Pozharska Kateryna is a researcher at the Department of Function Theory, Institute of Mathematics, National Academy of Sciences of Ukraine. Her work focuses on approximation theory, functional analysis, and harmonic analysis with applications to periodic and multivariate functions. Research Interests : Approximation theory, nonlinear approximation, widths and entropy numbers, sampling recovery algorithms, and function spaces like Nikol'skii-Besov and Lq. Scientific Awards : 2023 Joseph F. Traub Information-Based Complexity Young Researcher Award. Publications : Over 15 articles on approximation characteristics, D-optimal designs, and recovery of periodic functions (only the 15 most recent included). Contact : Email pozharska.k@imath.kiev.ua | Office Room 304 | Postal: Institute of Mathematics, 3 Tereshchenkovska St., Kyiv, Ukraine.
Prof. Sen Cheng is a Professor at the Institute of Neuroinformatics (INI), part of the Faculty of Computer Science at Ruhr-Universität Bochum. His research focuses on computational neuroscience, particularly the neural mechanisms underlying learning, memory, and spatial navigation. He leads a lab investigating hippocampal dynamics, combining mathematical modeling with optogenetic and electrophysiological data analysis. Collaborations span neuroscientists in Germany and internationally. Research interests include hippocampal replay mechanisms, episodic memory functions, and the interplay between sensory inputs and neural networks. His work bridges computational models (e.g., spiking neural networks) with experimental data from rodents and humans. Recent projects explore deep reinforcement learning agents for spatial navigation and the role of grid cells in cognitive mapping. Publications span Neuron , Current Biology , and eLife , addressing topics like hippocampal network stabilizations, spatial coding efficiency, and cerebellar contributions to fear extinction. Teaching includes courses on computational neuroscience, artificial neural networks, and mathematical psychology. Lab activities involve interdisciplinary research through colloquia like Brains in Space and supervision of master’s and bachelor’s theses in areas like reinforcement learning algorithms and embodied associative networks. The INI’s mission drives his work, linking biological insights to artificial cognitive systems design.
Joachim Frank is a Professor at Columbia University in the Departments of Biochemistry and Molecular Biophysics and Biological Sciences. He pioneered single-particle cryo-electron microscopy (cryo-EM), enabling 3D structural analysis of biomolecules. His work revolutionized structural biology, particularly in studying ribosomes, hemocyanins, and membrane proteins. Frank joined Columbia in 2008 after a 33-year tenure at the Wadsworth Center, NY State Department of Health. Education: PhD in Physics from Technical University Munich (1970), Postdoctoral fellowships at JPL (Caltech), Berkeley, and Cornell. Key mentors included Walter Hoppe (Munich) and Richard Henderson (Cambridge). Research Interests: Development of cryo-EM techniques, single-particle averaging, 3D reconstruction algorithms, and applications to molecular machines like the ribosome. His lab created SPIDER, a seminal image processing software system. Awards: Nobel Prize in Chemistry (2017), HHMI Investigator (1998–2017), Harkness Fellowship (1970), and Humboldt Fellowship (1994). Nobel Prize for developing cryo-EM to image biomolecules in atomic detail. HHMI funding supported groundbreaking projects on ribosome function and structural biology. Advising & Grants: Advised over 20 students/postdocs including Rajendra Agrawal (ribosome biochemist) and Christian Spahn (structural biologist). NIH grants sustained decades of cryo-EM research. HHMI provided critical funding for advanced microscopy infrastructure at Columbia. Labs & Collaborations: Frank Lab at Columbia focuses on cryo-EM applications. Notable collaborations with Jean Lamy (hemocyanin studies), Måns Ehrenberg (ribosome dynamics), and Ruben Gonzalez (single-molecule FRET).
Yanbin Chen is a researcher at the Technical University of Munich (TUM) within the Chair for Languages and Description Structures in Computer Science (Informatik 2). His research focuses on quantum computing optimizations, quantum programming languages, static analysis, ZX-calculus, and tensor networks. He collaborates with the Munich Quantum Valley (MQV) to advance quantum computing ecosystems, addressing challenges in quantum programming tools, algorithms, and hardware integration. He teaches courses such as Program Optimization, Quantum Computing at Compile Time, and Virtual Machines. His advising includes student theses on topics like quantum circuit synthesis, noise-tolerant algorithms, and quantum compiler design. Yanbin actively contributes to conferences and journals, publishing on circuit optimization frameworks, dead gate elimination, and probabilistic quantum models. Research collaborations include MQV, and he explores neutral atom-based quantum computing models. His work bridges quantum computing with high-performance computing (HPC), emphasizing practical tools and scalable solutions.
