Daniele Loiacono is an Associate Professor at Politecnico di Milano's Department of Electronics, Information, and Bioengineering (DEIB), affiliated with the Artificial Intelligence and Robotics Lab (AIRLab). His research focuses on interdisciplinary applications of Artificial Intelligence, Machine Learning, and Deep Learning in medical imaging, radiation therapy, and procedural content generation for games. He leads projects in synthetic image generation for radiotherapy quality assurance, automated treatment planning, and bias analysis in medical AI systems. Key research areas include medical image synthesis using GANs, radiation therapy optimization, and algorithmic game design. His contributions span clinical applications such as total marrow irradiation (TMI) planning and lymph-node segmentation, alongside innovations in shader generation and interactive evolutionary tools for game development. Loiacono collaborates on multi-center studies to validate AI-driven workflows in healthcare and has pioneered methods combining lean Six Sigma with machine learning for treatment process improvement. His work bridges clinical medicine and computer science, addressing challenges in radiation oncology, anatomical imaging, and procedural content automation. The AIRLab serves as a hub for his research, integrating AI advancements into real-world medical and engineering solutions.
Piotr Winkielman is a Professor of Psychology at the University of California, San Diego (since 2007), with affiliations at Warwick Business School (UK) and the Warsaw School of Social Sciences and Humanities (Poland). His research focuses on the interplay of emotion, cognition, embodiment, and consciousness in social cognition, employing methods from social/cognitive psychology and neuroscience. Key interests include unconscious emotion processing, facial mimicry, decision-making under emotional influence, and the neural basis of social perception. Education: PhD in Social Psychology (1997), University of Michigan MSc in Psychology (1991), University of Bielefeld, Germany BSc in Psychology (1988), University of Warsaw, Poland Research Interests: Winkielman explores how fleeting affective reactions influence behavior without conscious awareness, studying facial feedback mechanisms, emotional contagion, and the role of physiological signals in decision-making. His work bridges theories like Affect-As-Information and Facial Feedback, while challenging assumptions about the necessity of conscious processes in emotion. Grants & Funding: Supported by NSF Grant BCS-0350687 for studies on unconscious emotion and physiological feedback. Collaborations include projects on mimicry, stress effects, and cross-modal perception. Labs & Teams: Leads research at UCSD on embodied cognition and social neuroscience, with studies involving EEG, EMG, and behavioral experiments. International collaborations span UK, Poland, and Germany.
Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.
Professor Barry Porter is a faculty member at Lancaster University in the School of Computing and Communications . His research focuses on emergent software platforms that address software complexity through component models , meta-software platforms , and machine learning . Key areas include distributed systems, cloud integration with sensor nodes, green computing, and real-time visualization. Research Interests : Runtime adaptation in complex systems Self-assembling software architectures Machine learning for code optimization Distributed emergent systems at scale Green computing for multi-core environments Edge-cloud continuum integration Recent Publication Trends : His 2025 work explores genetic improvement for software using speciation algorithms , program geometry projection , and multi-agent decision frameworks . Earlier studies (2022-2024) investigate edge-cloud systems , neural transfer learning , and ecosystem curation in emergent software. Supervision & Projects : He supervises PhD student Ben Craine and leads projects like B-EGI (Bio-Enhanced Genetic Improvement) and BBC Prosperity Partnership for media delivery. Collaborations span environmental IoT, multi-agent learning, and fog computing. Labs & Groups : Affiliated with the Lancaster Intelligent, Robotic and Autonomous Systems Centre , Centre of Excellence in Environmental Data Science , and the Distributed Systems group.
Jeffrey L. Krichmar is a Professor in the Department of Cognitive Sciences and Department of Computer Science at the University of California, Irvine. His academic journey includes a B.S. in Computer Science from the University of Massachusetts Amherst (1983), an M.S. in Computer Science from The George Washington University (1991), and a Ph.D. in Computational Sciences and Informatics from George Mason University (1997). Prior to UCI, he served as Assistant Professor at George Mason University (1997-1999) and Senior Fellow at The Neurosciences Institute (1999-2007). University of California, Irvine (2007-present) George Mason University (1997-1999) The Neurosciences Institute (1999-2007) His research focuses on neurorobotics , exploring how embodied cognition and biologically plausible neural models can enhance robotic systems. Key areas include spiking neural networks , neuromodulation , path planning , and interactive tactile robots for therapeutic applications. His work bridges neuroscience , robotics , and cognitive science , with applications in autonomous vehicles , neuroprosthetics , and AI explainability . Recent publications emphasize spiking neural networks for navigation , neuromodulated attention , and neuromorphic hardware integration. The development of CARLsim, a GPU-accelerated spiking neural network simulator now in version 6.0, represents a major technical contribution. His team's work on socially assistive robots like CARL-SJR targets therapeutic applications for autism and ADHD. Scientific Awards IJCNN 2020 Best Paper Award Finalist for Best Student Paper at IJCNN 2018 Best Paper Award at IEEE IJCNN 2009 Grants include National Science Foundation funding for neural models of decision-making (2009). His lab (Cognitive Anteater Robotics Laboratory) develops systems that use large-scale brain simulations for autonomous behavior , with applications in adaptive robotics , sensorimotor learning , and neuroethology . Current projects explore neuromodulatory influences on attention systems and cognitive flexibility .
