Course Design and Instruction Experimental Methods (Mechanical Engineering): Redesigned and taught a course on probability, statistics, and measurement uncertainty, aligning lab components with modern experimental methods across engineering disciplines. Delivered 2-hour weekly lectures and supervised 4 lab sections, each 2.3 hours per week, teaching over 50 students per semester. Neuroplasticity and Disease (Kinesiology): Developed and instructed a course on neuromuscular disorders and neuroplasticity, focusing on physiological mechanisms and their pathological implications. Conducted 3-hour weekly lectures, teaching over 75 students per semester.

Interdisciplinary Research and Collaboration Led collaborative research initiatives in the field of Spinal Plasticity and Spinal Cord Injury. Fostered partnerships with academic institutions for joint research projects.

Student Mentorship and Management Mentored and managed a graduate student assistant, supporting their development and coordinating their contributions to course activities. Provided guidance and support to over 125 students each semester, promoting academic and professional growth.

Curriculum Development Contributed to enhancing departmental curricula, promoting interdisciplinary learning and ensuring alignment with industry standards.

• Spearheading the development of scientific infrastructure, driving research initiatives, and integrating cutting-edge algorithms into SeqAnalytics’ proprietary RNA sequencing pipeline.
• Delivering high-impact results and managing research consulting services for gene sequencing clients, ensuring top-tier project outcomes and customer satisfaction.
• Shaping the strategic scientific direction of SeqAnalytics’ consulting services, fostering collaborations and joint ventures with leading academic institutions.
• Leading funding acquisition efforts and effectively communicating complex scientific concepts to stakeholders and clients, ensuring alignment and support for SeqAnalytics’ innovative projects.

• Led 1 experimental and 1 in silico project investigating the molecular origin of photoreceptor discrete- and continuous-noise (2 firstauthor manuscripts in final preparations).
• Combined machine learning methods with electrophysiological recordings of single-photon responses to identify phosphodiesterase as the driver for photon-detection limits in rod photoreceptors.
• Mentored and trained a small team of post doc and staff research personnel on custom acquisition hardware, analysis software, and data analysis theory and methodology.

Thesis Title: Mechanisms of detection sensitivity and adaptation in the rod visual pathway.

• Co-created and lectured an undergraduate course named Life Sciences 40: Statistics for life sciences that focused on Monte Carlo simulation (bootstrap) methods. This course was highly successful and remains a popular course in the Life Sciences.
• Managed and created all course materials, including lectures, labs, and exams. I prepared and gave lecture 3 hours per week, and developed a 2-hour weekly, Python-based, computational lab for 86 undergraduates.
• Leveraged Python notebooks to teach students to develop algorithms for analyzing biological data. Students gained understanding of statistical principles for experimental-data analysis with probability distributions, confidence intervals, hypothesis testing, statistical independence, linear regression, ANOVA, statistical power and experimental design.

• 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.

Research Focus: Therapeutic use of olfactory ensheathing cells in treating spinal cord injury.

Completed coursework and research resulting in 2 publications, thesis unfinished to pursue Ph.D..

• In the laboratories of Patricia E. Phelps and V. Reggie Edgerton, I investigated the therapeutic use of olfactory ensheathing cells (OEC) transplantation for spinal cord injury which resulted in 2 publications (Khankan et al. 2016 & Thornton et al. 2018) and 4 conference abstracts.
• Managed and assisted in surgical implantation of stimulating and recording electrodes for behavioral experiments that assessed longterm functional recovery following spinal cord injury in rats.
• Leveraged experience in histology, immunochemistry, microscopy, statistical modeling, and machine learning to show functional recovery in spinal cord injured rats which received OEC transplantation had enhanced regeneration when combined with epidural (spinal) stimulation and climb-task training.

• Initially a field technician, I was quickly promoted to project and field management where I managed teams of up to 10 technicians to install voice, category-6, and optical fiber network infrastructure and control systems for medical labs, banks, educational institutions, oil refineries and private residences.