As of August 2021, I am a Machine Learning Scientist at Dyno Therapeutics. Before that, I recieved a Ph.D. in the Biophysics Graduate Group at the University of California, Berkeley, where I was a member of the Berkeley Artifical Intelligence Research Lab (BAIR) advised by EECS Professor Jennifer Listgarten. I am broadly interested in machine learning applications to biology, with a particular focus on developing tools for protein engineering. In the summer of 2019 I worked as an AI resident at Google X.

I recently gave a talk about a few different threads of my work as part of the MIA seminar series at the Broad Institute.

You can reach me at dhbrookes@gmail.com.

Publications

[1] D. H. Brookes, J. Otwinowski, and S. Sinai. Contrastive losses as generalized models of global epistasis. Accepted at NeurIPS (2024).

[2] D. Zhu*, D. H. Brookes*, A. Busia, A. Carneiro, C. Fannjiang, G. Popova, D. Shin, EF Chang, T. J. Nowakowski, J Listgarten, D. V. Schaffer. Optimal trade-off control in machine learning-based library design, with application to adeno-associated virus (AAV) for gene therapy. *Sci. Adv. 10, eadj3786 (2024). (*equal contributions)

[3] F. Damani, D. H. Brookes, T. Sternlieb, C. Webster, S. Malina, R. Jajoo, K. Lin, S. Sinai. Beyond the training set: an intuitive method for detecting distribution shift in model-based optimization. arXiv (2023). (* equal contributions)

[4] D. H. Brookes, A. Aghazadeh, and J. Listgarten. On the sparsity of fitness functions and implications for learning. Proceedings of the National Academy of Sciences 119(1), e2109649118 (2022).

[5] A. Aghazadeh, H. Nisonoff, O. Ocal, D. H. Brookes, Y. Huang, O. O. Koyluoglu, J. Listgarten, and K. Ramchandran. Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions. Nat. Commun. 12, 5225 (2021).

[6] D. H. Brookes, A. Busia, C. Fannjiang, K. Murphy, and J. Listgarten. A view of Estimation of Distribution Algorithms through the lens of Expectation Maximization. The Genetic and Evolutionary Computation Conference (2020).

[7] D. H. Brookes, H. Park, and J. Listgarten. Conditioning by adaptive sampling for robust design. Proceedings of ICML (2019). Selected for a 20 minute oral presentation (< 5% of submissions)

[8] D. H. Brookes and J. Listgarten. Design by adaptive sampling. NeurIPS Workshop on Machine Learning for Molecules and Materials (2018).

[9] E. Jurrus, D. Engel, K. Star, K. Monson, J. Brandi, L. E. Felberg, D. H. Brookes, L. Wilson, J. Chen, K. Liles, M. Chun, P. Li, T. Dolinsky, R. Konecny, D. Koes, J. E. Nielsen, T. Head-Gordon, W. Geng, R. Krasny, M. Gunner, G.-W. Wei, M. J. Holst, J. A. McCammon, N. A. Baker. Improvements to the APBS biomolecular solvation software suite. Protein Sci. 27 (1), pp. 112-128 (2018).

[10] L. E. Felberg, D. H. Brookes, E. Jurrus, N. Baker, and T. Head-Gordon. PB-AM: An open-source, fully analytical linear Poisson-Boltzmann solver. J. Comp. Chem. 38 (15), pp. 1275-1282 (2017).

[11] D. H. Brookes and T. Head-Gordon. Experimental inferential structure determination of ensembles for intrinsically disordered proteins J. Am. Chem. Soc. 138 (13), pp. 4530-4538 (2016).

[12] D. H. Brookes and T. Head-Gordon. The family of oxygen-oxygen radial distribution functions for water. J. Phys. Chem. Lett. 6 (15), pp. 2938-2943 (2015).

[13] L. E. Felberg, D. H. Brookes, J. E. Rice, T. Head-Gordon, and W. Swope. Role of hydrophilicity and length of diblock arms for determining star polymer physical properties. J. Phys. Chem. B. 119 (3), pp. 944-957 (2015).