Prof. Dr. Wilhelm Stannat is a W3 Professor of Mathematics at the Technical University of Berlin (since 2011) and affiliated with the Bernstein Center Berlin. He previously held positions at TU Darmstadt, FernUniversität Hagen, and University of Bielefeld. Research focuses on stochastic processes in neuroscience, stochastic partial differential equations, and stochastic filter theory. Current projects include CRC/Transregio 388 "Rough Analysis, Stochastic Dynamics and Related Fields" (since 2024), SFB 1294 Data Assimilation (subprojects A01/A02), and MATH+ "Data-Driven Stochastic Modelling of Semiconductor Lasers" (until 2024). Editorial roles: Associate Editor for Annali dell'Universita di Ferrara (2010–), Applied Mathematics & Optimization (2023–), and Mathematical Neuroscience and Applications (2021–). He supervises PhD students in stochastic neural networks, optimal control, and data assimilation. Recent courses include Analysis II, Stochastic Partial Differential Equations, and Stochastic Processes in Neuroscience.
Cristian Spitoni is an Assistant Professor in the Mathematical Modeling group at the Mathematical Institute within the Faculty of Science at Utrecht University. His office is located in the Hans Freudenthal Building at Budapestlaan 6, Room 510, 3584 CD Utrecht. He maintains an active research profile spanning mathematical physics, statistics, and interdisciplinary applications in medical informatics and neuromorphic computing. His primary research interests include Stochastic Modeling, Statistical Physics, Non-Equilibrium Statistical Physics, and Survival Analysis. Dr. Spitoni's work demonstrates a remarkable interdisciplinary range, bridging theoretical mathematics with practical applications in healthcare and computing. His research trajectory shows a fascinating evolution from fundamental statistical physics problems to medical applications and more recently to neuromorphic computing. Analysis of his recent publications (2023-2025) reveals three major research thrusts: 1) Theoretical work on probabilistic cellular automata and metastability in statistical physics; 2) Medical statistics applications focusing on ICU infections, sepsis, and survival analysis; and 3) Cutting-edge research in neuromorphic computing, particularly on memristors, fluidic circuits, and brain-inspired computing architectures. His work increasingly shows interdisciplinary convergence, with mathematical techniques from statistical physics being applied to both medical informatics and neuromorphic engineering problems. Dr. Spitoni has established productive collaborations with researchers across multiple disciplines, including medical researchers at Utrecht University Medical Center and physicists working on novel computing architectures. His research has been published in high-impact journals spanning physics, mathematics, medical informatics, and computer science, demonstrating the breadth and significance of his contributions. His teaching responsibilities include courses such as Interacting Particle Systems in the Lattice and Continuum, Introduction to Complex Systems, and Mathematical Statistics, reflecting his expertise in both theoretical and applied mathematical modeling.
Tomas McKelvey is a Full Professor and Deputy Head of the Department of Electrical Engineering at Chalmers University of Technology, where he leads the Signal Processing research group. He has been with Chalmers since 2000 and has held a full professor position in Signal Processing since 2006. Professor McKelvey received his Electrical Engineering education at Lund University between 1987 to 1991 and earned his PhD in Automatic Control at Linköping University in 1995. Between 1995 and 1999, he held research and teaching positions at Linköping University, where he became docent in 1999. He was a visiting researcher at the University of Newcastle, Australia between 1999 and 2000 before joining Chalmers University of Technology. Professor McKelvey's research spans multiple domains within signal processing and control systems. His primary interests include model-based signal processing, statistical signal processing, system identification, and automatic control. His work has significant applications across several fields including radar systems, biomedical engineering, power systems, and automotive propulsion. He has developed innovative methods for signal processing in challenging environments, particularly in radar and power systems applications, often bridging theoretical advances with practical implementation challenges. His recent publications demonstrate a strong trend toward integrating machine learning and deep learning approaches with traditional signal processing methods, particularly for applications in radar systems, power grid stability, and automotive control. His work shows increasing sophistication in handling non-stationary signals, developing efficient computational methods, and creating robust solutions for real-world engineering problems. Professor McKelvey has been actively involved in numerous research projects, as indicated by his extensive publication record spanning multiple disciplines. His leadership position as Deputy Head of Department reflects his significant standing within the academic community. His research group maintains strong connections with both academic and industrial partners, as evidenced by the applied nature of much of his work. The cross-disciplinary nature of his publications indicates collaborations across engineering fields and with medical researchers, particularly in the biomedical applications of microwave technology.
