Professor Thomas Lukasiewicz is a Full Professor and Head of the Artificial Intelligence Techniques research group at the Faculty of Informatics, Vienna University of Technology (TU Wien). His research focuses on enabling machines to mimic human-like intelligence through techniques spanning deep learning, symbolic reasoning, and predictive coding. Key areas include explainable AI, hybrid neurosymbolic systems, and applications in healthcare and law. He teaches courses such as Deep Learning for Natural Language Processing, Scientific Research and Writing, and multiple seminars in artificial intelligence and knowledge representation. His research projects include Explainable AI in Healthcare (2023–2027) and foundational work on predictive coding networks. His publications (15+ recent articles) address medical image segmentation, neurosymbolic frameworks, and language model evaluation in mathematics. Notable work includes neurosymbolic hybrid models (CCN⁺), reinforcement learning for medical report generation, and theoretical foundations of predictive coding networks.
Janusz Bujnicki is a Professor and head of the Laboratory of Bioinformatics and Protein Engineering at the International Institute of Molecular and Cell Biology in Warsaw (IIMCB), Poland. He holds concurrent roles in science policy advisory bodies, including the European Commission's Group of Chief Scientific Advisors (2015-2020, then expert) and the Polish Academy of Sciences’ advisory panel (2024-). He is also a founding member of the Association of ERC Grantees (AERG) and serves on the Scientific Advisory Board of Life Science Center at Vilnius University. Academia Europaea Member (2018-) EMBO Member (2018-) Leadership Academy for Poland (2018) His research spans structural biology, RNA modification, computational biology, and molecular evolution. He has pioneered computational methods like ModeRNA, SimRNA, and ClaRNA for RNA structure prediction and analysis, and developed databases like MODOMICS for RNA modification pathways. His work has applications in understanding RNA function and drug design targeting RNA-processing enzymes. The 15 most recent publications focus on RNA structural modeling (e.g., ModeRNA, ClaRNA, SupeRNAlign), RNA-ligand interactions (LigandRNA), and RNA modification biology (MODOMICS database). These works bridge computational methods with experimental validation in RNA enzymology and structure-function relationships. Scientific Awards: ERC Starting Grant (2010), EMBO Member (2018), Crystal Brussels Sprout (2016), Prime Minister’s Award (2014), Knight’s Cross of Polonia Restituta (2014) Grants & Leadership: Founded RNA bioinformatics infrastructure at IIMCB, led EU science policy advisory groups, and organized international research competitions (RNA Puzzles)
Olaf Steinbach is a University Professor (Univ.-Prof.) at the Institute of Applied Mathematics at Graz University of Technology. His academic career spans over three decades with continuous research activity from 1992 to the present, including publications scheduled for 2026. He serves as a project manager for several research initiatives including the Special Research Area (SFB) F90 Computational Electric Machine Laboratory, which runs from 2022 to 2026. Professor Steinbach's research interests primarily focus on Numerical Analysis and Computational Mathematics . His work centers around developing and analyzing advanced numerical methods, particularly Finite Element Methods (FEM) and Boundary Element Methods (BEM), for solving partial differential equations (PDEs) and optimal control problems. His research spans both theoretical aspects (such as error analysis, stability, and convergence) and practical applications (including electric machines, electromagnetics, and biomechanics). He has made significant contributions to space-time finite element methods, which treat time as an additional dimension in the discretization process, leading to more robust and efficient solvers for time-dependent problems. Analysis of his recent publications (2021-2026) reveals a strong focus on optimal control problems governed by partial differential equations, with particular emphasis on elliptic, parabolic, and hyperbolic PDEs. His work demonstrates a consistent pattern of developing robust numerical methods with rigorous error analysis, often incorporating regularization techniques to handle challenging constraints. The applications span computational electromagnetics (particularly electric machines), fluid dynamics, and wave propagation problems. His research increasingly incorporates advanced computational techniques including parallel computing and isogeometric analysis. Professor Steinbach has supervised numerous doctoral students and has been actively involved in organizing academic events, including summer schools on Boundary Element Methods. His collaborative network extends across multiple disciplines and institutions, reflecting the interdisciplinary nature of his work in computational mathematics. His research has been supported through multiple significant projects including DK-W1244 Doctoral Program on Partial Differential Equations, the EU CASOPT project on optimization of industrial devices, and the ongoing Special Research Area on Computational Electric Machine Laboratory. These projects demonstrate his leadership in establishing research frameworks that bridge theoretical mathematics with practical engineering applications. Professor Steinbach maintains an active research group within the Institute of Applied Mathematics, collaborating closely with researchers in computational engineering, electrical engineering, and biomechanics. His work on the Computational Electric Machine Laboratory represents a particularly strong interdisciplinary effort combining mathematical theory with electrical engineering applications.
