Graduate Student Researcher
- Identified and characterized the molecular basis for noise in rod photoreceptors and its implications on the threshold of photon detection by leveraging genetic, electrophysiological and statistical tools.
- Distinguished contributions of rod bipolar neurons to adaptation in the rod visual pathway, and identified the TRPM1-channel protein as a potential target for calcium that controls the boundary between rapid and prolonged adaptation to continuous light.
- In addition to thesis work funded by T32 training grant (T32EY07026, 2018 – 2021), I collaborated with internal and external labs to UCLA where I contributed statistical and experimental work to the successful funding from 4 different NIH grants, 3 publications (Zhang et al. 2020, Reingruber et al. 2020, Griffis et al. 2022) and several conference abstracts (Griffis & Sampath 2021, Chung et al. 2022, for example).
- Developed an open-source, MATLAB-based analysis framework and GUI, named Iris DVA, for transparent, documentable, and replicable analysis of physiological data.
- Developed and supported custom hardware and software for precision control over light stimuli, for use in microspectrophotometry, electroretinography, and single-cell patch physiology rigs.
1 March 2016
- Identified and characterized the molecular basis for noise in rod photoreceptors and its implications on the threshold of photon detection by leveraging genetic, electrophysiological and statistical tools.
- Distinguished contributions of rod bipolar neurons to adaptation in the rod visual pathway, and identified the TRPM1-channel protein as a potential target for calcium that controls the boundary between rapid and prolonged adaptation to continuous light.
- In addition to thesis work funded by T32 training grant (T32EY07026, 2018 – 2021), I collaborated with internal and external labs to UCLA where I contributed statistical and experimental work to the successful funding from 4 different NIH grants, 3 publications (Zhang et al. 2020, Reingruber et al. 2020, Griffis et al. 2022) and several conference abstracts (Griffis & Sampath 2021, Chung et al. 2022, for example).
- Developed an open-source, MATLAB-based analysis framework and GUI, named Iris DVA, for transparent, documentable, and replicable analysis of physiological data.
- Developed and supported custom hardware and software for precision control over light stimuli, for use in microspectrophotometry, electroretinography, and single-cell patch physiology rigs.