Adele Myers

Adele is doing her Ph.D. in Nina Miolane's Bioshape lab in the Electrical and Computer Engineering Department. Originally from Livermore, CA, she went to Pomona College for undergrad, where she majored in physics, minored in mathematics, and swam on the Pomona-Pitzer varsity swim team. In her first years of undergrad, her nuclear shockwave research recieved a best poster award and a Goldwater Scholarship. Next, her research shifted towards cosmology and general relativity. Now, she uses differential geometry to analyze biological shapes, supported by an NSF Data Driven Biology training grant fellowship.

Research

The shape of a biological object can provide insights about the internal state of the object. For example, while a healthy cell has a smooth boundary, a cancerous cell is more spikey, and the shape of an brain with Alzheimers' is different than the shape of a healthy brain. Thus, determine differences between biological objects is quite important. Often, biological shape are described with coarse qualitative attributes such as the area, perimeter, volume or convexity of the shape. However, these methods are not precise, statistically robust, or reproducible; they are not sufficient analyses when the objects are complex and the differences between shapes are subtle. Adele's research analyzes biological shapes in "shape spaces", where each point in the shape space represents a different shape outline. Then, I use differential geometry to develop robust methods for shape analysis.

Selected Publications

• Adele Myers and Nina Miolane. Regression-Based Elastic Metric Learning on Shape Spaces of Cell Curves. NeurIPS workshop: Learning meaningful representations of life. (2022).
• Allison M Gabbert; James A Mondo; Joseph P Campanale; Noah P Mitchell; Adele Myers; Sebastian J Streichan; Nina Miolane; Denise Montell (2022) Septins regulate border cell shape and surface geometry downstream of Rho Developmental Cell (submitted for publication)
• Myers et. al (2022) ”ICLR 2022 Challenge for Computational Geometry Topology: Design and Results”. Proceedings of Machine Learning Research. Volume “Topological, Algebraic, and Geometric Learning”. Accepted, In press.
• Nina Miolane; Saiteja Utpala; Nicolas Guigui; Alice Le Brigant; Hzaatiti; Johan Mathe; Niklas Koep; emaignant; xpennec; ythanwerdas; tgeral68; Christian; Olivier Peltre; pchauchat; Maya95assal; mortenapedersen; Lu ́ıs F. Pereira; Florent-Michel; Stefan Heyder; adelemyers99; Jules-Deschamps; Yann Cabanes; Quentin Barth ́elemy; Marius; Shubham Talbar; est271; Arthur Pignet; Oleg Kachan; Adrish Dey; Chris Harris. (2022). geomstats/geomstats: Geomstats v2.5.0 (2.5.0). Zenodo.
• Adele Myers, Greg Spriggs (2018), Water Entrainment in Nuclear Detonations: Discovery and Investigation, LLNL Report, LLNL-TR-758735