Prof. Alexander Gelfgat is a faculty member at the School of Mechanical Engineering , part of the Faculty of Engineering at Tel Aviv University . His research focuses on hydrodynamic stability , bifurcations , flow control , and computational fluid dynamics (CFD) , particularly in crystal growth , MHD , and high-performance computing contexts. Research Interests: Hydrodynamic stability and bifurcations Convection and rotating flows Shear layer dynamics Moving boundary tracking Crystal growth and melt flow His work spans three-dimensional flow instabilities , with applications to Czochralski crystal growth , Dean flows , and two-phase stratified channels . Recent studies emphasize non-modal disturbances and quasi-two-dimensional flow projections for enhanced visualization. Prof. Gelfgat has advised notable researchers including Yuri Feldman and Helena Vitoshkin . His publications address critical challenges in boundary layer instability , cross-flow wave propagation , and pressure-velocity coupled formulations for lid-driven flows in complex geometries.
Frans A. Oliehoek is a Full Professor in Interactive Learning and Decision Making at the Department of Intelligent Systems, Delft University of Technology. He co-leads the Sequential Decision Making group and serves as director and co-founder of the ELLIS Delft Unit. His academic affiliations include senior membership at AAAI, board membership at IFAAMAS, and associate editor roles for JAIR and AIJ. His research focuses on interactive learning and decision making , integrating AI, machine learning, and game theory to develop algorithms for agents interacting in complex, dynamic environments. Key themes include Bayesian reinforcement learning , influence-based abstraction , multi-agent coordination , and uncertainty modeling in real-world applications like traffic control, human-AI collaboration, and e-commerce agents. Recent publications highlight advancements in Bayesian RL with factored POMDPs , multi-agent safety guarantees , and state abstraction . His work spans theoretical foundations (e.g., Nash equilibria, MDP homomorphic networks) and practical frameworks (e.g., SHARPIE for human-AI experiments). Scientific contributions include: ELLIS Fellowship Senior AAAI Membership Best Paper Award at ALA 2021 Outstanding Paper Award at RLC 2024 He has advised students like Miguel Suau and Robert Loftin, with applications in multi-agent reinforcement learning , Bayesian planning , and human-AI interaction . Collaborations span institutions like UC Berkeley, University of Amsterdam, and MIT.
Prof. Dr. Carsten Gräser is a Professor in the Department of Applied Mathematics at Friedrich-Alexander University Erlangen-Nürnberg (FAU). His research focuses on numerical analysis, computational mechanics, and phase-field modeling, with an emphasis on nonsmooth optimization and multigrid methods for complex systems. 2025: Well-posedness of evaporation models for droplets. 2023: Multiscale fault systems and phase-field brittle fracture simulations. 2021: Membrane mechanics and DUNE framework developments. His work spans partial differential equations, computational geosciences, and biological membrane modeling, often involving finite element methods and nonlinear systems. Recent publications highlight applications in fluid dynamics, fracture mechanics, and geophysical fault simulation. His articles demonstrate a strong focus on numerical methods for phase-field models and nonsmooth systems, with recurring sub-fields like multigrid algorithms, membrane-mediated interactions, and finite element discretization. He collaborates widely on topics such as marine ice sheets and solder alloy coarsening. At FAU, Gräser teaches courses including Numerics of Incompressible Flows and Modeling, Simulation, and Optimization . His research integrates software development (e.g., the DUNE framework) with applications in physics, materials science, and geosciences.
Xin Liang is a Postdoctoral Researcher at the Max Planck Institute for Dynamics of Complex Technical Systems , Germany. His research focuses on Numerical Linear Algebra , Matrix Computation , and Matrix Theory , particularly addressing Eigenvalue Problems and associated principles in computational mathematics.
Ni Dang is a former Research Associate and PhD candidate at the Chair of Automatic Control Engineering (LSR) at Technical University of Munich (TUM), with a focus on advanced control strategies for autonomous vehicles. They earned a Master of Science in Circuits and Systems from Xidian University, China, and a Bachelor of Science in Electronic and Information Engineering from the same institution. PhD in Automatic Control Engineering (TUM, 2023) M.Sc. in Circuits and Systems (Xidian University, 2018) B.Sc. in Electronic and Information Engineering (Xidian University, 2015) Their research spans Autonomous Vehicles , Model Predictive Control , and Optimization , with interdisciplinary work in Epidemiology (2020) and Network Science (2018). Key contributions include applying Inverse Reinforcement Learning and Imitation Learning to model driving styles, integrating deep learning with control systems, and developing distributed stochastic MPC for traffic models. Their publications highlight trends in Autonomous Driving , Control Algorithms , and Network Analysis , balancing theoretical and applied approaches. A 2020 paper on a power-law model for pandemic spread demonstrates cross-domain adaptability of their methodologies.
