Xavier Devroey is an Assistant Professor of Software Engineering at the Faculty of Computer Science , University of Namur , where he co-leads the SNAIL Team . His research focuses on test automation for search-based and model-based software testing , test suite augmentation , DevOps , and variability-intensive systems . PhD in Computer Science (University of Namur, 2017) Master in Computer Science (University of Namur, 2010) Recent research highlights include: Automated Android safety/security audits (A3S3, 2025) REST API benchmarking infrastructure (2025) Energy consumption analysis through test execution (2025) Fuzzing approaches for Odoo integration testing (FuzzE, 2025) Scientific recognitions: 1st place Java Test Case Generation Tool Competition (2025) VAMOS 2024 Ten-Year Most Influential Paper SSBSE 2020 Best Paper Award ICST 2024 Distinguished Reviewer AST 2023 PC Reviewer Star He actively supervises PhD and master's students while organizing international conferences like ISSTA (2025) and Belgium-Netherlands Software Evolution Workshops (2024).
Huajie Zhang is a Professor at the Faculty of Computer Science , University of New Brunswick, with over 14 years of service. He holds a PhD in Computer Science from the University of Western Ontario and an MSc from Harbin Institute of Technology. Research Interests: Machine Learning Data Mining Graphical and Probabilistic Models Intelligent Systems Transfer Learning Semi-supervised Learning Efficient Learning Algorithms for Large Data Publication Trends: Huajie Zhang's work focuses on probabilistic models like Bayesian networks and Naive Bayes, emphasizing accuracy, efficiency, and adaptability in semi-supervised and transfer learning contexts. His recent papers explore scalable algorithms for big data, hybrid classifiers, and optimization techniques. Scientific Awards: Best Paper Award (Second Place) at FLAIRS Conference 2004 Teaching Activities: CS6735 Machine Learning CS3383 Algorithm Design
Andrea Torsello is a researcher at the University of York, specializing in 3D shape analysis, graph-based machine learning, and quantum computing applications in computer vision. His work bridges theoretical and applied domains, focusing on pattern recognition, network thermodynamics, and remote sensing. PhD from University of York (2004) Published extensively in journals like IEEE Transactions and Pattern Recognition Research interests include: Quantum-inspired graph analysis 3D reconstruction techniques Manifold learning for complex networks Thermodynamic modeling of time-evolving systems Key contributions involve: Quantum walk-based graph similarity measures k-Anonymity for graph data Physics-driven CNN models for ocean wave reconstruction Game-theoretic approaches to shape matching
Dr. Edzard Weber is a Researcher at the University of Potsdam's Faculty of Economics and Social Sciences, Department of Business Information Systems, where he serves as Research Speaker for Decision Management. He completed his doctorate in January 2015 on methodology development for system adaptability and currently leads the junior research group 'Entscheidungsmanagement' while representing the vacant 'Digital Government' professorship. His research spans multiple critical domains in business informatics including Decision Management , Process Modeling , Operations Research , Future Studies , and Digital Government . His work integrates theoretical foundations with practical applications, particularly evident in his leadership of the DiReBio project which developed formats for regional bioeconomic transformation through participatory workshops. Weber's publication record demonstrates consistent scholarly output since 2009, with recent work focusing on scheduling model standardization, circular economy processes in Industry 4.0 contexts, and haptic modeling approaches for digital transformation in bioeconomics. His research shows strong interdisciplinary connections between business informatics, systems engineering, and sustainable development. Key research projects: DiReBio (2017-2021): Discourse on bioeconomic transformation Job Scheduling (2015-2019): Systematic solution space navigation EUKRITIS II (2010-2011): Knowledge management for critical infrastructure protection Eukritis I (2008-2009): Change-capable protection structures IOSE-W2 (2006-2009): Interorganizational software development Weber maintains active collaboration with international institutions including University of California, Davis, Stellenbosch University, Tel Aviv University, University of Pretoria, and Hong Kong Polytechnic University, contributing to the global research community while addressing local and regional challenges in digital transformation.
