Steffen Jaap Skotvoll Bakker is an Associate Professor at the Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU). His research bridges energy systems, digitalization, and transport logistics through interdisciplinary modeling approaches. Specializes in energy system optimization under uncertainty Focuses on automated container terminal efficiency and freight decarbonization Combines econometrics with routing/scheduling algorithms for complex systems Recent work includes collaborations with SINTEF, IFE, and UIO on strategic energy scenarios, and contributions to bioconversion technologies for circular economies. His publications span operations research journals and logistics conferences, emphasizing data-driven decision-making frameworks.
Juan Antonio Fernández del Pozo is an Associate Professor of Statistics and Operations Research at the School of Computer Science, Technical University of Madrid (UPM), and a member of the Computational Intelligence Group. With over 60 publications and participation in more than 30 national and international research projects, he is a leading researcher in decision analysis and intelligent systems. His educational background includes: Doctorate in Computer Science from the School of Computer Science, UPM (Thesis: "Listas KBM2L para la síntesis de conocimiento en sistemas de ayuda a la decisión") His research focuses on probabilistic graphical models (Bayesian networks/influence diagrams) for decision support, evolutionary algorithm optimization, and machine learning for data streams with concept drift. Key application domains span Social Network Analysis, Air Traffic Management, structural health monitoring, Industry 4.0, and social service quality modeling in collaboration with Spanish Foundations. His work emphasizes knowledge acquisition in large decision tables, explanation synthesis, and sensitivity analysis. Publication trends reveal two phases: foundational work (2000-2013) on Bayesian networks and medical decision systems (e.g., neonatal jaundice modeling), and recent applied research (2016-2018) addressing environmental restoration, transportation scheduling, and social innovation networks. Scientific awards: No scientific awards were mentioned in the provided text. Advising and grants: Supervised 12+ Master's Degree projects Supervised 50+ final degree projects Participated in 30+ research projects Served on 18+ doctoral thesis evaluation panels Labs and teams: Active member of the Computational Intelligence Group at UPM
Mike Wallinga serves as a Lecturer in the Department of Computer Science at Northwestern College while concurrently holding a quarter-time position as Director of Institutional Research. With expertise spanning computational science and statistics, he teaches core computer science courses including introductory programming, data structures, databases, and parallel computing systems. His academic credentials include: Ph.D. in computational science and statistics from the University of South Dakota M.A. in computer science from the University of South Dakota B.A. in computer science and mathematics from Northwestern College Dr. Wallinga's research bridges bioinformatics, parallel computing, and pedagogical innovation. His bioinformatics work develops novel multiple sequence alignment algorithms using morphing techniques and particle swarm optimization, while his parallel computing research explores multi-core architectures for biological data processing. In computer science education, he designs immersive learning experiences such as semester-long game projects to engage programming students. Publication analysis reveals a strong focus on computational biology (71% of recent works), particularly multiple sequence alignment methodologies that dominated his 2017 output. His research trajectory shows progression from algorithmic development (2015-2017) toward educational innovation (2019) and theological integration in computing (2021), demonstrating interdisciplinary breadth within computer science. Recognition includes: Northwestern's 2018 Faculty/Staff Inspirational Service Award As a programming competition coach, he mentored two Northwestern College teams to global top-100 rankings in the ACM International Collegiate Programming Contest. His applied research extends to institutional analytics through his directorship, where he develops data-driven decision support systems using database technologies and visualization dashboards. Professional memberships include the Association for Institutional Research and Association for Computing Machinery. He maintains active leadership in competitive programming initiatives, creating structured team environments that prepare students for high-stakes algorithmic challenges through rigorous practice regimes and strategic problem-solving frameworks.
