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Michał PrzewoźniczekView profile

Professor

• Evolutionary Computation
• Multi-objective Optimization
• Many-objective Optimization
• +12 more

Michał Przewoźniczek is a Professor at Wrocław University of Science and Technology (PWr), actively contributing to the fields of evolutionary computation, optimization, and artificial intelligence. He is a key member of multiple research teams, including the Metaheuristics Team, Machine Learning Team, and Advanced Data Analysis Methods Team, reflecting his broad interdisciplinary impact. His research focuses on Evolutionary Computation , Multi- and Many-objective Optimization , Linkage Learning , Problem Decomposition , and Hybridization of metaheuristic methods, with applications in industrial process planning, network optimization, and real-world decision-making. He leads significant research projects such as greybox optimization (2023–2026) and multi-objective evolutionary methods with linkage learning (2021–2026), demonstrating sustained grant support and scientific leadership. The analysis of his recent publications reveals a consistent trend in advancing parameter-less evolutionary algorithms, empirical linkage learning, and scalable distributed implementations using containerization (e.g., Docker). His work bridges theoretical algorithm development with practical applications in optical networks, manufacturing, and distributed computing. His scientific honors include: Best Paper Nomination at the Genetic and Evolutionary Computation Conference (GECCO) Associate Editor, IEEE Transactions on Evolutionary Computation (since January 2023) Prof. Przewoźniczek supervises graduate students as a thesis advisor and collaborates extensively with researchers such as Piotr Dziurzanski, Marcin Komarnicki, Krzysztof Walkowiak, and Leandro Soares Indrusiak. He is actively involved in major conferences like GECCO, CEC, and CORES, and his work is indexed in Google Scholar, ResearchGate, and ORCID (0000-0003-2446-6473). He is affiliated with research groups focusing on Machine Learning, Computer Networks, Advanced Data Analysis, and Metaheuristics, indicating a strong collaborative and team-based research environment.

Pavlos PetoumenosView profile

Assistant Professor

• Compiler Optimization
• Runtime Systems
• Energy Efficient Computing
• +6 more

Pavlos Petoumenos is a Lecturer (Assistant Professor) in the Department of Computer Science at the University of Manchester and a Research Fellow of the Royal Academy of Engineering. He leads research within the Advanced Processor Technology (APT) group, focusing on compiler optimization and energy-efficient computing. Previously, he worked as a Research Associate and Senior Researcher at the University of Edinburgh under Hugh Leather. His research centers on compilers, runtime systems, and development tools designed to help programmers write fast, energy-efficient programs with minimal effort. With electronic systems consuming 10% of global electricity (projected to reach 20% by 2030), his work addresses the critical challenge of bridging the complexity gap between programmers and modern energy-efficient hardware. Key research areas include compiler optimization, machine learning applications in compilation, code size reduction, and quantum-classical programming integration. His recent publications reveal strong trends in applying machine learning to compiler optimization, particularly through active learning techniques and deep learning models. The research spans multiple domains including function merging, branch fusion, loop transformations, and energy profiling, with significant contributions to quantum programming language design. His work consistently targets real-world applications in multicore processors and interactive mobile environments. Award highlights include: Best Paper Award at CGO 2019 Distinguished Paper Award at ISSTA 2018 Best Paper Award at PACT 2017 Best Paper Award at CGO 2017 Best Paper Award at IISWC 2014 Royal Academy of Engineering Research Fellowship Petoumenos actively contributes to the research community through mentoring PhD students, organizing the biennial International Workshop on Code Optimisation for Multi and Many-Cores (COSMIC), and co-hosting the Compucast podcast where he serves as chief editor. His collaborations span multiple institutions including the University of Edinburgh, University of Lancaster, and University of St Andrews. He has secured significant research impact through tools like BenchPress and F3M that address practical challenges in compiler design and optimization. He leads the Advanced Processor Technology group and contributes to the Compucast podcast, which features interviews with computer science researchers, academic and industry news, and technical discussions. The podcast represents a collaboration between multiple UK universities and serves as an important outreach platform for computer science research.

