"I believe in intuition and inspiration. Imagination is more important than knowledge. For knowledge is limited, whereas imagination embraces the entire world, stimulating progress, giving birth to evolution. It is, strictly speaking, a real factor in scientific research.”
― Albert Einstein
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I solve hard problems with clean math, clear visuals, and code that doesn’t lie.
I am a recent bachelor of science graduate with a double major in Computer Science and Applied Mathematics, working as an AI/ML Research Assistant, AI Ed-Tech Startup Founder, and AI/ML Consultant.
My work focuses on visualization-driven machine learning, merging computation with human intuition to create transparent, interactive, and trustworthy artificial intelligence systems. I have developed methods in synthetic data generation, boosting classification models, and multidimensional clustering, alongside advancing feature engineering, visual knowledge discovery, and classification refinement. My approach bridges the gap between cutting-edge research and real-world impact, ensuring AI models are both interpretable and high-performing.
My work experience spans system administration, full-stack software development, and academic AI/ML research. I approach every challenge with a mindset of deconstructing complex problems, building modular tools, and verifying every result reproducibly.