Gözde Önder is an Assistant Professor in the Industrial Engineering Department at Başkent University, Ankara. Her academic profile indicates active engagement in operations research with focus on complex optimization problems relevant to transportation and logistics systems. Contact information shows affiliation with the Industrial Engineering Program through university email send@baskent.edu.tr and phone extension 4016. Her research interests concentrate on Operations Research , Supply Chain Management , and Mathematical Optimization with specific emphasis on time-constrained routing problems. Current investigations prominently feature traveling repairman problems, vehicle routing with time windows, and multi-depot optimization systems. Methodologically, she employs integer programming, biogeography-based metaheuristics, and combinatorial optimization techniques to address NP-hard problems in service scheduling and transportation networks. Analysis of her publication timeline reveals consistent scholarly output from 2015 through 2025, with increasing focus on time-window constrained systems and multi-depot extensions of classical routing problems. Her work demonstrates strong collaboration with Prof. İmdat Kara and other researchers at Turkish institutions, primarily published in operations research conferences and journals including Transportation Research Procedia and Journal of the Faculty of Engineering and Architecture of Gazi University. Teaching responsibilities include core industrial engineering courses: OLASILIK VE İSTATİSTİĞE GİRİŞ (Introduction to Probability and Statistics) TEDARİK ZİNCİRİ YÖNETİMİ (Supply Chain Management) YÖNEYLEM ARAŞTIRMASI I & II (Operations Research I & II) İSTATİSTİKSEL ANALİZ (Statistical Analysis) LOJİSTİK YÖNETİMİ (Logistics Management) Professional activities include serving as a reviewer for Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi (Gazi University Journal of Engineering and Architecture) in 2023, indicating active participation in academic quality assurance processes within her field.
Gabriel Kronberger is a Professor at Hagenberg University of Applied Sciences, specializing in Symbolic Regression, Genetic Programming, and Machine Learning. His work bridges theoretical advancements with industrial applications in mechatronics and engineering systems. Active in evolutionary computation and symbolic regression since 2006 Lead researcher at the Josef Ressel Center for Symbolic Regression Developed techniques for alarm flood reduction in critical infrastructure His research focuses on Symbolic Regression , where he explores algorithmic enhancements like redundant parameter reduction and equality graph integration. He applies these methods to material science (e.g., tensile strength prediction) and automotive engineering (e.g., powertrain modeling). Recent publications demonstrate a trend toward interactive tools (rEGGression) and hybrid approaches combining genetic programming with machine learning systems (neural networks, random forests). All 158 publications emphasize practical implementations in industrial contexts. He has organized key conferences like Genetic and Evolutionary Computation Conference (2017-2020) and led 6 major research projects from 2013 to 2026, including EREMA Recycling 4.0 and McTronic educational initiatives.
Prof. Dr. Ulrich Meyer is a full Professor of Computer Science at Goethe University Frankfurt, where he joined as a full professor in 2007 at the age of 35. He is also a Fellow at the Frankfurt Institute for Advanced Studies (FIAS) since 2020 and serves as the spokesperson for the DFG priority program "Algorithms for Big Data" (SPP 1736), which spans over 15 sites across Germany, Austria, and Switzerland. Professor Meyer received his Ph.D. in computer science from Saarland University in 2002. Following his doctorate, he worked as a postdoc and later as a senior researcher (W2) at the Max-Planck Institute for Computer Science in Saarbrücken before joining Goethe University Frankfurt. His research focuses on the design and engineering of algorithms and data structures for very large data sets, with particular emphasis on parallelism (Shared-Memory, clusters, GPUs), memory hierarchies (caches, SSDs, hard disks), and energy efficiency. His work spans the spectrum from theoretical worst-case results and average-case analyses to experimental evaluation of heuristics. A significant portion of his research involves graph algorithms, where his group has achieved more than a thousand-fold speed-up over state-of-the-art methods through algorithms with better access patterns. His recent work has focused on memory-efficient graph exploration in dynamically changing data, approximate solutions, and efficient generation of randomized graph test data. Professor Meyer has received numerous prestigious awards including the 2019 ESA Test of Time Award for the Delta-Stepping Algorithm, Best Paper Awards at ESA 2019 for research on Fragile Computing and Graph Generation, the 2011 Award Germany Land of Ideas for Ecosort, records in the JouleSort Competition in 2009/10, and the 2003 Best Dissertation Award from the University of Saarland. As spokesperson for the DFG priority program SPP 1736 "Algorithms for Big Data," Professor Meyer coordinates research across multiple institutions. His Algorithm Engineering group is also part of the DFG Research Unit FOR 2971 "Algorithms, Dynamics, and Information Flow in Networks" and the LOEWE Priority Programme CMMS. His research has been supported by various grants that have enabled significant contributions to parallel and external-memory graph algorithms, large-scale network generation, and energy-efficient computing. Professor Meyer's research group forms part of the Computer Sciences and AI Systems division at FIAS. They collaborate extensively with researchers across Europe through the DFG priority program and other initiatives. The group is particularly known for their work on memory-efficient graph algorithms, large-scale network generation, and contributions to understanding the algorithmic foundations of big data processing.
