Dr. Emir Demirović

Assistant Professor of Computer Science, TU Delft, The Netherlands

I lead the Constraint Solving ("ConSol") research group, where we develop combinatorial optimisation algorithms that can be applied to a wide range of (real-world) problems. I am also the co-director of the explainable AI in transportation lab ("XAIT") as part of the Delft AI Labs. Prior to my appointment at TU Delft, I worked as postdoc at the University of Melbourne (2017-2020), received my PhD from the Vienna University of Technology in 2017, and in between these positions held shorter term posts at a production planning and scheduling company MCP (Vienna, Austria) and the National Institute of Informatics (Toyko, Japan).

My focus now is on developing solving techniques based on constraint programming, optimising decision trees, and explainable methods for combinatorial optimisation. I am also interested in industrial applications, robust/resilient optimisation, and the integration of optimisation and machine learning.

My work is regularly published at leading AI conferences (e.g., AAAI, NeurIPS) and specialised venues (e.g., CP, CPAIOR). I have been invited to scientific events such as Dagstuhl seminars, Lorentz workshops, and the Simons-Berkeley programme. I enjoy organising both incoming and outgoing research visits (e.g., EPFL, ANITI/CNRS, CUHK, Monash University, TU Wien), and in general highly value collaboration with (inter)national researchers. In the past I participated in several algorithmic competitions, scoring first place, e.g., incomplete tracks of MaxSAT Evaluation 2018+, ROADEF/EURO 2012. Competitions serve as a good way of promoting my work beyond publications, e.g., Google OR-Tools adopted some of my ideas. Our approaches on optimising decision trees offer order-of-magnitude runtime improvements whilst support additional constraints. I am also happy to work on real-world problems together with academic and industrial partners.

My long term vision is to automate decision making that is currently done by humans, referring to scenarios where difficult decisions need to be made under complex constraints. This not only results in more efficient decision making and resource utilisation, but also relieves human experts of the burden of handling difficult and stressful tasks, leaving them more time to deal with creative and meaningful problems that have not (yet) been automatised.

Office: 4.E.400, Building 28
Email: e.demirovic@tudelft.nl
Publications: Google Scholar

  • Would you like to do your master thesis in our group? Get in touch!


We work on both fundamental and applied research, and collaborate with civil engineering, QuTech, and industry.

Data Science

specialised combinatorial optimisation algorithms for trustworthy machine learning, focussing on decision trees

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general-purpose optimisation techniques based on propositional logic and constraint programming

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methods that can provide human-understandable explanations in addition to good performance

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solving real-world problems such as timetabling, scheduling, and production planning

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Integration with Machine Learning

end-to-end learning with combinatorial problems (predict+optimise)

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Robust and

theory and algorithms beyond conventional optimisation

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Jeff Smits

Research Software Engineer

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Koos van der Linden

PhD candidate, optimal decision trees

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Maarten Flippo

PhD candidate, constraint programming

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Elif Arslan

PhD candidate, ride-sharing, forecasting

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Konstantin Sidorov

PhD candidate, explainable
combinatorial optimisation

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Imko Marijnissen

PhD candidate, scheduling for quantum

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Matthias Horn

Postdoc (2022), scheduling

Ana Tatabitovska


Imko Marijnissen

MaxSAT, now PhD student

Angelos Zoumis

Constraint Programming

Bob Dorland

Scheduling for Quantum Computers

Heqi Wang

Traffic Predictions

Jeroen van Dijk

SAT for Multi-Agent Path Finding

Thomas Bos

Explainable Predictive Maintainance

Isha Dijcks

Automating Puzzle Generation

Andrea Nardi

Graph Theory

Zhiyi Chen

(BSc honours) Algorithm Selection

Sander Waij

Multi-Agent Path Finding

Yorick de Vries

Reinforcement Learning for Logistics

Maxim Marchal

MaxSAT for Correlation Clustering

Jens Langerak


Max Ligtenberg

Predictions for Intensive Care

Bhavishya Palavali

Algorithm Selection



Dutch national funding agency


Delft AI Labs


TU Delft