Sebastian Risi is a Professor at the IT University of Copenhagen , where he directs the Creative AI Lab and co-directs the Robotics, Evolution and Art Lab (REAL) . His work bridges computational evolution, deep learning, and collective intelligence for applications in robotics, art, and video game design. His research focuses on self-organizing AI systems that grow or assemble through local interactions, inspired by biological development. Key areas include neuroevolution , neural cellular automata , and generative modeling , with applications in adaptive robotics, game content creation, and damage-resilient AI. Recent publications highlight trends in self-assembling neural architectures (NDPs) and 3D functional machine generation (Minecraft experiments). Awards include ERC Consolidator Grant (2022), Best Paper at FDG’21 , and Google Faculty Award (2019). Scientific Awards : ERC Consolidator Grant (GROW-AI), Best Paper FDG’21, Runner-Up IEEE Games’20, GECCO 2017 Competition Winner, Sapere Aude Grant, Amazon/Google Faculty Awards He advises on projects like GROW-AI (EU-funded), AI-TESTER (game testing), and C2SIM (military systems). Media coverage includes Science , Wired , and Popular Science .
Zakka Ugih Rizqi is a Researcher at Aalborg University's Department of Materials and Production within the Faculty of Engineering and Science. His research focuses on optimizing decision-making through the fusion of optimization techniques, multi-method simulation, and data science, with primary applications in supply chains, energy systems, and smart manufacturing. He currently contributes to the design of future aseptic production systems through the AP2030 project. He holds a Ph.D. in Industrial Engineering from National Taiwan University of Science and Technology (2022–2024). Research Interests: His work emphasizes realistic modeling of complex systems, including uncertainty and dynamic complexity. Key areas include simulation-based Digital Twin frameworks for smart warehouses, energy-efficient AS/RS configurations, and system dynamics modeling for policy analysis. He explores multi-objective optimization approaches to balance operational efficiency and environmental sustainability. Projects & Grants: He participates in the AP2030: BRD Aseptic Factory 2030 (2023–2027), focusing on aseptic production innovation. His research has been recognized with awards such as The Best Paper Award (2023) and The Best Presenter Award (2023). Collaborations: Recent visits include The University of Tokyo, Kyoto University, and Tohoku University (2024). He has presented at conferences like the National University of Singapore's Analytics for X 2023. Labs & Teams: Engaged in interdisciplinary teams within the Faculty of Engineering and Science, collaborating on projects blending automation, sustainability, and data-driven decision-making.
Djordje Grbic is a Lecturer at the IT University of Copenhagen, actively contributing to research in artificial intelligence, robotics, and maritime logistics. He is affiliated with the Creative AI Lab, Robotics, Evolution, and Art Lab, and The Maritime Hub Robotics, Evolution and Artificial Life Lab. His research focuses on: Deep reinforcement learning for complex planning tasks Evolutionary computation in artificial life systems AI optimization of maritime container stowage Procedural content generation for games Sequence evolution simulations for biological systems Recent publications (2023-2024) demonstrate expertise in applying deep reinforcement learning to maritime logistics problems and developing systems for sequence evolution simulation. Collaborations span computational logistics, bioinformatics, and game design domains. Current affiliations include: Creative AI Lab (ITU Copenhagen) Robotics, Evolution, and Art Lab The Maritime Hub for logistics research
Carsten Witt is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), working within the Algorithms, Logic and Graphs section. His research is centered on theoretical aspects of evolutionary computation, with a strong emphasis on runtime analysis, genetic algorithms, and randomized search heuristics. He is actively involved in guiding PhD research and has a substantial publication record in top-tier conferences and journals. PhD in Computer Science, Technical University of Dortmund, Germany Postdoctoral research at Max Planck Institute for Informatics Professor at DTU since appointment His primary research interests lie in evolutionary algorithms , runtime analysis , and probability theory in algorithmics . He investigates how bio-inspired optimization techniques such as genetic and compact genetic algorithms perform on benchmark problems like OneMax and LeadingOnes. His work often involves rigorous mathematical analysis to derive bounds on expected runtime and convergence behavior. The recent articles show a consistent trend in the theoretical foundations of evolutionary computation , particularly focusing on multi-valued representations, dynamic mutation strategies, neuroevolution models, and self-adjusting mechanisms. These works span subfields such as stochastic optimization, adaptive parameter control, and algorithmic analysis under probabilistic models. The dominant keywords include Computer Science, Theoretical Computer Science, Optimization, and Artificial Intelligence. Carsten Witt has not been explicitly listed with any scientific awards in the provided text. He has supervised several PhD students, including Adak, Rajabi, and Gießen, in projects related to nature-inspired algorithms and theoretical analysis. While specific grant names are not listed, his involvement in multiple funded PhD projects indicates active participation in research funding and academic leadership. Supervision roles include both main supervisor and examiner positions across various DTU research initiatives. Carsten Witt is affiliated with the Algorithms, Logic and Graphs group at DTU, which functions as a research lab focusing on foundational aspects of computing. This team conducts high-level theoretical research in algorithm design, discrete mathematics, and computational complexity, particularly in the context of heuristic and evolutionary methods.