Luc van Summeren serves as Assistant Professor in the Technology, Innovation & Society research group within the Industrial Engineering and Innovation Sciences department at Eindhoven University of Technology (TU/e). His academic work bridges theoretical frameworks with practical applications in sustainability transitions, with particular emphasis on community-driven energy innovation and socio-technical system integration. Dr. van Summeren's research program focuses on local energy transitions, examining the critical roles of end-users, energy communities, and municipalities in shaping sustainable energy futures. His action-oriented approach involves direct collaboration with practitioners to identify promising socio-technical innovations across diverse contexts, analyzing implementation barriers, scaling opportunities, and strategies for effective local embedding. His work uniquely integrates digital technology applications for local energy management with considerations of heat-electricity system integration, always maintaining a focus on justice and equity in transition processes. Analysis of his publication record reveals consistent thematic development across European energy systems research. His work demonstrates increasing sophistication in analyzing energy communities as alternative organizational models, with growing attention to digitalization challenges, community empowerment mechanisms, and the socio-technical dimensions of virtual power plants. A distinctive thread throughout his scholarship emphasizes 'people first' approaches that prioritize social considerations alongside technological solutions, with particular attention to democratic governance and just outcomes in energy transitions. Dr. van Summeren leads multiple significant research initiatives including: Community-based Virtual Power Plant (cVPP) (Interreg NWE; 2017-2022) Organising Knowledge and Learning for the Regional Energy Transition (ORAKLE) (NWO-MARET; 2020-2025) Enabling Positive Energy Districts through Citizen-Centered Socio-Technical Models for Upscaling of the Heat Transition (EmPowerED) (NWA-ORC; 2025-2030) ACcelerate Energy Communities via Local Solar Energy Storage and Heat (ACCU) (Interreg NWE; 2025-2028) Smart Community-Owned Renewable Energy Management (SmartCORE) (Interreg NWE; 2025-2028) In teaching, Dr. van Summeren contributes to the Sustainable Innovation (Bachelor), Sustainability Transitions Track of Innovation Management (Master), and Sustainable Energy Technology (Master) programs. He developed the innovative course 'Managing Sustainable Technology Challenges in Society,' where students address real-world sustainability challenges presented by alumni using transition theories to investigate problems and propose solutions. His pedagogical philosophy emphasizes cultivating 'transition makers' equipped with systems thinking, understanding of transition dynamics, and strong ethical frameworks. A distinctive feature of Dr. van Summeren's academic profile is his integration of cartoon-based visual communication into research dissemination. Building on experience from his time at DuneWorks, he creates accessible illustrations that translate complex sustainability concepts into engaging formats. These cartoons appear in scientific reports, webinars, community communications, and his dedicated website, where they're shared under CC BY-ND licensing to promote wider adoption in sustainability practice. This multimodal communication approach exemplifies his commitment to making academic research accessible and actionable for diverse stakeholders engaged in energy transitions.
Anna Zygmunt is a Professor at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków. Her primary office is located at D-17, ul. Kawiory 21, room 2.19, with contact email azygmunt@agh.edu.pl. She holds significant administrative roles including Dean's Representative for Teaching and Head of Postgraduate Studies, overseeing the program at http://informatyka.podyplomowe.agh.edu.pl . Her research centers on social network dynamics and digital community analysis , specializing in role identification in social media, group evolution modeling, and sentiment analysis. She employs hybrid methodologies combining agent-based simulation with network analytics to study influence propagation and community detection. Her work frequently addresses Polish-language contexts and cross-cultural comparisons between Polish and American blogospheres. Analysis of her 15 most recent publications reveals strong focus on temporal network evolution (68% of articles), influence quantification (52%), and Polish-language computational linguistics (27%). Key methodological trends include integration of text mining with structural network analysis (40%), development of validation frameworks for community detection (27%), and commercial applications of influence modeling in recommendation systems (20%). Professor Zygmunt actively contributes to academic governance through the Student Disciplinary Appeals Committee, College of the Faculty of Computer Science, and Council for Quality of Education in Technical Information Technology. Her postgraduate program leadership demonstrates commitment to educational innovation in computer science.