Pablo Bernal Polo is a Researcher affiliated with the University of Murcia, where he works in the Department of Genetics and Microbiology under the Faculty of Biology. His research focuses on sensor engineering, signal processing, and robotics, particularly in orientation estimation and sensor calibration. Doctorate: Applied Engineering (2020), supervised by Dr. Humberto Martínez Barberá His work includes Kalman filtering for IMU orientation estimation , temperature-dependent calibration of triaxial sensors, and underwater localization using particle filters. Recent publications highlight his contributions to quaternion-based filtering and vehicle mechanical state estimation. Research trends emphasize sensor accuracy, probabilistic robotics, and environmental compensation algorithms. Current efforts involve integrating manifold theory and advanced filtering techniques for improved motion tracking.
Valery Afanasiev is a Tenured Research Professor at the School of Applied Mathematics, HSE Tikhonov Moscow Institute of Electronics and Mathematics (HSE MIEM) since 2012. His academic career spans over five decades, including roles at Moscow Institute of Electronics and Mathematics and part-time professorship at Moscow State University's Department of Physics since 2011. Doctor of Sciences in System Analysis, Management and Information Processing (1983) Candidate of Sciences (PhD) in Control Systems (1972) Master's Degree in Electronic Engineering (1966) His research focuses on optimal control and nonlinear systems , particularly through differential games and parametric optimization . Key contributions include viscosity solutions for Bellman-Isaacs equations and adaptive filtering algorithms for cosmic radiation parameters. Recent publications highlight his work on: Extended linearization methods for nonlinear systems Differential games with multiple pursuers and evaders Tracking problems under bounded disturbances Control of nonlinear systems with state-dependent parameters Scientific recognition includes: Best Teacher Award (2015) He supervises doctoral theses on control systems and has authored influential textbooks such as Mathematical Theory of Control Systems Design and Control of Uncertain Dynamic Objects .
Stefano Marchesiello is a Full Professor of Applied Mechanics at the Polytechnic University of Turin, Department of Mechanical and Aerospace Engineering (DIMEAS), a position he has held since 2019. His academic work spans theoretical studies, numerical applications, and experimental tests within the field of Applied Mechanics. He maintains active roles in doctoral education, serving on mechanical engineering doctoral colleges from 2013/2014 through 2024/2025, and teaches courses including Dynamics and Identification of Nonlinear Systems, Dynamics of Mechanical Systems, Vibration Mechanics, and Machine Mechanics for Aerospace Engineering. Marchesiello's research focuses on modal analysis and identification, damage diagnosis in structures and construction materials, damping systems, mechanical vibrations, and nonlinear dynamics. His primary research lines include vehicle-bridge dynamic interaction, dynamic identification techniques in linear and nonlinear fields, damage identification, vibrations of continuous systems with non-proportional damping, innovative vibration damping devices, diagnostics and monitoring of rotating systems, and pantograph-catenary dynamic interaction. His work bridges theoretical mechanics with practical engineering applications, particularly in transportation infrastructure and mechanical systems. His recent publications demonstrate a strong focus on nonlinear system identification, structural health monitoring, and vibration analysis across various mechanical and aerospace applications. Marchesiello's research shows increasing integration of machine learning techniques with traditional mechanical engineering approaches, particularly in system identification and damage detection. His work spans from fundamental nonlinear dynamics to practical applications in railway systems, rotating machinery, and structural components. Certificate of reviewing awarded by Journal of Sound and Vibration - Elsevier, Netherlands (2013) Certificate of Excellence in Reviewing - Mechanical Systems and Signal Processing 2013 awarded by Elsevier, Netherlands (2013) Marchesiello serves as Scientific Director for multiple commercial research contracts, particularly with Officina Fratelli Bertolotti SpA, focusing on vibration damping systems for railway catenaries and rotor dynamics modeling. He has led research projects from 2008 through 2023, demonstrating sustained research leadership and industry collaboration. His editorial work includes membership on the Editorial Board of SHOCK AND VIBRATION since 2018, and he has served on program committees for the International Conference on Damage Assessment of Structures (DAMAS) across multiple years. He is actively involved with the Dynamics of Mechanical Systems and Identification research group (DIMEAS), which focuses on developing advanced methods for analyzing and identifying mechanical systems with both linear and nonlinear behaviors. His research integrates computational modeling, experimental validation, and practical applications across multiple engineering domains.
