Dr. Adriana San Miguel, a postdoctoral associate in the School of Chemical & Biomolecular Engineering under the supervision of Dr. Hang Lu, has been awarded a K99/R00 Pathway to Independence Award by the National Institutes of Health. With a proposed total budget of $927,000, her project is titled, “Elucidating synaptic regulators via high-throughput morphological characterization.” Using the nematode Caenorhabditis elegans as a model, the proposed work aims to understand how synapses (connections between neurons in our brain) are shaped by environmental and activity-dependent factors.
The K99/R00 Pathway to Independence Award is an award given by NIH to postdoctoral scientists to support their transition into an independent faculty appointment. This award provides support for a one- to two-year postdoctoral mentored phase and a successive three-year independent phase as a principal investigator. The main objective of this grant is to support promising scientists in the early stages of their career and accelerate their transition to an independent research position.
This competitive award is one of the few available for non-U.S. citizens and is a great complement for prospective faculty candidates. Current faculty members of the Georgia Tech community who have won this award include Dr. Brandon Dixon (Mechanical Engineering) and Dr. Matthew Torres (Biology).
After completing undergraduate studies in chemical engineering at Monterrey Institute of Technology (ITESM) and working in industry for a couple of years, Adriana moved to the United States from her native Mexico to pursue a graduate degree at Georgia Tech. She completed her Ph.D. in chemical and biomolecular engineering under the supervision of Dr. Sven Behrens, working on stimulus-responsive microcapsules and emulsions. She is now a postdoctoral fellow in Dr. Hang Lu’s lab, where she and others work on integrated engineering systems to perform high-throughput experiments with the nematode C. elegans to answer biological questions that cannot be addressed with conventional methods. Tools developed in the Lu lab, including microfluidics, machine learning and hardware automation, allow unbiased quantitative multidimensional characterization of micron-sized synaptic sites in large animal populations.