Jiannong Cao is a Chair Professor and Director of the University Research Facility in Big Data Analytics at the Department of Computing, Hong Kong Polytechnic University. He has held various academic roles since 1990, including Assistant Professor at City University of Hong Kong and Lecturer at Australian universities. PhD in Computer Science, Washington State University (1990) MSc in Computer Science, Washington State University (1986) BSc in Computer Science, Nanjing University (1982) His research focuses on cloud and edge computing , parallel and distributed computing , and mobile computing , with significant contributions to wireless sensor networks (WSN) for structural health monitoring (SHM) and software-defined networking (SDN) for vehicular communications. Recent work includes WiFi-based non-invasive health monitoring systems and multi-user computation partitioning in mobile cloud environments. Dr. Cao’s publications demonstrate trends in WSN optimization , SDN architectures , and cognitive modeling for network embedding , with applications in smart healthcare , transportation systems , and industrial IoT . Ministry of Education Natural Science Award (2018) ACM Distinguished Member (2017) IEEE Fellow (2014) Best Paper Awards at IEEE DSAA, SMARTCOMP, WCNC He has mentored numerous researchers, including Linchuan Xu , Xuefeng Liu , and Weigang Wu , who have authored key publications in top venues like ACM WSDM and IEEE INFOCOM . His professional roles include chairing IEEE committees and serving on grant panels for the Hong Kong Research Grant Council.
Professor Paul Midgley is a leading academic in Materials Science at the University of Cambridge's Department of Materials Science and Metallurgy, serving as Professor since 2007 and Head of Department from 2018–2020. He is a Fellow of Peterhouse College and holds multiple prestigious awards, including the Royal Society Fellowship and the Ernst Ruska Prize. Education: PhD in Physics (University of Bristol, 1991), MSc (Distinction) in Semiconductor Materials (1988), BSc (Hons) Physics (1987). Administration: Director of the Wolfson Electron Microscopy Suite, and active on various University committees including Research, Teaching, and REF. His research focuses on advanced electron microscopy techniques such as convergent beam diffraction, electron tomography, and nanostructure analysis, with applications in nanoscale materials science and 3D reconstruction using compressed sensing. He has pioneered methods like precession electron diffraction and multi-dimensional electron microscopy, contributing to fields like plasmonic nanoparticles and catalytic materials. Key Research Themes: Electron crystallography, nanomaterial characterization, energy materials, and defect analysis in perovskites. Midgley has delivered over 20 invited/plenary lectures globally, including at EUROMAT, the Welch Symposium, and the John Cowley Memorial Lecture. His grant income exceeds £16M as Principal Investigator. Labs/Teams: Leads the Wolfson Electron Microscopy Suite and collaborates internationally on microscopy advancements and materials innovation.
Prof. Wang Cheng-Xiang is a Professor of Wireless Communications at the Institute for Signal and System Processing (ISSS), School of Engineering and Physical Sciences (EPS), Heriot-Watt University, Edinburgh, UK since 2011. His academic journey includes roles as Deputy Head of ISSS (2014-2018), Programme Director for BEng Telecom Engineering (2015-2018), and Director for China Development Group (2006-2018). He holds a PhD from Aalborg University (2004) and prior research experience at Siemens AG and Universities in Germany, Norway, and the UK. His research focuses on applying artificial intelligence to wireless networks, 6G systems, and wireless channel modeling. He has published over 390 papers with an h-index of 57 and 13,460+ citations. Notable recognitions include IEEE/Clarivate Analytics' Highly Cited Researcher (2017-2019) and 11 Best Paper Awards. Prof. Wang serves as Executive Editorial Committee (EEC) Member for IEEE Transactions on Wireless Communications, and has held editorial roles in 10 journals. He actively organizes conferences, serving as chair for events like the IEEE World Congress on Computational Intelligence (2008) and 25th International Conference on Neural Information Processing (2018).
