I am Reza Alipour, PharmD, Ph.D. Data Scientist at Meta and Computational Biologist architecting infrastructure to extend human healthspan.
Biological aging is a physical engineering problem masquerading as an inevitability. While mind uploading represents the ultimate asymptote of human preservation, solving biological aging is the most feasible and critical engineering challenge of the 21st century.
Combining a PharmD, a Ph.D. in Neuroscience, and expertise in scaling machine learning systems, I am dedicated to dismantling the mechanisms of aging to radically increase human lifespan. My focus is applying AI and multi-omics infrastructure to discover fundamental biological drivers of decline, decode multimodal signatures of aging, and accelerate the transition from theoretical longevity research to clinically viable therapeutics.
An end-to-end, multimodal ensemble AI framework designed to detect extracapsular extension (ECE) in prostate cancer. Fuses MRI, histopathology, and clinical data from 1,001 patients into a unified predictive pipeline.
Utilizes lightweight CNNs engineered for clinical efficiency. Achieved expert-level performance, surpassing human radiologists in real-world testing. (Patented system, Co-author, and Web App Developer).
Developing a computational framework to predict autophagy flux scores across massive chemical spaces. This pipeline evaluates compounds for their potential to restore cellular proteostasis and clear intracellular debris.
By systematically predicting flux kinetics, this model accelerates the discovery pipeline for novel senolytics and geroprotectors designed to systematically target the biological hallmarks of aging.
Engineered an autonomous virtual mental health companion utilizing Large Language Models grounded strictly in evidence-based Cognitive Behavioral Therapy (CBT) techniques.
Replaces rigid decision trees with dynamic, empathetic NLP. Analyzes user context to deliver actionable psychological strategies (e.g., negative thought challenging, relaxation protocols) accessible 24/7.
Thesis: Deep Learning for Modeling Neural Dynamics
Top 1% Selectivity
Holder of two national patents (IR 79812, IR 82494).
These patents cover novel pharmacological formulations, translating molecular biology findings into actionable, targeted therapeutic delivery systems.