News

Nina Miolane is Featured in UCSB Convergence Revue

Nina Miolane, PI of the Geometric Intelligence Lab, is featured in UCSB Magazine of Engineering & Science (Convergence) for our research on quantifying the morphodynamics of migrating cells with geometric statistics !

The cells in our bodies are constantly on the move to achieve a myriad of tasks that are essential to life. What happens when cell motion is disrupted? What can we learn from the uncoordinated dances of abnormal moving cells? Could we read characteristics of pathologies such as cancers by decoding cell movements?

Adele Myers is Interviewed by the Machine Learning Street Talks Podcast @ NeurIPS

Adele Myers, Ph.D. student in the Geometric Intelligence Lab, is featured in the renowned Machine Learning Street Talks Podcast Series at NeurIPS 2022 for her work: "Regression-Based Elastic Metric Learning on Shape Spaces of Elastic Curves" with Nina Miolane. Watch her interview here!

Mathilde Papillon Receives the Best Paper Award at EAI ArtsIT

Mathilde Papillon, Ph.D. student in the Geometric Intelligence Lab, receives the prestigious Best Paper Award at the conference EAI ArtsIT for her work: "PirouNet: Creating Dance through Artist-Centric Deep Learning" with Mariel Pettee and Nina Miolane.

Watch Nina Miolane's Keynote at the CVPR Workshop on Deep Learning for Geometric Computing

Nina Miolane, PI of the Geometric Intelligence Lab, presented the work of our group at the workshop "Deep Learning for Geometric Computing". Watch the video here!

Nina Miolane Receives Regents' Junior Faculty Award

Nina Miolane, PI of the Geometric Intelligence Lab, receives the UC Regents' Junior Faculty Fellowship Award!

This award, presented by the UCSB Academic Personnel Office, supports outstanding junior faculty in their development of the substantial record in research and creative work necessary for advancement to tenure. 

We Are Awarded a NIH R01 Grant for Biological Shape Reconstruction

We are excited and honored that our group was awarded a NIH R01 grant for the introduction of geometric and deep learning methods to enhance 3D biological shape reconstruction. We aim to reveal the shapes of membrane proteins, these biomolecules targeted by over 50% of the pharmaceutical drugs, yet still difficult to image.

We Are Awarded the NSF SCALE MoDL Grant on Mathematical and Scientific Foundations of Deep Learning

We are excited and honored that our group was awarded the NSF SCALE MoDL Grant "Stimulating Collaborative Advances Leveraging Expertise in the Mathematical and Scientific Foundations of Deep Learning". We aim to provide a unified geometric and topological framework grounded in cell complex neural networks to explain and enhance deep learning architectures, with applications to biological shape analysis.

We win the 1st Prize in the C3.ai Covid-19 Grand Challenge!

In the C3.ai COVID-19 Grand Challenge, developers, data scientists, students, and creative minds around the world developed meaningful data-driven insights to inform decision makers and change how the world is fighting this pandemic.

Our solution "Modeling Population Heterogeneity by Providing Personalized Covid-19 Diagnostics" with C. Donnat and F. Bunbury won the first prize of $100,000!