I like to solve problems through technology, engineering and data. I am a data scientist at Google and build data products to help Google Cloud Sales and Marketing teams bring revenue. Before starting at Google I worked for three years in medical device research and development at Boston Scientific. Before that, I got my Ph.D. in biomedical engineering where I focused on computational modeling and machine learning applied to biomechanical systems. Below are projects from my Ph.D involving computational modeling, multivariable regression and heuristic optimization applied to the human musculoskeletal system. I also present some side projects I did to learn and implement data science tools on fun datasets.
I gathered experimental data consisting of finger movements and tendon excursions using a human cadaveric finger. Symbolic regression was implemented to infer the functions mapping joint angles to tendon excursions from this dataset. The form as well as the parameters of the mathematical function are simultaneously inferred from the experimental dataset. Multivariable regression, machine learning.
I developed a computational solver to model the interactions of tendon networks of the fingers that uses the Newton Raphson method and linear algebra to solve for fingertip forces. Mathematical modeling, Linear Algebra
I implemented a stochastic hill climbing algorithm to optimize the structure and parameters of tendon networks. I used this to optimize the finger model to best match the experimental dataset that was collected and evaluated the accuracy of this model by predicting on a test data set. I used the Matlab Parallel Computing Toolbox and Distributed Computing Server to run the search on multiple processors. Stochastic hill climbing, heuristic optimization
I downloaded my personal Fitbit data using the Python-fitbit API from the last 5 months and performed exploratory data analysis using Python/Pandas/NumPy/matplotlib to get insight in my day to day patterns. Exploratory data analysis, API
I performed exploratory data analysis using Python/Pandas/NumPy/matplotlib to understand which features were critical and gain more insight on the data. I then used Random Forest Regression in scikit-learn to predict the bike share count on the test data set. Exploratory data analysis, Random forest regression
I used SQL, Python to explore San Francisco bike share data and performed a k-nearest neighbor search to determine restaurants that are closest to bike stations. Exploratory data analysis, k-nearest neighbors
This is a machine learning classification project based on a small dataset. I used this project to learn Python and the tools for data science (NumPy, scikit-learn, Pandas, matplotlib). I performed basic data cleaning and pre-processing. I used Random Forest classification to predict survival of passengers on the test set provided.
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