• Led news coverage and editorial decisions by editing 5-7 news stories per week and managing 14 staff writers to strategize news coverage.• Developed news desk roadmap by planning future coverage in collaboration with several other desks, such as data • Reported on university administration, including biweekly interviews with university president, to investigate practices.• Wrote investigate data story on ChatGPT academic integrity cases at UMD, using Python (e.g. pandas) to process data.

• Reported two stories per week about critical news across Prince George’s County, ranging from educaton to politics.• Covered county council and school board, interviewing council members, school board members, and county executives.• Contributed to local breaking news coverage, including on campus and in College Park.

Worked in the Perception and Robotics group to develop a synthetic data generation tool in python to create synthetic oceanic environments in Blender to detect maritime objects, such as whales.• Developed a synthetic data generation tool for oceanic environments to detect marine animals with Python and Blender.• Trained convolutional neural networks with generated synthetic data to detect whales, outperforming real satellite images.• First author of the publication: Whale Detection Enhancement through Synthetic Satellite Images

CMSC132: Object Oriented Programming II (Data Structures) and CMSC216: Introduction to Computer Systems• Lead a discussion section with 30 students, teaching fundamental data structures concepts and answering student questions.• Hold office hours to answer student questions and clarify content taught in discussions.

• Parallelized several components of crucial business-oriented machine learning models using Kubeflow and Python.• Spearheaded effort to optimize downloads from AWS S3 to support machine learning models with thread-safe code.• Implemented internal feature selection algorithms with scikit-learn and Dask, removing interal dependencies.• Increased speed for data preparation and model prediction phases by 40% through parallelizaton and S3 downloads by 60%.

• Designed a New Relic Dashboard to display Jenkins and Aquasec data for company-wide software through a Flask server.• Implemented a Flask server, deployed in Openshift, that interacted with Jenkins, Aquasec, and New Relic APIs to periodically push new data to dashboard, documented in a Swagger API.• Automated process of adding and modifying widgets in New Relic using a GraphQL API.• Helped with developer support regarding an internal CI/CD tool that centralizes numerous container development services.• Decreased time to check project status by over 90% with New Relic Dashboard and created a blueprint for further expansion.

• Developed an internal website using React.js to centralize machine learning metadata of numerous Machine Learning tools.• Built a python command-line interface for NLP to pull metadata (e.g. packages and databases used) from a Github repository using abstract syntax trees and scraping techniques.• Centralized details of over 30 machine learning tools in a website with automated updates using Python NLP program.

Covered the Washington Nationals and Washington Commanders in the MLB and NFL Departments.

• Taught students, ranging from ages 5 to 16, fundamental mathematics concepts with unique and engaging methods.• Assisted more than 25 students per day using a personalized learning plan that is catered to their strengths and weaknesses.

• Developed a Java API to efficiently and automatically deploy and manage GCP, AWS EC2, and Azure virtual machines.• Provided built-in VM customization for users, such as network security groups, availability zones, and spin-up options.• Implemented a REST API with Flask to spin-up instances on each cloud provider given the results of a Machine Learning model, which reduces cloud costs for small businesses by finding the cheapest cloud instance.• Saved users an average of 2 minutes and 40 seconds per VM instantiation on GCP, AWS, and Azure.

Collaborated with Dr. Michael Hicks and the Lab for Programming Languages at the University of Maryland to further the viability of VOQC, a Verified Optimizer for Quantum Circuits.• Integrated VOQC (a Verified Optimizer for Quantum Circuits) into the compilation stacks of python-based quantum frameworks to provide interoperability between the native OCaml quantum compiler and python.• Defined OCaml to python bindings via a C wrapper class, the OCaml FFI and ctypes, and python ctypes.• Devised and implemented compiler passes to Qiskit and Cirq using their respective quantum APIs in python.• Performed regression testing and significantly improved runtime of the program after finding a bug in the OCaml ctypes library.• Discovered further quantum gate reduction, ranging from 7.7% − 24.4%, across 3 sets of benchmarks in Cirq and Qiskit.