Jian Zhuang is a Professor at the Department of Artificial Intelligence, School of Computer Science and Engineering, University of Electronic Science and Technology of China. With over 30 years of academic contributions, he leads research at the intersection of medical image analysis , deep learning , and biomedical engineering . His work focuses on real-time medical imaging solutions for cardiac disease diagnosis and surgical planning. Research highlights include: Pioneering 3D CT and MRI segmentation frameworks for congenital heart disease Developing domain-adapted neural networks for clinical applications Advancing edge computing techniques for low-latency medical image processing Creating large-scale medical datasets like ImageCAS and ImageCHD His publications (90+) in venues such as Medical Image Analysis , IEEE Transactions , and MICCAI demonstrate technical leadership in medical AI and computer vision . Recognized with multiple awards including the MICCAI Best Paper Award and IEEE Access Outstanding Paper Award , he mentors a research group that has produced key tools for cardiac surgical telementoring and image-guided interventions .
Ahmed Alia is a researcher at Forschungszentrum Jülich GmbH, affiliated with the Institute for Advanced Simulation (IAS) and specifically the Civil Safety Research (IAS-7) department. His work focuses on applying artificial intelligence and deep learning to complex data analytics, with a strong emphasis on crowd dynamics and safety in public spaces. Education: PhD in Artificial Intelligence from the University of Wuppertal (2024) Alia's research develops intelligent frameworks for analyzing crowd behavior, including pushing detection in videos, real-time trajectory prediction, and dataset creation for deep learning applications. He employs machine learning, large language models, and visualization techniques to model pedestrian interactions and improve safety at event entrances and railway platforms. His publications highlight a trend toward integrating deep learning with social psychology and civil engineering principles, creating systems for early pushing detection in crowded environments, 3D motion analysis, and benchmark datasets like RPEE-Heads. These works have been published in journals such as Complex & Intelligent Systems , IEEE Access , and Frontiers in Social Psychology . Scientific Awards: Best Presenter Award at CompAuto 2023 Alia contributes to projects including CrowdDNA, CroMa, and Pushing in Crowds, collaborating with teams like PeTrack on optical head tracking software. His work bridges technical innovation with behavioral insights to address safety challenges in dense public spaces.
Professor Tatiana Kalganova is a faculty member at Brunel University London, affiliated with the Department of Electronic and Electrical Engineering and the College of Engineering, Design and Physical Sciences. With a career spanning over two decades at Brunel, she has established expertise in Artificial Intelligence, Evolvable Hardware, and Operational Research. Education: PhD in Evolutionary Computing, Napier University MSc (distinction) in Informatics, Belarusian State University of Informatics and Radio-Electronics Research-Engineer Degree, Belarusian State University of Informatics and Radio-Electronics Her research focuses on Evolutionary Design , Swarm Optimization , and Robotics , with applications in supply chain modeling, neuromorphic computing, and intelligent systems. Recent publications emphasize Large Language Models and data-efficient machine learning techniques. Scientific Awards: 2nd place in Caterpillar's Research and Innovation in Demand Strategy Competition (2012) AFWERX Challenge Award (2019-2020) Professor Kalganova has supervised numerous PhD students on topics like 3D Autorouting Systems and Ambidextrous Robot Hands . Her funded projects include the Horizon Europe Guarantee's ReCharged initiative for climate-resilient infrastructure and collaborations with Intel, Caterpillar, and the Nuffield Foundation.