Massimiliano VasileView profile

Professor

• Computational Optimisation
• Astrodynamics and Mission Analysis
• Space Debris and Asteroid Manipulation
• +5 more

Massimiliano Vasile is a Professor in the Department of Mechanical and Aerospace Engineering at the University of Strathclyde. His expertise lies in computational optimisation, astrodynamics, and autonomous space systems. He leads major networks such as Stardust (asteroids/space debris), UTOPIAE (uncertainty treatment), and the EPSRC ENCORE network (resilience in engineering systems). Vasile has pioneered methods like Evidence Based Robust Optimisation and developed algorithms for debris removal, asteroid navigation, and multi-objective control. Research focuses include: Optimisation under uncertainty Space debris mitigation and active removal Autonomous spacecraft navigation AI-driven sensor fusion Space sustainability metrics Key achievements include: Innovative trajectory design for binary asteroids Development of CubeSat-based debris removal systems Advances in hyperspectral imaging for space object analysis He has received prestigious awards including Royal Astronomical Society Fellowship, AIAA Associate Fellowship, and IEEE Senior Membership. Current projects address space traffic coordination, solar power satellite engineering, and ethical use of spatial data in human rights contexts.

Clark BarrettView profile

Professor

• Formal Methods
• Automated Reasoning
• Verification
• +7 more

Clark Barrett is a Professor in the Department of Computer Science at Stanford University, where he conducts research in formal methods, automated reasoning, and verification. He is affiliated with several research centers including the Stanford Center for Automated Reasoning, Stanford Center for AI Safety, Stanford Agile Hardware Project, and Stanford Center for Blockchain Research. His research focuses on developing formal methods and tools for verifying complex systems, with particular emphasis on satisfiability modulo theories (SMT), verification of neural networks, hardware design verification, and security. His work bridges theoretical foundations with practical applications across multiple domains. Over the past decade, Barrett's research has evolved from foundational work in SMT solving to increasingly diverse applications including neural network verification, hardware verification, and AI safety. His recent publications demonstrate a strong focus on practical verification techniques for real-world systems, particularly in the areas of hardware design, neural networks, and programming languages. The trend shows an expansion from core verification techniques to broader applications in AI safety and secure systems design. 2021 CAV (Computer Aided Verification) Award Barrett leads several major research initiatives and collaborates extensively with industry partners. His work on the Marabou neural network verification framework, SMT-LIB standard, and Symbolic QED verification methodology have had significant impact in both academic and industrial settings. He has supervised numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. He is a key member of the Stanford Center for Automated Reasoning, which develops foundational technologies for automated reasoning, and the Stanford Center for AI Safety, where he focuses on formal methods for ensuring the safety and reliability of AI systems. His work on the Stanford Agile Hardware Project aims to revolutionize hardware design through formal methods and verification techniques.

Dongning GuoView profile

Professor

• Blockchain Technologies
• Information Theory
• Machine Learning
• +3 more

Dr. Dongning Guo is a Professor of Electrical and Computer Engineering at Northwestern University's McCormick School of Engineering, holding a courtesy appointment in Computer Science. His research focuses on blockchain technologies, information theory, machine learning, and wireless communications. He leads the Communications and Networking Laboratory and has contributed to projects involving spectrum sharing, next-generation cellular networks, and blockchain applications in wireless systems. Education: Ph.D. Electrical Engineering, Princeton University (2007) M.A. Electrical Engineering, Princeton University (2006) M.Eng. Electrical Engineering, National University of Singapore (2002) B.Eng. Electrical Engineering & Information Science, University of Science & Technology of China (2000) Research Interests: Dr. Guo explores the intersection of theoretical foundations and applied systems, with current emphases on: Fundamental limits of blockchain consensus mechanisms Spectrum management in 5G/6G and satellite constellations Machine learning for dynamic wireless resource allocation Asynchronous access protocols in multi-cell environments His work bridges information theory, optimization, and distributed systems to address challenges in modern communication networks. Recent Work Trends: Recent articles highlight advancements in MARL-based spectrum allocation, interference mitigation for LEO satellites, and scalable consensus protocols. His research increasingly integrates machine learning to solve complex resource allocation problems at scale. Labs/Teams: Directs the Communications and Networking Laboratory, focusing on next-gen wireless systems and distributed communication protocols. Collaborates with industry partners on satellite spectrum management and blockchain applications.

