Our Research Areas
We work at the intersection of multiple disciplines to understand and augment human memory in meaningful, responsible ways.
Cognitive Science
Understanding how human memory works—encoding, consolidation, retrieval—and applying those insights to build better digital memory systems.
AI & Machine Learning
Leveraging transformer models, RAG architectures, and embedding spaces to create context-aware memory assistants that enhance rather than replace human cognition.
Neuroscience
Drawing from neural mechanisms of memory formation, synaptic plasticity, and pattern completion to inform our augmentation strategies.
Mental Health & Well-being
Ensuring memory augmentation supports mental wellness, reduces cognitive load, and respects emotional context—never exploiting vulnerability.
Our Research Principles
Guiding values that shape how we approach memory augmentation research.
Open Science
We publish findings, share anonymized datasets (with consent), and contribute to the broader research community.
Privacy First
All research follows strict ethical guidelines—no data shared without explicit consent, and privacy-preserving methods are default.
Mental Health Centered
Memory augmentation must reduce stress, support well-being, and never manipulate or harm users' psychological state.
Interdisciplinary
We bridge cognitive science, AI, neuroscience, and clinical practice to build holistic solutions grounded in human understanding.
Current Research Projects
Active and planned research initiatives with academic and clinical partners.
Semantic Memory Networks
ActiveMapping how personal memories cluster and connect over time using graph neural networks and embedding spaces.
Emotional Context & Recall
ActiveUnderstanding how emotional valence affects memory retrieval and building sentiment-aware search algorithms.
Cognitive Load Metrics
PlanningDeveloping validated measures of cognitive offloading effectiveness and stress reduction in daily memory tasks.
Privacy-Preserving Embeddings
ActiveExploring federated learning and differential privacy techniques for personal memory systems that never expose raw user data.
Who We Partner With
We collaborate with diverse research institutions and clinicians worldwide.
University Labs
- Memory & cognition research groups
- Human-computer interaction labs
- Computational neuroscience centers
Clinical Researchers
- Cognitive behavioral therapy practitioners
- Neuropsychology clinics
- Mental health technology researchers
AI & ML Institutes
- Natural language processing groups
- Responsible AI research teams
- Privacy-preserving ML labs
Join Our Research Community
Whether you're a researcher, clinician, or student working on memory, cognition, AI, or mental health—we'd love to explore collaboration opportunities.
Email us at [email protected]
Latest Research Insights
Recent findings and developments from our research partnerships.
Memory Consolidation in Digital Systems
How spacing algorithms can improve long-term retention in personal knowledge bases.
Ethical AI for Mental Health
Frameworks for building memory assistants that respect psychological safety and autonomy.
Vector Embeddings & Human Memory
Comparing semantic similarity in neural networks with human associative memory structures.