Mr. Nicolas THIBAULT is a Lecturer in Computer Science at Paris-Panthéon-Assas University, affiliated with the Center for Research in Economics and Law (CRED). His academic career combines teaching and research in theoretical computer science, with a focus on algorithms and network optimization. His research interests include: Algorithms (particularly randomized and truthful scheduling) Dynamic graph maintenance and incremental/decremental tree problems Online computation and bicriteria optimization Network interconnection and parallel machine scheduling Recent publications highlight his work on: Truthful mechanisms for weighted completion times Disturbance minimization in connection trees Hardness results for multi-group interconnection Competitive analysis of online scheduling Optimal rebuilding strategies for dynamic trees Scientific awards include the Best Young Researcher Article at AlgoTel 2006 for his work on connection tree updates. He co-heads the Professional License program in Organizational Management, specializing in Network and Information Systems Management.
Giovanni Iacca is an Associate Professor at the Department of Information Engineering and Computer Science (DISI) of the University of Trento, Italy, where he leads the Distributed Intelligence and Optimization Lab (DIOL). He serves as Coordinator of the Master's Degree in Computer Science and Deputy Director of the Information Engineering and Computer Science Doctoral School. Dr. Iacca has over 15 years of industrial experience in mechatronics and optimization applied to engineering, logistics, and scheduling. Dr. Iacca received his PhD in 2011 from the University of Jyväskylä, Finland, and his MSc in 2006 from the Technical University of Bari, Italy. His academic career includes: 2021-present: Associate Professor, University of Trento 2018-2021: Tenure-track Assistant Professor, University of Trento 2017-2018: Postdoc, RWTH Aachen University, Germany 2013-2016: Postdoc, EPFL and University of Lausanne, Switzerland 2012-2016: Postdoc, INCAS³, The Netherlands Dr. Iacca's research bridges fundamental and applied aspects of artificial intelligence with particular emphasis on evolutionary computation and explainable AI. His work spans machine learning, optimization techniques, distributed systems, and their practical implementations. Recent research directions include federated learning, interpretable reinforcement learning, neural architecture search, and optimization for resource-constrained environments. He teaches courses on Computer Architectures, Introduction to Machine Learning, Bio-Inspired Artificial Intelligence, Optimization Techniques, and AI in Medicine. His publication record demonstrates a strong trend toward developing transparent and efficient AI systems. Recent papers focus on making complex AI models more interpretable while maintaining performance across diverse domains from healthcare to supply chain management. His work on evolutionary approaches to explainable AI has gained significant recognition in the computational intelligence community. Scientific Awards and Editorial Roles EvoApplications Best Paper Award (2017) UKCI AWARENESS Best Paper Award (2012) IEEE CIS Outstanding Student-Paper Award (2011) IEEE Senior Member (2023) Associate Editor, Evolutionary Intelligence (2024) Editorial Board Member, Memetic Computing (2024) Associate Editor, IEEE Transactions on Evolutionary Computation (2023) Dr. Iacca has successfully supervised multiple PhD students including Andrea Ferigo, Hyunho Mo, and Leonardo Lucio Custode. His research is supported by various grants and collaborations with industry partners like MyAv. He serves as chair for PPSN 2026 and has organized workshops including the Workshop on Awareness and Consciousness in Artificial Intelligence (ACAI). As leader of the Distributed Intelligence and Optimization Lab (DIOL), Dr. Iacca oversees a research team working at the intersection of evolutionary computation, machine learning, and distributed systems. The lab focuses on developing novel algorithms that balance computational efficiency with interpretability, with applications spanning from embedded systems to large-scale distributed computing environments. Current projects include interpretable reinforcement learning, federated neuroevolution, and optimization for edge computing.
Günter Hotz is a full Professor at the Department of Computer Science (Fachrichtung Informatik), Universität des Saarlandes , Germany. His academic career spans over five decades, including roles as Director of the Computer Center (1972-1974) Spokesperson for SFB 100 (1982-1984) and SFB 124 (1991-1992) . Research Interests include Theoretical Computer Science Circuit Design Computational Complexity Information Theory Geometric Motion Planning Formal Verification . His work focuses on analytic machines, hardware verification, and motion planning algorithms, with applications in linguistics and VLSI design. Publications emphasize formal methods for hardware, computational models over real numbers, and efficient parsing algorithms. Key trends involve integrating mathematical theory with practical circuit design and motion planning solutions. Scientific Awards include Leibniz Prize (1986) Konrad Zuse Medal (1999) Grand Cross of Merit (1998) . Students : Supervised 40 dissertations, with over a third of his advisees becoming professors in mathematics and computer science. Labs & Projects : Led major research initiatives like SFB 100 (COMSKEE system) and SFB 124 (VLSI design methods). Collaborative works with international institutions in France, the U.S., and Georgia.
