Grzegorz Filcek is a Researcher at the Department of Computer Science and Systems Engineering , Faculty of Information and Communication Technology , Wrocław University of Science and Technology, Poland. His work focuses on multi-criteria optimization , evolutionary algorithms , and scheduling with applications in transportation systems , logistics , and industrial automation . Email: grzegorz.filcek@pwr.edu.pl Office: Building C-3, Room 14, Janiszewskiego 11/17, 50-372 Wrocław His research spans multi-criteria decision-making , location-scheduling integration , and transportation network optimization . Recent work includes frameworks for Pareto optimal solutions in MIP and rescheduling trains under track closures . He also explores evolutionary computation for freight parking planning and carpooling systems . Key trends in his publications include multi-criteria optimization (2019-2024), evolutionary algorithms (2020-2021), and transportation/logistics applications (2016-2023). His work often combines mathematical programming with real-world problem-solving in supply chains , railways , and emergency power systems .
Nuno Fachada is an Assistant Professor at Lusófona University's School of Communication, Arts and Information (ECATI) and a researcher at COPELABS (Cognitive and People-centric Computing). He teaches Programming and AI in the Videogames Bachelor's program and Research Software in the Informatics PhD program, with research spanning Artificial Intelligence, Machine Learning, Modeling and Simulation, High Performance Computing, and Computer Science Education. His work integrates computational methods with applications in game development, wildfire management, and networking. Education: Bachelor's degree in Electrical and Computer Engineering from IST (2005) Master's degree in Electrical and Computer Engineering from IST (2008), focusing on immune system simulation PhD in Electrical and Computer Engineering from IST (2016) with thesis 'Agent-Based Modeling on High Performance Computing Architectures', awarded 'Pass with Distinction and Honour' Research interests emphasize Agent-Based Modeling for complex systems like wildfires and immune responses, Particle Swarm Optimization algorithms, and OpenCL for parallel computing. He develops educational tools for game development curricula and applies AI to environmental monitoring and wireless networks, with strong output in simulation frameworks and human-centric computing. Recent publications (2024-2025) reveal three dominant trends: wildfire modeling using satellite data and agent-based approaches, large language models for engineering code generation (e.g., LoRaWAN), and game AI for procedural content and rehabilitation. His work bridges theoretical computer science with practical applications in environmental science and healthcare, while maintaining focus on education through tools like TextCL and cf4ocl. Scientific Awards: No scientific awards, fellowships, or medals were mentioned in the provided text Advising and Grants: Fachada serves as a researcher in ILIND-funded projects, notably the 'Cybersecurity Awareness Training Simulator' (2024-2025) with six collaborators. He supervises PhD students in Informatics but specific advisees aren't listed. His grant activity primarily involves institutional projects through Lusófona University's research center, with emphasis on simulation-based tools for real-world applications. Labs and Teams: He is a core researcher at COPELABS, focusing on cognitive and people-centric computing projects including wildfire simulation and cybersecurity training. Prior to Lusófona, he conducted postdoctoral work at LaSEEB/ISR (Institute for Systems and Robotics), maintaining connections to IST. His team collaborations span international researchers in environmental modeling and AI, with recent projects involving Portuguese and European institutions.
