Lam James is a Chair Professor of Control Engineering at the University of Hong Kong (HKU), affiliated with the Faculty of Engineering. He holds a BSc (1st Hons.) in Mechanical Engineering from the University of Manchester, and MPhil/PhD degrees from the University of Cambridge. His academic journey includes roles as a Croucher Fellow, Lecturer at the University of Melbourne, and faculty member at City University of Hong Kong before joining HKU in 1993. Professor Lam serves as Editor-in-Chief for four international journals including IET Control Theory and Applications and Journal of The Franklin Institute . His research focuses on networked control systems, vibration control, control theory, and multi-agent systems. With 600+ peer-reviewed publications, he maintains an H-index of 102 (Web of Science) and 108 (Scopus), recognized as a Highly Cited Researcher in multiple fields. Education: BSc Manchester (Mechanical Engineering), MPhil/PhD Cambridge Editorial Roles: 20+ journals, including leadership roles since 2012 His awards include Foreign Membership of Academia Europaea (2024), National Academy of Artificial Intelligence membership (2025), and two State Natural Science Awards (2015, 2019). He is a Fellow of multiple institutions including IEEE, IMechE, and HKIE.
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.
Hans Tompits is an Associate Professor in the Department of Knowledge-Based Systems at Technische Universität Wien (Vienna University of Technology). His research focuses on computational logic, declarative logic programming, and formal methods, with a particular emphasis on Answer-Set Programming (ASP). He coordinates the Master's program in Logic and Computation and leads projects in areas such as formal methods for optimization, fault-tolerant autonomous systems, and algorithmic composition. His work bridges theoretical advancements with practical applications, including tools like SeaLion (an ASP IDE with debugging support) and dlvhex (an ASP-based semantic web reasoner). He has contributed to foundational topics like program equivalence, debugging techniques, and integration of ASP with external systems. His recent projects address challenges in autonomous vehicle architectures, music composition algorithms, and safety-critical system design. Tompits has published extensively on topics ranging from nonmonotonic reasoning and modal logics to the development of declarative programming tools. His interdisciplinary approach spans computer science, mathematics, and AI, with applications in both academic and industrial contexts.
Georgios B. Giannakis is a Full Professor, Endowed Chair, and Presidential Chair in the Department of Electrical and Computer Engineering at the University of Minnesota since 1999. He directs the Digital Technology Center and has held academic roles at the University of Virginia (1987-1999) and USC (1982-1986). His research spans Data Science, Wireless Communications, Network Science, and Statistical Signal Processing , with applications to IoT and power systems. Diploma in Electrical Engineering, NTUA (1981) MSc in Electrical Engineering, USC (1983) MSc in Mathematics, USC (1986) PhD in Electrical Engineering, USC (1986) His publications (470+ journals, 770+ conferences, 34 patents) focus on fading channel modeling, UWB localization, blind signal estimation, and cross-layer wireless design . Articles emphasize multicarrier systems, time-varying channels, and ultra-wideband communication , with citations exceeding 76,000 (H-index 145). Scientific Awards : EURASIP 'Athanasios Papoulis' Society Award (2020) IEEE Fourier Technical Field Award (2015) Gugliermo Marconi Prize Paper Award (2003) 9 Best Journal Paper Awards (IEEE/SPS & ComSoc) IEEE SPS Technical Achievement Award (2001) He has mentored over 50 PhD students and 25 postdocs, served IEEE as Distinguished Lecturer, and contributed to Greek university accreditation panels. His work bridges theoretical signal processing and practical communication systems .
Anna Beer is a researcher in the Faculty of Computer Science, specializing in data mining and machine learning with a focus on density-based clustering, spectral clustering, and interactive clustering frameworks. She holds a BSc and MSc in computer science and maintains an ORCID profile (https://orcid.org/0000-0002-6890-997X) for her research contributions. Research Themes: Development of clustering algorithms (e.g., DISCO, Scar, LUCKe), fairness in density-based clustering (FairDen), and applications to molecular dynamics and climate research (DROPP). Collaborations: Works with colleagues like Ira Assent, Christian Plant, and Lars Krieger, with recent contributions to conferences like ICLR 2025. Activities: Presented research on density-connectivity distance at a 2023 oral contribution. Publications: 9 publications since 2019, including 3 in 2025 and 6 in 2024, covering topics from cluster evaluation to deep active learning strategies.
