Experimental design, data analyses, and algorithm/ML model development for a smart thrombectomy device.

Senior Research Fellow, Dept. of Psych. + Adjunct Assistant Professor, Dept. of Neuroscience.Investigated neuromodulation via closed-loop fMRI to study working memory:Applied machine learning techniques (multivariate pattern analysis) to neuroimaging data to determine whether neuromodulating competition between items in working memory affects long-term memory.

Applied Neuroscience R&D: Responsible for developing and executing portfolio of research studies to explore design decisions and novel indications for the next generation of deep brain stimulation devices. Key highlights:• Designed and executed research studies on digital health applications for deep brain stimulation patients• Shortened timeline from study design to kickoff, including IRB approval and contract execution, to 3 months, a team record• Developed intellectual property: Co-inventor on 2 patents

Adjunct Research position affiliated with my research program at St. David's Medical Center (2013-2015, see below).Developed and delivered new neurotechnology and brain-computer interfaces curriculum for upper level undergraduate neuroscience majors (2019-2020).

Machine learning project lead for healthcare client:• Consulted for major kidney dialysis healthcare organization with goal of developing machine learning models for predicting infections and hospitalizations in chronic kidney disease• Performed literature review to elucidate potential factors resulting in dialysis and inform feature engineering• Acquired neural networks and deep learning certificate

Hired to create novel research program in Parkinson’s disease and deep brain stimulation. Promoted twice within 2 years, ultimately to Director of Research.• Led strategic business development in translational neuroscience• Managed scientifically diverse team consisting of two Principal Investigators and three additional research staff• Secured renewal of institutional funding and new research funding• Mentored team of undergraduate biomedical engineering students in their senior year capstone project• Smartphone Parkinson’s project: engineered features for classification of severity of Parkinson’s motor symptoms with AUC of 92%: demonstrated that PD can be quantitatively assessed via smartphone-as-a-sensor• Keyboarding Parkinson’s project: engineered features and developed classifier for detecting PD with ~65% accuracy based solely on keyboarding dynamics• Initiated and executed research studies on deep brain stimulation (DBS) in patients with Parkinson’s disease: the first/only researcher in Austin to set up protocol of acquiring electrophysiology signals during awake DBS neurosurgery; investigated effects of DBS on cognition in PD patients.

Researcher in Human Motor Control lab, hired to design and execute research studies on deep brain stimulation in movement disorders patients; acquired and analyzed novel datasets:• Executed research studies in movement disorders laboratory, specifically investigating pathophysiological basis of Parkinson’s disease and effects of deep brain stimulation on the motor system• Acquired dozens of data sets during awake brain surgery; applied statistical signal processing techniques to analysis of data• Developed custom code for analyzing simultaneously recorded kinematic behavioral data and electrophysiology time series data from multiple brain regions

Researcher in Applied Neuroscience R&D for start-up company, hired to manage research studies to helpdetermine the technology roadmap for a wireless EEG system and a non-invasive brain stimulation and recording system:• Developed Matlab scripts for EEG analysis• Tested algorithms for real-time feature extraction and inverse modeling of EEG data• Designed experiments on simultaneous non-invasive brain stimulation and recording in collaboration with academic partners• Project Manager of EU-funded R&D project on concurrent brain measurement and stimulation with 7 partners from 6 countries