Beatrice Ombuki-BermanView profile

Professor

• Computational Intelligence
• Evolutionary Computation
• Swarm Intelligence
• +6 more

Beatrice Ombuki-Berman is a Professor and Chair of the Department of Computer Science at Brock University, Canada. She holds a PhD in Intelligent Systems Engineering from the University of the Ryukyus, Japan, along with an ME in Information Engineering and certifications in Japanese Language Studies. Her research focuses on computational intelligence, evolutionary computation, swarm intelligence, and optimization techniques, with recent work on complex networks and drug discovery applications. Education: PhD, Intelligent Systems Engineering, University of the Ryukyus, Japan ME, Information Engineering, University of the Ryukyus, Japan BSc, Double Major in Mathematics and Statistics, Minor in Computer Science, Jomo Kenyatta University of Agriculture and Technology, Kenya Research Interests: Development of efficient computational intelligence algorithms for optimization problems, including evolutionary algorithms, particle swarm optimization, and neural networks. Applications span combinatorial optimization, multi-objective optimization, network robustness, and bioinformatics. Grants & Funding: Her research is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) and industry collaborations through Brock-Niagara VPMI and FedDev Ontario. Professional Activities: Co-Director of the Bio-Inspired Computational Intelligence Group. She is also a publicity chair for IEEE WCCI 2024 and IEEE SSCI 2025. Labs & Teams: Leads the Bio-Inspired Computational Intelligence Group, focusing on algorithm development and real-world applications.

Xiaofeng WangView profile

Associate Professor

• Model Predictive Control
• Robust Adaptive Control
• Networked Control Systems
• +8 more

Xiaofeng Wang is an Associate Professor in the Department of Electrical Engineering at the University of South Carolina, affiliated with the Molinaroli College of Engineering and Computing. He holds a Ph.D. in Electrical Engineering from the University of Notre Dame (2009) and prior degrees in Applied Mathematics and Control Theory from East China Normal University. Education Ph.D., Electrical Engineering, University of Notre Dame (2009) M.S., Operation Research and Control Theory, East China Normal University (2003) B.S., Applied Mathematics, East China Normal University (2000) His research spans control systems, robotics, and machine learning, with applications in cyber-physical systems (CPS), autonomous vehicles, and urban air mobility (UAM). Key methodologies include Lebesgue-approximation model predictive control, robust adaptive control, and physics-informed neural networks. Recent projects focus on DeSimplex architecture for safe learning-enabled systems, morphing drones, and resilient CPS under cyber-physical threats. The 15 most recent publications emphasize sampled-data control, distributed systems, and deep learning integration. Notable trends include lebesgue sampling for nonlinear systems, generative adversarial networks for aerial image analysis, and adaptive control for uncertain environments. Awards include the 2014 PHM Society Best Paper Award and 2013 ICCPS Best Paper Finalist. Scientific Awards Best Paper Award, Prognostics and Health Management Society (2014) Best Paper Finalist, International Conference on Cyber-Physical Systems (2013) He has supervised numerous students, including PhD graduates Xin Zhang, Rabab Abdelfattah, and Lixing Yang, many of whom now hold academic or industry positions. Current research involves NSF-funded work on guaranteed tubes for safe learning in autonomy architectures and collaborations with institutions like UIUC and Zhejiang University.

