Microtechnologies and Deep Learning for Neurogenetics

Description:
My lab is interested in engineering machine learning tools and micro systems to address questions in systems neuroscience, developmental biology, and cell biology that are difficult to answer with conventional techniques. Microfluidics provide the appropriate length scale for investigating molecules, cells, and small organisms; moreover, one can also take advantage of unique phenomena associated with small-scale flow and field effects, as well as unprecedented parallelization and automation to gather quantitative and large-scale data about complex biological systems. In parallel, ML technologies have now vastly expanded the capabilities for scientific inquiry, both in data processing and data interpretation.
We are particularly interested in the questions of how the brain is assembled during development (and changes during aging) and information is processed by brain circuits. We work with a powerful genetic system - the free-living soil nematode C. elegans. In this talk, I will introduce powerful mathematical and physics-based tools to accelerate the biological understanding. I will talk about two recent developments in discrete microfluidic systems exploiting multiphase and dynamical behavior of the fluids and microswimmers (i.e. C. elegans) (Aubry, Small, 2022; Sun, Adv Healthcare Mat’l, 2021). I will also talk about a powerful graph-theory-based framework to build probabilistic models of brain atlases (Chaudhary, eLife, 2021), as well as a deep-learning tool to denoise calcium activities in the brain (Chaudhary, NComm, 2022). These machine-learning approaches greatly reduce bias, enable automated and robust cell identification and signal extraction, and will enable a variety of applications including gene-expression analysis, whole-brain imaging, and connectomics.

Speaker: Hang Lu - Georgia Institute of Technology
Hang Lu is the C. J. “Pete” Silas Chair and Professor of Chemical & Biomolecular Engineering, the Director of the Interdisciplinary Bioengineering Program at Georgia Tech, and the associate director of the Southeast Center for Mathematics and Biology supported by NSF and Simons Foundation. Her research interests are microfluidics, machine learning, and their applications in neurobiology, cancer, and biotechnology. Her awards/honors include ACS Analytical Chemistry Young Innovator Award, NSF CAREER award, Alfred P. Sloan Foundation Research Fellowship, DuPont Young Professor Award, and DARPA Young Faculty Award; she was also named an MIT Technology Review TR35 top innovator, and invited to give the RPI Van Ness Award Lectures in 2011, the Saville Lecture at Princeton in 2013, and the Humphrey Distinguished Lecturer at Lehigh University in 2023. She is an elected fellow of AAAS, AIMBE, and RSC (UK).