Prof. Ben Paechter is a leading academic at the School of Computing Engineering and the Built Environment , Edinburgh Napier University, specializing in evolutionary algorithms and swarm robotics. As a Professor , he has shaped research directions in computational optimization and adaptive systems for over two decades. Research Themes : Evolutionary Swarm Robotics, Multi-Objective Optimization, Hyper-heuristics, Neural Architecture Search Key Projects : FOCAS (European Commission), KTP: Intelligent Agents (Innovate UK), PerAda (Self-Aware Systems) Collaborations : CAVES Research Group, Centre for Algorithms, Visualisation and Evolving Systems His article trends demonstrate expertise in cross-domain hyper-heuristics, swarm behavior tree evolution, and neural network architecture optimization. Recent works (2023-2024) focus on GPU-accelerated evolutionary algorithms and hierarchical swarm control systems. Scientific Awards : Fellow of the British Computer Society (FBCS) Chartered IT Professional (CITP) GECCO Best Paper Nomination (2018) Advising & Grants : Supervised 5 PhD students including Paul Lapok (planar mechanisms) and Andreas Steyven (swarm diversity). Secured £2.2M+ in funding across 13 projects, including EU FOCAS (£639,999) and Innovate UK grants.
Thorsten Pachur is a Professor for Behavioral Research Methods at the Technical University of Munich (since 2022) and a Senior Research Scientist at the Max Planck Institute for Human Development in Berlin. His work focuses on judgment and decision-making, particularly in social contexts, adaptive rationality, and risky choices. He has led projects such as " Simple decision-making strategies " and " Search and Learn " at the Max Planck Institute. Education : Habilitation (2012, University of Basel), PhD in Psychology (2006, Free University of Berlin), Dipl.-Psych. (2002, Free University of Berlin), MSc in Health Psychology (2002, University of Sussex) Research Themes : Pachur investigates how social memory structures influence decision-making, the toolbox of cognitive strategies in risky choices, and the role of attentional processes in decisions. His work bridges prospect theory and heuristic models , examining how attention shapes probability weighting and outcome evaluation. Scientific Contributions : Recent publications include studies on the description-experience gap in intertemporal choices, age differences in risk perception , and attentional biases in sequential sampling . His 2024 work on COVID-19 vaccine refusal highlights deliberate ignorance and cognitive distortions. Scientific Awards : 2017 Fellow of the Association for Psychological Science (APS), 2001 DAAD Scholarship, 2005 Max Planck Postdoctoral Fellowship Grants : Funded by the Swiss National Science Foundation (SNSF), German Research Foundation (DFG), and Biäsch Foundation Editorial Roles : Consulting Editor for Journal of Experimental Psychology: Learning, Memory, and Cognition (2013–2025), Associate Editor for Cognitive Psychology (2025–), and member of boards for journals including Decision and Psychological Review .
Anjith George is a researcher at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, working within the Biometrics Security and Privacy Laboratory. His research focuses on advancing face recognition systems with particular emphasis on security, efficiency, and cross-domain applications. He maintains a strong collaborative relationship with Professor Sébastien Marcel's research group at EPFL. George's research interests span multiple critical areas in modern biometrics including face recognition systems, face anti-spoofing techniques, heterogeneous face recognition across different modalities (such as visible to infrared), and efficient model deployment for edge devices. His work addresses fundamental challenges in biometric security by developing robust systems that can withstand presentation attacks while maintaining high accuracy across diverse conditions. He has made significant contributions to the field of synthetic data generation and utilization for improving face recognition systems, exploring how knowledge can be effectively transferred from synthetic to real-world domains. Analysis of George's recent publications reveals a clear research trajectory focused on solving practical challenges in face recognition. His work has evolved from fundamental eye tracking and gaze direction research in the early 2010s toward increasingly sophisticated face recognition systems addressing security vulnerabilities, efficiency constraints, and domain adaptation problems. A significant portion of his recent work explores the potential of synthetic data to overcome limitations in real-world training data, while also investigating how to bridge the gap between different face recognition modalities. His research demonstrates a strong emphasis on practical applications, particularly in resource-constrained environments where edge deployment is necessary. George has been actively involved in major biometrics competitions and challenges, including the FRCSyn Challenge and EFaR (Efficient Face Recognition) competition, contributing to community benchmarking efforts and advancing state-of-the-art solutions. His collaborative work spans multiple institutions globally, reflecting the international nature of biometrics research.