Alan Bovik is the Cockrell Family Regents Endowed Chair Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin's Cockrell School of Engineering. He also holds positions at The Institute for Neurosciences and serves as Director of the Laboratory for Image and Video Engineering (LIVE). With a career spanning over three decades at UT Austin, he has progressed from Assistant Professor (1984-1988) to Associate Professor (1988-1994) to his current position as Full Professor (1994-present). Dr. Bovik received his Ph.D. in Electrical and Computer Engineering in 1984 from the University of Illinois, Urbana-Champaign. Professor Bovik's research focuses on image and video quality assessment, visual perception, and digital media processing. He is renowned for developing groundbreaking algorithms including the Structural Similarity (SSIM) index, Visual Information Fidelity (VIF), and various blind quality assessment models like BRISQUE and NIQE. His work bridges engineering and neuroscience, creating perception-based models that optimize visual media delivery while reducing bandwidth consumption. These innovations have had profound industry impact, with his algorithms processing a significant proportion of global internet video traffic. His recent publications demonstrate continued leadership in perceptual quality assessment, with increasing focus on AI-generated content, high dynamic range (HDR) video, and novel applications in medical imaging. The research shows a clear trajectory toward more sophisticated, neural network-based quality metrics that better align with human visual perception across diverse content types. Professor Bovik has received numerous prestigious awards recognizing his contributions to the field: John Fritz Medal (2024) IEEE Edison Medal (2022) IAMB BaM Award (2022) Elected to the United States National Academy of Engineering (2022) Technology and Engineering Emmy Award (2021) IEEE Fourier Award for Signal Processing (2019) Progress Medal from The Royal Photographic Society (2019) Named Honorary Fellow of The Royal Photographic Society (2019) Primetime Emmy Award (2015) Edwin H. Land Medal from The Optical Society (2017) As Director of the Laboratory for Image and Video Engineering (LIVE), Professor Bovik has secured substantial research funding from organizations including the National Science Foundation and the National Institute for Standards and Technologies. His lab has produced numerous influential datasets including the LIVE Image and Video Quality Databases. He has mentored many successful students who have gone on to make significant contributions in academia and industry, though specific student names are not provided in the source materials. The Laboratory for Image and Video Engineering (LIVE) under Professor Bovik's direction has become a world-renowned center for research in perceptual image and video quality. The lab maintains close collaborations with major technology companies including Netflix, Amazon, and YouTube, ensuring that research has direct practical applications. LIVE has developed numerous influential tools and databases that are widely used in both academic research and industrial applications worldwide.
Henry Jäger is a Professor in Food Technology at the University of Natural Resources and Life Sciences, Vienna (BOKU) since 2014. Previously, he worked as a Project Manager at Nestlé (2012-2014) and Lecturer at TU Berlin (2012-2017), following his PhD/Postdoc at TU Berlin (2006-2012) and Diploma in Food Technology (2000-2006). His research focuses on electrotechnologies in food processing , particularly pulsed electric fields (PEF) and ohmic heating , for microbial inactivation, food preservation, and quality optimization. He explores applications in plant material processing , gluten-free baking , and novel food preservation methods . His work also addresses edible insect processing for allergenicity reduction and protein recovery. Recent publications (2025-2024) analyze synergies between PEF and ohmic heating, biofilm imitation systems for hygiene validation, and computational models for sterilization processes. These studies span food safety , sustainable processing , and functional food design . Henry Jäger actively contributes to scientific communities, serving on the EFFoST managing board , as scientific advisor for food conferences, and as reviewer for journals like Food Chemistry and Trends in Food Science & Technology . He has organized workshops on PEF applications and contributed to EU food technology initiatives.