Ueli Grossniklaus is an Ordinary Professor at the University of Zurich within the Faculty of Mathematical and Natural Sciences , affiliated with the Department of Plant and Microbiology . His work focuses on plant developmental biology, particularly epigenetic and genetic mechanisms governing reproduction and adaptation. Key Courses: Epigenetics, Plant Biology Workshop, Group Seminars on Current Research Laboratory Techniques: Advanced methods in plant cell mechanics, transcriptomics, and genome editing Research Interests span plant epigenetics, reproductive biology, and the interplay between environmental stress and genetic regulation. He investigates: Mechanistic control of gametogenesis and fertilization Epigenetic contributions to plant adaptation Evolutionary implications of asexual reproduction Biophysical forces in plant cell growth Publication Trends (2025–2018) reveal expertise in: Arabidopsis and fern model systems Epigenetic regulation (DNA methylation, histone dynamics) Apomixis and hybrid seed failure mechanisms Biomechanics of pollen tubes and carnivorous plants Genome editing tools (CRISPR) and long-read sequencing Scientific Collaborations include interdisciplinary projects on: Microfluidic devices for plant cell analysis Gene drive ecology and ethics 3D imaging of plant reproductive structures Advising and Grants focus on mentoring through research internships in developmental biology, genetics, and systems biology. His lab engages in: Epigenetic response to environmental stress Cell wall mechanics in reproduction Computational modeling of plant growth Laboratory Teams integrate plant biologists, bioengineers, and computational scientists to study: Mechanistic gene regulation Evolutionary developmental biology Microrobotics for cellular force measurement
Florian Muijres is an Associate Professor and Chairholder at the Experimental Zoology Group, Wageningen University & Research, where he leads the Animal Flight Lab. His research focuses on the biomechanics, aerodynamics, and flight control of natural flyers such as insects, birds, and bats, with applications in bio-inspired robotics and ecological solutions like mosquito traps and flapping-wing drones. He holds a PhD from Lund University (Sweden) and conducted postdoctoral research at the Dickinson Lab, University of Washington (USA). Research Interests: Merging experimental and computational methods, his work explores primary research on flight mechanics (e.g., mosquito evasion, butterfly gliding) and applied studies (e.g., drone design, pollinator behavior in greenhouses). His lab uses advanced videography and robotic models to study flight dynamics under real-world conditions. Labs & Teams: The Animal Flight Lab collaborates with biologists, physicists, and engineers to investigate flight adaptations in mosquitoes, bumblebees, and pied flycatchers. Projects include developing high-efficiency traps and analyzing flight performance in complex environments.
Dr. Ting Hu is an Associate Professor in the School of Computing at Queen's University, affiliated with the Faculty of Arts and Science. She leads the Machine Intelligence & Biocomputing (MIB) Laboratory, focusing on bio-inspired AI and bioinformatics. Her research bridges evolutionary computing, machine learning, and biomedical data analysis. Dr. Hu holds a PhD in Computer Science from Memorial University and completed postdoctoral training at Dartmouth College. She teaches courses with strong student evaluations, winning the Howard Staveley Teaching Award (2019-2020) and recognition as a Mental Health Champion (2023). Education: B.Sc. in Computational Mathematics, Wuhan University M.Sc. in Computer Science, Wuhan University PhD in Computer Science, Memorial University Postdoctoral Fellowship, Geisel School of Medicine, Dartmouth College Research Interests: Evolutionary algorithms and genetic programming Interpretable and explainable AI Biomedical data mining (metabolomics, genomics) Complex network analysis Applications in precision medicine and disease prediction Awards & Recognition: Queen's AMS Undergraduate Mentorship Award (2025) IEEE CIBCB Best Student Award (2022) Howard Staveley Teaching Award (2019-2020) NSERC Discovery Grant Reviewer (2019) Memorial University's Best Professor Award (2016) Lab & Collaborations: MIB Lab develops tools like geneDRAGNN (graph neural networks for gene-disease prioritization) Active roles in IEEE Computational Intelligence Society and EuroGP Advances include vaccination strategies via graph-RL and interpretable clustering methods
Stéphane Doncieux is a University Professor in Computer Science at Sorbonne University, where he is affiliated with the Institute of Intelligent Systems and Robotics (ISIR), a joint research laboratory with CNRS. Since January 2024, he has served as Director of ISIR, following a term as Deputy Director from 2019 to 2023. He leads the ASIMOV research team and is based at the Pierre and Marie Curie Campus in Paris. His primary research interests lie in cognitive and developmental robotics, with a strong focus on open-ended learning, evolutionary algorithms, and adaptive systems. He investigates how robots can autonomously learn diverse skills through mechanisms such as novelty search, quality-diversity optimization, and intrinsic motivation. His work bridges theoretical foundations in artificial life and practical applications