Dieter SchmalstiegView profile

Professor

• Augmented Reality
• Virtual Reality
• Computer Graphics
• +2 more

Dieter Schmalstieg is the Alexander von Humboldt Professor of Visual Computing at the University of Stuttgart's Faculty 5: Computer Science, Electrical Engineering and Information Technology, leading the Institute for Visualization and Interactive Systems (VIS). He also holds an adjunct professorship at Graz University of Technology's Institute of Computer Graphics and Vision. His research focuses on augmented reality (AR), virtual reality (VR), computer graphics, visualization, and human-computer interaction. He earned his Dipl.-Ing. (1993), Dr. techn. (1997), and Habilitation (2001) from Vienna University of Technology. He has authored/co-authored over 400 peer-reviewed publications, with over 29,000 citations and numerous awards, including the IEEE ISMAR Career Impact Award (2020) and the IEEE Virtual Reality Technical Achievement Award (2012). His professional roles include editorial leadership in top journals like IEEE Transactions on Visualization and Computer Graphics, and steering committee memberships in major conferences like IEEE ISMAR. Research interests span AR/VR applications, situated visualization, coherent rendering, spatial interaction, and medical visualization. Notable projects include the Christian Doppler Laboratory for Handheld Augmented Reality (2008–2015), and collaborations with industry partners like Qualcomm and VRVis. He has advised over 50 PhD students, many now professors or researchers globally. Grants include the Alexander von Humboldt Professorship (2023), SNAP Mixed Reality Lab funding, and EU projects like MiReBooks (mining education). His work bridges theory and application, with impactful contributions to AR/VR systems, medical visualization tools, and GPU techniques for real-time rendering.

Xilu WangView profile

Research Fellow

• Data-driven Optimization
• Artificial Intelligence
• Machine Learning
• +5 more

Dr. Xilu Wang is a Surrey Future Fellow at the University of Surrey, affiliated with the School of Computer Science and Electronic Engineering. They are part of the Nature Inspired Computing and Engineering Research Group and the Surrey Institute for People-Centred Artificial Intelligence (PAI). Their research focuses on data-driven optimization, trustworthy AI, and machine learning, with specialties in multi-objective optimization, Bayesian optimization, evolutionary machine learning, and federated optimization. Dr. Wang holds a PhD and can be contacted via email . Research interests include developing algorithms for fair, privacy-preserving machine learning and optimization. Key topics span neural architecture search, federated optimization, and transfer learning. Dr. Wang has supervised multiple postgraduate students, including Abel Alexander (University of Surrey), Adrian Kruse (Bielefeld University), and Scheu Louis Lamar (Bielefeld University), focusing on Bayesian optimization and federated learning applications. Publications emphasize large-scale optimization, federated systems, and secure evolutionary algorithms. Notable works include surrogate-assisted optimization methods and privacy-preserving frameworks. Their contributions bridge theoretical advancements with practical applications in autonomous driving, SAR imagery, and resource-constrained computing. Dr. Wang’s work has been featured in IEEE Transactions on Evolutionary Computation and other top-tier journals. They actively explore interdisciplinary collaborations, particularly in trustworthy AI and federated learning security. Current projects aim to enhance algorithmic fairness, scalability, and real-world deployment in complex systems.

Jens SchedelView profile

Researcher

• Distributed Systems
• Operating Systems
• Invasive Computing
• +3 more

Jens Schedel is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander University Erlangen-Nuremberg. His academic activities focus on invasive computing, distributed systems, and operating systems. He is affiliated with the Faculty of Engineering and collaborates with the ergoo group (Erlangen Research Group on Distributed Objects and Operating Systems). Education: 2010: Diploma Thesis: 'Design and implementation of a system abstraction layer for the I4Copter framework (QC_OSAL)' 2009: Study Thesis: 'Design and implementation of a distributed management and control software for projectors' Research Interests: Invasive Runtime Support Systems (iRTSS), aspect-oriented operating systems (CiAO), interdisciplinary quadrocopter projects (I4Copter), resource-aware programming, and embedded systems. His work emphasizes adaptive algorithms, parallel computing, and type-safe migration in safety-critical environments. Teaching: Taught courses (SP1/SP2) across multiple semesters (2010–2015), including FITS (Formal IT Systems) and PASST (Programming and Software Systems). Labs/Teams: Member of the ergoo group and contributes to the SFB/TRR 89 Invasive Computing project.

