Our Experts
The neuroscience researchers and cognitive training experts behind Supertos
Every brain training game, article, and coaching recommendation at Supertos is grounded in peer-reviewed neuroscience. Our research team brings 40+ years of combined experience in cognitive neuroscience, clinical neuropsychology, and computational neuroscience from institutions including Stanford, MIT, UCLA, and Carnegie Mellon.
Research Team
Dr. Chen leads the research team at Supertos, translating peer-reviewed neuroscience into evidence-based brain training programs. Her doctoral research at Stanford focused on the neural mechanisms of working memory training in adults aged 25-65. She has published extensively on adult neuroplasticity, contributed to secondary analysis of the landmark ACTIVE study (2,832 participants, 29% dementia risk reduction), and co-authored a meta-analysis of 87 brain training studies. At Supertos, she oversees the scientific foundation of all 49 brain training games and designed the adaptive difficulty algorithms used in BrainGym AI.
Expertise: Adult Neuroplasticity, Working Memory Training, Processing Speed, Cognitive Assessment, Memory Improvement, Brain Training Research
View full profile and articles →Dr. Rivera brings clinical expertise to the Supertos research team, ensuring brain training protocols are appropriate for diverse adult populations. His background in clinical neuropsychology at UCLA focused on cognitive assessment and dementia prevention strategies. He developed the brain age assessment methodology used in the BrainGym AI platform and oversees the clinical safety review of all training protocols. His research on cognitive decline prevention in adults 50+ has informed the platform's approach to age-appropriate difficulty calibration.
Expertise: Clinical Neuropsychology, Cognitive Assessment, Brain Aging, Dementia Prevention, Brain Age Measurement, Cognitive Rehabilitation
View full profile →Dr. Watson leads the AI and data science division at Supertos, developing the machine learning algorithms that power BrainGym AI's personalized coaching system. Her research at Carnegie Mellon combined computational neuroscience with machine learning to model individual differences in cognitive training response. At Supertos, she built the adaptive training engine that adjusts difficulty in real-time based on user performance, and the AI coaching system that creates personalized daily training plans targeting each user's weakest cognitive domains.
Expertise: Computational Neuroscience, Machine Learning, Adaptive Training Algorithms, AI Coaching, Personalization, Behavioral Data Analysis
View full profile →Our Research Approach
At Supertos, every feature starts with the science. Our research process follows a rigorous methodology:
- Literature Review: We analyze peer-reviewed studies from journals including the Journal of Cognitive Enhancement, Neuropsychologia, and Frontiers in Aging Neuroscience
- Protocol Design: Training exercises are designed to target specific cognitive domains (memory, processing speed, attention, logic, cognitive flexibility) based on validated protocols
- Adaptive Calibration: AI algorithms personalize difficulty based on real-time performance data, ensuring each user trains at their optimal challenge level
- Outcome Measurement: Brain age assessments and cognitive profiling track user progress across all five cognitive domains
- Continuous Improvement: Training protocols are updated based on the latest published research and user outcome data
Key Research the Platform is Built On
Published Research & Resources
The Science of Brain Training (PDF)
Comprehensive research overview
State of Brain Training 2026 (PDF)
19-page industry research report
Research Compendium 2026 (PDF)
Key findings collection
Science & Research Hub
All brain training research articles
Last Updated: March 2026