Pau Segovia Castillo is a Beatriz Galindo Fellow in the Department of Automatic Control at Universitat Politècnica de Catalunya (UPC), where he conducts research in large-scale systems control. He is affiliated with the Advanced Control Systems (SAC) research group at the Research Center for Supervision, Safety and Automatic Control (CS2AC). Previously, he held postdoctoral positions at IMT Lille Douai (2019–2020), Delft University of Technology (2020–2023), and UPC (2023). BS/MS in Industrial Engineering, Universitat Politècnica de Catalunya (UPC), 2015 Joint PhD in Automatic Control, Robotics and Vision, UPC and IMT Lille Douai, France, 2019 His research focuses on non-centralized predictive control approaches for managing large-scale systems, with applications in water resources systems, intelligent transportation systems, and energy systems. He applies model predictive control, digital twin technologies, and switching control strategies to real-world infrastructure such as canals, irrigation networks, and inland waterways. His work emphasizes real-time optimization, robustness, and scalability in complex environments. The recent publications highlight a strong trend in model predictive control applied to waterway and transportation systems. Key themes include coordination of vessels and bridges, digital twin implementation, multilayer control architectures, and observer design for nonlinear systems. The research spans both theoretical advances in control theory and practical implementations using real databases and simulations. Scientific recognition includes: Beatriz Galindo Fellow He advises PhD students, including Javier Pedrosa Alias, and has been involved in collaborative research projects across institutions in Spain, France, and the Netherlands. His work is supported by his fellowship and integrated within the SAC research group, contributing to national and international efforts in smart infrastructure and automation. The research group CS2AC provides a multidisciplinary environment for supervision, safety, and automatic control applications. He is a member of the Advanced Control Systems (SAC) research group at CS2AC, which focuses on developing advanced control methodologies for safety-critical and large-scale systems. The team works on real-time monitoring, fault detection, and optimization of industrial and environmental processes.
Mahdi Shahbakhti is an Adjunct Professor in the Mechanical and Aerospace Engineering department at Michigan Technological University . He has a PhD in Mechanical Engineering from the University of Alberta and joined MTU in 2012 after postdoctoral work at UC Berkeley and 3.5 years in automotive R&D. His research focuses on advanced control techniques for energy systems in transportation and buildings, which account for 68% of U.S. energy consumption. Education: PhD in Mechanical Engineering, University of Alberta, Canada Research interests include thermo-kinetic physical modeling , model order reduction , grey-box modeling , and nonlinear controls for hybrid electric vehicles, internal combustion engines, and HVAC systems. His work bridges academic and industrial applications in vehicle emissions , aftertreatment systems , and smart grid integration . Recent publications demonstrate expertise in HCCI engine modeling, hybrid powertrains, emission control, and building energy systems. He actively collaborates with researchers in automotive and energy sectors. Affiliations: Co-Advisor of Alternative Energy Enterprise at MTU Active member of ASME Dynamic Systems & Control Division Professional roles include Vice-Chair of ASME Energy Systems technical committee, Secretary of Automotive Transportation Systems technical committee, and session organizer in automotive and building energy control. His industry experience informs real-world applications of adaptive parameter estimation and model-based control .
L.F.P. (Pascal) Etman is an Associate Professor in the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e), specializing in system design and optimization within the Control Systems Technology group. He has developed advanced methods such as the Augmented Lagrangian Coordination (ALC) method for distributed optimization and Effective Process Time (EPT) modeling for manufacturing systems, with applications spanning infrastructure, mechatronics, and additive manufacturing. His academic background includes MSc and PhD degrees in Engineering Mechanics from TU/e in 1992 and 1997, respectively. After his PhD, he joined TU/e's Systems Engineering group before transitioning to Control Systems Technology in 2015. Pascal has held visiting positions at institutions including Philips Semiconductors (1998), the University of Michigan (2001), and universities in South Africa (2005–2006). Research interests focus on Model-based design of complex systems Design structure matrix modeling for system analysis Optimization algorithms for engineering systems Aggregate modeling of discrete manufacturing processes Integration of requirements engineering and systems architecting His work aligns with UN Sustainable Development Goals through collaborations with semi-industry partners like Rijkswaterstaat, TNO, and NXP. He teaches graduate courses on Engineering optimization with numerical methods Design structure matrix modeling for integrated systems and supervises bachelor's design projects. Pascal has advised six PhD students and co-supervises five ongoing PhD projects, while guiding over 40 master’s theses.
