I work on statistical learning and inference problems that arise in data-driven design: the design of novel objects with desired properties, such as proteins or small molecules, in a way that is learned from data. How can we quantify uncertainty or estimate risk when we deploy design algorithms? How can we understand the inductive biases of generative models used for design? I am particularly interested in these questions in the context of protein design.

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

Google Scholar

preprints

  • Clara Fannjiang, Stephen Bates, Anastasios N. Angelopoulos, Jennifer Listgarten, and Michael I. Jordan. 2022. Conformal prediction for the design problem. Proceedings of the National Academy of Sciences, to appear. arXiv code

  • Danqing Zhu, David H. Brookes, Akosua Busia, Ana Carneiro, Clara Fannjiang, Galina Popova, David Shin, Edward F. Chang, Tomasz J. Nowakowski, Jennifer Listgarten, and David V. Schaffer. Machine learning-based library design improves packaging and diversity of adeno-associated virus (AAV) libraries. 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)

journals

  • Chloe Hsu, Hunter Nisonoff, Clara Fannjiang, and Jennifer Listgarten. 2022. Learning protein fitness models from evolutionary and assay-labelled data. Nature Biotechnology. PDF publication

  • 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

workshops

  • 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

* equal contribution


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