Isabel Nha Minh Le is a Researcher and doctoral candidate in the Quantum Computing Group under Prof. Mendl at the Technische Universität München (TUM). She holds a B.Sc. and M.Sc. in Physics from RWTH Aachen University, specializing in Quantum Technologies. Her research focuses on quantum algorithms for quantum chemistry, tensor network methods, and quantum machine learning. She teaches tutorials and seminars on quantum computing and ethics. Her work includes advancements in Riemannian quantum circuit optimization and symmetry-invariant quantum ML force fields, presented at conferences such as ICFO-IMPRS Joint Workshop and APS Global Summit. She is affiliated with the Department of Computer Science at TUM School of CIT, with an office at SAP Labs Munich. Education: B.Sc. in Physics, RWTH Aachen University M.Sc. in Physics, RWTH Aachen University Research Interests: Development of quantum algorithms for simulating complex quantum systems Integration of tensor networks and machine learning for quantum simulation Optimization techniques for quantum circuits Teaching Roles: Tutorial Leader: Introduction to Quantum Computing (2023–2025) Seminar Leader: Advanced Topics of Quantum Computing (2023–2025) Seminar Leader: Science & Ethics (2024–2025) Labs/Teams: Member of the Quantum Computing Group at TUM, collaborating with Prof. Mendl's team on quantum algorithms and applications.
Martina Nibbi is a Researcher and PhD candidate at the Technical University of Munich (TUM) within the TUM School of CIT , Department of Computer Science. Her work focuses on quantum algorithms, quantum chemistry, and quantum simulation of fermionic systems. She holds a B.Sc. and M.Sc. in Physics from Università degli Studi di Milano (2016–2022) and has conducted a visiting research stint at the Lawrence Berkeley National Laboratory in Spring 2025. Research Interests: Martina’s research explores quantum computing applications in chemistry and physics, including block encoding techniques for matrix product operators, tensor networks, and multiwavelet-based methods. She investigates computational methods to optimize wavefunctions and simulate fermionic systems at the complete basis set limit. Teaching: She contributes to courses such as Informatics for Engineering (C programming) and Advanced Concepts of Quantum Computing , spanning both lectures and seminars. Publications: Her recent work includes advancements in quantum algorithm design and computational methods, such as optimizing wavefunctions using multiwavelets and DMRG, and developing block encoding frameworks for matrix product operators. Supervised Work: Cecilie Priller (B.Sc. 2024): Computational complexity analysis of linear combination of unitaries block encoding Robert-Mihai Budai (M.Sc. 2024): Quantum algorithms using Lindbladian dissipation Defne Tolun (B.Sc. 2025): Complexity analysis of linear combination of unitaries in quantum chemistry
Professor Martina Peuser serves as Professor of General Business Administration with specialization in Organization and Project Management at Leibniz University of Applied Sciences in Hannover, where she also heads the Business Administration program. With over a decade of academic leadership since 2014, she has established herself as a prominent figure in business education and applied research. Education Background: Diplom-Ökonomin in Business Administration and Economics, University of Hannover (2002) Doctorate (Dr. rer. pol.) from University of Hannover with dissertation on "Competency-Oriented Brand Cooperations of Energy Suppliers in the B2B Sector" (2007) International Business Administration studies at Maastricht University (1999-2000) Industrial Management apprenticeship at Stadtwerke Hannover AG (1995-1998) Her research focuses on three interconnected domains: Agile Business Management and Organizational Structures , examining how companies can develop flexible frameworks for digital transformation; Project Management and Agile Methodologies , particularly in complex organizational contexts; and Marketing Management and Digital Transformation , with special attention to energy sector applications. Her work consistently bridges theoretical frameworks with practical business applications, demonstrated through numerous industry collaborations. Analysis of her recent publications reveals a clear trajectory toward practical applications of agile methodologies across diverse sectors. Her 2022-2024 work shows increasing focus on generational workforce dynamics, digital signage optimization in retail, and decision-making patterns across international contexts. The consistent theme across her publication history is the adaptation of traditional business frameworks to digital and agile environments, with particular emphasis on energy, retail, and manufacturing sectors. Professional Recognition: Professional Scrum Master I certification (2020) Longstanding jury membership for Startup Competition at hannoverimpuls GmbH (2017-present) Former member of the Presidential Council of the German Project Management Association (GPM) (2017-2019) Professor Peuser actively bridges academia and industry through her extensive program management activities and student support initiatives. Her research projects frequently involve direct collaboration with major German corporations including E.ON, Continental AG, and Deutsche Messe AG. She has developed specialized curricula in agile project management and digital marketing that directly respond to industry needs, particularly in the energy and retail sectors. Her leadership extends to program development for both dual-study and full-time business administration tracks. While not explicitly mentioning dedicated laboratories, her work demonstrates strong connections to industry innovation ecosystems through her extensive project management activities and corporate collaborations. Her research approach emphasizes practical application within real business contexts rather than isolated laboratory settings.