Dr. Borivoje Dakic is an Associate Professor at the University of Vienna , affiliated with the Faculty of Physics and the Quantum Optics, Quantum Nanophysics and Quantum Information department. His research spans foundational and applied aspects of quantum theory. Operational reconstruction of quantum formalism Quantum interference as a resource for communication Tomography of large-scale quantum systems Macroscopic quantum phenomena His work includes scalable verification techniques for quantum devices and collaborations with experimental teams like Philip Walther’s and Markus Aspelmeyer’s groups. He received the Marko Jarić Prize (2025) for his contributions. Recent projects focus on diagnostics of quantum devices (FWF BeyondC SFB), information-theoretic foundations of quantum interference (FWF P36994), and local operations in quantum field theory (Cluster of Excellence QuantA). His research on quantum coherence in networks and macroscopic entanglement challenges traditional assumptions about quantum-classical boundaries. Publications emphasize resource-efficient tomography, device-independent verification, and foundational frameworks for quantum statistics and field theory. Teaching: Quantum Information (2025W), Theory in Quantum Optics (2025S), VCQ Summerschool Labs: Dakić Group at University of Vienna
Atakan Aral serves as an Associate Professor at the Faculty of Computer Science, University of Vienna, where he leads research in edge computing, distributed systems, and environmental monitoring applications. His work focuses on developing efficient and resilient computing systems for environmental applications, with particular emphasis on neuromorphic edge AI and the cloud-edge continuum. He maintains an active teaching schedule offering courses in Distributed Systems Engineering, Cloud Computing, and Practical Software Courses with Bachelor's Thesis work across multiple semesters through 2025. Dr. Aral's research interests span several critical areas in modern computing including edge computing architectures, federated learning approaches, neuromorphic computing for environmental monitoring, and resilient systems design. His work addresses fundamental challenges in resource-constrained environments, particularly focusing on latency-sensitive applications and energy-efficient computation. The interdisciplinary nature of his research bridges theoretical computer science with practical environmental applications, developing systems that can operate effectively in remote or resource-limited settings. Analysis of his recent publication trajectory reveals a clear evolution from foundational cloud computing research toward increasingly specialized edge intelligence systems. Early work focused on resource allocation and scheduling in cloud environments, while his current research emphasizes neuromorphic approaches for sustainable environmental monitoring. His publications demonstrate growing interdisciplinary collaboration, particularly with environmental scientists, and increasing focus on practical implementations of theoretical concepts in real-world monitoring systems. Dr. Aral leads significant research projects including TROCI (Towards Resilient Operation of Critical Infrastructure), an ongoing initiative, and SWAIN (Sustainable Watershed Management Through IoT-Driven AI), which ran from February 2021 to February 2024. His work spans multiple dimensions of computing systems, from hardware-aware algorithms to application-level implementations, with consistent contributions to major conferences and journals in distributed systems and edge computing. He is an active member of the Scientific Computing research group at the University of Vienna, working from Room 6.49 at Währinger Straße 29. His research environment includes collaboration with the Environment and Climate Research Hub, reflecting the interdisciplinary nature of his work that bridges computer science with environmental applications. His publications indicate strong international collaboration across European institutions and research groups.
Claudia Plant is a Professor in the Faculty of Computer Science , leading the Research Group Data Mining and Machine Learning . Her research focuses on clustering algorithms, data mining, and machine learning applications in areas like biomedical data, wind energy, and causality inference. She has contributed to projects such as Knowledge-infused Deep Learning for Natural Language Processing (2020–2028) and Hybrid Computational Sciences (2021–2021). Plant has authored over 160 publications, with recent work emphasizing deep learning, anomaly detection, and GPU-optimized algorithms. She actively engages in academic activities, including talks on clustering methods and interdisciplinary projects like Governing Algorithms: The Politics of Data and Decision-Making . Her research interests span clustering algorithms , graph neural networks , causality discovery , and ethical digital transformation . Notable projects include causal analysis of wind farm dynamics and AI-enhanced education tools. Plant’s work bridges computational methods with societal challenges, such as empowering marginalized communities through ethical technology adoption.
Paolo Samorì is a full-time Professor at the Université de Strasbourg , where he serves as Director of the Nanochemistry Laboratory and Emeritus Director of the Institut de Science et d'Ingénierie Supramoléculaires (ISIS) . He is affiliated with multiple prestigious academies, including the German National Academy of Science and Engineering (ACATECH) , Royal Society of Chemistry (FRSC) , and European Academy of Sciences (EURASC) . Education: Laurea (MSc) in Industrial Chemistry (University of Bologna, 1995), PhD in Chemistry (Humboldt University Berlin, 2000, summa cum laude). His research focuses on Nanochemistry , 2D materials , and supramolecular systems at interfaces , with applications in organic electronics , optoelectronics , and sensing . He pioneered methods for scanning probe microscopies and photoresponsive nanodevices , including graphene-based systems and diarylethene molecular switches. His scientific awards include the ERC Advanced Grant (2019) , Blaise Pascal Medal (2018) , and Catalán-Sabatier Prize (2017) , among 20+ honors. He has trained over 130 students and researchers , including 34 professors now active globally.
