Celso C. Ribeiro is a Member of the Brazilian Academy of Sciences and of the National Order of the Scientific Merit in Brazil. He is a Full Professor at the Institute of Computing of Universidade Federal Fluminense, Brazil. He chaired the Departments of Electrical Engineering (1983-1987) and Computer Science (1993-1995) of the Catholic University of Rio de Janeiro, Brazil. He was the Director of the Department of Modernization Programs of the Brazilian Ministry of Education (2005-2007) and acted as Subsecretary of Education of the State of Rio de Janeiro (2007–2008). He obtained his doctorate in Computer Science at Ecole Nationale Supérieure des Télécommunications, France, in 1983 and his Habilitation at Université Paris XIII, France, in 1990. He was a President of the Brazilian Operations Research Society (1989-1990), a President of the Latin-American Association of Operations Research Societies (1992-1994), and a Vice-President of the International Federation of Operational Research Societies (1998-2000). His research is funded by the Brazilian Council of Scientific and Technological Development (CNPq) and by the Rio de Janeiro State Foundation for Research Support (FAPERJ). He is the editor of six books and the author of more than 150 papers in international journals and 25 book chapters. He has supervised 32 doctorate dissertations and 37 master theses. Dr. Ribeiro is the General Editor of the journal International Transactions in Operational Research. He is also the coauthor of the book “Optimization by GRASP: Greedy Randomized Adaptive Search Procedures”, published by Springer in 2016, as well as an Associate Editor of the journals Engineering Applications of Artificial Intelligence, Discrete Optimization, Journal of Heuristics, and RAIRO Operations Research.Second column division content.

Andrea Schaerf received his PhD in Computer Science from "Sapienza" University of Rome (Italy) in 1994, where he has been Assistant Professor from 1996 to 1998. From 1998 to 2005 he has been Associate Professor at University of Udine (Italy), where starting 2005 he is Full Professor. From 2015 to 2021 he has been the Head of the Bachelor and Master Programs in Management Engineering at University of Udine. His main research interests are: Scheduling and Timetabling Problems, Local Search & Metaheuristics for Combinatorial Problems, and Problem Specification Languages and Tools. He has always been very active in the PATAT community: he has attended all twelve past PATAT conferences (co-chairing PATAT-2016), he has co-organized two timetabling competitions and two nurse rostering competitions, and he has written many papers on the definition and the solution of various fundamental timetabling problems and datasets.





Dr. Deepak Ajwani is an Assistant Professor at the School of Computer Science, University College Dublin. His research is at the confluence of algorithm design and engineering, combinatorial optimization and machine learning. He received his Ph.D. from Max Planck Institute for Informatics in 2008, for his work on I/O-efficient graph traversal algorithms. Thereafter, he worked as a Postdoctoral Researcher at MADALGO - Centre for Massive Data Algorithms, where he developed shared memory multicore algorithms for discrete optimization problems. In 2010, he was awarded a grant of around 75,000 Euro from the Irish Research Council for Science, Engineering and Technology (IRCSET) and IBM Research for a project on designing graph partitioning and repartitioning techniques in the context of Exascale stream computing systems. As part of this Postdoctoral work carried out at University College Cork, he proposed a methodology to co-optimize the inter-related hard problems of interconnect topology configuration, graph partitioning and routing, that provided fairly good solutions in very little time. From 2012 to 2018, he worked as a research scientist at Nokia Bell Labs. In this role, he was involved in the design and development of a cognitive computing tool for interpreting, organizing and navigating unstructured content.

Peter Nightingale is a lecturer at the Department of Computer Science, University of York, and a member of the Artificial Intelligence Group. His research is on modelling and solving constraint satisfaction and optimization problems, with a particular focus on automating constraint modelling. The way in which a constraint optimization or satisfaction problem is modelled (presented to the solver) makes a huge difference to the performance of the solver, and modelling currently requires extensive human expertise. One aspect of automated modelling is finding an appropriate formulation of the variables and constraints to target a chosen solver or class of solvers (e.g. MIP, SAT, or CP). He is the main author of the automated modelling tool Savile Row, which includes a set of reformulations to improve existing models while specialising them for a chosen solver class. One example of a reformulation is 'tabulation', where some chosen sub-problems are pre-solved and replaced in the model. Tabulation can, in some cases, improve solver performance by orders of magnitude. He has also worked on the Constraint Modelling Pipeline project (with collaborators at the University of St Andrews), which is able to produce multiple models from a single high-level specification of a problem class. One or a portfolio of models can then be selected for solving, mimicking human exploration of the space of models. He is also interested in SAT encodings, i.e. effective, scalable representation of constraints as Boolean formulas to be solved by a SAT solver. His ongoing research in automated modelling will be funded by UK EPSRC grant "Solver Feedback Loops for Automated Constraint Modelling." Prior to this, his research focused on constraint solvers, in particular efficient handling of global constraints in a backtracking solver. He was awarded his Ph.D. by the University of St Andrews in 2007 for his work on quantified constraint satisfaction, an extension of the standard constraint satisfaction formalism that is able to represent uncertainty.