Matej Jusup

Matej Jusup

Doctoral Researcher

ETH Zurich

I am a PhD student at ETH Zurich, supervised by Prof. Francesco Corman and Prof. Andreas Krause. My research interests are sequential decision-making and data-driven algorithms. I am particularly passionate about reinforcement learning, multi-agent learning, safe learning, and their applications in transportation systems.

Before starting a PhD, I worked as a quantitative researcher at Morgan Stanley and as a team-lead data scientist in a startup, Cantab Predictive Intelligence.


Team Lead Data Scientist
March 2019 – July 2020 Zagreb, Cambridge

Lead a team of five data scientists.

Behavioral Credit Scoring:

  • Built a PySpark gradient-boosting model to predict consumer default risk probability, achieving a market-leading Gini metric results of up to 75%.

ML-Driven Marketing Campaign:

  • Devised a data-driven campaign for promoting a heart disease drug to doctors on behalf of a top pharmaceutical company, which led to a 10% sales increase during A/B testing.
  • Statistical analysis was conducted using Statsmodels, SciPy, and Python plotting packages.

Personalized Newsletter and E-Commerce Recommender Systems:

  • Constructed a hybrid recommender system combining content-based and collaborative filtering, which achieved a 1.5% click-through rate during the proof-of-concept phase.
  • Utilized Databricks, Python, PyTorch, and AWS in the technology stack.

Delivery Delay Estimation:

  • Developed a customer support system for a shopping mall during the COVID-19 pandemic, which predicted delivery delays using a time-series ARIMA model supplemented with supervised learning techniques.
  • The technology stack comprised Pandas, NumPy, and Sklearn.
Quantitative Researcher
October 2017 – March 2019 Budapest

Systemic Risk Model Execution Efficiency:

  • Created a parallel version of a hill climber heuristic that made the optimization problem practically tractable. The heuristic’s runtime was limited to 3 minutes and, on average, generated solutions within 5% of the optimum, with the reported worst-case being 15% for tractable test-set instances.
  • Employed a technology stack encompassed Python, CPLEX, and OR-Tools.

Treasury Department Cash Traceability:

  • Constructed an uncollateralized debt tracking system by amalgamating diverse daily feeds to generate comprehensive firm-wide reports within seconds.
  • Employed Q/kdb+, Python, PyQ kernel, and SQL for the development.

E-Trading Execution Limits Calibration:

  • Fine-tuned an in-house model to prevent real-time executions during high-risk scenarios, employing a statistical analysis of e-trading clients.
  • Utilized Pandas for the calibration process.

Listed derivatives liquidity:

  • Created proof-of-concept for data-driven liquidation model based on intraday futures trading.
  • Utilized Q/kdb+ for data processing.
Software Developer
December 2016 – October 2017 Budapest
Implemented and unit-tested features for the Java-based margin calculator microservice.
Technology Analyst Program
August 2016 – December 2016 New York and London
Participated in a 15-week annual grad program among 50 globally selected interview-passing students.
Junior Teaching Assistant
October 2013 – March 2014 Zagreb
Selected to deliver problem-solving lectures by achieving the highest course score among 70 students.


  • Stefano-Franscini-Platz 5, Zurich, 8093
  • Find me at Honngerberg campus HIL F 12.2