in robotic manipulation, perception, and control. The recent publications highlight a consistent trend in advancing robotic learning under sparse rewards and in open-ended environments. Key themes include quality-diversity optimization for grasping, state representation learning, sim-to-real transfer, and the development of behavioral repertoires. These works are published in high-impact journals such as IEEE Transactions on Robotics, Evolutionary Computation, and Frontiers in Robotics and AI. Coordinator, DREAM FET H2020 project (2015–2018) Principal Investigator, ANR projects on Creative Adaptation by Evolution, Learning Movement Skills, and Grasping with Multimodal Feedback Involved in European initiatives including VeriDREAM and HumanE-AI-Net He has supervised numerous PhD and Master’s students, including Leni Le Goff, Giuseppe Paolo, Alban Laflaquière, and Achkan Salehi, often in collaboration with leading researchers like Olivier Sigaud and Jean-Baptiste Mouret. He teaches computer science and robotics at both undergraduate and graduate levels at Sorbonne University. Doncieux has been instrumental in shaping research directions in evolutionary and developmental robotics, notably through his leadership in the IEEE Task Force on Evo-Devo-Robotics and his editorial contributions. His lab, ASIMOV, fosters interdisciplinary research integrating computer science, neuroscience, and engineering to create more autonomous and intelligent robotic systems.
Leif Ristroph is an Assistant Professor of Mathematics at the Courant Institute of Mathematical Sciences , New York University . His research bridges experimental physics and applied mathematics , focusing on fluid-structure interactions in both biological and geophysical contexts. Key research areas include: Biophysical Flows : Aerodynamics of insect flight ( Applied Mathematics Laboratory ), hydrodynamics of fish schooling, and flow-sensing mechanisms via the fish lateral line system Geophysical Flows : Shape evolution during erosion, dissolution patterns in fluid flows, and bubble formation dynamics Recent publications (2019-2015) explore: Flow interactions in flapping swimmers and hovering systems Evolutionary optimization of wing shapes and self-sculpting processes Stability mechanisms in tandem flapping and insect flight His work has been featured in major media outlets like Nature , New York Times , and ScienceDaily . Ristroph's lab at NYU's Applied Mathematics Laboratory combines physical experiments, computational models, and theoretical analysis to study complex fluid-structure interactions.
Jens Krause is a Professor and Head of Department at the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) in Berlin, leading the Research Group on Mechanisms and Functions of Group-Living. He holds a full professorship in Fish Ecology at Humboldt-Universität zu Berlin, Faculty of Life Sciences, Thaer-Institute, and since 2018 has been an Adjunct Professor at Technical University Berlin within the Excellence Cluster 'Science of Intelligence'. His research is centered on collective intelligence, social networks, decision-making, and behavioural ecology in fish and other animals. Full Professor in Fish Ecology, Humboldt-Universität zu Berlin Adjunct Professor at Technical University Berlin (since 2018) Head of Department, IGB Berlin PhD, University of Cambridge Diploma, Free University Berlin His work integrates experimental biology, network analysis, and biomimetic robotics to understand how animals make collective decisions. His expertise spans animal behaviour, evolution, and ecological physiology, with a strong focus on group-living dynamics. Recent research explores group hunting, predator evasion, social foraging, and the impact of environmental stressors on collective behaviour. The analysis of his recent publications reveals a strong trend in understanding collective behaviour in fish, including escape waves, social foraging, group hunting in marlins and sailfish, and the use of robotic agents to study social integration. His interdisciplinary approach combines marine biology, physics, robotics, and data science to uncover the mechanisms behind collective intelligence in both animal and human systems. Editorial Board, Behavioral Ecology Editorial Board, Fish and Fisheries Executive Board, Excellence Cluster 'Science of Intelligence' Advisory Board, Bimini Biological Field Station Foundation He advises numerous PhD students and postdoctoral researchers, and leads major research projects, including 'Developing exploration behaviour' funded by the Excellence Cluster. His work has been supported by extensive collaborations across Europe and North America, and he frequently publishes in top-tier journals such as Nature , Science Advances , Proceedings of the Royal Society , and Current Biology . His lab employs cutting-edge methods including automated tracking, social network analysis, and interactive robotics to study animal groups. His research group, 'Mechanisms and Functions of Group-Living', is embedded within the Excellence Cluster 'Science of Intelligence', where they investigate collective cognition, social information use, and the role of individual differences in group performance. The team combines field studies with laboratory experiments and computational modelling to understand the evolution and function of collective behaviour across species.
