Doctoral Program

Schedule

9:00 Open Edward Lam Welcome
9:05 Invited speaker Emir Demirović Lessons learned, unwritten rules, and the role of luck in academia
9:45 Main conference paper Samuel Cloutier Cumulative Scheduling with Calendars and Overtime
9:50 Main conference paper Erdem Kuş Frugal Algorithm Selection
9:55 Main conference paper Panteleimon Iosif A CP/LS heuristic method for maxmin and minmax location problems with distance  constraints
10:00 Main conference paper Melle Van Marle Exponential steepest ascent from valued constraint graphs of pathwidth four
10:05 Main conference paper Maarten Flippo A Multi-Stage Proof Logging Framework to Certify the Correctness of CP Solvers
10:10 Rebuttal workshop presentation Kostis Michailidis Constraint modelling with LLMs using in-context learning
10:30 COFFEE BREAK
11:00 Rebuttal workshop Edward Lam  
11:10 DP paper Josep Maria Salvia Hornos Constraint programming applied to minimizing switching on a binary sorter
11:20 DP paper Christian Perez Reinforcement Learning for Energy Efficiency in Unrelated Parallel Machine Scheduling Problem
11:30 DP paper Carlos March Moya A Constraint Programming Solver Selector for JSP Addressing Energy Efficiency and Tardiness
11:40 DP paper Julien Rouzot Scheduling Data Transfers in Space Missions with Constraint Programming
11:50 DP paper Elifnaz Yangin A Tableau Calculus for Non-Clausal Regular MaxSAT
12:00 DP paper Tim Luchterhand About the Usefulness of Learned Heuristics in Tight Scheduling Problems
12:10 DP paper Imko Marijnissen Incremental time-tabling propagation for the cumulative global constraint
12:20 DP paper Pol Barrachina Hernandez Explainable Interactive Decision Support Systems
12:30 LUNCH
14:00 Invited speaker Tias Guns Tips for a successful PhD, and how to win an award with it
14:40 Rebuttal workshop presentation Augustin Delecluse Black-box value heuristics for solving optimization problems with constraint programming
15:00 Rebuttal workshop Edward Lam  
15:10 DP paper Konstantin Sidorov A branch-and-bound procedure for finding the shortest refutation of an unsatisfiable propositional formula
15:20 DP paper Carla Davesa Sureda Towards High-Level Modelling in Automated Planning
15:30 COFFEE BREAK
16:00 Main conference paper Hugo Barral Acquiring Constraints for a Non-linear Transmission Maintenance Scheduling Problem
16:05 Main conference paper Filipe Souza An Investigation of Generic Approaches to Large Neighbourhood Search
16:10 DP paper Shuolin Li Get the Better Cores through Unlocking Mechanism in Unit Propagation
16:20 DP paper Joseph Loughney Symmetry Breaking in the Subgraph Isomorphism Problem
16:30 DP paper Orhan Yigit Yazicilar Automated Nogood-Filtered Fine-Grained Streamliners for Constraint Satisfaction Problems
16:40 End of DP    
20:00 Dinner   Location: Taverna d'El Foment
23:00 End of dinner    

Invited Speakers

Emir Demirović

Emir Demirović is an assistant professor at TU Delft (Netherlands). He leads the Constraint Solving ("ConSol") research group, which advances combinatorial optimisation algorithms for a wide range of (real-world) problems, and co-directs the explainable AI in transportation lab ("XAIT") as part of the Delft AI Labs. Prior to his appointment at TU Delft, Emir worked at the University of Melbourne, Vienna University of Technology, National Institute of Informatics (Toyko), and at a production planning and scheduling company.

The focus point of Emir's current work is solving techniques based on constraint programming, optimising decision trees (machine learning), and explainable methods for combinatorial optimisation. He is also interested in industrial applications, robust/resilient optimisation, and the integration of optimisation and machine learning. He publishes in leading AI conferences (e.g., AAAI, NeurIPS) and specialised venues (e.g., CP, CPAIOR), attends scientific events such as Dagstuhl seminars, Lorentz workshops, and the Simons-Berkeley programme, and frequently organises incoming and outgoing visits, e.g., EPFL, ANITI/CNRS, CUHK, Monash University, TU Wien.

Tias Guns

Tias Guns is Associate Professor at the DTAI lab of KU Leuven, in Belgium. Tias' expertise is in the hybridisation of machine learning systems with constraint solving systems, more specifically building constraint solving systems that reason both on explicit knowledge as well as knowledge learned from data. For example learning the preferences of planners in vehicle routing, and solving new routing problems taking both operational constraints and learned human preferences into account; or building energy price predictors specifically for energy-aware scheduling, and planning maintenance crews based on expected failures. He won the ACP 2013 and ECCAI 2013 doctoral dissertation award. He was awarded a prestigious ERC Consolidator grant in 2021 to work on conversational human-aware technology for optimisation and currently leads a lab of 8 PhD students and 4 postdocs (3 of which are ACP dissertation awardees).