Summary
Azim Amirabad is a Principal Artificial Intelligence Scientist in Cambridge, MA with nine years of experience developing ML and deep learning methods that bridge LLMs, multimodal models, and single-cell multi-omics to accelerate biological discovery. He has led integrative analyses across scRNA-seq, scATAC-seq, CITE-seq and imaging data at Johnson & Johnson and Sanofi, and previously developed causal and generative models for single-cell regulatory landscapes during postdoctoral work at MIT/CSAIL and the Broad. His background combines a PhD in Computer Science from the Max Planck Society with hands-on expertise in geometric deep learning, diffusion models, and statistical optimization, enabling translation of complex high-dimensional data into actionable therapeutic hypotheses. Known for applying algorithmic rigor to biological heterogeneity, he has a track record of finding cell-type–specific drivers and potential drug targets across cancers and immune cell atlases.
9 years of coding experience
8 years of employment as a software developer
Postdoctoral Associate Computer Science, Postdoctoral Associate Computer Science at Massachusetts Institute of Technology
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Max Planck Society
Bachelor's degree Biotechnology, Bachelor's degree Biotechnology at Shahed University
Online Leadership Principles, Online Leadership Principles at Harvard Business School
English, Turkish, Persian, German