Volkmar Sauerland is a Researcher in the Biogeochemical Modelling Unit at GEOMAR Helmholtz Centre for Ocean Research Kiel. He has been working at GEOMAR since November 2022 (with a brief gap between March and October 2022), focusing on algorithms for discrete and continuous optimization problems applied to biogeochemical ocean models. Prior to this, he spent seven years (2013-2020) as a PostDoc and Research Associate in the Discrete Optimization Group at Christian-Albrechts-Universität zu Kiel (CAU). Dr. Sauerland completed his PhD in 2012 at CAU with a thesis titled "Algorithm Engineering for some Complex Practice Problems: Exact Algorithms, Heuristics and Hybrid Evolutionary Algorithms" and earned his Diploma in 2003 with research on "Mathematical optimization in the design of cosine-modulated filter banks." His educational background reflects the dual focus that characterizes his research career spanning both mathematical optimization and oceanographic applications. Sauerland's research interests bridge two distinct domains: mathematical optimization and marine biogeochemistry. His work focuses on developing and adapting algorithms for parameter optimization and calibration of biogeochemical ocean models. He is currently involved in the EU project OceanICU "Understanding Ocean Carbon," which examines the ocean's role in the global carbon cycle. His research combines theoretical work in optimization algorithms with practical applications in ocean modeling, creating a unique interdisciplinary niche. Analysis of his publication record reveals a clear trajectory from purely theoretical optimization work toward increasingly ocean-focused applications. His earlier publications (2007-2013) focus primarily on combinatorial optimization, permutation problems, and evolutionary algorithms. Starting around 2015, his work shifts toward oceanographic applications, with nearly all recent publications (2017-2023) addressing biogeochemical modeling challenges. The most recent papers demonstrate sophisticated approaches to model calibration, parameter estimation, and uncertainty analysis in complex marine systems. Dr. Sauerland has been involved in significant research projects including the EU's OceanICU initiative and has presented his work at major conferences such as the Ocean Sciences Meeting 2020 in San Diego and the Ocean Deoxygenation conference in Kiel. His collaborative work spans multiple institutions, with frequent co-authorship with researchers from GEOMAR and CAU. While specific grant information isn't detailed in the provided text, his ongoing EU project involvement suggests successful grant acquisition. Based at GEOMAR's Kiel facility, Sauerland works within the Marine Biogeochemistry research division, specifically in the Biogeochemical Modelling Unit. His office is located in Room 5.506, Tower 5, Floor 5 at GEOMAR's Wischhofstraße 1-3 address. His research contributes to GEOMAR's broader mission of understanding ocean processes and their role in Earth's climate system, particularly through the development of advanced computational methods for model calibration and evaluation.
Pablo Salinas is a Research Fellow in the Novel Reservoir and Simulation group (NORMS) at Imperial College London's Department of Earth Science & Engineering. He holds affiliations with the Applied Modelling and Computation Group and NORMS. His primary role involves advancing reservoir simulation through computational methods. Current Position: Research Fellow (2018–Present) Previous Roles: Post-doctoral research associate (2013–2018) Research focuses on subsurface energy systems, multiphase flow dynamics, and numerical methods like multigrid solvers and unstructured mesh optimization. He is the lead developer of the Imperial College Finite Element Reservoir Simulator (IC-FERST), pioneering coupled physics/chemistry simulations with dynamic mesh adaptation. Salinas currently supervises 5 PhD projects and contributes to the UK's national core studies program addressing the COVID-19 pandemic, advising SAGE. His work integrates geothermal energy, contaminant transport modeling, and well optimization. Labs/Teams: NORMS group, leading IC-FERST development.
Warren Hare is a Professor and Associate Head of the Graduate Program in the Department of Computer Science, Mathematics, Physics and Statistics at the University of British Columbia Okanagan. He holds a PhD in Mathematical Optimization from Simon Fraser University. His research focuses on structured blackbox optimization, emphasizing algorithm development for applications such as road design and computer simulations. He serves as an Associate Editor for Set Valued and Variational Analysis and the Pacific Journal of Optimization , and co-authored the book Derivative-Free and Blackbox Optimization . Research Interests: Mathematical optimization, nonconvex analysis, derivative-free optimization, bundle methods, and applications in road design. He explores structured blackbox optimization problems where mathematical structures (e.g., max functions) can be leveraged to design efficient algorithms. Advising & Grants: Supervises graduate students in optimization and has secured funding for projects involving road alignment optimization and medical imaging applications. Collaborates on interdisciplinary initiatives combining optimization with civil engineering and medical physics. Labs/Teams: Engaged with UBC Okanagan’s optimization research group and collaborates with industry partners on infrastructure and healthcare optimization challenges.