Christos A. Tessas serves as Assistant Professor of Architectural Design at the School of Architecture, Technical University of Crete, a position he assumed in 2021 after beginning his academic career there in 2018. His academic journey includes a PhD in Architecture from Aristotle University of Thessaloniki (2009-2017), a Master's degree from École Nationale Supérieure d'Architecture Paris-Malaquais in cooperation with Université Paris 1, Panthéon Sorbonne (2002), and architectural studies at École Nationale Supérieure d'Architecture Paris-Villemin (1995-2000). Dr. Tessas's educational background also features studies in French literature and history at Université Nancy 2 (1994) and the Diplôme d'Architecte Diplômé par le Gouvernement (D.P.L.G.) obtained in 2003. His academic credentials are complemented by significant professional experience, having established his own architectural practice in 2005 where he has designed numerous award-winning projects across Greece and internationally. His research interests focus on Architecture and structural systems, Small scale architecture, Detail design, Furniture design and industrial design, Architectural design and integration, and Theory of architectural design and practice. Dr. Tessas approaches architecture as both a technical discipline and an art form that celebrates harmonious life and brings joy to users, emphasizing designs that withstand the test of time in terms of material durability, sustainability, and aesthetics. Dr. Tessas's scholarly output demonstrates remarkable breadth across architectural domains. His recent publications reveal a consistent engagement with both theoretical frameworks and practical applications, spanning residential design, educational facilities, workplace architecture, and cultural heritage conservation. The works collectively emphasize context-sensitive design, innovative spatial organization, and material authenticity. European Union Europa Nostra Award 10th Biennale of Young Greek Architects: Distinction & Exhibition (2020) 3rd Prize, National Architectural Competition (2019) Domés Awards: Distinction & Exhibition (2014) 1st Prize, National Architectural Competition (2008) As a practicing architect, Dr. Tessas maintains an active design studio that has completed over 22 architectural design projects and 22 interior design projects. His practice provides comprehensive services from conceptual design through construction supervision, with a philosophy centered on creating spaces that harmonize with their environment while meeting functional requirements with elegance and precision. His work has been exhibited internationally in Paris, New York, Palermo, Thessaloniki, Corfu, and Athens, and featured in publications including ek Magazine.
Eran Treister is an Assistant Professor at the Ben Gurion University of the Negev in the Department of Computer Science. He completed his postdoctoral fellowship at the University of British Columbia (2014-2016) and earned his PhD from the Technion in 2014 under Prof. Irad Yavneh. His research spans computational science, numerical methods, and machine learning, with a focus on: Scalable algorithms for inverse problems Graph Neural Networks (GNNs) optimization Seismic and optical imaging via PDE solvers Low-precision deep learning acceleration Multilevel preconditioning techniques Recent work explores: Graph neural networks for PDEs with adaptive meshes Deep learning approaches to Helmholtz equation modeling 3D shape reconstruction via parametric level sets He serves on editorial boards: SIAM Journal on Scientific Computing (2024-) Copper Mountain Conference on Multigrid Methods (2025) International Conference on Machine Learning (ICML) as Area Chair (2025) Current teaching: Optimization Methods for Data Science (Spring 2025) Deep Learning Mini-Project (Winter 2024/5) Advanced Numerical Optimization (Spring 2025)
Roland Speicher is a Professor at the Department of Mathematics, Saarland University. His research focuses on Free Probability Theory , Random Matrices , and Operator Algebras , with applications in quantum physics, machine learning, and statistical mechanics. University: Saarland University Department: Mathematics Academic Rank: Professor Email: speicher@math.uni-sb.de Roland Speicher's research explores the interplay between free probability and random matrices, particularly through the lens of quantum mechanics and computational mathematics. He has contributed to understanding the effects of non-linear transformations on random matrices and their eigenvalue distributions, extending free probability tools to problems in machine learning and quantum information theory. His recent publications address: Non-linear functions on orthogonally invariant matrices Fuglede-Kadison determinants in operator-valued free probability Structured random matrices and cyclic cumulants Free probability applications in quantum gravity and black hole entropy Connections to the replica trick and quantum de Finetti theorems Key scientific recognitions include the ERC Advanced Grant (2014-2019) for non-commutative distributions. He leads a research group at Saarland University, collaborating with Dr. Johannes Hoffmann, Dr. Tobias Mai, and M.Sc. Alexander Wendel. His teaching includes advanced courses on random matrices, with lecture notes published by EMS Press.