Yasutaka Furukawa is a Principal Scientist at Wayve and an Associate Professor of Computing Science at Simon Fraser University (SFU). He leads the SFU Visual Computing Group, ranked 12th globally in Computer Vision and Graphics. His research focuses on 3D reconstruction, neural networks, and computer graphics, with notable contributions in multi-view stereo, CAD modeling, and diffusion models. Furukawa holds a Ph.D. from the University of Illinois at Urbana-Champaign (2008) and has held roles at Washington University in St. Louis, Google, and the University of Washington. Education: Ph.D., Computer Science, UIUC, 2008 Postdoctoral Research, University of Washington (with Prof. Steven Seitz and Brian Curless) Research Interests: Furukawa’s work bridges computer vision and graphics, emphasizing robust 3D reconstruction techniques. His projects include PuzzleFusion++ (diffusion-based fracture assembly), BrepGen (B-rep CAD generation), and HouseDiffusion (floorplan synthesis). He explores applications in autonomous systems, AR/VR, and robotics. Key Projects: Generative Adversarial Networks for architectural design (House-GAN series) Neural networks for inertial navigation (RoNIN) Diffusion models for spatial puzzles and CAD modeling Awards: Recipient of the PAMI Longuet-Higgins Prize (2020), NSF CAREER Award (2015), and multiple Google Faculty Research Awards. Teaching: Courses include CMPT412 Computer Vision and CMPT762 Advanced Computer Vision at SFU. Active in supervising over 30 graduate students and postdocs.
Robyn Michele Woollands is an Assistant Professor in the Department of Aerospace Engineering with additional affiliation at the Coordinated Science Lab. Her research focuses on advanced aerospace control systems and orbital dynamics. Research Interests: Primary expertise lies in optimal control theory applied to spacecraft systems, including orbital transfer maneuvers, multi-agent coordination in space environments, and resilient control under actuator failures. Her work integrates computational mathematics with aerospace engineering to solve complex trajectory optimization problems. Publication Focus: Recent publications demonstrate strong emphasis on constrained motion planning for space applications, fault-tolerant control architectures, and efficient numerical methods for solving bang-bang control problems. Her research consistently bridges theoretical control concepts with practical aerospace challenges. Awards: AFOSR Young Investigator Award (2024) Affiliations: Conducts research through the Coordinated Science Lab, focusing on interdisciplinary aerospace control systems and collaborative projects.
James Demmel is a Professor of Computer Science and Mathematics at the University of California, Berkeley, with joint appointments since 1990. His research focuses on numerical linear algebra, high-performance computing, and parallel algorithms. He co-developed widely used libraries like LAPACK and ScaLAPACK. Demmel holds ACM, SIAM, and IEEE Fellowships, and is a member of both the National Academy of Engineering and Sciences. Education: Ph.D. in Computer Science, UC Berkeley, 1983 B.S. in Mathematics, Caltech, 1975 Research Interests: Demmel’s work emphasizes numerical methods for linear algebra, including algorithms for eigenvalue problems, matrix factorizations, and high-performance computing architectures. His contributions bridge theory and practice, addressing challenges in floating-point arithmetic and algorithm scalability. Publications & Awards: Over 150+ publications, including foundational papers on LAPACK and iterative methods. Recipient of the SIAM Activity Group Linear Algebra Best Paper Prize (1988, 1991), Wilkinson Prize (1993), and ACM Supercomputing Test of Time Award (2019). Labs & Teams: Demmel collaborates with groups like Berkeley Benchmarking and Optimization Group (BeBOP) and CLIMB, focusing on algorithmic efficiency and exascale computing.