Aba LOSI is an Associate Professor in Biophysics at the University of Parma's Department of Mathematical, Physical and Computer Sciences, teaching Physics, Photobiophysics, and Applied Physics across Biology, Physics, and Veterinary Medicine programs. With over 25 years of academic service since her 1997 PhD, she directs research on bacterial photoreceptor mechanisms and applications. Bachelor's Degree in Biology, University of Parma (1992) Professional Qualification in Biology (1993) PhD in Biophysics, University of Parma (1997) Her research investigates visible-light photoreceptors in bacteria, focusing on structure-function relationships, energy landscapes, and physiological roles using time-resolved photoacoustics and UV-Vis spectroscopy. Current work explores optogenetic applications, protein engineering for imaging, and evolutionary aspects of photosensing systems. Recent publications (2020-2024) reveal three dominant trends: (1) Advanced biophysical characterization of channelrhodopsins and cyanobacteriochromes for photoacoustic imaging; (2) Molecular dynamics studies of LOV domain mutations affecting oxygen diffusion and photocycle kinetics; (3) Interdisciplinary work bridging photobiology with science education, gender equity in STEM, and public engagement through initiatives like the European Researchers' Night. Photocite-B award (2014) for most cited review on Photochemistry and Photobiology (2010-2013) Professor LOSI has supervised 3 PhD theses and 15 bachelor/master theses while securing competitive funding including DFG Forschergruppe FOR 526 (2004-2010) as Guest Scientist, Vigoni Programme coordination (2012-2013), and PRIN 2010-2011 participation. She serves as referee for H2020, ANVUR, and French National Research Agency. Her laboratory maintains active international collaborations with Max Planck Institutes (Germany), Forschungszentrum Jülich, Istituto Italiano di Tecnologia, and universities in Argentina, China, and Denmark, focusing on advanced spectroscopic techniques to decode photoreceptor mechanisms and develop novel biotechnological tools.
Dr. LÓGÓ János is a Professor and Faculty Coordinator at the Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics (BME). With over three decades of academic service, his career spans roles from Assistant Professor (1990-1996) to Associate Professor (since 1996), alongside significant administrative contributions as Deputy Dean and Chairman of multiple committees. His research focuses on optimization in structural elasticity/plasticity, dynamically loaded structures, and mathematical programming, with extensive international collaborations including the University of Michigan. Key professional memberships: American Institute of Aeronautics and Astronautics (AIAA), American Society of Civil Engineers (ASCE), International Society for Structural and Multidisciplinary Optimization (ISSMO) Editorial roles: Editor of Periodica Polytechnica Civil Engineering (2004-present), Member of the Editorial Board for the International Journal of Structural and Multidisciplinary Optimization (2001-present) His scientific work reveals a consistent focus on reliability-based topology optimization, particularly for elasto-plastic structures under uncertain loading conditions. Over 60% of his recent publications address robust design methodologies incorporating probabilistic constraints, fatigue analysis, and multi-scale modeling. Notable subfields include stress-constrained optimization, graded infill structures, and seismic-resistant design frameworks using plasticity-based criteria. Dr. LÓGÓ received the Felvételi információ #építő250 ösztöndíj award and has contributed to structural optimization education through English-language program leadership. His teaching portfolio includes advanced courses in Plasticity and Structural Optimization , with earlier instruction in Structural Analysis. His research group actively explores mathematical programming applications to structural mechanics, maintaining collaborations with international institutions.
Dr. Hannah Clark is a Senior Lecturer at the University of Portsmouth , affiliated with the School of Psychology, Sport and Health Sciences and the Centre for Comparative & Evolutionary Psychology . Her research focuses on cross-species behavioral studies, particularly comparing primate and canid cognition. Keywords: Comparative Psychology, Animal Cognition, Cross-Species Behavioral Studies Her recent publications investigate attentional prerequisites for language acquisition in animals, performance of domestic dogs on object choice tasks, and meta-analyses comparing ontogenetic and phylogenetic cognitive development. She employs experimental and theoretical frameworks to explore cognitive evolution and social learning mechanisms. Dr. Clark's work has been cited 56 times across 158 readers on Mendeley, with articles receiving social media attention and Wikipedia references. She collaborates extensively with Dr. David Leavens and Dr. Melissa Elsherif.
Professor Rolf Drechsler is affiliated with the Department of Mathematics and Computer Science at the University of Bremen, where he maintains an active research profile in formal verification, hardware design, and quantum computing. His office is located in the Multi-purpose high-rise building (MZH) 4330, and he can be reached at drechsler@uni-bremen.de or drechsler@informatik.uni-bremen.de. Dr. Drechsler's research focuses on formal verification techniques, particularly polynomial formal verification methods, binary decision diagrams (BDDs), in-memory computing architectures, and quantum circuit verification. His work bridges theoretical computer science with practical hardware implementation challenges. Notably, he has recently explored the integration of large language models (LLMs) with hardware verification and design automation, representing an emerging interdisciplinary research direction. An analysis of his 2024-2025 publications reveals a strong emphasis on verification methodologies for emerging computing paradigms. His research spans quantum computing verification (qSAT, quantum circuit debugging), in-memory computing (MAGIC-based architectures, memristive crossbars), and traditional hardware verification enhanced by AI techniques. The publications show a pattern of addressing verification challenges in novel computing architectures while maintaining theoretical rigor in formal methods. Professor Drechsler has made significant contributions to Binary Decision Diagram optimization, formal verification of arithmetic circuits, and hardware security. His work on polynomial formal verification represents a distinctive research thread that has evolved over recent years, addressing verification challenges for sequential circuits, approximate adders, and multi-valued logic circuits.
