I am a second-year undergraduate at UC San Diego pursuing a Bachelor of Science in mathematics and computer science. I'm specializing in machine learning, mathematical finance and statistics/probablity.
I am a member of the Mathematical Neuroscience lab advised by Gabriel Silva, where I am researching adaptive and dynamic neural networks. Last summer, I interned at CareFusion BD as a data scientist working on time-series forecasting and machine learning models.
Developed time-series forecasting, machine learning models to predict drug shortages, and price changes. Effectively analyzed and visualized datatsets with more than 10 Million drug usage and transaction records. Used dimension-reduction techniques (PCA, SVD, LDA), fourier and log transformations and resampling techniques (bagging, boosting) to identify correlations between variables and extract underlying patterns of the data. Worked with multiple regression and classificatoin models - linear models, ARIMA, boosted trees (xgboost), random forests and SVMs.
Tutor for Object-Oriented Programming (CSE 11) and Data Structures (CSE 12). Worked with the instructor to design and write programming assignments and their specifications. Held office hours and led review sessions to explain programming concepts and assist students in implementing programming assignments by analyzing and debugging their code. Graded homework, exams and wrote submission/grading scripts for programming assignments.
Wrote python scripts to setup a Continuous Integration server to automate package builds. Developed multiple native Linux (Ubuntu, CentOS, Debian) packages using bash and python for Kolibri - Learning Equality's flagship application. Optimized software setup on all platforms by implementing efficient installation scripts.
Developing dynamic, adaptive neural networks using data assimilation training methods. Applying non-linear transfer functions between neurons and layers to create complex dynamic geometric networks.
Analysis of the negative effects of Gentrification in San Diego in the 21st Century. Visualized the change in demographics of all neighborhoods in San Diego using heat maps. Identified neighborhoods effected the most by Gentrification and found patterns between multiple socio-economic factors such as Poverty, Population, Uninsurance and Property value. Languages/Tools Used: Python (Pandas, NumPy, Matplotlib, Patsy), Jupyter Notebooks.
Data science powered web application to perform sentiment analysis on YouTube comments. Applied machine learning techniques on the model using a training dataset of 1 Million tweets. Wrote python scripts for web scraping and performing sentiment analysis on the comments. Languages/Tools Used: Python, Natural Language Toolkit, Flask.
Android application to ease the process of connecting with people on multiple social media. Integrated database, added Location tracking and developed the app structure. Languages/Tools Used: Java (Android), XML, Google Firebase.