Lindsey J. Heagy is an Assistant Professor in the Department of Earth, Ocean, and Atmospheric Sciences at the University of British Columbia. Her research focuses on geophysics, data science, and inverse theory, with applications in resource exploration, groundwater, and environmental studies. She leads a research group developing machine learning and inversion methods for geophysical data analysis. Key affiliations: University of British Columbia (Department of Earth, Ocean, and Atmospheric Sciences) Her work emphasizes open science, contributing to projects like SimPEG (an open-source Python package for geophysical simulations) and GeoSci.xyz (collaborative educational resources for geophysics). She advocates for reproducible workflows and open-source software in the geosciences. Research interests include electromagnetic methods, inverse problem solutions, and interdisciplinary applications of data science. Notable contributions include advancements in 3D electromagnetic modeling, UXO detection via machine learning, and open-source tools for airborne and ground-based geophysical data analysis. Publications span peer-reviewed journals like Geophysical Journal International and Exploration Geophysics , focusing on numerical methods, inversion techniques, and open-source software development. Her work bridges computational geophysics with real-world challenges in environmental monitoring and resource management. She collaborates on projects like SimPEG and GeoSci.xyz, fostering community-driven advancements in geophysical education and research. Her efforts in capacity building include geophysical training in regions like Myanmar to improve water security.
Dr. Thanos Avramidis is a Lecturer in Operational Research at the School of Mathematical Sciences, University of Southampton. His research focuses on Markov Decision Processes , stochastic simulation , and financial derivatives pricing , with applications in dynamic pricing under demand uncertainty. He has held academic roles at Cornell University and research positions at the University of Montreal, collaborating with Prof. Pierre L'Ecuyer. Education & Career: PhD in Industrial Engineering, Purdue University Assistant Professor, Cornell University (1997–2001) Researcher at University of Montreal (2002–2006) Joined University of Southampton in 2007 Research Themes: Current work develops algorithms for pricing in unknown demand environments. Past contributions include variance reduction methods, call-center optimization models, and Monte Carlo analysis in finance. Awards: Recipient of the INFORMS Simulation Society Award (2009) and George Nicholson Prize (1993). Teaching: Courses include Introduction to Operational Research and Stochastic OR Methods . Lab/Teams: Member of the CORMSIS research group.
Dr. Julio Martinez is a Professor of Civil Engineering at Purdue University's College of Engineering. His research focuses on advanced simulation techniques, 3D visualization for construction processes, and optimization of earthwork operations. He has pioneered methodologies for integrating discrete-event simulation with virtual reality and augmented reality technologies to enhance construction planning and risk management. His work emphasizes sustainable construction practices, dynamic resource allocation, and real-time user interaction in simulation systems. Notable contributions include developing the STROBOSCOPE simulation framework and the VITASCOPE visualization toolkit. He has authored over 50 papers on topics ranging from stochastic modeling to scalable scene rendering algorithms. Dr. Martinez’s research bridges engineering and computer science, applying cutting-edge visualization techniques to traditional construction challenges. His recent work explores the integration of wireless communication simulations in earthmoving operations and the validation of complex construction models through immersive visualizations. His publications consistently address practical construction scenarios, with a focus on improving operational efficiency and safety through advanced modeling and simulation approaches. Current research trends include enhancing simulation scalability, improving 3D animation realism, and applying simulation for sustainable infrastructure development.
Lorenzo Rossi is an Assistant Professor specializing in the intersection of process mining, robotics, and business process management (BPM). His research focuses on advancing methodologies for analyzing robotic systems, IoT environments, and collaborative processes through formal models like BPMN. Key areas include process discovery, digital twins, and system resilience in smart environments. Expertise: Process Mining, Multi-Robot Systems, BPMN Semantics, Cyber-Physical Systems Key Tools: BEAR (BPMN animator), MIDA (multi-instance animator), UBBA (Unity-based BPMN animator) His work emphasizes reproducibility and practical frameworks for rapid prototyping. Recent trends in his articles highlight the integration of process mining with robotics for data-driven decision-making, as well as formal verification of BPMN collaborations to ensure system correctness. Awards: None explicitly mentioned in the provided texts. Advising & Grants: No student/advisor relationships or grant details documented here. Labs/Teams: Collaboration with Unity-based simulation tools (UBBA, MIDA) and serious game platforms (PlayWithUnicam).
