PeopleofSuperintelligence

Researcher

Shaul Markovitch

Also known as S. Markovitch

Shaul Markovitch is a professor at Technion whose work spans artificial intelligence, machine learning, and multi-agent systems. In 1989, he and Paul Scott introduced DIDO, a system for exploratory learning. With Evgeniy Gabrilovich, he developed Explicit Semantic Analysis in 2007, which represents text with Wikipedia concepts. In 2012, he and colleagues published Pundit for predicting future news events; he co-chaired AAAI-17 in 2017 and became a EurAI Fellow in 2019. His 2022 and 2025 work made embedding spaces and language models easier to interpret; as of October 2026, he is a professor at Technion's Data & Knowledge Lab.

Now at
Technion
Works
123
Citations
6,508
h-index
31

Publication numbers from OpenAlex, as of 15 October 2026.

Timeline

  1. 1989

    Exploratory learning with DIDO

    In 1989, he and Paul Scott introduce DIDO, a system designed for exploratory learning.

  2. 2007

    Explicit Semantic Analysis

    In 2007, he and Evgeniy Gabrilovich publish Explicit Semantic Analysis, which represents text as weighted vectors of Wikipedia concepts.

  3. 2012

    Pundit predicts future events

    In 2012, he and Kira Radinsky and Sagie Davidovich publish Pundit, an algorithm that generalizes causality pairs to predict future news events.

  4. 2017

    Co-chairing AAAI-17

    He co-chairs AAAI-17 with Satinder Singh in 2017.

  5. 2019

    A EurAI Fellowship

    He becomes a EurAI Fellow in 2019.

  6. 2022

    Interpreting embedding spaces

    With Adi Simhi, he presents a 2022 method that transforms latent embeddings into a comprehensible conceptual space.

  7. 2025

    Adding Concept Layers

    In 2025, he and Or Raphael Bidusa introduce Concept Layers, a method that makes large language models easier to interpret and adjust.

  8. As of October 2026

    Professor at Technion

    As of October 2026, he is a professor at Technion's Data & Knowledge Lab. His work covers artificial intelligence, machine learning, and multi-agent systems.

Sources and credits

Photo: We have no published photo of Shaul Markovitch yet. Send us a free-licensed photo.

  1. Learning Novel Domains Through Curiosity and Conjecture — Lacuna lacuna.tiptreesystems.com. Checked 15 October 2026.
  2. Explicit Semantic Analysis gabrilovich.com. Checked 15 October 2026.
  3. Abstract | IJCAI ijcai.org. Checked 15 October 2026.
  4. [1402.0574] Learning to Predict from Textual Data arxiv.org. Checked 15 October 2026.
  5. AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence - AAAI aaai.org. Checked 15 October 2026.
  6. Hall of Fame – Digital Ecai 2020 digital.ecai2020.eu. Checked 15 October 2026.
  7. [2209.00445] Interpreting Embedding Spaces by Conceptualization arxiv.org. Checked 15 October 2026.
  8. [2502.13632] Concept Layers: Enhancing Interpretability and Intervenability via LLM Conceptualization arxiv.org. Checked 15 October 2026.
  9. People — TD&K tdk.cs.technion.ac.il. Checked 15 October 2026.