisy real-world setting.
- EEG
- Machine learning
- Real-world data
AI in health
Clinical questions, practical machine learning
Contributed to work spanning deep-learning analysis of medical imaging, brain-simulation-informed dementia classification and the adoption barriers facing health AI.
- Health AI
- Deep learning
- Brain simulation
Research software
Methods built for other researchers to use
Created LFSpy, a scikit-learn-compatible local feature-selection library, and FBAR, a real-time single-channel EEG artifact-detection method designed for practical neurotechnology systems.
- Python
- Open science
- Signal processing
About
A translator between disciplines—and a builder within them.
My work sits at the intersection of machine learning, neuroscience and neuroengineering.
I earned a PhD in Computational Science and Engineering from McMaster University, where my research focused on user-centred, co-adaptive brain-computer interfaces. Since then, my work has crossed EEG, neurofeedback, brain simulation, medical imaging and AI in healthcare.
I currently work as Principal Engineer for AI for Human Learning at Babbel Labs, developing intelligent systems at the intersection of machine learning and how people learn.
I’m most useful when a problem is technically complex, the evidence is imperfect, and multiple disciplines need to find a shared way forward.
- Doctorate
- Computational Science & Engineering, McMaster University
- Foundation
- Psychology & Statistics, York University
- Methods
- Machine learning, biosignal analysis, study design
- Leadership
- 20+ researchers supervised across graduate and undergraduate projects
- Translation
- Workshops and advisory work for health, government and technical teams
Have a knotty technical problem?
Let’s make it tractable.
Send a short note about the question, the data and where you’re stuck. I’ll tell you candidly whether I can help.
kiretd@gmail.com