Matej Jusup
Matej Jusup

PhD Student

About Me

I co-developed a Chess Champ Gem for Gemini—the first language model to reach Grandmaster-level performance in Chess with a human-comparable search budget per move. It integrates search-based planning techniques to enhance multi-step reasoning in games like Chess, Chess960, Connect Four, and Hex. This work was part of my Student Research at Google, hosted by Eric Malmi and Aliaksei Severyn.

I am a PhD student at ETH Zurich and an Associated Researcher at ETH AI Center, supervised by Prof. Francesco Corman and Prof. Andreas Krause.

Before starting a PhD, I worked as a Quantitative Researcher at Morgan Stanley and as a Senior Data Scientist, leading a team of four, in a tech startup, Cantab Predictive Intelligence.

Download CV
Interests
  • Artificial Intelligence
  • Reinforcement Learning
  • Large Language Models
  • Planning and Reasoning (with LLMs)
  • Sequential Decision Making
  • Multi-Agent Systems
  • Probabilistic Learning
  • Safe Learning
  • Data-Driven Algorithms
  • AI in Board Games
  • Mean-Field Control
Education
  • PhD in Artificial Intelligence (expected: June 2025)

    ETH Zurich

  • MSc in Mathematical Statistics

    University of Zagreb

  • Visiting Student

    University of Bielefeld

  • BSc in Mathematics

    University of Zagreb

Featured Papers
Recent & Upcoming Talks

CroAI – ML Pub Meetup

Zagreb, Croatia

Invited talk on Superhuman Planning with Large Language Models hosted by CroAI.

ZurichNLP Meetup

Zurich, Switzerland

Invited talk on Mastering Board Games With Language Models hosted by ZurichAI.

Experience

  1. Student Researcher

    Google

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    Zurich, Switzerland
    • Key Contribution: The first LLM that plays chess at the world champion level using human search budget.
    • Hosts: Eric Malmi and Aliaksei Severyn
    • Publication: First co-author of a spotlight paper at ICML 2025 — https://arxiv.org/abs/2412.12119
    • Planning with LLMs: Enhanced LLMs with search-based planning techniques to improve multi-step reasoning.
    • Asynchronous MCTS: Introduced dynamic virtual counts to balance exploration–exploitation with few simulations.
    • Prompt Engineering: Assisted in designing board-game prompts and test-time internal search linearization.
    • Technology Stack: Python, Transformer Pre-Training, Supervised Fine-Tuning, Tree-Search Methods
  2. Senior AI Researcher

    Cantab Predictive Intelligence (tech startup)

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    Zagreb & Cambridge
    • Key Contribution: Lead a team of four researchers on a few projects running in parallel.
    • Behavioral Credit Scoring: Gradient-boosting model for default risk, achieving a market-leading Gini of 75%.
    • AI-Driven Marketing: Boosted heart drug sales by 10% via data-driven A/B-tested campaign for pharma client.
    • Personalized Newsletter: Built a hybrid recommender (content-based + collaborative); 1.5% CTR in PoC.
    • Delivery Delay Estimation: Predicted COVID-era mall delays using ARIMA and supervised learning.
    • Technology Stack: Python, PyTorch, PySpark, Databricks, Statsmodels, AWS/Azure, Sklearn, Numpy, Pandas, Git
  3. Quantitative Researcher

    Morgan Stanley

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    Budapest, Hungary
    • Key Contribution: Built scalable models for risk, liquidity, and trade execution in financial systems.
    • Systemic Risk Model: Built a parallel hill climber heuristic, solving the problem in 3 minutes, averaging 5% from optimal.
    • Cash Traceability System: Developed a real-time uncollateralized debt tracker from daily data feeds.
    • E-Trading Limits Calibration: Tuned model to block high-risk trades via statistical analysis of client behavior.
    • Listed Derivatives Liquidity: Developed a PoC liquidation model driven by intraday futures data.
    • Technology Stack: Python, CPLEX, OR-Tools, Q/kdb+, PyQ, SQL, Pandas
  4. Software Engineer

    Morgan Stanley

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    New York, London & Budapest
    • Annual Grad Program: Participated in a 15-week program for 50 globally selected students.
    • Margin Calculator Microservice: Implemented and unit-tested features for NYSE and HGK stock exchanges.
    • Technology Stack: Java, C++, Spring Beans, JUnit
  5. Junior Teaching Assistant

    University of Zagreb

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    Zagreb, Croatia
    • Euclidean Spaces: Delivered problem-solving lectures after achieving the top score in a class of 70.

Education

  1. PhD in Artificial Intelligence (expected: June 2025)

    ETH Zurich

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    Zurich, Switzerland
    • Key Contribution: Operating a fleet of tens of thousands of agents in real time while satisfying safety constraints.
    • Thesis: Safe and Scalable Ride-Sourcing Vehicle Rebalancing: A Constrained Mean-Field RL Approach
    • Supervisors: Prof. Francesco Corman and Prof. Andreas Krause
    • Research Area: Reinforcement Learning, Multi-Agent Systems, Sequential Decision Making, Data-Driven Algorithms
  2. MSc in Mathematical Statistics

    University of Zagreb

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    Zagreb, Croatia
    • Thesis: Network Optimization in Railway Transport Planning
    • Supervisor: Prof. Marko Vrdoljak
    • Distinction: Graduated with honors.
    Read Thesis
  3. Visiting Student

    University of Bielefeld

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    Bielefeld, Germany
    • Research Visit: Two semesters funded by Erasmus+ during which I wrote my MSc thesis.
    • Host: Prof. Andreas Dress
  4. BSc in Mathematics

    University of Zagreb

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    Zagreb, Croatia
Skills
Research
RL
LLMs
MCTS
Safe RL
MFRL
MFC
BO
Programming
Python
C++
SQL
Java
C
Bash & CLI
Q/kdb+
Cloud & Packages
Git
PyTorch
AWS & Databricks
Numpy
Sklearn
Pandas
PySpark
Languages
90%
English
100%
Croatian
10%
German
Challenge
Chess puzzle diagram

White to move and win.

Find a solution such that white to move wins.

I am happy to hear your solution if you can solve it even with the assistance of an engine! I am also open to discussing why many modern engines fail to solve it.

Computer Chess:

I co-developed a Chess Champ Gem for Gemini, which enhances language models with search-based planning techniques to improve multi-step reasoning in board games such as Chess, Chess960, Connect Four, and Hex. It achieves Grandmaster-level performance in Chess with a search move count per decision comparable to human players.

Notable Achievements:

  • Silver medalist at the individual Croatian junior (under 20 years) championship in 2011.
  • Played in Croatian, German, Hungarian and Swiss leagues.
  • A personal best Elo rating of 2585 on www.chess.com ranks me within 3 thousand best players on the platform among over 100 million registered users (99.999% percentile).
  • The official Elo rating of 2250 places me among the top 3% of globally registered chess players.