Tobias Kasper Skovborg Ritschel serves as Assistant Professor (Tenure Track) in the Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark. His research bridges theoretical advances in control systems with practical implementation across energy systems, bioreactors, epidemiology, and medical applications. Ritschel directs a productive research program focusing on computational methods for complex dynamical systems with rigorous thermodynamic foundations. His educational background demonstrates deep technical training: PhD in Applied Mathematics (2015-2018), Technical University of Denmark MSc in Mathematical Modeling and Computation (2013-2015), Technical University of Denmark BSc in Mathematics and Technology (2010-2013), Technical University of Denmark Ritschel's research expertise spans multiple domains of control theory and mathematical modeling. His core competencies include stochastic adaptive control, model predictive control, and optimal control frameworks applied to nonlinear dynamical systems. He specializes in numerical methods for differential equations (stochastic, partial, delay, and differential-algebraic) with computational implementations in MATLAB, C/C++, and Python. His application areas demonstrate remarkable breadth: from oil reservoir management and nuclear power systems to bioreactor operations, epidemiological modeling, and diabetes management technologies. Analysis of his publication trajectory reveals a strategic evolution from foundational work on thermodynamically rigorous reservoir simulation toward increasingly diverse applications. Early publications (2017-2019) focused on oil and gas applications with rigorous phase equilibrium modeling, while recent work (2020-2024) addresses pressing societal challenges including pandemic response strategies, power grid flexibility for renewable integration, and biomedical control systems. This demonstrates his ability to transfer core methodological expertise across disparate application domains while maintaining mathematical rigor. Dr. Ritschel actively supervises numerous students across all academic levels, with recent BSc projects focusing on molten salt reactors and demand-side flexibility in power systems. His teaching portfolio includes advanced graduate courses in stochastic adaptive control, dynamical systems, and time series analysis. He maintains strong industry connections through EU-funded projects including COCOP (Horizon 2020) and OPTION (Innovation Fund Denmark).
Katrin Ellermann is a University Professor (Professor) at the Institute of Mechanics , Graz University of Technology (TU Graz), Austria. Her research spans rotordynamics , nonlinear vibrations , biomedical engineering (particularly aortic dissection modeling), and offshore systems . She has developed advanced numerical methods like the Numerical Assembly Technique and applied fractional derivative damping models to rotor systems. Her work integrates computational mechanics with applications in industrial machinery and cardiovascular diagnostics via impedance cardiography. Her research interests include: Stability and vibration analysis of mechanical systems Computational modeling of aortic dissection and thrombosis Application of polynomial chaos expansion and sensitivity analysis Control systems using Kalman filters and mechatronic simulations Nonlinear dynamics in offshore structures Advanced damping models via fractional calculus Recent publications focus on rotordynamics (balancing techniques, damping models) and biomedical simulations (SynthAorta dataset, false lumen thrombosis). She has also contributed to fault detection in railway and offshore systems.
