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 independent thinker. I write about AI at the intersection of technology, ethics, society, and education. My perspective comes from an unusual path — one that did not follow the standard route from computer science to machine learning.
That path matters. It is what allows me to understand AI technically, think about it philosophically, and explain it to anyone.
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 North Shore Hebrew Academy, I built a three-track AI curriculum currently teaching AI Principles 1 and 2, covering supervised and unsupervised learning, CNNs, NLP, and reinforcement learning — all implemented in Python on real-world datasets, with ethics woven into every layer. Also teaches 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.
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.