There's a lesson here, and I'm not going to be the one to figure it out.
Educated in several acronyms across the globe (UNISR, SFI, MIT), I was co-founder and CTO of Tooso, a NLP / IR startup in San Francisco acquired by TSX:CVO.
I led Coveo’s A.I. and MLOps roadmap from scale-up to IPO, and built out Coveo Labs, an applied R&D practice rooted in collaboration (e.g. Stanford, Outerbounds, Netflix, Farfetch, Microsoft, NVIDIA), open source and open science.
I’m a proud NLP advisor for Plural, and Adj. Professor of ML at NYU, which is mostly notable because it is the only job I ever had that my parents (sort of) understand.
I recently started investing in tech startups, both directly and as LP in AI funds: I’m always happy to chat with founders about DataOps, MLOps and AI.
I’m now building Bauplan.
I occasionally share code, ideas and teaching materials; if you have no intention of selling me anything, you can also try me on Linkedin.
I talk a lot, and I’m sometimes invited to do so by friends in industry (Home Depot, Lowe’s, Farfetch, eBay, NVIDIA, Pinterest) and research (KDD, SIRIP, CiE): some of my talks and papers are highlighted (for readers with insomnia) at the end of this page.
My recent research sits at the intersection of language, learning and retrieval.
I am co-organizer of SIGIR eCom (2022, 2023), Industry Sponsorship Chair for CIKM 2022, and I’ve been part of the program committee for many conferences (COLING, ECONLP, ECNLP, EMNLP, ACL, SIRIP). My work has been presented in venues such as NAACL, WWW, RecSys and Nature journals: our paper on cognitively-inspired query embeddings won the Best Paper Award at NAACL 21.
As a true SFI alumnus, I am an old-fashioned generalist, and I gave tiny contributions to other fields mostly as a way to spend more time with old friends: logic and computation, cellular automata, computational social sciences, networks, philosophy of mind, political science.
In previous lives, I managed to get a Ph.D., simulate a pre-Columbian civilization, document biases in national elections and give an academic talk on videogames. Some of my improbable “achievements” received ample press coverage in national outlets.
Having built end-to-end data and ML pipelines at garage, growth and IPO scale, I happily shared all my mistakes in a series of articles that introduced the concept of Reasonable Scale.
Some time before Brad Pitt’s movie, I led one of the first attempts of running sophisticated analytics for a professional basketball team, and spearheaded the first data science effort on Milan’s bike-sharing service (no bikers or bureaucrats were harmed during the project).
The content of jacopotagliabue.it are released under the BY-NC-ND license; my chibi has been designed by the incredibly talented wisesnail.
Last update: May 2023.
Quick links to some selected projects, talks, papers, datasets.
Aside from research and tutorials, our datasets have been successfully used by dozens of master students to defend their thesis at Tillburg University and Politecnico in Milan.