Linda Gurvin Opheim is an Associate Professor at the Department of Mathematical Sciences, University of Agder, focusing on mathematics education and teacher training. Her research emphasizes understanding teachers' perspectives and enhancing pedagogical strategies for pre-service educators, particularly in multilingual and vocational contexts. She has contributed extensively to curriculum design, assessment methods, and professional development programs. PhD in Mathematics Education (2022), MSc in Mathematics Education (2011), General Teacher Education (2005) Her research spans task design , self-efficacy of multilingual teacher students , and didactical time constraints , often integrating technology for practical classroom solutions. Recent work includes collaborative algebra activity development and ethical student data usage. Articles highlight trends in mathematics pedagogy , inclusive education , and digital learning , with a focus on bridging theory-practice gaps. Awards include 2014 recognition in the TV show Hjernekamp and roles in educational ICT projects like DVM-U. She serves as a consultant for high-stake mathematics exams and has advised students since 2011, balancing academic rigor with accessible teaching methods. Her career includes teaching positions in youth schools and leadership in research groups like Mathematical Thinking in Schools (MaThS) .
Florian Frohn is a tenured lecturer ("Lehrkraft für besondere Aufgaben") in the Programming Languages and Verification research group at the Department of Computer Science, RWTH Aachen University. He holds a Dr. rer. nat. (2018), MSc (2013), and BSc (2011) in Computer Science, all from German institutions, with his bachelor's studies completed part-time. His research interests center on formal methods for software verification, including automated termination and complexity analysis of imperative programs, satisfiability of Constrained Horn Clauses (CHCs), SMT solving with integer exponentiation, loop acceleration, term rewriting systems, and abstract interpretation. He is a key contributor to several influential tools: LoAT (Loop Acceleration Tool), AProVE (Automated Program Verification Environment), and SwInE; he also worked on Astrée and the CAGE toolchain developed under the DARPA STAC program. His recent publications (2023–2024) demonstrate continued leadership in top venues such as FM, IJCAR, FoSSaCS, and SAS, with work advancing loop acceleration, non-termination proofs, and SMT solving. These contributions reflect a strong trend in developing practical, automated techniques for program verification and analysis. IJCAR Best paper honourable mention (2024) EASST Award for best ETAPS paper (2020) iFM Best Tool Paper Award (2017) ISR Best Poster Award (2017) SEFM Recognition Award (2016) CADE Woody Bledsoe Travel Award (2016) Florian Frohn actively contributes to the research community through program committee roles for major workshops and conferences including TACAS, HCVS, WST, SMT, and LPAR. He has advised no listed students but has been involved in mentoring through research collaborations. His teaching portfolio includes courses on verification techniques, satisfiability checking, and advanced programming concepts. He leads the Termination and Complexity Competition (termCOMP) and participates in organizing key events in the formal methods community.
Pietro Micheli is an Associate Professor of Organizational Performance at Warwick Business School and a Visiting Fellow at Cranfield School of Management's Centre for Business Performance . He holds a PhD and Master by Research from Cranfield School of Management, and a Master in Management and Production Engineering from Politecnico di Milano. His research focuses on Performance Management Systems , Innovation Strategies , and Design Thinking , with a particular emphasis on organizational ambidexterity, strategic alignment, and process improvement. He has published extensively in top-tier journals like Long Range Planning , Public Administration Review , and Journal of Product Innovation Management . Micheli's work bridges theory and practice, addressing challenges in both private and public sectors. He co-founded the Evidence-based Management Collaborative and contributed to the Advanced Institute of Management Research (AIM) . His recent articles explore mitigating negative effects of performance measurement through strategic design, emphasizing ethical and responsible innovation frameworks. He has authored multiple book chapters and edited volumes on performance measurement systems, business ecosystems, and design thinking applications. His teaching includes modules on Performance Culture , Designing Performance Indicators , and Target Setting , reflecting his commitment to translating academic insights into actionable management practices. He advises organizations on balancing performance metrics to foster innovation while maintaining operational efficiency.
