I currently work on machine learning methods for data-driven design, the design of novel objects with desired properties, such as proteins, molecules, or materials, in a way that is learned from data. How should the use of generative and predictive modeling for design differ from traditional uses of these technologies? How can we reason about and quantify uncertainty when we extrapolate? I am particularly interested in these questions as they apply to the design of proteins with improved fitness.

I also maintain an interest in applications of machine learning for exploring life in our last great frontier, the ocean.


  • Chloe Hsu, Hunter Nisonoff, Clara Fannjiang, Jennifer Listgarten. A systematic assessment of methods for combining evolutionary and assay-labelled data for protein fitness prediction. accepted at Nature Biotechnology. bioRxiv

  • Akosua Busia, George E. Dahl, Clara Fannjiang, David H. Alexander, Elizabeth Dorfman, Ryan Poplin, Cory Y. McLean, Pi-Chuan Chang, and Mark DePristo. A deep learning approach to pattern recognition for short DNA. bioRxiv

refereed conferences

  • Ghassen Jerfel*, Serena Wang*, Clara Fannjiang, Katherine Heller, Yian Ma, Michael Jordan. Variational refinement for importance sampling using the forward Kullback-Leibler divergence. UAI 2021. arXiv

  • Clara Fannjiang and Jennifer Listgarten. Autofocused oracles for model-based design. NeurIPS 2020. arXiv proceedings code

  • David H. Brookes, Akosua Busia, Clara Fannjiang, Kevin Murphy, and Jennifer Listgarten. A view of estimation of distribution algorithms through the lens of expectation-maximization. GECCO 2020. proceedings arXiv (extended version)


  • Katherine Lee, Orhan Firat, Ashish Agarwal, Clara Fannjiang, and David Sussillo. Hallucinations in neural machine translation. NeurIPS 2018 Workshop on Interpretability and Robustness for Audio, Speech, and Language. PDF


  • I. Masmitja, J. Navarro, S. Gomariz, J. Aguzzi, B. Kieft, T. O‚ÄôReilly, K. Katija, P. J. Bouvet, C. Fannjiang, M. Vigo, P. Puig, A. Alcocer, G. Vallicrosa, N. Palomeras, M. Carreras, J. Del-Rio, J. B. Company. 2020. Mobile robotic platforms for the acoustic tracking of deep-sea demersal fishery resources. Science Robotics, Vol. 5, Issue 48, eabc3701. PDF publication

  • Clara Fannjiang, T. Aran Mooney, Seth Cones, David Mann, K. Alex Shorter, and Kakani Katija. 2019. Augmenting biologging with supervised machine learning to study in situ behavior of the medusa Chrysaora fuscescens. Journal of Experimental Biology, 222, jeb207654. PDF publication jellyfish footage code

  • Clara Fannjiang. 2013. Optimal arrays for compressed sensing in snapshot-mode radio interferometry. Astronomy & Astrophysics, 559, A73. PDF publication

* equal contribution

Benthocodon hyalinus, after photo by K. Raskoff in Matsumoto et al. (2020).