TL;DR: After 8 years at Meta, I'm starting a new chapter as Professor of Practice at Central European University (CEU) in Vienna — teaching, building, and staying hands-on with AI. CEU is a rare institution: global in outlook, committed to academic freedom and critical thinking. This is the move.
Writing
Selected writing
I write mostly on LinkedIn — about becoming a manager, teaching data science, and the practical edges where AI tooling meets real work. A growing selection lives here.
This paper from Anthropic is so valuable in my role as Professor of Practice at CEU. My first reading: using AI while learning a new technical skill can measurably reduce actual understanding, even when it doesn't reliably reduce performance. That gap — between what you can do with the tool and what you actually know — is the whole pedagogical problem of this moment.
Nobody told me the job was running 1:1s, aligning roadmaps, writing growth plans, sitting in calibration meetings arguing over whether someone's work was 'senior-level' or just very strong mid-level. I figured it out eventually — by being a mediocre manager for longer than I'd like to admit, while good people depended on me to be better. So I taught a two-day intensive at CEU compressing that learning, and open-sourced the whole course.
Yesterday was the last day of my Data Science 4: Deep Learning and Topics in Applied AI course at Central European University. Teaching deeply technical, cutting-edge topics was an incredible experience — made possible by a group of students who were just genuinely game.
My MS Business Analytics students at CEU Vienna are heading into interview season, and I wanted them to have the best shot they could. So I built resumasher — an open-source Claude Code skill (also runs on Codex CLI, Gemini CLI, and similar) that tailors resumes by mining evidence from your actual project folder.
This fall I taught ECBS5294 at CEU — SQL, JSON normalization, reproducible pipelines — a required core course in the MSBA program. The practical data skills I spent years learning piecemeal. I'd spent months building it out with AI as a true co-author. What surprised me wasn't speed. It was the quality of the back-and-forth.
Szilard Pafka and I just published a piece on using AI agents to iteratively improve XGBoost models. Everyone knows Claude can write code — but it turns out it can research, try, evaluate, keep what works, discard what doesn't, and push model quality upward across repeated experiments.
Not because the people are bad at statistics. Because nobody asked: What decision does this actually inform? What breaks if we succeed? Who might block us, and why? I've been teaching a course at CEU called Designing Analytics Projects on exactly this — the work that has to happen before the code.
One of the great pleasures of this moment is that for a few hundred dollars a month, Claude Code and Colab Pro+ buy me an absurd amount of cognitive leverage. They are fuel for curiosity. They let me chase ideas quickly, prototype, test, learn, refine, and keep going. Build build build.
I'm teaching an applied deep learning course at CEU this semester: six weeks, free Kaggle GPUs, and one cumulative problem — a long-tail 113-class classifier with a 2,666:1 class imbalance. Week 3 needed OLMo 2 sequence classification in HuggingFace transformers; the implementation didn't exist. So I wrote it. PR merged in 24h.
I keep encountering a failure mode in LLMs that is subtler than hallucination. It's that memory masquerades as reasoning. I asked Claude to figure out if the sunset would be visible over the ocean from a specific beach in Thailand. Within minutes, it built a working tool with NOAA solar calculations — and confidently produced an answer that turned out to be wrong in an instructive way.
Last week I posted 'There should be more medleys' on BlueSky. Then I spent today actually doing something about it. I have 350+ guitar tabs backed up from Ultimate Guitar. The problem: how do you build a medley that actually flows? Not just 'these songs are in the same key' but actually makes musical sense?