Felix Kallenborn is an Academic Staff Member at Johannes Gutenberg University Mainz specializing in High Performance Computing, with active research and teaching engagements documented through 2024. His work bridges computational biology and physics through advanced algorithm development. His research focuses on developing context-aware computational methods for genomic sequencing data analysis and physics simulations. Key contributions include the CARE series of bioinformatics tools for error correction and read extension in DNA sequencing, which evolved to incorporate machine learning techniques for improved accuracy. He also applies GPU-accelerated parallel computing to complex physics problems like neutrino oscillation modeling, demonstrating cross-disciplinary expertise in optimizing computational workflows. Analysis of his 2019-2024 publications reveals a strategic evolution from physics-oriented high-performance computing (neutrino simulations) toward bioinformatics dominance, with machine learning integration becoming increasingly prominent. The consistent thread across all works is the development of massively parallel algorithms that leverage hardware acceleration to solve computationally intensive scientific problems, particularly in genomic data processing where accuracy and scalability are critical. He operates within Johannes Gutenberg University Mainz's High Performance Computing research infrastructure, contributing to the development of specialized computational tools that address fundamental challenges in both life sciences and physics through innovative parallel processing approaches.
Richard Hill is a prominent researcher specializing in Internet of Things systems, edge computing architectures, and security protocols for distributed systems. His scholarly contributions span over a decade with consistent publication output through 2025, demonstrating ongoing research activity and academic leadership in computer science. His research interests focus on Industrial IoT optimization , edge intelligence , cybersecurity frameworks , and applied machine learning across industrial and educational contexts. Recent work demonstrates particular expertise in resource allocation models, authentication protocols for sensor networks, and educational applications of computing technologies. Hill's publication trends reveal strong interdisciplinary engagement, bridging computer science with industrial applications, healthcare diagnostics, and educational technology. His work frequently addresses real-world implementation challenges in IoT systems, cloud-edge architectures, and security protocols. As evidenced by his book publications including Guide to Industrial Analytics (Springer, 2021) and Edge Intelligence and the Industrial Internet of Things , Hill has established significant scholarly authority in his fields of expertise. His collaborative research network includes extensive partnerships with Hussain Al-Aqrabi, Ashiq Anjum, and other international researchers, with particular emphasis on UK-based educational applications and industrial implementations.
Prof. Dr. Felix Dietrich holds the Professorship for Physics-Enhanced Machine Learning at the Technical University of Munich (TUM), within the TUM School of CIT and Department of Computer Science. He is actively engaged in teaching courses including Scientific Computing and Machine Learning for the Winter semester 2025/26 and supervises Master's theses in Machine Learning in Crowd Modeling & Simulation. His research focuses on the intersection of scientific computing and machine learning, particularly through his 'Harmonic AI' framework that bridges linear operator theory with deep learning. Key research areas include physics-enhanced machine learning, Koopman operator theory, scientific machine learning, and numerical algorithms for complex systems. His work applies to diverse domains such as crowd dynamics, quantum systems, molecular dynamics, and civil engineering applications like structural defect detection. Prof. Dietrich leads several major research initiatives including the Emmy Noether group 'Harmonic Artificial Intelligence based on Linear Operators', the Scientific Machine Learning Focus Group with the IAS, GNI projects DeepMonitor and FORWARD, and AutoMD-AI for molecular dynamics simulations. His recent publications demonstrate strong activity in connecting mathematical rigor with machine learning applications. His scientific contributions span developing data-driven algorithms that combine the reliability of traditional scientific computing with the flexibility of AI approaches. This work has resulted in numerous publications in top-tier journals including SIAM Journal on Scientific Computing, Chaos, Nature Communications, and PNAS Nexus. Emmy Noether group on Harmonic AI Scientific Machine Learning Focus Group with IAS GNI project DeepMonitor for structural defect detection GNI project FORWARD for pedestrian flow prediction AutoMD-AI for molecular dynamics surrogate modeling Prof. Dietrich actively mentors PhD students and postdoctoral researchers, with several advisees including Chinmay Datar, Erik Lien Bolager, and Vladyslav Fediukov appearing as co-authors on recent publications. His research group (SCML) develops open-source software for physics-enhanced machine learning applications.