Thiago FerreiraView profile

Assistant Professor

• Software Engineering
• Search-Based Software Engineering
• Artificial Intelligence in Software Engineering
• +7 more

Thiago Ferreira is an Assistant Professor in the Department of Information Technology & Informatics at the College of Innovation & Technology, University of Michigan-Flint. He holds a Ph.D. in Computer Science from the Federal University of Paraná (2019) and has held postdoctoral and teaching roles at the University of Michigan-Dearborn prior to joining UM-Flint. His research is centered on improving software engineering practices through artificial intelligence and optimization techniques. Research Interests: His work primarily focuses on Search-Based Software Engineering (SBSE) , software refactoring , software testing , and requirements engineering . He investigates how user preferences and AI—particularly large language models—can be leveraged to automate and enhance software development decisions, including objective function selection, refactoring, and product line testing. His recent publications emphasize empirical studies on Docker refactorings and AI-driven software design. Article Trends: His latest research (2023–2025) demonstrates a growing integration of large language models and AI in software engineering tasks, with a strong emphasis on reproducibility, explainability, and empirical validation. He frequently collaborates on interdisciplinary projects involving accessibility, STEM equity, and assistive technology, reflecting a commitment to socially impactful computing. Honors & Awards: Nominee, 2024 Outstanding Student Organization Advisor Awardee, 2024 Lois Matz Rosen Junior Faculty Excellence in Teaching Award Fall 2024 Golden Apple Recognition Awardee, Early Career Award Academic (2023) 🥇Best Paper at SBCARS '24 Advising and Grants: Dr. Ferreira actively mentors students across research projects and publications. He is the Principal Investigator (PI) on multiple funded grants, including a Google Research Scholar Program award (2025, $60,000) and the NSF REU Site on Digital Accessibility (2024–2027, $429,397). He also leads projects on community-based STEM engagement and software refactoring, demonstrating strong leadership in both technical and social innovation. Labs and Teams: He leads a research lab focused on AI and search-based software engineering at UM-Flint, frequently collaborating with students and faculty across disciplines, particularly in occupational therapy and community service projects like Play Beyond Boundaries and Kaden's Car . These initiatives highlight his role in fostering interdisciplinary, student-driven innovation.

Gordon FraserView profile

Professor

• Software Engineering
• Software Testing
• Automated Testing
• +9 more

Gordon Fraser is a Professor and holds the Chair of Software Engineering II at the Faculty of Computer Science and Mathematics, University of Passau, since 2017. His academic journey includes a doctorate from Graz University of Technology and research experience at Saarland University and the University of Sheffield. Research Interests: His work centers on software analysis, software development, and programming education. Key themes include preventing software errors through automated testing, improving developer productivity using AI-based tools, and enhancing programming training—especially for beginners and children. He actively explores the role of gamification, large language models, and search-based techniques in software testing and education. Recent Research Trends: His recent publications (2023–2024) show a strong focus on flaky tests in Python, automated test generation for Android and block-based environments (Scratch), gamification of testing in IDEs, and educational studies on pair programming, gender dynamics, and feedback mechanisms in primary computing education. The integration of AI and machine learning in software testing is a recurring theme, especially in the context of large language models and neuroevolution. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: He leads several research projects, including DFG-funded TYPES4STRINGS and DeepMBT , and the DeepCode project which explores AI for code quality. He mentors a large number of students and researchers, particularly in the areas of automated testing and programming education. His advising spans PhD and Master’s students, many of whom are co-authors on his publications. Labs and Teams: He leads a research group at the University of Passau focused on software testing, analysis, and education. The team develops tools like Pynguin for automated Python testing and Code Defenders for gamified software testing education. They also work on LitterBox , a linter for Scratch programs, and various gamification plugins for IDEs and educational platforms.