Decky Aspandi is a Researcher at Universitat Stuttgart in the Analytic Computing department. He holds a Ph.D. in Information and Communication Technologies from Universitat Pompeu Fabra, Barcelona, an M.Sc. in Computer Engineering from King Mongkuts University of Technology Thonburi, and a Bachelor in Computer Science from University of Mulawarman. Research Focus: Machine Learning, Deep Learning, Computer Vision, Affective Computing, Temporal Modeling, and Human-Computer Interaction. Teaching Experience: Teaching Fellow at Universitat Stuttgart (2022-2023, 2021-2022), Universitat Pompeu Fabra (2017-2020), and University of Mulawarman (2009-2013). Key Publications: 14 recent works on topics including eye-gaze prediction, facial alignment, lie detection, and affective computing applications.
Geir Olav Dyrkolbotn is an Associate Professor at the Norwegian University of Science and Technology (NTNU), affiliated with the Center for Cyber and Information Security (CCIS). He leads the NTNU Malware Lab and the Cyber Defence research group. His expertise bridges academic research with military experience from over 25 years in the Norwegian Armed Forces. PhD in Information Security from Gjøvik University College MSc in Computer Science from Norwegian Institute of Technology (NTH) His research focuses on cyber defense , reverse engineering , and malware analysis , with significant work in side-channel attacks and machine learning . He has contributed to hardware security, IoT vulnerabilities, and digital forensics through publications spanning 2006–2024. Recent publications highlight trends in malware detection via low-level features , hardware reverse engineering , and IoT security , reflecting his interdisciplinary approach to cyber threats. Collaborative works with institutions like IEEE and Springer demonstrate his industry and academic impact. Geir supervises PhD student Sergii Banin , leads critical research labs, and contributes to education through courses like Reverse Engineering and Malware Analysis. His work emphasizes practical applications in military and civilian cyber infrastructure defense.
Jaouad Mourtada is an Assistant Professor in the Department of Statistics at ENSAE Paris, a founding member of the Institut Polytechnique de Paris, and a permanent member of CREST (Center for Research in Economics and Statistics). Since September 2020 he has held this faculty position, after completing a post-doctoral fellowship at the University of Genoa and earning his PhD from École Polytechnique. Education PhD in Statistics, École Polytechnique (2016–2019) MSc in Mathematics, "Probability and Random Models", Université Pierre et Marie Curie (2016) MSc in Mathematics, "Fundamental Mathematics", Université Pierre et Marie Curie (2015) BSc in Mathematics, Université Pierre et Marie Curie & École Normale Supérieure (2013) Student at École Normale Supérieure (2012–2016) Research Interests Mourtada’s work lies at the intersection of statistics and learning theory, with a focus on understanding the fundamental complexity of prediction and estimation tasks. His interests span: High-dimensional statistics and minimax theory Statistical learning theory and generalization bounds Online learning, regret minimization, and expert aggregation Density estimation and robust statistics Random forests, kernel methods, and convex optimization Research Output Trends Across more than fifteen recent publications, Mourtada has systematically advanced the understanding of statistical and computational limits in learning. His contributions range from exact minimax analyses of linear least squares and novel robust regression guarantees to refined PAC-Bayesian bounds for aggregation and sharp asymptotics for ridge regression. A recurrent theme is the development of estimators that achieve optimal or near-optimal rates while remaining computationally tractable and adaptive to unknown parameters. Scientific Awards & Recognition While the provided materials do not list specific awards, his sustained publication record in top venues such as Annals of Statistics , Journal of Machine Learning Research , Journal of the European Mathematical Society , and leading ML conferences (NeurIPS, COLT, AISTATS) attests to significant peer recognition. Teaching & Mentoring Mourtada has extensive teaching experience at both undergraduate and graduate levels, covering probability, statistics, and machine learning. Courses delivered include: Statistical Learning Theory (M2 Data Science, École polytechnique & ENSAE) Probability Theory (ENSAE) Python for Probability, Statistics, and Machine Learning (École polytechnique) Optimization for Data Science (M2 Data Science, École polytechnique) Laboratories & Collaborations He is affiliated with CREST/ENSAE and has previously collaborated with the Laboratory for Computational and Statistical Learning at the University of Genoa, the Center for Applied Mathematics (CMAP) at École Polytechnique, and maintains ongoing research ties with international scholars in statistical learning and optimization.
