Core Skills

Languages

Python, C/C++, Java, Javascript

Backend Development

Data Structures & Algorithms, RESTful APIs, Servers, Databases

Data Analysis

Python (Numpy, Pandas, Matplotlib), Matlab, R, SQL, Tableau

AI/ML

Sci-kit Learn ML models, OpenAI/Langchain applications, PyTorch Deep Learning Neural Networks

Projects

Spring 2024 Data Science Projects

This repository contains various data science projects I have completed in Python over the course of the Spring 2024 (my Junior Spring) semester at Johns Hopkins University. My projects derive from two of my courses: Computational Stem Cell Biology (CSCB) and Information Retrieval and Web Agents (IRWA).

Computer System Fundamentals

This course covers the design and performance of computer systems, ranging from simple 8-bit microcontrollers to 32/64-bit RISC and x86 CISC architectures. It starts with logic gates and digital circuits, progressing to topics like arithmetic and logic units, registers, caches, memory, and pipelined execution. The course also includes instruction set architectures, interrupts, and peripheral communication protocols, with practical programming projects in assembly language and processor simulators.

Fall 2023 Data Science Projects

This repository contains various basic data science projects I have completed over the course of the Fall 2023 (my Junior Fall) semester at Johns Hopkins University. The majority of my projects were completed as part of the Biomedical Data Science course along side its accompanying Biomedical Data Science Laboratory labeled under the BME-DS folder. These projects range from basic classification tasks using only the Sci-Kit Learn library to more advanced image classification tasks using convolutional neural networks using Pytorch. On the other hand, the AI folder contains my final project for the Artificial Intellgience course. This project measures the effectiveness of various neural network model configurations on a standard classification task using Pytorch.

K-RCPS

K-RCPS is a high-dimensional extension of the Risk Controlling Prediction Sets (RCPS) procedure that provably minimizes the mean interval length by means of a convex relaxation. Under the supervision of JHU CS Professor Jeremias Sulam and graduate student Jacoppo Teneggi, I worked towards implementing the K-RCPS algorithm in HuggingFace and publishing it for official use as part of the HuggingFace Transformers library.

Coursera-JHU Data Science Specialization

This repository contains various projects I have completed as part of the Coursera JHU Data Science Specialization. The specialization consists of 10 courses that cover the fundamentals of data science, including data manipulation, data visualization, machine learning, and more.