Andrea Borghesi is an Assistant Professor (tenure-track) at the Department of Computer Science and Engineering (DISI), University of Bologna, Italy. His research focuses on optimization techniques, Machine Learning, and energy-efficient computing in High-Performance Computing (HPC) systems. He holds an M.S. (2013) and Ph.D. (2017) in Computer Engineering from the University of Bologna, and has been awarded the Italian National Scientific Qualification for Associate Professor in Computer Engineering (2018-2020). **Employment History**: 2020–Present: Executive Scientific Representative at ALMA-AI (Inter-departments Center for Artificial Intelligence, University of Bologna) 2019–Present: Assistant Professor (RTDA) at DISI, University of Bologna 2018–2019: Postdoctoral Fellow at DISI 2017–2018: Research Intern at University of Bologna’s Department of Electrical, Electronic and Information Engineering **Research Interests**: Borghesi’s work addresses complex systems, predictive maintenance, and energy efficiency. Key areas include HPC scheduling, AI-driven monitoring, and interdisciplinary AI applications. His projects span energy-aware scheduling, federated learning for anomaly detection, and AI fairness in healthcare and education. **Grants & Projects**: Lead or contributed to EU-funded initiatives such as AEQUITAS (AI fairness), StairwAI (AI accessibility), and OPRECOMP (transprecision computing), alongside national projects like vertOpt (AI application matching) and EXADATA (exascale monitoring). **Labs & Collaborations**: Active in the Inter-departments Center for Artificial Intelligence (ALMA-AI) and collaborated with institutions like CINECA and industry partners such as ENI and YANMAR. His work integrates AI, optimization, and simulation to solve real-world challenges in computing and healthcare.
Dr. Yurii Khomskii holds multiple academic positions across European institutions. He serves as a Lecturer and Academic Tutor at Amsterdam University College, a Privatdozent (equivalent to Senior Lecturer/Associate Professor) in the Department of Mathematics at Universität Hamburg, and a Guest Researcher at the Institute of Logic, Language and Computation (ILLC) at the University of Amsterdam. His work bridges mathematical logic, set theory, and foundations of mathematics across these institutions. Dr. Khomskii obtained his PhD from the University of Amsterdam in 2012 under the supervision of Prof. Benedikt Löwe and Prof. Jörg Brendle, with a dissertation titled Regularity Properties and Definability in the Real Number Continuum . He completed his Habilitation at Hamburg University in 2018 with the work Cardinal Characteristics, Regularity Properties, Definability and the Structure of the Real Line and the Generalised Real Line . Prior to these achievements, he held postdoctoral positions at the Kurt Gödel Research Center for Mathematical Logic at the University of Vienna and Universität Hamburg, and was a Marie Sklodowska-Curie research fellow. His research focuses on mathematical logic and set theory, specifically exploring the structure of the real line, regularity properties of sets of reals, descriptive set theory, forcing techniques, and generalized real numbers and Baire spaces. His work combines deep theoretical investigations with connections to foundational questions in mathematics. Dr. Khomskii's approach often involves analyzing cardinal characteristics, investigating definability constraints, and examining the interplay between combinatorial principles and regularity properties in various set-theoretic contexts. Analysis of his recent publications reveals a consistent trajectory in advanced set theory, with increasing focus on generalized Baire spaces and alternative set-theoretic frameworks. His work spans from concrete investigations of cardinal characteristics and regularity properties to more foundational explorations of paraconsistent and paracomplete set theories. The evolution shows a progression from classical descriptive set theory toward more abstract and generalized frameworks, while maintaining rigorous mathematical standards. Dr. Khomskii has been actively involved in teaching and mentoring across multiple institutions. He regularly coordinates intensive projects at the ILLC on advanced topics in set theory, forcing, and mathematical logic. At Universität Hamburg, he guides student seminars on logic and set theory topics. His teaching spans foundational courses like propositional logic and Boolean algebras to advanced topics such as infinite games, forcing, and generalized Baire spaces. He has supervised numerous student projects and coordinated semester-long intensive courses that combine theoretical instruction with practical problem-solving sessions. His research activities include directing student projects like the 2023 Forcing and Independence Proofs coordinated project at ILLC, where students explored forcing techniques and independence proofs in set theory. He has also led seminars on topics including infinite computability, descriptive set theory, and advanced set theory at both Universität Hamburg and the ILLC. His research collaborations span across Europe, with frequent co-authorships with researchers from Vienna, Amsterdam, and other mathematical logic centers.
Richard B. Johnson is a Ph.D. graduate in Computer Science from the University of Maryland, College Park. He holds a dual M.S. in Computer Science and Information Technology & Management. His research focuses on high-performance computing (HPC), parallel programming models, and persistent memory systems. Current roles include Instructorship for courses like CMSC 389N and extensive teaching assistant duties across multiple computer science disciplines. He has taught over 15 courses spanning programming languages, web development, computer architecture, and cybersecurity. Notable publications include work on Chapel language optimization, hybrid parallel models, and vulnerability analysis in shared codebases. His doctoral thesis (2024) explores hybrid-PGAS memory hierarchies for next-generation HPC systems. Academic coursework spans advanced topics in HPC systems, cybersecurity, machine learning, and computational linguistics. Collaborative research includes projects on password datasets analysis, facial recognition classification, and multilingual transliteration systems. His work emphasizes bridging theoretical computer science with practical, high-performance system implementations.
