Hi, I'm George! I am a biophysics researcher specializing in the use of fluorescence-based experimental techniques and computational approaches to study the biomolecules. My current research makes use of super-resolution single molecule localization microscopy and live cell microscopy to investigate the effects of oncogenic mutations on KRAS dynamics in cells as well as the molecular mechanisms of V(D)J recombination. My prior research has included work on the conformational dynamics of PSD-95, the TPP riboswitch, P27 and Cdk/Cyclin A, and FoxP1/FoxP2 using single molecule FRET. I began my biophysics training during my undergraduate studies at NCSU under Dr. Keith Weninger, where I learned the essentials of TIRF microscopy and wet lab skills. My graduate research performed with Dr. Hugo Sanabria at Clemson University and Dr. Prof. Claus AM Seidel at Heinrich-Heine-Universität Düsseldorf has focused on using time-resolved, fluorescent-probe based spectroscopy to study single biomolecule conformational dynamics in vitro. Currently I am located at NYU Grossman School of Medicine for my postdoctoral research with Dr. Eli Rothenberg using SMLM-STORM and live cell microscopy to study biomolecular interactions, structure, and dynamics in the relevant cellular contexts. In my research, I have also applied quantitative liquid chromatography, molecular dynamics simulations (including Gromacs, discrete MD with replica exchange, and LAMMPS), and bioinformatics tools in conjunction with confocal experiments to perform integrative biophysical studies of several systems. Further, I have used Python for developing tools for single-molecule experimental design and data analysis, including selection of fluorescent probe placement within single-molecule systems for FRET studies and analysis of time-trace fluorescence data. In addition to these experiences, I have received hands-on training in optical microscopy (including widefield phase–contrast, widefield multi-color fluorescence, scanning confocal, and fluorescence lifetime based imaging) as well as machine learning using Python with Scikit-learn and Tensorflow.