Edo Liberty

Edo Liberty — Teaching

Teaching Statement

While I was never a full-time professor, I love teaching. I taught for four years at different universities, and the two courses I created are both shared as open-source material for educators anywhere. I've been lucky to mentor many talented interns over the years; they have all gone on to phenomenal careers. Finally, I often give public and academic talks about my work.

Columbia

COMS 6998 - Fall 2026: Algorithms in Large Language Models, Columbia University. I only teaching a part of this class as an Adjunct together with Alex Andoni

This class covers algorithmic aspects of various parts of modern-day LLMs, with a focus on theoretically grounded ideas. The class will primarily cover the theoretical/formal computational frameworks within which one can reason about best algorithms for specific problems. Most of the class will be devoted to mathematical analysis of algorithms.

Princeton

COS 597A - fall 2023: Long Term Memory in AI - Vector Search and Databases, Princeton University. Co-instructed with Matthijs Douze

Long Term Memory is a foundational capability in the modern AI Stack. At their core, these systems use vector semantics. Vector search is also a basic tool for systems that manipulate large collections of media like search engines, knowledge bases, content moderation tools, recommendation systems, etc. As such, the discipline lies at the intersection of Artificial Intelligence and Database Management Systems. This course covers the theoretical foundations and practical implementation of vector search applications, algorithms, and systems.

Tel Aviv University

0368-3248-01-Algorithms in Data Mining - Tel Aviv University 2011-2013

The course covered algorithmic tools for data mining massive data sets with an emphasis on randomization and streaming algorithms. The class notes are provided for free academic use. The algorithms are presented in their most basic form for didactic reasons. The proofs are simple and self-contained. Each is presentable in roughly one long frontal session. An advanced undergraduate or graduate student with some hands-on experience in probability, linear algebra, algorithms, and combinatorics should be able to follow this course.

Mentorship

Interns and students I've had the privilege to mentor, and where they are now:

Keynotes & Tutorials