farhand7@gmail.com

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Hi. I'm Farhan Damani.

I work at the intersection of machine learning and drug design. I am broadly interested in tackling biological design problems using data, models, and computation.

about me.

I am a machine learning research scientist at Dyno Therapeutics where I work on designing new delivery vectors for gene therapy. My overall research goal is to design novel therapeutics using machine learning. In recent years, my work has focused on a variety of problems in this space including protein design, molecular design, reaction condition design, and chemical synthesis design. Previous to Dyno, I consulted for Pfizer R&D on molecular design (see here)

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In 2019, I completed a master's degree in computer science at Princeton University where I worked on machine learning for computational neuroscience and chemistry with Jonathan Pillow and Ryan Adams. Previous to that, I completed my undergraduate degree in computer science at Johns Hopkins University, where I worked with Alexis Battle on computational genomics.



recent work.


Efficient Design of Optimized AAV Capsids using multi-property Machine Learning Models Trained across Cells, Organs and Species.
Damani F., Dyno Therapeutics, Sinai S., Kelsic E.
Representation Learning in Biology, ISMB/ECCB 2021. (talk)


Bayesian Reaction Optimization as a Tool for Chemical Synthesis.
Shields B., Stevens J., Li J., Parasram M., Damani F., Janey J., Adams R., Doyle A.
Nature 2021


Black Box Recursive Translations for Molecular Optimization.
Damani F., Sresht V., Ra S.
Neural Information Processing Systems Workshop on Machine Learning for Molecules 2020.
[paper]


Discrete Object Generation with Reversible Inductive Construction
Seff A., Zhou W., Damani F., Doyle A., Adams, R.P.
Neural Information Processing Systems (NeurIPS) 2019
[Manuscript][Code]


Inferring animal learning rules from behavior with probabilistic models.
Damani F., Roy, N., Akrami A., Brody C., Adams, R.P., Pillow, J.W.
Computational and Systems Neuroscience (Cosyne) Extended Abstract.


A framework for predicting tissue-specific effects of rare genetic variants
Damani F., Kim Y., Li X., Tsang E., Davis J., Chiang C., Zappala Z., Strober B., Scott A., Hall I., GTEx Consortium, Montgomery S., Battle A.
In preparation.
[Manuscript][Code][Slides][Talk]


The impact of rare variation on gene expression across tissues
Li X., Kim Y., Tsang E., Davis J., Damani F., Chiang C., Zappala Z., Strober B., Scott A., Ganna A., Merker J., GTEx Consortium, Battle A., Montgomery S.
Nature 2017
[Paper][Code]


The impact of structural variation on gene expression
Chiang C., Scott A., Davis J., Tsang E., Li X., Kim Y., Damani F., Ganel L., GTEx Consortium, Montgomery S., Battle A., Conrad D., Hall I.
Nature Genetics 2017
[Paper]