There's a lesson here, and I'm not going to be the one to figure it out.
My name is Apo and I’m a mighty pirate
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.
Having built end-to-end data pipelines at garage, growth and IPO scale, I happily shared all my mistakes at Reasonable Scale in a series of articles.
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’m building a new company now and I’m always happy to chat about DataOps, MLOps and NLP.
Where is my mind?
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 asked to do so by friends in industry - from startups to Fortune 500 - and academia: popular projects and papers are highlighted (for readers with insomnia) at the end of this page.
A.I. research
My recent research sits at the intersection of language, learning and retrieval.
I am co-organizer of SIGIR eCom, Industry Sponsorship Chair for CIKM 2022, and I’ve been involved in many NLP and IR events (COLING, ECONLP, ECNLP, EMNLP, 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: computational social sciences, networks, philosophy of mind, political science.
Old stuff
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. For some reasons, few of my improbable “achievements” received ample press coverage in national outlets.
Some time before Brad Pitt’s movie, I led one of the first attempts of running sophisticated analytics for a professional basketball team.
I also led the first data analysis and vizualization effort on Milan’s bike-sharing service. No bikers (and no bureaucrats) were harmed for the project.
About this page
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: February 2023.
Appendix
Quick links to selected projects, talks, papers, datasets: if there’s a paper, talk, deck you know I have, but you can’t find it (here or elsewhere), please do get in touch directly.
Selected open source projects
- RecList is a testing library for recommender systems: RecList spawned a sponsored open source package, a competition, and three papers (WWW, Nature Machine Intelligence, CIKM).
- “You don’t need a bigger boat” is an introduction to modern pipelines at the Reasonable Scale, first presented at the Stanford MLSys Lectures in 2021. A simpler setup with the same principles can be found in the Post-Modern Data Stack project.
Selected talks
- Mo’ models, mo’ problems: CLIP-like models and generalization in modern e-commerce platforms, KDD 2022 (Invited Talk), 08/22
- Are we there yet? Meaning in the age of large language models, CiE 2022 (Invited Talk), 07/22
- Recs at reasonable scale (slides, repo), NVIDIA RecSys Summit (Keynote), 07/22
- RecList, StitchFix Algo Hour, 03/22
- I (don’t) know what you did last summer, Lowe’s, 11/21
- Not all those who browse are lost: few-shot and zero-shot personalization with deep architectures, Haystack, 07/20
Selected papers
- A challenge for rounded evaluation of recommender systems (Nat. Mach. Int. 2023)
- Contrastive language and vision learning of general fashion concepts (Nat. Sci. Rep. 2022)
- Beyond NDCG: behavioral testing of recommender systems with RecList (WebConf 2022)
- You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack (RecSys 2021)
- Query2Prod2Vec: Grounded Word Embeddings for eCommerce (NAACL 2021, Best Paper Award)
- Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction (NAACL 2021)
- Cellular Automata (SEP 2017)
Datasets
- EvalRS 2022, packaging the LFM-1b dataset into an easy-to-use, “testable” format for our data challenge.
- SIGIR eCom Challenge 2021, the largest and most complete session-based dataset of all time.
- Shopper Intent Prediction, from our paper.
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.