As a Data Engineer Intern at DataGrokr Analytics, I am actively involved in building and optimizing data pipelines that are critical for data-driven decision-making. My role encompasses transforming and cleaning large datasets to ensure their quality and reliability for analysis. This experience allows me to apply and expand my expertise in key technologies that are integral to efficient data processing and automation, including Python, SQL, Docker, and Jenkins.At DataGrokr, I have the opportunity to work on a variety of projects that require a diverse skill set. This includes developing business logic using advanced SQL concepts, leveraging cloud services such as AWS, GCP, and Azure to automate and scale solutions, and building dashboards using tools like Power BI and Quicksight for data visualization. A common thread across most projects is ETL (Extract, Transform, Load) processes, which I integrate as part of broader initiatives like data onboarding, business logic development, orchestration, and visualization.

For my Undergraduate Senior Capstone, I was deeply engaged in pioneering Quantum Computing research under Dr. Yipeng Huang from Rutgers University. My research focused on the generalization of stabilizer states of higher-dimensional quantum units (qudits, as opposed to qubits) within quantum systems. My work revolved around creating a tableau for the simulation of qudits on a classical computer and attempting to run qudit stabilizer codes for Quantum Error Correction. In the course of this endeavor, I meticulously analyzed existing literature concerning the simulation of quantum systems as well as stabilizer formalization for quantum systems.I completed an Honors Thesis for this project, and we presented our team's accepted paper at the YArch ‘24 Workshop at ASPLOS, the ACM International Conference on Architectural Support for Programming Languages and Operating Systems.My commitment to pushing the boundaries of quantum computing is reflected in my project, the details of which can be explored further through the attached links.

In my role as a Learning Assistant, I played a pivotal role in the Introduction to Computer Science and Data Structures courses, predominantly instructed using Java. I took charge of organizing presentations and leading sessions, employing innovative strategies tailored to the diverse abilities and interests of students to reinforce course material effectively. My commitment to fostering an inclusive learning environment led me to actively participate in professional development workshops, particularly focusing on addressing bias within the technology industry. Beyond my responsibilities in aiding fellow learners, I conducted thorough session reviews and assessments for other Learning Assistants, offering constructive formative feedback to enhance our collective impact.

As an undergraduate research assistant, I undertook a comprehensive exploration of floating-point arithmetic and its intricacies, delving into the realm of errors and exceptions with the overarching goal of enhancing program accuracy. This involved conducting a thorough literature search and crafting a detailed literature review. My work on this project not only deepened my understanding of the nuances in computational precision but also allowed me to contribute to the ongoing discourse in the field. For those interested in exploring the project further, the details can be found at this link: https://drive.google.com/drive/folders/1J52oesos-QU5zrizvhJAq8cvtzddJkCe?usp=sharing