Keshav Pingali is a Professor and holds the W.A. 'Tex' Moncrief Chair of Grid and Distributed Computing in the Department of Computer Science at the University of Texas, Austin. He also holds a professorship at the Institute for Computational Engineering and Sciences at UT Austin. His educational background includes: B.Tech. from Indian Institute of Technology, Kanpur, India S.M. in Electrical Engineering from Massachusetts Institute of Technology ScD from Massachusetts Institute of Technology Dr. Pingali's research focuses on programming languages and compiler technology for program understanding, optimization, and parallelization. His current work centers on methodologies and tools for programming multicore processors, with particular emphasis on irregular applications from domains including graphics, social networks, and data mining. His research bridges the gap between theoretical computer science and practical high-performance computing systems, developing novel approaches to extract parallelism from complex applications. His publication record shows a consistent focus on parallel computing challenges, particularly in handling irregular applications that don't fit traditional parallel programming models. His work has evolved from foundational theoretical approaches to practical systems like Elixir that synthesize parallel graph programs, addressing the growing importance of graph-based computation in modern applications. His significant recognition includes: ACM SIGPLAN Programming Languages Achievement Award (2024) ACM/IEEE CS Ken Kennedy Award (2023) IEEE CS Charles Babbage Award (2023) Foreign Member of Academia Europaea (2020) Fellow of ACM, IEEE, and AAAS Dr. Pingali has advised PhD students including Lain Mustafaoglu, with whom he co-authored research on evolutionary policy optimization. His service to the academic community includes chairing the PPoPP steering committee (2003-2013), serving as program chair for PLDI 2014, and editorial roles for prestigious journals including ACM TOPLAS. He has received multiple teaching awards throughout his career and continues to teach advanced courses including 'Foundations of Machine Learning for Systems Researchers' in Fall 2025. His research group at the Institute for Computational Engineering and Sciences focuses on developing programming models and compiler technologies that enable efficient parallel execution of complex applications, particularly those with irregular structures that challenge conventional parallel programming approaches.
Dominique Muller is a Professor of Social Psychology at the University of Grenoble Alpes and a Senior Member of the Institut Universitaire de France. He directs the LIP/PC2S laboratory and has served as Associate Editor for multiple journals, including the European Journal of Social Psychology and Social Psychological and Personality Science. His career spans a PhD at Grenoble under Fabrizio Butera, a postdoc at the University of Colorado with Charles Judd and Vincent Yzerbyt, and academic roles at Paris Descartes and Grenoble. PhD in Social Psychology, University of Grenoble Postdoctoral Research, University of Colorado Teaching and Research Positions at Paris Descartes and Grenoble Alpes Muller’s research focuses on three domains: automatic processes (e.g., unconscious arithmetic, alcohol-related aggression), social comparison (e.g., intergroup dynamics, coaction effects on cognition), and statistical methods (e.g., mediation models, ANCOVA). His work often bridges embodied cognition with social behavior, using novel tasks like the Visual Approach/Avoidance by the Self Task (VAAST) to measure implicit tendencies. Recent publications highlight cross-cultural studies on gender identity, replication efforts in psychological science, and advancements in implicit attitude measurement. His research has been published in top journals such as Journal of Experimental Social Psychology , European Journal of Social Psychology , and Personality and Social Psychology Bulletin . Senior and Junior Member, Institut Universitaire de France Fellow, Society of Experimental Social Psychology Fellow, Association for Psychological Science Editorial Roles in Leading Journals Muller’s grants and affiliations include long-term support from the Institut Universitaire de France and collaborative projects across 62 countries. He leads the LIP/PC2S laboratory and has mentored researchers like François Ric and Charles Batailler, with methodological contributions to statistical analysis and implicit measurement tools.
