Building full-stack features at an early-stage startup with a focus on expanding B2B functionalities for a ticketing system that enables teams to manage and track operational tasks.

Designing and optimizing data generation pipelines, publishing a paper on the Sato-Tate conjecture in a team of 4. My role involved:

• Developing high-performance algorithms in Python and Java to generate and validate over 700 datasets, crucial for the team's research analysis.

• Optimizing data generation pipelines, improving execution speed by 3x, enabling the team to handle larger datasets.

• Building Python modules with libraries like SciPy to streamline the computation of iterated integrals.

• Owning and maintaining the team’s GitHub repositories, ensuring consistent structure and version control.

• Actively participating in weekly team meetings, providing ideas and insights that drove key decisions in algorithm design and project direction.

Conducting one-on-one and group peer tutoring sessions on Java and Data Structures. Facilitated multiple exam review sessions for 50+ students.

Chosen from 2,600+ applicants, received one-on-one mentorship and guidance from Google Software Engineers to improve technical problem solving skills.

Developed a Python pipeline to automate data generation and validation from 2,700+ hours of audio data using PyTorch, enhancing analysis workflows for an open-source tool on communication challenges. Ensured data accuracy with a 0.03% error margin and streamlined code adoption by creating simplified Jupyter Notebook examples.