Sara van de Geer is a Full Professor at the Seminar for Statistics within the Department of Mathematics at ETH Zürich since 2005. She previously held academic positions at the University of Leiden, Université Paul Sabatier (Toulouse), and others. She earned a Master's (1982) and Ph.D. (1987) in Mathematics from Leiden University. Her research focuses on high-dimensional statistics, empirical processes, and mathematical foundations of machine learning. Van de Geer has received prestigious recognitions including the Van Wijngaarden Award (2016), Knight in the Order of Orange-Nassau (2015), and membership in Leopoldina (2013). She served as President of the Bernoulli Society (2015–2017) and Chair of the Seminar for Statistics at ETH Zürich. Her contributions include landmark works on statistical learning theory and high-dimensional inference, with key publications in top journals like Annals of Statistics and SIAM/ASA Journal on Uncertainty Quantification. Her academic leadership includes organizing Saint Flour Lectures, Wald Lectures (2016), and delivering plenary lectures globally. Her research bridges theoretical statistics with applied methodologies, emphasizing rigorous mathematical frameworks for modern data analysis challenges.
Ansgar Jüngel is a Full Professor for Analysis of Nonlinear Partial Differential Equations (PDEs) at the Technische Universität Wien (TU Vienna), affiliated with the E101-Institute for Analysis and Scientific Computing. His academic journey includes roles at universities in Berlin, Konstanz, Mainz, and Vienna since 1991. He specializes in mathematical analysis of cross-diffusion systems, entropy methods, semiconductor models, and quantum fluid dynamics. Notable achievements include an ERC Advanced Grant (2021) and the Tsungming-Tu Award (2011). Research focuses on nonlinear PDEs with applications in physics, engineering, and biology, emphasizing rigorous existence theory, numerical methods, and entropy-based approaches. Recent projects include 'Emerging network structures and neuromorphic applications' and 'Taming complexity in partial differential systems.' His teaching includes courses on partial differential equations, calculus of variations, and computational finance. Publications span over 200 works, with key contributions on cross-diffusion models, quantum hydrodynamics, and energy-transport systems. He has supervised numerous PhD students and collaborates internationally on topics like semiconductor simulations and stochastic interacting particle systems. Grants include an FWF Special Research Programme and ERC funding.
Dragi Kimovski is a Habilitated Assistant Professor in Distributed Systems at Klagenfurt University, Austria, focusing on Edge Computing and AI. He previously held roles at the University of Innsbruck and the University of Information Science and Technology in Macedonia. His research spans Edge/Fog/Cloud computing, multi-objective optimization, and high-performance computing. He has coordinated major projects like 6GContinuum and KärtnerFog, and led initiatives such as DataCloud and ASPIDE. His teaching includes courses on Distributed Computing, Cloud Computing, and IoT. He is the co-creator of the Carinthian Computing Continuum and maintains a blog on Edge AI World. His work emphasizes sustainable and efficient computing solutions for emerging technologies. Education: Not explicitly listed in the provided text. Research Interests: Edge Computing, Fog Computing, Cloud Computing, Multi-objective Optimization, High-Performance Computing, AI in Distributed Systems. His work addresses challenges in resource management, latency reduction, and scalability across heterogeneous environments, with applications in healthcare, IoT, and 6G networks. Projects: 6GContinuum (Coordinator): Focuses on AI services over 6G networks. KärtnerFog (Scientific Coordinator): Develops adaptive Fog infrastructures over 5G. DataCloud (WP5 Leader): Manages Big Data pipelines on the Computing Continuum. ASPIDE (Scientific Coordinator): Advances exascale programming models for data processing. Teaching: Klagenfurt University: Courses include Distributed Computing, IoT, Cloud Computing, and Advanced Programming. University of Innsbruck: Taught Advanced Parallel and Distributed Systems. University of Information Science and Technology: Courses in High-Performance Computing and Network Architectures. Labs/Teams: Co-created the Carinthian Computing Continuum, an automated SDN testbed for Edge computing research. Active in interdisciplinary teams addressing extreme data processing and sustainable computing.