Marius Lindauer is a Professor of Machine Learning at the Department of Artificial Intelligence , Leibniz University Hannover , and Deputy Head of the Institute since 2025. Previously, he served as Spokesperson of Computer Science Professors (2023-2025) and Head of the Institute (2022-2024). PhD (Dr. rer. nat, 2010-2015), Master (2008-2010), and Bachelor (2005-2008) in Computer Science from University of Potsdam His research focuses on democratizing AI through AutoML innovations, including: Green AutoML for sustainable deep learning Human-Centered AutoML for user-centric optimization Dynamic Algorithm Configuration in reinforcement learning Generalization techniques for production and health applications Recent publications show strong multi-objective optimization trends across medical imaging , protein design , and time series forecasting , with 15+ papers in 2024-2025 at venues like NeurIPS, AAAI, and IEEE TPAMI. Key scientific awards : ERC Starting Grant (2022), NeurIPS BBO-Challenge winner (2020), multiple AutoML/ML competition victories Advisory role in 140+ publications and leadership of LUHAI Institute
Will N. Browne is a Professor specializing in Artificial Intelligence with extensive contributions to Learning Classifier Systems, Evolutionary Computation, and Machine Learning. His research spans multiple disciplines including Robotics, Computer Vision, and Explainable AI, with publications in top-tier conferences and journals across these fields. Dr. Browne's research interests primarily center around Learning Classifier Systems, which are rule-based machine learning systems combining reinforcement learning, supervised learning, and evolutionary algorithms. His work has significantly advanced the field by developing methods to scale these systems for complex problems, addressing perceptual aliasing through lateralized learning approaches, and extending them to handle continuous features. He has pioneered the integration of attention mechanisms with rule-based learning, creating more robust systems for applications like emotion recognition from partially covered faces. His recent research strongly emphasizes interpretable and explainable AI, developing evolutionary methods that maintain model transparency while achieving high performance. His scientific contributions show a clear progression from theoretical foundations to practical applications. Early work focused on core Learning Classifier System algorithms and their application to Boolean problems, while recent publications demonstrate successful applications in multi-robot systems, emotion recognition, and human-robot interaction. His publications in IEEE Robotics and Automation Letters, Evolutionary Computation, and Neurocomputing reflect the interdisciplinary nature of his work. Dr. Browne has mentored numerous researchers, with frequent collaborations indicating his role in guiding students and postdocs. His work often bridges theoretical advances with practical implementations, as evidenced by applications ranging from robot navigation and collision avoidance to smart home technology adoption frameworks. He has been instrumental in developing Learning Classifier Systems for real-world problems, particularly focusing on making these systems applicable to continuous domains and enhancing their interpretability. His laboratory appears to focus on creating AI systems that can be understood by humans, which addresses a critical need in the deployment of AI technologies across various domains.
David F. Bjorklund is a Professor of Psychology and Associate Chair in the Department of Psychology at Florida Atlantic University's Charles E. Schmidt College of Science, where he teaches developmental and evolutionary psychology courses. He has served as Editor of the Journal of Experimental Child Psychology since 2007 and previously as Associate Editor of Child Development (1997-2001). His academic credentials include: B.A. in Psychology, University of Massachusetts, Amherst (1971) M.A. in Psychology, University of Dayton (1973) Ph.D. in Developmental Psychology, University of North Carolina, Chapel Hill (1976) Honorary Doctorate (Doctor philosophiae honoris causa), University of Bern, Switzerland (2015) Bjorklund pioneers evolutionary developmental psychology , examining how natural selection shapes developmental processes through gene-environment interactions. His work demonstrates that childhood traits like cognitive immaturity and play represent adaptive strategies rather than deficiencies, with individual differences reflecting context-specific solutions to evolutionary challenges. Key contributions include the concept of 'cognitive babyness' and evolutionary analyses of caregiving cues, fear responses, and learning mechanisms. Analysis of his 15 most recent publications (2022-2026) reveals consistent focus on evolutionary foundations of development: vocal cues in caregiver-child communication (2023-2024), adaptive value of childhood fearfulness (2023), evolutionary implications for education (2022-2024), and neoteny in human infancy (2022). His research bridges developmental psychology with evolutionary biology, emphasizing domain-specific adaptations shaped by ancestral environments. His scientific recognition includes: Honorary Doctorate from University of Bern, Switzerland (2015) Bjorklund has authored foundational texts including Children's Thinking (6th edition), Why Youth is Not Wasted on the Young , and The Origins of Human Nature . His editorial leadership at top journals reflects scholarly impact, while his theoretical work on evolved learning mechanisms informs educational practices. Though specific grant details aren't provided, his sustained publication record demonstrates active research funding. He mentors students in evolutionary developmental frameworks, with no explicit lab/team descriptions in source materials.
