About me

Welcome! I am a Postdoctoral Fellow at the Broad Institute of MIT/Harvard in the Eric and Wendy Schmidt Center. My work focuses on improving the prediction and inference of biological and physical systems by blending machine learning, mechanistic modeling, and data assimilation techniques. I aim to build robust, unifying theory for these approaches, as well as develop concrete applications. I have worked substantially in the biomedical sciences, and enjoy collaborating on impactful applied projects.

I am always interested in developing new collaborations. If you think our work is complementary, please send me a message!

Updates:

• CD_Dynamax: a new JAX repository for learning dynamical systems from irregularly sampled, partially observed, noisy time-series data (extends discrete-time state-space approach in Dynamax). Joint with Iñigo Urteaga.

• August 2023: I co-organized a minisymposium on randomized machine learning at ICIAM 2023 in Tokyo with Oliver Dunbar, Nick Nelsen, and Georg Gottwald. Thank you to our wonderful speakers, attendees, and co-organizers for such enriching sessions!

• July 2023: I visited Prof. Iñigo Urteaga at the Basque Center for Applied Mathematics (BCAM) in Bilbao, Spain. Thank you to Iñigo and BCAM for hosting me! I look forward to continuing our collaboration on uncertainty quantification for hybrid dynamical models.

• May 2023: I defended my PhD thesis Machine Learning and Data Assimilation for Blending Incomplete Models and Noisy Data. I graduated with a PhD in computing and mathematical sciences at Caltech advised by Andrew Stuart. I am grateful to Andrew for his mentorship and support throughout my PhD. I am also grateful to my committee members, Yisong Yue, Houman Owhadi, and Katie Bouman for their insightful feedback and guidance.