Volker Mehrmann is a full professor at the Department of Mathematics and Natural Sciences, Technische Universität Berlin. He previously served as Chair of the DFG Research Center MATHEON (2008-2016) and held faculty positions at TU Chemnitz (1993-2000) and RWTH Aachen (1990-1992). His career spans 40+ years, including roles at Universität Bielefeld (1980-1993) and visiting positions at Kent State University and IBM Scientific Center. Education: Habilitation in Mathematics (1988), PhD in Mathematics (1982), Diploma in Mathematics & Physics (1979), all from Universität Bielefeld Research Interests: Mehrmann's work focuses on numerical mathematics, particularly differential-algebraic equations (DAEs), matrix theory, control theory, and operator theory. His research includes nonlinear eigenvalue analysis, optimal control, model order reduction, and parallel computing for mathematical problems. He has authored over 787 citations (as of 2006) and pioneered numerical methods for unstructured nonlinear DAEs and structured polynomial eigenvalue problems. Scientific Trends: His publications reveal a progression from foundational work in linear quadratic control (1991) to modern challenges in nonlinear eigenvalue problems (2013), with consistent emphasis on DAEs, matrix theory, and numerical methods. Collaborations include major industrial partnerships with DaimlerChrysler, Audi, and CST GmbH. 2011: ERC Advanced Grant for Numerical Mathematics 2006-2013: Leadership in nonlinear eigenvalue problems 1999-2006: Development of SLICOT control theory software 1991-2005: Advancements in DAE analysis and control Honours: Mehrmann is a SIAM Fellow (2011), recipient of the Radon Lecture (2017), Gauss Lecture (2015), and ICIAM Plenary speaker (2015). He served as President of GAMM (2011-2013) and Vice President of the European Mathematical Society (2017-2018). Professional Service: He has held numerous leadership roles including Editor-in-Chief of Linear Algebra and its Applications (1999-), and advisory board positions at RICAM Linz (2014-), Fraunhofer ITWM (2013-), and CERFACS (2009-).
Manuel Woschank is a Senior Lecturer at Montanuniversität Leoben, affiliated with the Chair of Industrial Logistics. He holds academic degrees including a Diplom-Ingenieur (FH), MSc, and Dr.sc.admin., and serves as Deputy Chair and contact for international cooperation. Education: Dipl.-Ing. (FH), MSc, Dr.sc.admin. His research focuses on Industrial Logistics and Circular Economy integration in SMEs, Industry 4.0/5.0 technologies for sustainability, and Competence-Based Education frameworks. Recent work addresses decarbonization strategies, digital twins, and real-time data in production planning. Key trends in his publications include reverse logistics in circular economy adoption, competence barriers in SMEs, and digitalization in logistics education. He contributed to projects like 'Engineering Excellence for the Mobility Value Chain' (EE4M) and 'CoR-ILog.' Woschank co-developed the LogiLegoLab , a problem-based learning approach for logistics education. He actively explores human-centricity in Industry 5.0 and cross-regional competence studies (Asia, Central Europe).
Christian Vogel is a Senior Lecturer affiliated with the Institute for Signal Processing and Speech Communication at TU Graz. His work focuses on advanced signal processing techniques, analog/digital conversion systems, and mixed-signal architectures. Key research areas include time-interleaved ADC error correction, digital predistortion for RF systems, and EVM estimation methodologies. Education: Holds a Dr.techn. (Doctor of Engineering) and Dipl.-Ing. (Diploma in Engineering). His research emphasizes practical applications in mixed-signal systems, including calibration techniques for high-resolution ADCs, nonlinear system compensation, and energy-efficient transmitter design. Recent work explores optimization-based quantization, crest factor reduction in envelope tracking, and adaptive filtering for digital predistortion. Publications span over 15 years, addressing topics ranging from PWM-based RF transmission to all-digital phase-locked loops. His contributions bridge theoretical signal processing with hardware implementation challenges in modern electronic systems. Teaching expertise includes analog and digital signal processing, with authorization to teach at TU Graz. Active in industry-academia collaboration, his work aims to improve system performance in telecommunication and embedded electronics.
Balwin Bokor is a Researcher at Steyr University of Applied Sciences, affiliated with the Department of Production and Operations Management. His work focuses on industrial systems, simulation modeling, and sustainable manufacturing practices. Bachelor of Arts (BA), Master of Science (MSc) Research Interests: Bokor's research spans production engineering and operations management, emphasizing energy efficiency, logistics optimization, and simulation-based analysis. His studies address material requirements planning (MRP), constant work-in-process (CONWIP) systems, and flexible capacity adjustments in multi-stage production environments. Article Trends: Recent publications highlight simulation-driven approaches to production planning, energy cost balancing, and control system optimizations. These works align with Industry 4.0 trends in smart production and sustainable logistics. Scientific Awards: Recipient of the Würdigungspreis (2022), an Austrian award recognizing academic merit. Collaborations: Active in cross-institutional research networks, with collaborations in production system engineering, battery manufacturing, and energy reduction strategies. His work contributes to the 'fingerprint' research areas identified by Steyr University of Applied Sciences.