Le Chen is a Doctoral Researcher at the Empirical Inference Department of the Max Planck Institute for Intelligent Systems and ETH Zurich , advised by Prof. Bernhard Schölkopf and Prof. Dieter Büchler. Previously, he obtained his M.S. in Electrical Engineering and Information Technology from ETH Zurich and gained research experience at Microsoft Mixed Reality & AI Lab, Tencent AI Lab, and Tencent Robotics X Lab. Research Interests: Le Chen focuses on the intersection of robotics and machine learning, with a specific emphasis on reinforcement learning for dexterous manipulation, visual-inertial calibration, and uncertainty-aware robotic perception. His work spans dynamic motion control, policy gradient subspaces, and novel algorithms for 3D/4D reconstruction. Key Contributions: He co-developed the RP1M dataset for bimanual piano manipulation and contributed to tendon-driven robot design ( Safe & Accurate ) and LEAP-VO for robust visual odometry. His research also includes Gaussian splatting dynamics for 4D content creation ( GaussianFlow ). Scientific Awards: Best Systems Paper Finalist at RSS 2024 for the RP1M dataset
Buddhika W. Nettasinghe is an Assistant Professor in the Department of Business Analytics at the Tippie College of Business, University of Iowa. He holds a PhD in Electrical and Computer Engineering from Cornell University, along with advanced degrees from Cornell and the University of British Columbia, and a BSc in Electrical and Electronic Engineering from the University of Peradeniya. His research focuses on network science, complex systems, and uncertainty quantification, with applications to social networks, polarization, and information diffusion. Research Interests Network Science Complex Systems Uncertainty Quantification His recent work explores affective polarization, structural inequalities in networks, and statistical inference methods like friendship paradox-based sampling. Publications highlight his contributions to directed network analysis, conformal prediction for hidden Markov models, and computational approaches to social segregation. Scientific Awards Recognition of Meritorious Reviewers (2024)
Olga Battaïa is Senior Professor in the Department of Operations Management and Information Systems at KEDGE Business School, where she also serves as Associate Dean for Research. Her academic journey includes a PhD in Industrial Engineering from École nationale supérieure des mines de Saint-Étienne (2007) and accreditation to supervise research (HDR, 2014). Education PhD in Industrial Engineering, École nationale supérieure des mines de Saint-Étienne, 2007 HDR (Accreditation to Supervise Research), 2014 Research Focus Professor Battaïa’s research lies at the intersection of operations management , supply-chain optimization , and decision-science methods . She develops mathematical models and algorithms that improve the design and performance of production systems, reconfigurable manufacturing lines, closed-loop supply chains, and collaborative human-robot systems. Her work embraces sustainability challenges, ergonomic considerations, and uncertainty management. Recent investigations include: Dynamic vaccine-allocation models responding to pandemic realities Ergonomic integration in human-robot collaborative workstations Electric-bus charging-network design for sustainable urban mobility Robust resource-leveling techniques under project uncertainty Publication Impact Across 200+ refereed publications, her 2023–2025 output alone spans International Journal of Production Economics , IJPR , Computers & Industrial Engineering , Transportation Research Part E , and flagship conferences such as CIRP ANNALS. Thematic trends reveal deepening emphasis on (i) sustainable and resilient supply-chain design, (ii) advanced exact and heuristic optimization for reconfigurable manufacturing, and (iii) human-centric production systems. Editorial & Professional Service Associate Editor, Journal of Manufacturing Systems (Elsevier) Associate Editor, IISE Transactions Associate Editor, Omega—The International Journal of Management Science Associate Member, International Academy for Production Engineering (CIRP) Member, IFAC Technical Committee on “Manufacturing Modelling for Management and Control” Doctoral Advising & Research Leadership Professor Battaïa has co-supervised 11 PhD thestrong>ses to completion and delivered more than 15 invited lectures worldwide. She coordinates multi-partner research projects with industry and academic institutions, securing sustained funding for large-scale optimization and sustainability initiatives. Laboratories & Teams She leads research activities within KEDGE’s Operations, Supply Chain and Information Management group, collaborating closely with the Reconfigurable Manufacturing Systems and Sustainable Supply Chain Design research teams.
