We turn biological shapes into biological insights.
Modeling the healthy and pathological shapes of proteins, cells and organs is critical for research in biology and medicine: ranging from our understanding of cancers to the diagnosis of neurodegenerative diseases.
We work in collaboration with experts in molecular and cell biology, as well as with clinicians, to advance the frontiers of biomedical knowledge and AI-assisted medical practice.
Our lab leverages tools from geometry, topology, computer vision, machine learning and deep learning to create computational representations of biological structures in the human body at different scales. We are dedicated to contributing to the transformation of biology and medicine into statistically-grounded and computationally-enabled disciplines.
BioShape Research: The Hidden Geometries of Life
Molecular & Cellular Shape Analysis
For Computational Biology
Proteins and cells adopt varying shapes to fulfill essential functions in living organisms. How can we best observe this extremely coordinated "dance of life" that happens at the nano- and micro-scales? How can we image protein shapes with a sub-atomic resolution? What information do protein shapes reveal about their function? Unusual shapes also play the role of biological whistleblower. What information do cancer cell shapes reveal about cancer types and stages? Learn more.
Anatomical Shape Analysis
For Computational Medicine
Bioshape analysis may hold the key to unlock outstanding mysteries in medicine. What does a brain shape tell us about the progression of Alzheimer's disease? How do brain shapes vary with hormonal levels, e.g., during the menstrual cycle of a woman? Knowing that women are twice at risk of Alzheimer's compared to men: could answers to the two previous questions be related? Overall, how can AI best assist medical researchers and clinicians? Learn more.
Foundations in Geometry & Learning
For Shape Analysis
Shape analysis raises a number of fascinating questions at the intersection of geometry and AI. How can we quantify biological shapes with their normal and pathological variability? How can we build reliable shape analysis methods for computational biomedicine? Beyond biomedicine, we research foundations of geometric learning and ask: what is the geometry of a deep learning model? can we build a geometric model of the (artificial) mind? Learn more.
Latest News
Nina Miolane named Hellman Fellow
Nina Miolane was named a 2023 Hellman Fellow. The Hellman Fellows Fund, founded by Chris and Warren Hellman in 1994, provides support to promising junior faculty in the earliest stages of their academic career. The award supports emerging leaders across the University of California, the American Academy of Arts and Sciences, Stanford University, Harvard Business School and Williams College.
Read MoreNina Miolane named Hellman Fellow
Adele Myers Receives the Prestigious NSF Graduate Research Fellowship (GRFP)
Adele Myers, Ph.D. in the BioShape Lab, has been awarded the prestigious NSF GRFP fellowship. She will receive 3 years of funding to develop novel cutting-edge methodology to analyze biological shapes.
Read MoreAdele Myers Receives the Prestigious NSF Graduate Research Fellowship (GRFP)
Sophia Sanborn Receives the Prestigious PIMS-Simons Fellowship!
Sophia Sanborn, postdoctoral fellow in the BioShape Lab, has been awarded the prestigious PIMS-Simons fellowship from the Pacific Institute for Mathematical Sciences and the Simons Foundation.
This fellowship recognizes her as an "outstanding young researcher in the mathematical sciences”!
Read MoreSophia Sanborn Receives the Prestigious PIMS-Simons Fellowship!