Utilizing Python to conduct end-to-end analysis of ECG signals including data pre-processing, parameter estimation, feature engineering, statistical analysis, data visualization, and presentation of quantitative insights to scientists and cardiologists to identify new predictors of cardiac disease.Developing tools to orthogonalize signals, eliminate baseline wander via FFT, reduce noise via signal averaging, detect QRS boundaries, and estimate wavelet parameters via evolutionary optimization to automate data pre-processing.Utilizing numerical and wavelet analysis to analyze the magnitude, orientation, motion, spectral content, and bispectral coupling of the heart’s equivalent dipole from ECG signals to determine if we can gain more useful information about cardiac disease.Collaborating with scientists at Ohio State to test this new method’s effectiveness in detecting eccentric left ventricular hypertrophy in mice using bootstrap sampling and nonparametric statistical tests to identify meaningful group differences.Designed, developed, and deployed a Shiny web app to enable scientists to easily transform, analyze, and visualize patterns in ECG signals to generate deeper insights on pre-clinical studies for pharmaceutical clients.

Utilized Python to develop and validate ODE / regression models of bioengineered cell activity and patient physiology, simulate cell-based therapeutic interventions in virtual patients to predict efficacy, and visualize simulation results to generate quantitative insights for metabolic and immunology programs.Developed tools to estimate kinetics of cellular and physiological processes from metabolomics / clinical data via evolutionary optimization / MCMC / Bayesian methods and accelerate simulations via parallel computing to advance our in-silico platform.Analyzed, interpreted, and presented simulation results demonstrating the value of mathematical modeling in drug development to scientists and executives resulting in a promotion and conversion to FTE within 5 months of joining as a contractor. Collaborated with scientists, clinicians, and program managers to design experiments to characterize cellular metabolism, understand patient physiology, and analyze external clinical data sources to quantify ADME properties of target metabolites.Collaborated with stakeholders across R&D to leverage mathematical models to support quantitative decision making around study interpretation, first-in-human dosing, safety margins, and drug product storage conditions for 5 programs.

PI: Eduardo ReateguiLab: Reategui Group for Bioengineering Research Utilized microfabrication techniques like photolithography and soft lithography to design and construct various microfluidic chips for encapsulation of cancer cells into hydrogels to enable biological studies.Conducted experiments to optimize the cell encapsulation protocol by determining oil / surfactant type, reagent concentrations, and flow rates that minimized variability and maximized stability of microparticles.Developed documentation for the new lab protocol, trained the incoming PhD student on it, and presented key research findings to the lab PI and head of the Chemical Engineering department.

Graded assignments and held weekly office hours to help students understand course concepts related to data analysis, numerical methods, and mathematical modeling and simulation of biological systems using MATLAB.Guest lectured for a cell and tissue engineering course on how to develop and implement an ODE model to simulate stem cell differentiation using MATLAB and created a homework assignment to assess students’ computational skills.

Department: Technology Commercialization Office Conducted a process improvement project for the legal contracts team by creating a value stream map to model the legal contracts process and analyzing their process data to assess if they needed to hire more people. Suggested workflow changes to improve process efficiency to the head of the contracts department.Worked with accountants to analyze financial statements and invoice clients for patent and legal fees.

Department: Technology Commercialization Office Evaluated invention disclosures for marketability and commercial potential by conducting market research and generating documents to describe the invention and its value proposition.Analyzed the competitive landscape to identify potential licensees for Ohio State's disruptive technologies and conducted prior art searches for various invention disclosures to assess the patentability of the invention.

Facilitated individual and small group tutoring sessions and developed supplemental material for students to aid in their understanding of the course content.

Developed a MATLAB script to numerically integrate pre-clinical time series data to estimate drug exposure to enable scientists to quantitatively assess cardiovascular safety and efficacy of drugs for pharmaceutical clients. Developed a MATLAB script to utilize Fourier analysis to estimate the frequency and phase content of blood pressure signals in dogs to develop an algorithm to predict central aortic pressure signals from peripheral aortic pressure signals and discussed potential pre-clinical applications of the algorithm with the scientific director.

PI: Andre PalmerLab: Andre Palmer Laboratory for Artificial Blood ResearchLed a research project managing 2 undergrads to develop a multi-physics model in COMSOL of in vivo oxygen (O2) generation from alginate microparticles containing calcium peroxide (CaO2) and catalase to predict and optimize the spatiotemporal dynamics of tumor oxygenation. Collaborated with lab members to design experiments to characterize CaO2 decomposition and catalase enzyme kinetics to quantify the rate of O2 generation under different experimental conditions using UV-Vis spectroscopy.Developed a MATLAB script to transform absorption data to concentrations using the Beer-Lambert law and fitted the spectroscopic data to a chemical kinetics model using non-linear least squares regression to estimate the kinetic parameters related to CaO2 decomposition to inform model development.

Assisted students in understanding the fundamental concepts and skills of engineering. Graded homework assignments and gave students feedback to help them improve.