Marde HelbigView profile

Senior Lecturer

• Dynamic Multi-Objective Optimization
• Computational Intelligence
• Evolutionary Algorithms
• +4 more

Dr. Marde Helbig is a Senior Lecturer at Griffith University , School of Information and Communication Technology. Previously held positions include Deputy Director of the Griffith Sciences Partnership Office (2022-2025), Senior Lecturer at the University of Pretoria, and researcher at the Council for Scientific and Industrial Research (CSIR) in South Africa. Research focuses on dynamic multi-objective optimization using computational intelligence algorithms (evolutionary and swarm intelligence). Active in decision-making visualization and algorithm design for time-varying optimization problems. Recent grants include: AI-driven decision-making for smart infrastructure (CSIRO, 2025-2028) Ocean carbon capture optimization (CSIRO, 2025-2028) She is an Associate Editor for leading journals in Swarm and Evolutionary Computation and IEEE Transactions on Evolutionary Computation , with over 15 years of editorial and committee involvement in IEEE societies. Awards include the 2023 Griffith Sciences Pro Vice Chancellor’s Excellence Award for industry engagement and the 2019 TW Kambule-NSTF Emerging Researcher Award . Supervision includes 6 PhD and Master’s students across optimization and cybersecurity domains.

• Adaptive Optics
• High Performance Computing
• Astronomical Instrumentation
• +3 more

Dr. Alastair Basden is a leading researcher in astronomical adaptive optics and high-performance computing at Durham University's Department of Physics. He serves as the Head of the COSMA HPC Service, a Tier 1 national supercomputing facility supporting global astrophysical research. His work spans the development of real-time control systems, simulation platforms, and hardware innovations for Extremely Large Telescopes (ELTs).

• Systems Engineering
• Sustainable Manufacturing
• Cyber-Physical Systems
• +5 more

Dr. Ali Asghar Bataleblu is a Senior Research Fellow at the Sustainable Manufacturing Laboratory within the Faculty of Engineering at the Free University of Bozen-Bolzano . His work focuses on transdisciplinary systems engineering for resilience, sustainability, and circular economy , with over a decade of experience in uncertainty-based Multidisciplinary Design Optimization (MDO) . He was previously the director of an MDO research center for nearly ten years. Academic Rank: Research Fellow Key Research Areas: Systems Engineering, Cyber-Physical Systems, Resilience Engineering, Sustainable Manufacturing Dr. Bataleblu’s research bridges model-based systems engineering (MBSE) , digital twins , and surrogate-assisted optimization to address sustainability challenges in manufacturing and mobility. He employs multi-agent systems and system-of-systems frameworks to analyze complex interactions in industrial and environmental contexts. His work includes requirements management and axiomatic design for holistic system decomposition. The 15 most recent articles highlight his focus on resilient cyber-physical production systems , sustainable mobility , AI-integrated MBSE , and multi-agent optimization for wildfire management. These works span disciplines like industrial engineering , computational intelligence , and urban planning , with subfields covering digital twins , dynamic system modeling , and automated routing . Scientific Awards: TOWARDS South Tyrol International Postdoctoral Fellowship (2023) Young Scientist Award at ICQIS-2024 (2024) He leads the Sustainable Factory Design Decomposition (SFDD) project funded by the Autonomous Province of Bolzano , collaborating with Purdue University Fort Wayne . His research integrates graph database visualization for supply chain resilience and life cycle assessment (LCA) automation in fast-moving consumer goods. He also explores reinforcement learning for aerial vehicle design and robust control systems for drones.