Every chapter of my career added a layer. Together, they crystallized something the field needs and rarely finds.
I am a Data Scientist, AI Consultant, and Educator based in New York. I write, teach, consult, and build curricula. My students go on to do original research at places like Yale and UPenn. My teaching focuses on what AI does and why it does it - and what to watch out for. My curriculum on AI and Machine Learning covers both the technical parts, and how the advances in AI can affect society.
Managing complex, multi-stakeholder productions under high pressure taught me systems thinking and coordination at scale. Opera is, in many ways, a real-time optimization problem — hundreds of moving parts, every night, with no room for error.
Running a gallery sharpened aesthetic judgment, curatorial thinking, and an understanding of how form communicates meaning. It also taught business insight: how to tell a story, build an audience, and assess value — both financial and cultural.
I made the transition into data science and machine learning, bringing quantitative rigor to complement what came before. I developed curricula covering supervised and unsupervised learning, neural networks, CNNs, GANs, AI ethics, and the linear algebra underlying it all — implemented in Python on real-world datasets.
At a prestigious private high school in New York, I built a three-track AI curriculum covering AI Principles 1 and 2 — supervised and unsupervised learning, CNNs, NLP, and reinforcement learning — all implemented in Python on real-world datasets, with ethics woven into every layer. Also taught Computer Science Essentials, Python, and mathematics through Calculus 3. Students from that program are now at Yale and UPenn doing original AI research.
I consult on AI product strategy, AI product auditing, corporate training, workshop facilitation, and curriculum development. I offer private instruction for business leaders seeking to understand AI and ML. I write publicly about AI at the intersection of technology, ethics, society, and education.
I serve as a judge at the Long Island Math Fair at Hofstra University and at the CIJE Robotics Competition — recognizing and encouraging the next generation of mathematicians and engineers.
True AI literacy is not about using AI tools. It is about understanding the principles behind them — the mathematical foundations, the design choices, the value judgments encoded in every training objective, every dataset, every deployment decision.
AI systems are not neutral. They reflect choices — what to optimize for, what to include in the training data, whose feedback shapes the reinforcement learning. Those choices have consequences. The only way to evaluate them responsibly is to understand them.
I bring to this work something that pure technical training rarely provides: the ability to sit with ambiguity, to ask why before how, and to communicate across the gap between what the algorithm does and what it means.