Kristina Monakhova

Kristina Monakhova is an Assistant Professor in the Department of Computer Science at Cornell University, where she leads the Computational Imaging Lab. Her research focuses on co-designing optics and algorithms to develop advanced imaging systems such as smaller, more capable cameras and microscopes.

• Education: PhD in Electrical Engineering and Computer Sciences from UC Berkeley (2020), supported by the NSF GRFP fellowship. Postdoctoral work at MIT (2020-2022) with the MIT Postdoctoral Fellowship for Engineering Excellence.
• Affiliations: Cornell University (2022-present), MIT (2020-2022), UC Berkeley (2015-2020).

Monakhova’s research spans computational imaging, physics-informed machine learning, and adaptive acquisition methods. She integrates deep learning with optical system design to address challenges in low-light imaging, hyperspectral acquisition, and microscopy. Her work emphasizes trustworthiness through uncertainty quantification, reducing model hallucinations in medical and scientific applications.

Her recent publications focus on single-shot HDR imaging, uncertainty-driven microscopy, and isotropic 3D deblurring. These studies leverage optical innovations (e.g., spectral filter arrays, global reset release shutters) paired with tailored neural networks for real-time, high-fidelity imaging.

• Scientific Awards
  • NSF GRFP Fellowship (PhD)
  • MIT Postdoctoral Fellowship for Engineering Excellence

Monakhova mentors PhD students including Shamus Li, Cassandra Ye, Yujin Lee, and Hasindu Kariyawasam. She has taught courses like CS6662: Computational Imaging (Cornell, Fall 2024) and guest lectured on computational systems at MIT and UCSD. Her lab collaborates on interdisciplinary projects bridging optics, machine learning, and biomedical imaging.