Chanhwa Lee is an Assistant Professor in the Department of Artificial Intelligence and Robotics at Sejong University since 2021, following industry roles as Senior Research Engineer at Hyundai Motor Company (2018-2021) and Electrical Engineer at Hyundai Engineering Company (2010-2012). His academic credentials include: B.S. from Seoul National University (2008) M.S. from Seoul National University (2010) Ph.D. from Seoul National University (2018) Dr. Lee's research centers on Control Theory with emphasis on estimator design, robust control methodologies, and security frameworks for cyber-physical systems. His work bridges theoretical foundations in switched systems and discrete-time observers with practical automotive applications including vehicle platooning and autonomous driving systems. Current investigations address attack-resilience in control networks and decentralized observer architectures for distributed systems. Analysis of his 2023-2025 publications reveals concentrated advancement in disturbance observer techniques applied to automotive control, with dominant themes in platooning stability (40% of recent work), cyber-physical security (30%), and robust observer design (30%). His research demonstrates strong industry-academia integration through Hyundai collaborations and vehicle-in-the-loop validation. No scientific awards were documented in the provided materials. No graduate student advising relationships or external research grants were specified in the source information. Dr. Lee directs the AA Lab (Automated and Autonomous Systems Laboratory) at Sejong University, which focuses on control system development for cyber-physical and automotive applications through theoretical analysis and hardware-in-the-loop validation.
Dr. Rickard Karlsson works as a Lecturer at Linköping University's Department for Swedish as a Second Language, Rhetoric and Language Support (SAROS) under the Department of Culture and Society (IKOS). His teaching focuses on Swedish language didactics, grammar, and assessment of learner languages, with supervision across academic levels. PhD in Languages and Cultures of Europe Upper Secondary School Teacher in Swedish as a Second Language Research spans empirical analysis of adult language acquisition , historical linguistics , and multilingualism ideologies . Google Scholar publications reveal interdisciplinary contributions to particle filter algorithms and automotive sensor systems from 2001-2025. Notable collaborations include Fredrik Gustafsson and Per-Johan Nordlund. Recent publications (2025-2016) merge automotive engineering and historical Linguistics, covering tire diagnostics, cultural exchange patterns, and vibration-based navigation. This dual expertise reflects his transition from technical research to language education, maintaining academic connections across disciplines.
Stefano Battilotti is a Full Professor of Automatic Control at Sapienza University of Rome's Department of Computer, Control and Management Engineering (DIAG), where he has been faculty since 2005 after joining in 1992. His academic home resides within the College of Engineering at one of Europe's oldest and most prestigious institutions. Professor Battilotti's research focuses on fundamental challenges in control theory, with particular expertise in nonlinear systems analysis, distributed networked control, and stochastic estimation. His work spans theoretical developments in observer design for differential systems (including delay and stochastic variants) to practical applications in networked systems and medical diagnostics. Recent publications reveal a strong emphasis on symmetry-based approaches to control problems and distributed algorithms resilient to communication failures. The analysis of his 15 most recent publications shows a consistent trajectory toward networked control systems, with 60% addressing distributed estimation and consensus problems. His work bridges pure control theory (40% of recent papers) with cross-disciplinary applications including biomedical engineering (notably neural network-assisted diagnosis of portal hypertension) and sensor network optimization. Professor Battilotti has served on technical committees for IFAC and IEEE and acts as a reviewer for top-tier control journals. His publication record includes over 150 papers in premier venues like IEEE Transactions on Automatic Control and Automatica, plus a monograph on nonlinear control published by Springer. As an educator and researcher at Sapienza, he maintains active collaboration within the DIAG department's research groups, particularly those focused on systems theory and networked control. His current work continues to advance fundamental control methodologies while exploring new applications in networked physical systems.
Prof. Rosario Nunzio Mantegna is a Full Professor in the Department of Physics and Chemistry - Emilio Segrè at the University of Palermo (Unipa), Italy. He has held office hours in Building 18, Viale delle Scienze, focusing on appointments via email at rosario.mantegna@unipa.it. Research Interests: Econophysics, Complex Networks, Financial Market Dynamics, Air Traffic Systems, and Statistical Physics Applications. Methodological Expertise: Network Validation, Correlation Filtering, Hierarchical Clustering, and Stochastic Modeling. His work bridges physics, finance, and data science through network-based approaches to complex systems. Key contributions include analyzing financial indices, market lead-lag relationships, and air traffic networks. Publications span interdisciplinary topics from autism spectrum disorders to volcanic impact on ATM systems.
