Peter Bosman

Full Name
Prof.dr. P.A.N. Bosman
Function(s)
Scientific Staff Member
Email
Peter.Bosman@cwi.nl
Telephone
+31 20 592 4265
Room
M276
Department(s)
Life Sciences and Health

Research

Dr. Peter A.N. Bosman is a senior researcher heading the Medical Informatics (MI) subgroup of the Life Sciences and Health (LSH) research group at the Centrum Wiskunde & Informatica (CWI) (Center for Mathematics and Computer Science) located in Amsterdam, the Netherlands. Dr. Bosman was formerly affiliated with the Intelligent Systems research group of CWI and before that with Utrecht University, where he also obtained his M.Sc. and Ph.D. degrees in Computer Science.

Dr. Bosman's fundamental research focus is on the design and application of evolutionary algorithms (EAs) for single- and multi-objective optimization, and machine learning. The optimization problems considered are typically complex to an extent where a black-box optimization (BBO), or at least a grey-box optimization (GBO), perspective is required, i.e. virtually no information (BBO) or limited information (GBO) is available (or properly understood) about the optimization problem at hand. The designed EAs are moreover mostly model-based, meaning that a specific model is used to capture and exploit problem-specific features to guide the search for high-quality solutions more effectively and efficiently and get the most out of previously performed evaluations. Such models may be derived by hand or, if this isn't possible (as in e.g. the BBO case), be learned online, i.e. during optimization, using techniques from fields such as machine learning and data mining. For problems where efficient (problem-specific) heuristic optimization techniques (i.e. local search (LS) techniques) are available or can be derived, model-based EAs are furthermore a very solid basis for hybridization to obtain the best of both worlds in terms of efficiency and effectiveness, resulting in state-of-the-art optimization algorithms for specific problems.

Dr. Bosman's applied research focus is on the use of (model-based) EAs to solve key problems in the Life-Science and Health (LSH) domain that require optimization and/or machine learning. A specific focus is on improving mathematics and computer science related aspects in radiation oncology, such as automated treatment planning, deformable image registration and 3D dose reconstruction. Previous application areas have included (smart) energy and logistics, dynamic pricing of goods for revenue management, optimization of patient flows in hospitals and dynamic routing of vehicles for transportation purposes.

Dr. Bosman has (co-)authored over 100 refereed publications, out of which 4 received best paper awards. According to Google Scholar, his h-index is 29 with a total of 2750 citations to his works (as measured on December 1, 2017). He is program committee member of various major conferences and journals in the EA field and related fields. In 2017, Dr. Bosman was the General Chair of the main conference in the field of EAs: the Genetic and Evolutionary Computation Conference (GECCO). He has furthermore organized various workshops and tutorials on various EA related topics and has been (co-)track chair and (co-)local chair at GECCO.

Finally, the (co-)acquired research grant funding by Dr. Bosman totals over €4M, which includes one STW-KWF partnership project, four NWO projects, one KiKa project, and one EIT ICT Labs project that together fund(ed) 5 postdocs, 13 Ph.D. students, a radiation therapy technologist, a scientific programmer, and various high-performance computing hardware.

Publications

Current projects with external funding

  • 3D dose reconstruction for children with long-term follow-up Toward improved decision making in radiation treatment for children with cancer
  • ICT based Innovations in the Battle against Cancer – Next - Generation Patient -Tailored Brachytherapy Cancer Treatment Planning
  • Improving Childhood Cancer Care when Parents Cannot be There - Reducing Medical Traumatic Stress in Childhood Cancer Patients by Bonding with a Robot Companion
  • Fusible Evolutionary Deep Neural Network Mixture Learning from Distributed Data for Robust Medical Image Analysis (FEDMix)
  • Multi-Objective Deformable Image Registration (MODIR) – An Innovative Synergy of Multi-Objective Optimization, Machine Learning, and Biomechanical Modeling for the Registration of Medical Images with (MODIR)

Professional activities

  • Committee member: Genetic and Evolutionary Computation Conference - [GECCO]
  • Committee member: International Conference on Parallel Problem Solving from Nature - [PPSN]
  • Committee member: European Workshop on Evolutionary Computation in Transportation and Logistics -[EvoTRANSLOG miv 2010 EvoApplications]
  • Committee member: European Workshop on Evolutionary Algorithms in Stochastic and Dynamic Environments - [EvoSTOC miv 2010 EvoApplications]
  • Committee member: The IEEE Congress on Evolutionary Computation
  • Committee member: Benelux Conference on Artificial Intelligence - [BNAIC]
  • Committee member: Member review committee - Research Grants Council, Hong Kong
  • Committee member: Member program committee,
  • Editor: Journal: Journal of Applied Metaheuristic Computing - [IJAMC]
  • Lecturer: Technische Universiteit Delft - [TUD]
  • Organizer: Tutorial organizer - Genetic and Evolutionary Computation Conference - [GECCO] - Model-Based Evolutionary Algorithms
  • Side Position: Treasurer - Koninklijk Wiskundig Genootschap - [KWG]

Grants

  • ᅠNVIDIA GPU Grant Programme,ᅠ"Support for the GPU-based Acceleration of Gene-pool Optimal Mixing Evolutionary Algorithms" (2017)
  • STW-KWF Partnership Programme,ᅠᅠ"Improving Childhood Cancer Care when Parents Cannot be There - Reducing Medical Traumatic Stress in Childhood Cancer Patients by Bonding with a Robot Companion" (2016)
  • NWO Innovatieve PPS in ICT (IPPSI) - Technology Area (TA) programme,ᅠᅠ"ICT-based Innovations in the Battle against Cancer - Next-Generation Patient-Tailored Brachytherapy Cancer Treatment Planning",ᅠᅠ2015. (2015)

Awards

  • Best paper award: Genetic and Evolutionary Computation Conference 2015, Madrid, Spain (2015)
  • Best paper award: Genetic and Evolutionary Computation Conference 2013, Amsterdam, The Netherlands (2013)
  • Best paper - Genetic and Evolutionary Computation Conference , Portland, Oregon, USA (2010)
  • Best paper: BNAIC (Benelux Conference on Artificial Intelligence) - Eindhoven, The Netherlands (2009)