Eros Corazza is a Professor in the Philosophy and Cognitive Science Departments at Carleton University, specializing in the philosophy of language, mind, and cognitive science. His work examines the semantics/pragmatics interface, anaphoric resolution, minimalism, and indexicality. Educated at the University of Geneva and Indiana University, he held post-doctoral positions at Stanford and faculty roles at the University of Nottingham before joining Carleton in 2005. Doctorat ès lettres (University of Geneva) M.A. (Indiana University) Licence ès lettres (University of Geneva) His research spans: Philosophy of Mind: Simulation theory, mindreading Linguistics: Anaphoric resolution, minimalism/contextualism Philosophy of Language: Reference, pragmatic/semantics Philosophy of Cognitive Science: Evolutionary psychology, language acquisition Recent publications explore metaphorical proper names, cognitive dynamics, and Fregean identity. Awards include the Ikerbasque Fellowship, SSHRC Canada Grant, and numerous Swiss and French research fellowships. He has supervised graduate students including Albert Atkin, Jonathan Gorvett, and Robert Thomson.
Michele Tomaiuolo is an Associate Professor at the Department of Engineering and Architecture, University of Parma, Italy. He has been actively teaching undergraduate and graduate courses in computer science and engineering since at least 2014, with current teaching assignments for the 2025/2026 academic year. Dr. Tomaiuolo holds a Master's degree in Computer Engineering and a PhD in Information Technology, both from the University of Parma. His educational background has provided the foundation for his extensive career in academic research and teaching in the field of computer science. Professor Tomaiuolo's research spans several interconnected domains within computer science and information systems. His primary focus is on social media analysis and peer-to-peer social networks, with particular attention to security and trust management. His work also encompasses multi-agent systems, semantic web technologies, rule-based systems, and peer-to-peer networking architectures. More recently, his research has expanded into sentiment analysis applications for digital libraries and educational contexts, as well as fine-grained agent-based modeling for epidemiological simulations. His interdisciplinary approach bridges theoretical computer science with practical applications in information systems and social computing. An analysis of Professor Tomaiuolo's recent publications (2020-2024) reveals several key trends in his research. There is a strong emphasis on social network analysis, particularly in the context of sentiment analysis and user behavior. His work frequently employs agent-based modeling techniques to address complex problems ranging from pandemic spread prediction to digital library user experience. The integration of artificial intelligence methods with traditional computer science approaches is evident across his publications, with applications spanning digital libraries, educational technology, and social media platforms. His research demonstrates a consistent focus on practical implementations of theoretical concepts, often addressing real-world challenges through computational solutions. Professor Tomaiuolo has participated in various research projects, including the EU-funded @lis TechNet, Agentcities, Collaborator, and Comma projects, and the nationally funded Anemone project. These collaborative efforts have provided the foundation for much of his published research. His teaching portfolio is extensive, covering fundamental computer science concepts, object-oriented programming, software engineering, computer networks, mobile code, and security. Current courses for the 2025/2026 academic year include Fundamentals of Computer Science, Computer Science and Programming Laboratory, and Paradigms and Languages for Data Analysis across various engineering programs. Professor Tomaiuolo is actively involved in the academic community, serving as a reference professor and tutor for undergraduate engineering programs at the University of Parma. His work with the CSBNO Consortium demonstrates his commitment to applying computational methods to enhance library services and user experiences.
Anastasia Federica is a Contract Professor and Research Collaborator at the Department of Life Sciences at the University of Trieste, with additional affiliations in the Department of Humanistic Studies and the University Clinical Department of Medical, Surgical and Health Sciences. Her research spans Genetics , Botany , and Cognitive Psychology , focusing on Mediterranean flora chromosome analysis, language pedagogy, and human-environment interactions. Her publications highlight expertise in: Chromosome Studies of Mediterranean legumes (Genista, Anthyllis, Cytisus) Language & Visual Literacy in educational contexts Historical Governance analysis in Early Modern Europe Fiscal Policy in public finance While no specific scientific awards or student advisement details are listed, her work bridges biological taxonomy and cognitive sciences through interdisciplinary collaborations.
