HarryHu
I turn ambiguous real-world problems into deployable ML systems— building computer vision pipelines, multimodal AI applications, and data products that ship with measurable impact.
Curated highlights across work experience and research projects — emphasizing real constraints and measurable impact.
Under-utilized space uncovered
Blueprint processing time cut
Vision pipeline mAP gain
Production ML deployments
Washington University in St. Louis
M.Sc. in Data Analytics & Statistics
McKelvey School of Engineering
Aug 2024 – Dec 2025
New York University
B.Sc. in Data Science & Mathematics
College of Arts & Science
Jan 2021 – May 2024
I'm an AI/ML Engineer and Data Scientist with a background in data science, statistics, machine learning, and applied research. My interests sit at the intersection of modeling, systems thinking, and real-world problem solving — especially in computer vision, multimodal AI, and decision-focused analytics.
I'm most energized by messy, high-ambiguity problems that require more than just fitting a model: defining the right objective, working through constraints, choosing practical tradeoffs, and building toward something that can actually be used.
Every project here started from a real need — mine or my family's. Claude is the collaborator that shortens the loop from 'I want this' to 'this exists' enough that the ideas actually ship.
What collaborators, stakeholders, and teammates say about working with me.
Harry exemplified enthusiasm, energy, and real depth from day one. He supported new technologies I launched for space-utilization and analytics, and he approached every challenge with a level of maturity well beyond an internship role. What stood out most was his consistency — he asked thoughtful questions, iterated quickly, and delivered work that directly strengthened our operational decision-making. Harry is a standout data scientist with strong instincts, a collaborative spirit, and a genuine commitment to meaningful work.
Exploring multimodal AI systems, applied computer vision, and production ML workflows — while building case-study-driven projects at the intersection of research and real-world deployment.
Let's build
something useful.
I'm always interested in thoughtful conversations about machine learning, applied AI, computer vision, and building useful systems.