Fabian Pfitzner is a Research Associate at the Technical University of Munich (TUM) , specifically within the Chair of Computing in Civil and Building Engineering led by Prof. Dr.-Ing. André Borrmann. His work focuses on leveraging data mining , computer vision , and knowledge graphs to enhance construction site monitoring and process automation. Research Interests: Data Mining & AI in Construction: Developing intelligent systems to extract actionable insights from construction site data. Computer Vision for Process Monitoring: Applying advanced image analysis techniques to track construction progress and activities. Knowledge Graph Construction: Creating semantic models to represent construction processes and enable better decision-making. Digital Twinning: Building real-time digital replicas of construction sites for enhanced control and optimization. His recent publications span Automation in Construction , Forum Bauinformatik , and CIB W78 , focusing on concrete pouring monitoring , rebar installation prediction , and robotic construction monitoring . These works consistently integrate AI-driven analytics with real-world construction challenges . Teaching & Supervision: Mr. Pfitzner teaches Bau- und Umweltinformatik 1 and Softwarelab , and has supervised multiple Master’s and Bachelor’s theses on topics including BIM-based progress monitoring, digital twins, and automated environmental impact calculations. Contact: fabian.pfitzner@tum.de | Room 0501.03.161 | Tel: +49 (89) 289-25064
Dr. George B. Mertzios is an Associate Professor at the Department of Computer Science, Durham University , UK. His career spans roles including Senior Lecturer (2015-2017) and Lecturer (2011-2015) at Durham, with additional positions as an Invited Assistant Professor at LaBRI, University of Bordeaux/CNRS, France (2012), and postdoctoral research fellowships at the University of Haifa and Technion, Israel (2010-2011). He earned his PhD in Computer Science from RWTH Aachen University (2009) and a Diplom in Mathematics from Technische Universität München (2005). Education PhD in Computer Science, RWTH Aachen University, 2009 Diplom in Mathematics (minor in Computer Science), Technische Universität München, 2005 Research Interests focus on efficient algorithms and computational complexity in temporal graphs , with significant contributions to dynamic network optimization , evolutionary graph theory , and parameterized complexity . His work bridges combinatorial optimization , algorithmic game theory , and intersection graph models . Scientific Activities include organizing Co-Chair of the PC for SAND 2026 Co-Organizer of Dagstuhl Seminar 26251 (2026) Organizer of the Algorithmic Aspects of Temporal Graphs workshops (2018-2025) His 15 most recent articles (2025-2021) address problems in temporal graph realization , dynamic network optimization , Hamiltonian cycles , and epidemic control on temporal networks , published in top venues like Theoretical Computer Science , Journal of Computer and System Sciences , and Algorithmica . Scientific Awards include a Gold Medal in the 1998 Balkan Mathematical Olympiad, Distinguished Diploma in the 1998 Bulgarian National Mathematical Competition, and Best Paper Awards at ALGOWIN 2025 and ICALP 2010 Track C. Research Supervision includes advising PhD students: David Fairbairn (in progress), David Kutner (2025), Nina Klobas (2024), Charles Murray (2021), Sepehr Meshkinfamfard (2016), Ioannis Lignos (2016) Postdoctoral researchers: Christoforos Raptopoulos (2020), Viktor Zamaraev (2017-2019), André Nichterlein (2016-2017), Archontia Giannopoulou (2014-2015), Konrad Dabrowski (2012-2013) Grants led include EPSRC grants EP/P020372/1 (2017-2020) on algorithmic aspects of temporal graphs and EP/K022660/1 (2013-2015) on intersection graph models. He also contributed to the EU IP MULTIPLEX (2012-2016).
Danny Hucke is a researcher affiliated with the University of Siegen, Department of Electrical Engineering and Computer Science. His work focuses on advanced data compression techniques, algorithmic complexity, and formal verification of streaming systems. Education: PhD in Grammar-based compression for strings and trees (University of Siegen, 2019) Research Interests: Driven by challenges in grammar-based compression, empirical entropy metrics, and formal language processing in streaming environments, his research bridges theoretical computer science and practical algorithm design. Key areas include: Data Compression for Trees and Strings Sliding-Window Algorithms Circuit Complexity Algorithmic Entropy Analysis Scientific Contributions: His work has been recognized with a Best Paper Award at SPIRE 2016. Publications span top-tier venues like IEEE Transactions on Information Theory , ACM TOCT , and conferences including ICALP, STACS, and LATIN. Contact: Department of Electrical Engineering and Computer Science, University of Siegen, Hölderlinstrasse 3, D-57076 Siegen. Email: hucke@eti.uni-siegen.de . Phone: +49-271-740-3415. Office: Room H-A 7104. Collaborations: Conducted research within the group of Prof. Markus Lohrey, collaborating extensively with Moses Ganardi, Louisa Seelbach, and Eric Nöth.