Yue Jiang is an incoming Assistant Professor at the University of Utah (Fall 2025) and is currently finishing her Ph.D. at Aalto University and the Finnish Center for Artificial Intelligence (FCAI). Her research focuses on human-centered technologies in HCI, computer vision, and deep learning, particularly in computational user interface understanding, eye tracking, and adaptive GUI layouts. She has held visiting roles at Carnegie Mellon University (CMU) and collaborated with institutions in Canada and the UK. She serves on program committees for CHI, VL/HCC, and IUI, and has organized computational UI workshops at CHI conferences. Education: Ph.D. in Intelligent Systems (Aalto University & FCAI, 2025) Visiting Ph.D. Student (CMU, 2024) M.Sc. in Computer Science (University of Maryland, 2020) B.Sc. in Computer Science (University of Toronto, 2018) Research Interests: Yue explores computational representations of UIs, human behavior modeling via eye tracking and motion capture, and adaptive interfaces. Her work bridges HCI, computer vision, and machine learning to enhance human capabilities through AI-driven systems. Awards: Meta Research PhD Fellowship (2023–2025) Heidelberg Laureate Forum Young Researcher (2024) Google Europe Students with Disabilities Scholarship (2022) Advising & Grants: Yue mentors students in areas like multimodal generative AI and seeks researchers for her group. She has received grants from FCAI, Adobe Research, and the National Science Foundation. Labs & Collaborations: Active in the Computational Behavior Lab (Aalto) and BIG Lab (CMU), she collaborates on projects like OR-Constraints for adaptive GUIs and graph-based UI modeling.
Fulvio Corno is a Professor in the Department of Control and Computer Engineering at the Polytechnic University of Turin, Italy. With a research career spanning over three decades since 1992, he has established himself as a prominent researcher in Internet of Things, Human-Computer Interaction, and Ambient Intelligence. His research focuses on making technology accessible to end-users, particularly through work on End-User Development in IoT systems, assistive technologies for people with disabilities, and security challenges in IoT environments. Corno's work bridges the gap between technical complexity and user needs, developing tools that simplify interaction with smart environments. His publication trends show a consistent focus on IoT systems, with recent work emphasizing security issues for novice programmers, computational notebooks for prototyping, and natural language interfaces for configuring smart environments. His research has evolved from foundational work on semantic approaches to IoT to practical tools addressing real-world implementation challenges. Corno has collaborated extensively with researchers including Luigi De Russis, Alberto Monge Roffarello, and Dario Bonino, producing significant contributions to the field of smart environments and end-user programming. He has contributed to education in Ambient Intelligence, developing courses that prepare engineers for the challenges of intelligent environments, and has supervised numerous students in this emerging field. His laboratory work centers on practical implementations of IoT systems, with projects spanning healthcare support systems, notification management across devices, and accessibility solutions for people with motor disabilities.
Dr. Vincent T'KINDT is a Researcher in the Computer Science Department at the Polytechnic School of the University of Tours. He serves as Head of the "Scheduling and Management" axis at the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT, UR 6300) and as Head of Corporate Relations for his department. His research focuses on Scheduling , Multi-criteria Optimization , and Operational Research , contributing to theoretical and applied advancements in these fields. Recent publications highlight his work on Multi-objective scheduling for parallel machines Heuristic algorithms for dynamic scheduling Just-in-Time scheduling models Rescheduling with setup times His research combines exact and approximate methods to solve complex industrial scheduling challenges. He has authored key works in journals like the Journal of Scheduling , Computers & Operations Research , and European Journal of Operational Research , with notable contributions including the book Multicriteria Scheduling: Theory, Models and Algorithms (Springer, 2006). Dr. T'KINDT collaborates with the LIFAT laboratory (UR 6300) and has participated in international conferences such as PMS'08, MISTA'07, and MAPSP2007. His methodological approaches include Integer Programming, Beam Search algorithms, and Pareto optima enumeration.
