Farhan Damani
About Farhan Damani
Farhan Damani is a Senior Scientist at Dyno Therapeutics with extensive experience in machine learning and artificial intelligence, particularly in gene therapy and sequence modeling.
Company
Farhan Damani is currently employed at Dyno Therapeutics as a Senior Scientist. Dyno Therapeutics specializes in using artificial intelligence to create enhanced gene therapy vectors.
Title
Farhan Damani holds the position of Senior Scientist at Dyno Therapeutics. His role involves leading advanced research and development projects in gene therapy and machine learning.
Education and Expertise
Farhan Damani pursued his education at prominent institutions. He studied Machine Learning and Artificial Intelligence at Princeton University from 2017 to 2019. Prior to that, he graduated with a degree in Computer Science & Applied Math from The Johns Hopkins University, where he studied from 2012 to 2016. He has significant expertise in high-dimensional discrete design problems, focusing on machine learning methods such as sequence modeling, deep learning, and generative design.
Professional Background
Farhan Damani's professional journey includes significant roles at various institutions. At Dyno Therapeutics, he has worked as Scientist I from 2020 to 2021, Scientist II from 2021 to 2022, and currently as a Senior Scientist. He was also one of the first AI hires at the company. Before Dyno Therapeutics, he worked at Pfizer as a Machine Learning Research Intern for 11 months in 2019. His early career includes positions such as Graduate Student Researcher at Princeton University, Research Assistant at the Machine Learning Group at JHU / Center for Computational Biology, and SPUR Scholar at The Johns Hopkins University Applied Physics Laboratory. Additionally, he served as a Debate Coach at Loyola Blakefield from 2012 to 2013.
Publications and Research Contributions
Farhan Damani has contributed to the field through several important publications. He authored 'Generative design for gene therapy: an in vivo validated method' and 'Beyond the training set: an intuitive method for detecting distribution shift in model-based optimization.' His research has focused on predicting the mutational effects of genetic mutations, small molecule generative design, lead optimization, reaction condition optimization, and protein design applied to gene therapy.