Hans-Georg Brachtendorf is a Professor at the Research Center Hagenberg Embedded Systems within the School of Informatics, Communications and Media at the University of Applied Sciences Upper Austria - Hagenberg Campus. His work focuses on advanced circuit simulation techniques, RF engineering, and digital signal processing with significant contributions to harmonic balance methods and nonlinear circuit analysis. His research interests span electronic circuit design, RF power amplifiers, digital signal processing, and nonlinear dynamics. Specializing in harmonic balance methods and oscillator circuits, he has developed innovative techniques for steady-state circuit simulation, nonlinear adaptive filtering, and multiplier-less digital filter design. His work bridges theoretical mathematics with practical electronic engineering applications, particularly in THz-wave detection and wireless communication systems. Analysis of his recent publications (2022-2025) reveals a strong focus on nonlinear dynamics identification using symbolic regression, optimization of digital filters through multiplier-less architectures, and governing equation reconstruction from measurement data. His work demonstrates consistent advancement in computational efficiency for circuit simulation while addressing modern challenges in RF and THz technologies. While no specific scientific awards are listed in the available data, his research has generated significant academic impact with 43 publications since 1994 and an h-index reflecting substantial citation influence. Professor Brachtendorf has supervised at least two academic works according to institutional records, though specific student names are not provided. His research activities include collaborations evidenced by joint publications with researchers such as Steiger, Dalpiaz, and Kronberger across multiple institutions. The Research Center Hagenberg Embedded Systems serves as his primary laboratory environment, focusing on digital transformation within ICT strength areas with particular emphasis on circuit simulation and signal processing applications.
Lukas Exl is a Senior Lecturer at the University of Vienna and a Research Director at the Wolfgang Pauli Institute (WPI), where he leads the Mathematical AI/ML Research Division. He holds a habilitation (venia docendi) in Computational Science from the University of Vienna, the first in this interdisciplinary field. Research Platform MMM Mathematics-Magnetism-Materials Wolfgang Pauli Institute (WPI), Vienna His research integrates Applied Mathematics, Computational Physics, and Scientific Machine Learning, focusing on numerical methods for PDE-based simulations, data-driven modeling, and reduced-order approaches. Key applications include computational micromagnetism for green energy materials and developing physics-informed neural networks (PINNs) with interpretable architectures. Recent publications emphasize machine learning techniques for magnetic material optimization, stray field computation, and trustworthy AI (TAI/XAI). He supervises students in Computational Science, Applied Mathematics, and Physics, with a focus on Extreme Learning Machines (ELMs), PINNs, and tensor decomposition methods. Data-driven Reduced Order Approaches for Micromagnetism (FWF Project, €484k, 2024-2028) Design of Nanocomposite Magnets by Machine Learning (FWF Project, €254k, 2022-2027) Reduced Order Approaches for Micromagnetics (FWF Project, €402k, 2018-2024) His team includes researchers like Dr. Sebastian Schaffer (PhD graduate), Kein Gjordeni, and Caroline Maitz. Collaborations span Danube University Krems and Technical University of Denmark's Energy Conversion and Storage department.
Dipl.-Ing.(FH) Dr. Markus Gusenbauer is a researcher at the University for Continuing Education Krems , affiliated with the Center for Modelling and Simulation . His work focuses on computational materials science, micromagnetics, and machine learning applications in magnetic materials. He leads projects such as "Towards the digital twin of a permanent magnet" and "Magnetism at interfaces: from quantum to reality" , funded by organizations like FWF and Bundesländer. His research includes optimizing permanent magnet performance, analyzing nanoscale defects, and developing machine learning models for material design. Key research areas include: Coercivity enhancement in magnets Micromagnetic characterization of MnAl-C materials Deep learning for hysteresis property prediction Stochastic micromagnetism and mesh-independent simulations His recent publications (2020-2024) address topics like rare-earth reduction in magnets, twin boundary effects, and conditional physics-informed neural networks. He actively participates in international conferences, presenting work on topics such as machine learning-assisted interface analysis and magnet design strategies.
