We're excited to announce our paper has been published at NeurIPS 2025

Healthy Longevity with
Dignity and Identity

Reteena is an applied ai lab focused on creating human-friendly
solutions that enable more accessible alzheimer's diagnosis and therapy

supports from
JOHNS HOPKINS PAVA CENTER MICROSOFT STARTUP PROGRAM JOHNS HOPKINS PAVA CENTER MICROSOFT STARTUP PROGRAM

Remembrance project illustration 01
Remembrance Launching in Q3 2026

Remembrance is an emotionally intelligent AI memory companion for Alzheimer's patients that delivers reminiscence therapy through natural conversations. By storing memories in interconnected knowledge graphs that mirror how human memory actually works, Remembrance helps patients maintain their sense of identity and connection to their life stories even as cognitive decline progresses.


Clarity Open Source project illustration 02
Low Field MRI Framework IEEE BigData 2024 Publication

Enhances low-field MRI scans for Alzheimer’s diagnosis using deep learning-based brain segmentation and volumetric analysis. By enabling 96% diagnostic accuracy on affordable, low-strength scanners, the framework provides a faster, cost-effective alternative to conventional high-field MRI while preserving clinically meaningful structural information for early and accessible neurodegenerative assessment.


GeneAttentionNet project illustration 03
Lark Multi-Stakeholder LLM Agents NeurIPS Workshop 2025 Publication

Lark is a biologically inspired decision-making framework that combines LLM-based reasoning with an evolutionary, stakeholder-aware multi-agent system. Through plasticity, duplication and maturation, ranked-choice aggregation, and compute-aware optimization, Lark generates concise, aligned strategies while balancing stakeholder trade-offs, performance, and computational cost across decision cycles.


SALSA project illustration 04
GeneAttentionNet MIT URTC 2025 Presentation, Preprint

GeneAttentionNet is an interpretable, biologically informed neural architecture for Alzheimer’s classification from gene expression data. Using attention-based tokenization, pathway gating, and protein-interaction–masked heads, it captures gene-level context, improving accuracy over baselines while enabling trustworthy, mechanism-aware predictions for transcriptomic disease modeling and biomedical research.


NeuroMorph project illustration 05
CortiForge Submitted to ICLR 2026, Preprint

CortiForge is an open-source, biologically grounded framework for modeling cortical microcircuits that links spiking neural network simulations with experimental brain data. Through standardized neuron libraries, AllenSDK integration, and end-to-end workflows, CortiForge enables interpretable, validated circuit models that bridge theory, experiments, and neuroscience research at scale reproducibly.