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 .
Shaukat Ali serves as Research Professor and Head of the Department of Engineering Complex Software Systems at Simula Research Laboratory, concurrently holding the title of Chief Research Scientist. His academic leadership drives innovation at the critical nexus of quantum computing, artificial intelligence, and software engineering, with concentrated expertise in verification, validation, and testing methodologies for complex systems including cyber-physical infrastructures and autonomous robotics. His primary research domains encompass: Verification and Validation Search-Based Software Engineering Autonomous Driving Systems Cyber-Physical Systems Engineering Digital Twin Technologies Quantum Software Engineering Analysis of recent publications (2024-2025) reveals a decisive trend toward quantum-AI convergence in software engineering, particularly through quantum software testing frameworks and AI foundation models applied to cyber-physical systems. His work systematically addresses noise mitigation in quantum hardware, uncertainty quantification in adaptive robotics, and novel testing paradigms using vision-language models for industrial robotics—demonstrating both theoretical rigor and industrial applicability. As department head, Ali spearheads strategic research directions in complex software systems, fostering cross-disciplinary collaboration while actively shaping quantum software engineering through workshops like QAI2024 and Q-SANER 2024. His invited presentations at venues including JYU Quantum Electronics and EU-Korea Quantum Forums underscore his influence in defining emerging research landscapes.
Prof. Iain D. Couzin is Director of the Max Planck Institute of Animal Behavior and holds a Professorship in Biodiversity and Collective Behavior at the University of Konstanz. He co-leads the German Research Foundation (DFG) Excellence Cluster “Centre for the Advanced Study of Collective Behaviour” and previously served as a full Professor in Princeton University’s Department of Ecology and Evolutionary Biology. His research focuses on uncovering the principles of collective behavior across biological scales, from neural networks to animal swarms and human societies. Research interests span experimental and theoretical approaches, leveraging cutting-edge technologies like AI-driven behavioral analysis and virtual reality for animals. These methods enable studies on coordination mechanisms in systems such as fish schools, locust swarms, and primate groups, with applications to robotics and social sciences. Key awards include the Leibniz Prize (Germany’s highest research honor), Royal Society Fellowship, and multiple citations as a Highly Cited Researcher. His leadership roles include directing interdisciplinary teams at the Max Planck Institute and the Konstanz Excellence Cluster, fostering collaborations across physics, computer science, and biology. Awards: Royal Society Fellowship (2025), Leibniz Prize (2022), Fyssen Prize (2024), Lagrange Prize (2019) Labs/Teams: Collective Behavior Lab (Max Planck Institute), Centre for the Advanced Study of Collective Behaviour (DFG Cluster)
Alice C Helliwell is an Assistant Professor in Philosophy at Northeastern University London, specializing in AI art and computational creativity. Her research addresses philosophical questions about machine creativity, AI ethics, and the aesthetics of AI-generated art. She holds a PhD from the University of Kent and has held roles including Interim Director of the Doctoral Research School at NU London (2023-24). Her current projects include a Visiting Fellowship at Friedrich-Alexander-Universität exploring AI responsibility frameworks. Education includes: PhD in History and Philosophy of Art (University of Kent) MA in History and Philosophy of Art (University of Kent, Paris) PGCert in Occupational Psychology (Birkbeck, University of London) MA(Hons) in Philosophy and Psychology (University of Edinburgh) Her research spans: Philosophy of technology Creativity theory Applied AI ethics Landscape of AI ethics in creative industries Recent publications focus on AI alignment problems, cluster accounts of art, and Wittgensteinian analyses of AI governance. She co-edited volumes on Wittgenstein and AI (2024) and authored key works linking Darwinian creativity models to computational systems. Awards include the Paris Scholarship and Vice Chancellor’s Scholarship. Professional roles include Associate Editor at AI & Society (2024–present) and Trustee of the British Society of Aesthetics. Teaching focuses on AI/data ethics and aesthetics at NU London, with supervision of dissertations in AI art, technology ethics, and aesthetics. She holds Fellowship of Advance HE (2024).