Dr. William N. Caballero is an Assistant Professor of Data Science in the Department of Operational Sciences at the Air Force Institute of Technology (AFIT). His research focuses on developing statistical and mathematical models for decision support in uncertain, multi-agent environments, with applications in defense and security. Methodologically, his work integrates deterministic/stochastic optimization, Bayesian analysis, and interpretable machine learning. Education: Doctor of Philosophy in Operations Research, Air Force Institute of Technology (2019) Master of Science in Operations Research, Air Force Institute of Technology (2017) Bachelor of Science in Industrial Engineering, University of Houston (2011) Research Interests: His interdisciplinary research bridges statistics and operations research, emphasizing Bayesian decision analysis for security problems and modern data science applications in defense contexts. Primary domains include adversarial risk analysis, security games, ethical AI systems, automated driving technologies, and military personnel training optimization. Publication Trends: Recent articles demonstrate strong focus on machine learning applications in national security (LLMs, pilot selection), adversarial modeling (security games, data poisoning), and autonomous systems (driving mode management, ethical frameworks). Methodological innovations frequently combine Bayesian approaches with optimization techniques. Awards and Honors: Seiler Award for Mathematical Sciences Research (2023) Finalist for Clemen-Kleinmuntz Decision Analysis Best Paper (2022) USAF-MIT AI Accelerator Datathon: 5 awards including Overall Winner (2021) Multiple Field/Company Grade Officer of the Quarter awards (2013-2024) Distinguished Graduate honors (AFIT, OTS, Squadron Officer School) General Omar Nelson Bradley Fellowship (2018) Inductee to Omega Rho and Tau Beta Pi honor societies
Professor Francisco Saldanha da Gama is a Chair in Supply Chain Management at the University of Sheffield Management School. His expertise spans facility location, supply chain optimization, and stochastic programming, with over 150 conference presentations and editorial leadership as Editor-in-Chief of Computers & Operations Research . He has co-edited the seminal Springer volumes Location Science and contributed to humanitarian logistics, robust optimization, and multi-period network design. PhD in Statistics and Operations Research from University of Lisbon Research interests focus on integrating strategic supply chain decisions with operational constraints under uncertainty. Key areas include: Facility location under stochastic and risk-averse frameworks Dynamic lot-sizing and assembly line rebalancing Hub location-routing with congestion modeling Humanitarian logistics for sheltering and evacuation Recent publications (2023-2025) address multi-stage stochastic districting, risk-averse transportation planning, and battery swapping station optimization. Collaborators include Stefan Nickel and Gilbert Laporte. Editor-in-Chief, Computers & Operations Research (2020-present) Editorial Advisory Board, Journal of Operational Research Society (UK) Consulting Editor, Social Sciences & Humanities Open Supervises PhD researchers like Jue Huang (Electric vehicle supply chain sustainability). Professional affiliations include INFORMS, Operational Research Society (UK), and leadership roles in EURO working groups on stochastic optimization and location analysis.
Cyril Allignol is a Lecturer and Researcher in the OPTIM team at the National School of Civil Aviation (ENAC) research laboratory. His work focuses on two primary themes: (1) solving combinatorial optimization problems related to air traffic and airport operations using constraint programming , and (2) formalizing reactive languages to ensure guaranteed properties for air traffic control and piloting assistance tools. PhD in Computer Science and Telecommunications (2011) from the University of Toulouse ENAC Engineer (2006) Master's in Computer Science and Telecommunications (2006) from the University of Toulouse His research spans air traffic conflict resolution , detect-and-avoid algorithms for UAVs/UAS , and formal methods in reactive language compilation . He has contributed to constraint programming frameworks, robust gate allocation models, and 3D trajectory deconfliction systems. His work integrates metaheuristics , geometrical algorithms , and formal verification techniques. Publications reveal expertise in mathematical optimization , UAS integration , and bigraph-based modeling for avionics systems. He collaborates with institutions across France, Italy, Georgia, and the United States through conferences like ICRAT , ATM Seminar , and ROADEF . His team affiliation ( OPTIM ) and technical focus on conflict resolution , self-separation , and navigation accuracy highlight his contributions to air traffic safety and efficiency . Current projects include 4D-trajectory deconfliction and formal methods for avionics software .