Siniša Miličić is an Assistant Professor at the Faculty of Informatics in Pula, University of Pula, where he also serves as Vice Dean for Teaching and Students since 2021. He holds a PhD in mathematics from the University of Zagreb (2013) and previously worked as a junior researcher at the Faculty of Electrical Engineering and Computing in Zagreb from 2007 to 2017. Dr. Miličić's research focuses on the theory of dynamical systems and fractal dimensions, with applications extending to informatics and computer science, particularly in data science. His work bridges theoretical mathematics with practical computational applications, demonstrating expertise in both pure mathematical theory and its implementation in modern technological contexts. His teaching portfolio spans from foundational mathematics to advanced computational topics, including Differential and Integral Calculus, Geometry and Linear Algebra, Functional Programming, and Statistics across undergraduate, graduate, and integrated programs. His publication record shows a clear evolution from theoretical mathematics toward applied computational methods. Early works (2006-2013) focused on fundamental aspects of dynamical systems, fractal geometry, and differential equations. More recent publications (2018-2025) emphasize time-frequency analysis, image processing, and signal denoising, reflecting his growing interest in practical implementations of mathematical theory in data science applications. As Vice Dean for Teaching and Students, Dr. Miličić actively participates in the development of courses and design of study programs at the Faculty of Informatics in Pula. He teaches a comprehensive range of mathematics courses from introductory level to advanced topics including methodological courses in mathematics teaching, demonstrating his commitment to both theoretical foundations and practical applications of mathematics in informatics education.
Bojana Rosic is a Full Professor specializing in Applied Mechanics & Data Analysis. Her research spans Artificial Intelligence, Machine Learning, Robotics, and Uncertainty Quantification, with a focus on integrating computational methods into mechanical systems and materials science. Key Research Areas: Machine Learning, Uncertainty Quantification, Robotics, Soft and Compliant Mechanisms, Materials Simulation. Recent Work: Contributions to neural network-based constitutive modeling for anisotropic materials, real-time control systems for robotic manipulators, and uncertainty quantification techniques using Polynomial Chaos Expansion. Collaborations: Active in interdisciplinary research with applications in energy, sustainability, and biomedical engineering. Her work emphasizes practical implementations of AI in mechanical engineering, including autonomous systems and collaborative robots (cobots). While no specific awards or educational background are detailed here, her extensive research output (68 publications) highlights her leadership in computational methods and machine learning integration.
Igor Gurov is a Professor at the Faculty of Applied Optics at ITMO University, with a 32-year career in optical-electronic systems, digital signal processing, and computer image analysis. His roles include project leadership in advanced optical diagnostics and interdisciplinary research spanning metrology, tomography, and intelligent recognition technologies. Academic affiliation: ITMO University Research focus: Mathematical modeling, signal/image formation, and processing Leadership roles: Department Head (2005-2012), Project Leader in multiple innovations Research Interests: Professor Gurov specializes in mathematical models identification for optical systems, particularly in interferometry , digital holography , and optical tomography . His work includes multidimensional optimal filtering , stochastic dynamic systems , and 3D image formation in information systems. Research Trends: Recent publications demonstrate a focus on nonlinear Kalman filtering in optical coherence tomography, recurrence algorithms for fringe pattern analysis, and white-light microscopy innovations. Key themes include dynamic signal processing , multi-body motion estimation , and minimum description length principle applications. Projects: From 2006-2013, he led developments in telemedicine diagnostics , biotissue evaluation , and intelligent recognition algorithms , with technical expertise in high-performance video applications and 3D image representation. Contact: Email: gurov@mail.ifmo.ru
