As a beginning freshman student, the idea of carving my own path and making a tangible difference quickly became more than a curiosity—it became a mandate. My drive for problem‑solving, paired with an early focus on artificial intelligence and automation, pushed me beyond “just writing code” into building systems that turn creative ideas into reality.

At 18, that instinct turned into action when I launched an AI consulting and data systems business. What began as an experiment in entrepreneurship evolved into a lab for creative problem‑solving and technical growth—where market research, workflow design, and long‑term automation strategy all converged. The agency grew into a dependable partner for more than ten businesses, but the real value was in the proof that thoughtful engineering can move real companies forward.

That early spark matured into MyProgrammatic.com, my AI consulting agency founded during college. I treat every client challenge as a design problem: how to turn messy data into predictive insight. Projects like my Time Series Optimization Engine and Cultural & Language Tutor Chatbot are the result of combining algorithmic thinking, domain research, and user empathy into something genuinely new.

The chatbot, for example, demanded multimodal thinking—blending NLP, reinforcement learning, and pedagogical design into a single product. Work like this taught me that AI development is fundamentally creative: you imagine new possibilities, design simple but powerful systems, and iterate until the implementation matches the vision.

Alongside client work, I build open‑source tools, ship digital teaching products that have reached hundreds of learners, and mentor peers through ACM and the Machine Learning Club. As I move into upper‑division CS, I bring not just a business story but a creative philosophy: that engineering is problem‑solving elevated to art—and I want to keep deepening that craft through advanced algorithms, research, and impactful systems.

Computer and data science shape how I think about problems, structure projects, and ship reliable systems. At UC Merced, I've completed core CS and math sequences—including Advanced Programming, Discrete Math, Data Structures, calculus, and probability—which have strengthened my algorithmic intuition and disciplined debugging habits.

To close the gap between coursework and industry, I completed Harvard's ML & AI with Python and Stanford's Machine Learning Specialization. These programs deepened my understanding of supervised and unsupervised learning, neural networks, and model validation—concepts I immediately pushed into production‑style projects.

As founder of MyProgrammatic and a leader in ACM and the Machine Learning Club, I focus on making advanced CS and AI accessible. Through workshops, team projects, and open‑source work, I help peers deploy models, review code with intent, and explain their work to employers.

I pay particular attention to students who feel intimidated or underrepresented in tech. In ACM and ML Club, I organize beginner‑friendly sessions and outreach, prioritizing clear explanations, patient troubleshooting, and an environment where questions are expected—not judged.