Louisa Seelbach Benkner is a postdoctoral researcher at the University of Siegen , affiliated with the Faculty of Electrical Engineering and Computer Science. Her research focuses on data compression, particularly grammar-based compression, succinct data structures, universal source coding, and analytic combinatorics. She has contributed to theoretical advancements in tree compression and entropy analysis. Education: PhD in Computer Science from University of Siegen (2023). Research interests center on optimizing data compression techniques for tree structures, analyzing entropy bounds, and applying combinatorial methods to algorithm design. Her work bridges theoretical computer science and practical data encoding solutions. Selected publications highlight her expertise in hypersuccinct tree compression, empirical entropy comparisons, and fringe subtree analysis. Key collaborations include researchers like Markus Lohrey, Stephan Wagner, and Travis Gagie. Scientific awards: Capocelli Prize at DCC 2019 Best Student Paper Award at SPIRE 2020 Teaching roles include formal languages, complexity theory, logic, and algorithmics courses. She has also contributed to mathematics instruction in analysis and linear algebra.
Steffen Hage is a Senior Lecturer at the University of Tübingen's Faculty of Mathematics and Natural Sciences, specifically affiliated with the Department of Biology and Institute of Neurobiology. As a part-time teaching staff member, he focuses on neurobiological and physiological research aspects of vocal communication and auditory feedback mechanisms in primates and other animals. Institute of Neurobiology Animal physiology Department of Biology His research spans evolutionary perspectives on speech and language, neural control of vocalization, auditory processing defects in neurodevelopmental disorders, and adaptive strategies to environmental acoustics. Key methodologies include electrophysiological recordings, behavioral analysis, and computational modeling. Recent publications emphasize vocal coding efficiency in marmosets, neural synchronization during vocal production, and environmental influences on auditory behavior. His work bridges comparative neuroscience and biomedical applications, particularly in hearing rehabilitation technologies.
Fatma Deniz is a Full Professor at the Faculty of Electrical Engineering and Computer Science, Technische Universitaet Berlin. She combines computational neuroscience , data science , and artificial intelligence to investigate how complex information is encoded in the human brain during natural tasks. Her lab develops machine-learning approaches for analyzing multimodal brain data, with a current focus on multilingual brain representation . Education: PhD in Computational Neuroscience (Bernstein Center Berlin), M.Sc. in Computer Science (Technical University of Munich), thesis work at Caltech Past Appointments: Helen Wills Neuroscience Institute (UC Berkeley), Berkeley Institute for Data Science, International Computer Science Institute (Berkeley) Her research spans scientific reproducibility (co-editor of a UC Press book), image-based authentication (MooneyAuth), and Mooney image databases . She has received prestigious fellowships including Moore-and-Sloan Data Science Fellow and DAAD Postdoctoral Fellowship . Her GitHub repository denizenslab/pymooney contains Python tools for generating Mooney images. Scientific Awards: Moore-and-Sloan Data Science Fellow DAAD Postdoctoral Fellow Her work has been featured in major media outlets including Nature Neuroscience , MIT Technology Review , Discover Magazine , and ScienceDaily .
Prof. Dr. Martin Bogdan is a faculty member at the University of Leipzig since 2008, currently holding the Professorship for Neuromorphic Information Processing in the Faculty of Mathematics and Computer Science . His academic career spans roles as a research assistant, assistant professor, and department head at institutions including the University of Tübingen and University of Leipzig. Education : Studied technical computer science at Fachhochschule Offenburg (1987–1993) and industrial informatics at Université Grenoble I (1991–1993); earned PhD in 1998 from University of Tübingen. Research Interests focus on: Neuromorphic Information Processing Spiking Neural Networks Brain-Computer Interfaces (BCI) Embedded Systems for Bio-Analogous Processing Real-Time Signal Processing in Medicine Machine Learning Applications in Neurology Mainframe Computing Techniques Article Trends show expertise in: spiking neural networks for real-time applications; BCI systems for locked-in syndrome patients; hyperspectral imaging for agricultural analysis; FPGA-based evolving hardware; and machine learning applications in medical diagnostics. His work bridges neuroscience, computer science, and biomedical engineering. Academic Roles include leadership of the NeuroTeam (2000–2015), editorial positions, and extensive teaching experience in technical computer science and neuromorphic systems. Labs & Teams : Leads the Neuromorphic Information Processing division; collaborates with researchers including Dr. Sophie Adama, Dr. Jörn Hoffmann, and engineers like Max Braungardt.