Christian Fikar is a Professor of Food Supply Chain Management at the Faculty of Life Sciences: Food, Nutrition and Health at the University of Bayreuth, based at the Kulmbach campus. His research focuses on business management issues in food value chains, particularly time-critical logistics processes and computer-based decision support systems. Professor Fikar studied Supply Chain Management at the Vienna University of Economics and Business, completed his doctorate and habilitation at the University of Natural Resources and Life Sciences in Vienna, and has held academic positions at both institutions before joining the University of Bayreuth. He has extensive international experience from research periods in the USA, Taiwan, Spain, and Finland. His methodological expertise spans Operations Research, Operations Management, and Business Analytics , with applications focused on increasing the resilience, sustainability, and efficiency of food value chains. Professor Fikar's research particularly examines logistics decisions in the food mail order industry and short food supply chains (regional supply networks) in Upper Franconia. His work enables more efficient processes that can contribute to reducing food waste while maintaining high-quality food delivery systems. Key research areas include perishable food distribution, digital logistics platforms, and crowd logistics for local food systems. Analysis of his recent publications reveals a strong focus on computational approaches to food supply chain challenges, integrating simulation and optimization techniques to address problems in perishable food distribution, crowd logistics, and regional food systems. His work shows increasing attention to digital transformation in food supply chains, quality preservation in logistics, and sustainable delivery models. Professor Fikar has advised doctoral students including Florian Cramer, whose 2025 dissertation focused on retail access models. His research has been supported by various projects examining sustainable food distribution systems, e-grocery operations, and resilience in short food supply chains. He actively contributes to the sustainable development of regional food networks in Upper Franconia through both research and teaching initiatives at the University of Bayreuth.
Melissa Greeff is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen's University. She is a faculty affiliate with the Vector Institute for Artificial Intelligence and works with the Robora Lab , focusing on robotics and control systems. Research Interests : Aerial robots, vision-based navigation, safe learning-based control systems. Education : BASc in Engineering Science, PhD from the University of Toronto. Research Focus : Her work integrates robotics, control theory, and machine learning to enable safe, efficient UAV operations in complex environments, including maritime applications and GPS-denied navigation. Key methodologies involve differential flatness and model predictive control (MPC) enhanced by learning-based approaches. Publications : Recent articles emphasize vision-based navigation, multirotor control under disturbances, and benchmarking safe learning systems. Topics span UAVs in infrastructure assessment, cooperative control, and embedded predictive control solutions. Labs & Teams : Collaborates with the Robora Lab and Ingenuity Labs Research Institute on robotics and AI research.
Alexander von Rohr is a postdoctoral researcher at the Technical University of Munich , affiliated with the Learning Systems and Robotics Lab . Previously, he was a doctoral researcher at the Max Planck Institute for Intelligent Systems and RWTH Aachen University , supported by IAV . His educational background spans Computer Science (RWTH Aachen), Electrical Engineering (BHT Berlin), and University of Lübeck . Research Focus : Embodied AI, Bayesian optimization, risk-aware reinforcement learning, robust control, probabilistic models, data-driven controller synthesis. His recent publications (2024-2025) emphasize event-triggered learning for time-varying systems, diffusion models with constraints , and robust safety via entropy regularization in RL. These works demonstrate applications in robot manipulators , digital twins , and underactuated systems . Awards include the Best Reviewer Award at AISTATS 2025 . Collaborations include Prof. Sebastian Trimpe and Angela P. Schoellig , with co-authorships on 15 recent articles. His lab focuses on probabilistic control for dynamical systems with formal guarantees.
