• Work cross-functionally with Data, Quality, Software, Hardware, and Firmware teams in a start-up company to drive system integration of the non-invasive CGM (continuous glucose monitoring) product

• Write user needs and system requirements for the CGM product

• Write verification tests for the system requirements and execute tests. Utilize tools like a DMM and SQL to verify signal measurements and data records respectively

• Test GraphWear app product for functionality bugs and submit defect reports to Software team

• Test system-level integration (apps, CGM device, cloud architecture) and submit defect reports

• Document system use-cases and identify failure-modes for testing

• Write Work Instructions for process tasks like Device Management in clinical trials

• Support Data team with data analysis efforts and maintain the interactive dashboard of signal performance metrics

• Analyze glucose timeseries signals generated by GraphWear sensor relative to a gold standard reference and competing CGM systems

• Use the DSP (digital signal processing) algorithm stack to convert voltage signals to calibrated plasma glucose concentrations

• Use Git-based tools SourceTree and BitBucket to add calculation and visualization tools, as well as signal correction algorithms to master branch

• Create an interactive dashboard with Plotly Dash for executive team to view real-time distribution analysis of data

• Analyze key metrics that describe signal quality and data compliance to FDA iCGM standards by generating 2D and 3D data visualizations in Plotly by various category groupings

• Understand database architecture and collaborate with Data Engineers to address data requirements (e.g. type, format) for effective data analysis. Ensure database adjustments are stable across the system (app, cloud, devices)

• Used the Python web-crawling framework Scrapy with CSS to extract CE (continuing education) course information from up to twenty providers

• Cleaned and standardized scraped data to optimize inputs to Recommendation Engine in CE App

• Compared NLP (Natural Language Processing) models TF-IDF, Word2vec, and BERT for Recommendation Engine in CE App

• Used MLflow to track NLP model changes and assess model performance

• I am the 2nd author of a paper published in Sensors (basel) peer-reviewed journal which expands on the concepts explored in my Masters thesis (see Publications)

• Completed thesis on novel gut-brain PAC (phase amplitude coupling) to understand the association of visceral states and cognitive performance

• Analyzed EEG (electroencephalogram) and EGG (electrogastrogram) data using Python data science & visualization libraries NumPy, SciPy, Pandas, Scikit-learn, and matplotlib to identify periods of inter-signal modulation

• Processed timeseries data using filtering, Hilbert transform, and spectral analyses to isolate low-frequency gastric signals and high-frequency EEG signals

• Led weekly meetings between project collaborators