Alessandro Astolfi is a Professor of Nonlinear Control Theory in the Department of Electrical and Electronic Engineering at Imperial College London, where he has been faculty since 1996. He also holds a professorship at the Department of Civil Engineering and Computer Science Engineering at the University of Rome Tor Vergata since 2005. From 2022 to 2025, he serves as College Consul for the Faculty of Engineering and Business School, and previously served as Head of the Control and Power Group from 2010 to 2022. His research expertise spans multiple areas of control theory: Nonlinear control theory Adaptive and robust control Discontinuous stabilization Model reduction Geometric control theory Observer design Astolfi's work has produced significant applications across diverse fields including aerospace systems (under-actuated satellites, autonomous aircraft), power systems (multi-machine systems), game theory (population dynamics, multi-agent systems), and industrial processes (model reduction applications). His research contributions have been recognized with numerous prestigious awards: 2003 Philip Leverhulme Prize for outstanding research achievements of young scholars 2007 IEEE Control Systems Society Antonio Ruberti Young Researcher Prize 2009 IEEE Fellow for contributions to nonlinear control theory 2009 Googol Best New Application Paper Award 2012 George S. Axelby Outstanding Paper Award 2012 IEEE CSS Distinguished Member Award 2015 Sir Harold Hartley Medal from the Institute of Measurement and Control (UK) Professor Astolfi has authored more than 120 journal papers, 30 book chapters, and over 240 conference papers. He co-authored the monograph 'Nonlinear and Adaptive Control with Applications' (Springer-Verlag). He currently serves as Chair of the IEEE CSS Conference Editorial Board and has participated in program committees of numerous international conferences. His research group at Imperial College London maintains strong connections with academic institutions worldwide including ETH Zurich, Rice University, and SUPELEC, with whom he has established visiting lecturer relationships spanning decades.
Professor Moncef Gabbouj is a distinguished academic and researcher currently serving as Professor of Signal Processing at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Finland. Previously, he held the same position at Tampere University of Technology before the merger in 2019. He has also held visiting professorships at prestigious institutions including Hong Kong University of Technology and Science, University of Southern California, and Purdue University. Ph.D. and MSc. in Electrical Engineering from Purdue University, USA (1989 and 1986) B.Sc. in Electrical Engineering from Oklahoma State University, USA (1985) Prof. Gabbouj's research spans multiple domains within signal and image processing, with a strong focus on machine learning applications. His primary research interests include artificial intelligence, machine learning, Big Data analytics, multimedia content-based analysis, indexing and retrieval, nonlinear signal and image processing, voice conversion, and video processing and coding. His work bridges theoretical advancements with practical applications across various industries, particularly in multimedia communications and biomedical applications. His extensive publication record demonstrates a clear evolution from traditional signal processing techniques toward more sophisticated machine learning and deep learning approaches. Recent work shows increasing focus on convolutional neural networks for various applications including ECG classification, video processing, financial time-series analysis, and image recognition tasks, reflecting the broader trend in the field toward deep learning methodologies while maintaining strong foundations in signal processing theory. IEEE Fellow (2011) Member, Finnish Academy of Science and Letters (2014) Knight, First Class, of the Order of the White Rose of Finland (2006) Nokia Foundation Recognition Award (2005) Nokia Foundation Visiting Professor Award (2012) Finnish Cultural Foundation for Art and Science Award (2017) TUT Foundation Grand Award (2015) Prof. Gabbouj has supervised 64 doctoral and 72 Master's theses, demonstrating his significant contribution to academic mentoring. His research has been supported by substantial funding, including research grants totaling 8.5 million Euro (2001-2015). He has served as Academy of Finland Professor during 2011-2015 and has been involved in numerous EU research projects including Horizon, ESPRIT, HCM, IST, COST, Tempus and Erasmus programs. As Editor, Guest Editor or member of the Editorial Board of 6 international scientific journals, he has significantly influenced the academic discourse in his field. He leads the Signal Analysis and Machine Intelligence (SAMI) research group at Tampere University and serves as the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics. His research unit focuses on applying advanced machine learning techniques to solve complex problems in signal processing, computer vision, and multimedia analytics, with applications ranging from healthcare to multimedia communications and financial analysis.
Cornelia Schneider is a Professor and Head of the Institute of Computer Science at the University of Applied Sciences Wiener Neustadt. She leads research in digital health and care technologies, focusing on applications like Augmented Reality, sensor data analysis, and social robotics for vulnerable populations. Her work emphasizes user-centered design and has been supported by projects funded by FFG and the Active Assisted Living Programme. Her research spans telemedicine infrastructure, elderly care robotics, and health monitoring systems. Notable projects include 24/7-Digital (2023-2026), Care about Care (2021-2023), and AgeWell (2019-2022). She has pioneered solutions like CARU cares and DigiCare training programs. Dr. Schneider has been recognized with the AAL Award (2014) and the tecnet | accent Innovation Award (2021). She coordinates interdisciplinary teams and actively participates in conferences like Health Informatics Meets Digital Health. Her lab collaborations include FOTEC and Salzburg Research, where she previously led the e-Health Competence Center until 2019.
