Chemical Engineering Seminar
Title: Illuminating Natural Intelligence with Smart Experimental Systems and Advanced Machine Learning
Abstract: My lab is interested in engineering machine learning tools and microtechnologies 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 machine-learning/statistical and physics-based tools to accelerate the understanding of C. elegans brain in the context of neural development and aging, sensorimotor integration, higher cognitive functions such as learning. The technological approaches greatly reduce bias, enable automated and robust cell/synapse identification, and will enable a variety of applications including gene-expression analysis, whole-brain imaging, and connectomics.
Bio: Hang Lu is the C. J. "Pete" Silas Professor of Chemical and Biomolecular Engineering and the Associate Dean for Research and Innovation of College of Engineering at Georgia Tech. She graduated summa cum laude from the University of Illinois at Urbana-Champaign in 1998 with a B.S. in Chemical Engineering. She obtained her Ph.D. in Chemical Engineering in 2003 from MIT. Between 2003 and 2005, she was a postdoc at UCSF and the Rockefeller University in neuroscience. She has been an assistant professor (2005-2010), associate professor (2010-2013), and professor (2013-present) of chemical & biomolecular engineering at Georgia Tech. Her current research interests are in microfluidics, automation, quantitative imaging, data science, and their applications in neurobiology, cell biology, cancer, and biotechnology. Her award and honors include the Pioneer of Miniaturization Lectureship, the ACS Analytical Chemistry Young Innovator Award, a National Science Foundation CAREER award, an Alfred P. Sloan Foundation Research Fellowship, a DuPont Young Professor Award, a DARPA Young Faculty Award, Council of Systems Biology in Boston (CSB2) Prize in Systems Biology, Georgia Tech Junior Faculty Teaching Excellence Award, and Georgia Tech Outstanding PhD Thesis Advisor Award; she was also named an MIT Technology Review TR35 top innovator, and invited to give the Rensselaer Polytechnic Institute Van Ness Award Lectures in 2011, and the Saville Lecture at Princeton in 2013. She is an elected fellow of American Association for the Advancement of Science (AAAS), of Royal Society of Chemistry (RSC), and of the American Institute for Medical and Biological Engineering (AIMBE). She is currently the associate director of the Southeast Center for Mathematics and Biology (SCMB) at Georgia Tech, supported by NSF and Simons Foundation. Her lab's work has been/is supported by >$37M ($17M to her lab) from US NSF, NIH, private foundations and others.