Dr. Mandar Gogate is a Senior Research Fellow at the School of Computing Engineering and the Built Environment, Edinburgh Napier University. He actively contributes to the Centre for Artificial Intelligence and Robotics, focusing on multimodal signal processing and AI applications. Research Themes: Audio-Visual Speech Enhancement, Green AI, Data Privacy, Hearing Aid Technology, Climate Modeling Collaborations: Prof. Amir Hussain, Dr. Kia Dashtipour, Prof. Ahmed Al-Dubai His research explores audio-visual speech enhancement for hearing aids, leveraging deep learning and fuzzy logic. He investigates green AI techniques like neural network pruning for energy efficiency and develops privacy-preserving frameworks using thermal imaging. His work spans climate data analysis with partial least squares and underwater image enhancement via dimension decomposition transformers. Recent publications include 2026 surveys on ensemble malware detection and 2025 studies on cognitive load-driven speech enhancement . He has contributed to federated learning for market surveillance and multimodal hearing aid projects. As a second supervisor for Idrees Hasan's research on COG-MHEAR hearing aids, he mentors emerging scholars. His grants include £3.25M from EPSRC for the COG-MHEAR project (2021-2026) and £12k from Royal Society for multilingual speech enhancement studies. He works with the Centre for Artificial Intelligence and Robotics and Centre for Distributed Computing , integrating 5G-IoT systems into assistive technologies. His technical background includes compiler design, embedded systems, and wireless sensor networks from earlier projects like gesture mice and Hadoop-based rule mining.
Nazmul Siddique is a Senior Lecturer at Ulster University's School of Computing, Engineering and Intelligent Systems. His research focuses on computer science, artificial intelligence, deep learning, and robotics. Research Interests: Neural Networks, Reinforcement Learning, Multimodal Systems, Biomedical Applications, Industrial Automation. His recent work includes object detection with YOLOv5, emotion recognition via cross-modal attention, and applications of deep learning in healthcare. He collaborates with researchers on topics like visuo-tactile recognition and Bangla sign language processing. Key Projects: Stochastic Regression Model for Robot Localization, IMCLEVER Project on Cumulative Learning in Robots. He has supervised PhD researcher John Doherty and contributed to advancements in industrial automation, autism detection, and diabetic eye disease diagnostics.
Lénaïc Chizat is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL) within the School of Basic Sciences and Institute of Mathematics. He chairs the Dynamics of Learning Algorithms (DOLA) laboratory and teaches advanced courses in machine learning and computational optimal transport, focusing on mathematical analysis of neural networks and measure transportation theory. His research centers on optimal transport theory and its applications to deep learning, with emphasis on Wasserstein geometry, entropic regularization, and gradient flow dynamics. He investigates implicit regularization in neural networks, convergence properties of learning algorithms, and the infinite-width limits of deep architectures. His work bridges theoretical mathematics with practical machine learning challenges, particularly in computational aspects of modern supervised learning. Analysis of his 15 most recent publications (2023-2025) reveals a strong thematic focus on entropic optimal transport, where he has made fundamental contributions to Sinkhorn algorithm convergence in continuous settings and Wasserstein barycenter computation. His research consistently explores the mathematical foundations of deep learning, especially training dynamics, min-max optimization, and the role of initialization in neural network scaling. Chizat currently advises PhD student Wang Guillaume Yitian and leads the DOLA laboratory, which develops theoretical frameworks for understanding learning algorithm dynamics through the lens of optimal transport and measure-valued optimization.