Thomas Schnake is a postdoctoral researcher at the Machine Learning Lab of the Technical University of Berlin and the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds a Ph.D. in Machine Learning from TU Berlin and prior degrees in Mathematics and Scientific Computing from Humboldt University of Berlin. His research focuses on Explainable AI (XAI), Natural Language Processing, and the mathematical foundations of machine learning. He has also gained industry experience at ebuero AG and GFaI e.V. in Berlin. Education: B.Sc. Mathematics & Philosophy, Humboldt University Berlin (2014) M.Sc. Mathematics, Humboldt University Berlin (2018) M.Sc. Scientific Computing, Technical University Berlin (2018) Ph.D. Machine Learning, Technical University Berlin (2024) Research Interests include: Explainable AI for complex domains like quantum chemistry and histopathology Graph neural network interpretability through walk-based explanations High-resolution data synthesis with minimal input Unsupervised anomaly detection in text and energy systems His recent publications (2021-2025) demonstrate contributions to XAI frameworks, graph neural network explanations, and transformer model interpretability. He is affiliated with two prominent institutions and maintains active research in interdisciplinary areas combining mathematics and machine learning.
Sonja Schlachter is a Lecturer at the Hildesheim University Foundation (GHR 300) and a doctoral candidate at the Chair of Political Didactics and Political Education, University of Hildesheim. She holds a dual degree in Political Science and German from Johann Wolfgang Goethe University Frankfurt am Main and the University of Birmingham. Her research focuses on migration policy, digitalization in politics, and innovative approaches to political education, emphasizing student-centered learning and participatory methods. Education: Master's in Political Science and German Studies (2007), supplemented by extensive international teaching experience in Vietnam, Denmark, Mozambique, and Germany. Professional Experience: Secondary school teacher since 2020, with prior roles in early childhood education and teacher training. Her doctoral project explores collaborative textbook development with students to enhance democratic engagement through participatory didactics. Current teaching includes seminars on political practice phases for trainee teachers. Research interests intersect migration governance, digital policy tools, and educational innovations. Recent work examines algorithmic systems for refugee integration (Match'In project), municipal security challenges, and narrative strategies in policy framing. Publications highlight critical perspectives on integration policies, digital governance frameworks, and the interplay between migration and urban security. Active in interdisciplinary networks addressing refugee reception challenges and local integration strategies.
Giuseppe Thadeu Freitas de Abreu is a Professor of Electrical Engineering at the School of Computer Science & Engineering, Constructor University Bremen, Germany. He holds a Docent (Habilitation) in Statistical Signal Processing and Communication Theory from the University of Oulu (2006), a PhD in Physics from Yokohama National University (2004), and a Master’s in Physics from the same institution (2001). His research focuses on wireless communications, signal processing, and next-generation networks, including 5G/6G systems, massive MIMO, and physical-layer security. He has held roles such as Full Professor at Ritsumeikan University (2015–2018) and Adjunct Professor at the University of Oulu (2006–2011). Education: Docent (Habilitation): Statistical Signal Processing and Communication Theory, University of Oulu, 2006 PhD in Physics, Yokohama National University, 2004 Masters in Physics, Yokohama National University, 2001 Bachelor in Electrical Engineering, Universidade Federal da Bahia, Brazil, 1997 Research Interests: Machine Learning for Wireless Communications, Cell-Free Massive MIMO, 5G/6G Systems, Millimeter Wave, Physical-Layer Security, Localization, and Random Matrix Theory. His work emphasizes practical applications in next-gen networks, including ISAC (Integrated Sensing and Communications) and quantum-aided systems. Grants & Projects: Continental AG Project ADVANTAGE Phase 2 (2021–2024) EIG Concert Japan Integrated Project ORACLE (2021–2024) EU H2020 HIGHTS (2015–2018) EU FP7 BUTLER (2011–2014) Awards: IEEE Fellowships, Heiwa Nakajima Fellowship (2013), and numerous grants from JSPS, NICT, and others. His contributions span editorial roles in IEEE Transactions on Wireless Communications and leadership in international research initiatives. Labs & Groups: Leads the Abreu Group (abreugroup.pages.constructor.university), focusing on cutting-edge wireless research with industrial collaborations.
Prof. Marc-Thorsten Hütt is a Professor of Computational Systems Biology at the School of Science, Constructor University Bremen gGmbH. His research integrates mathematical, physical, and computational approaches to understand biological systems, including gene regulatory networks, metabolic systems, and neural dynamics. He leads a research group focused on systems biology, network analysis, and interdisciplinary applications in medicine and environmental science. His work bridges theoretical frameworks with experimental data, addressing challenges in bioinformatics, epigenetics, and complex systems. Research interests include the interplay between network topology and dynamics, particularly in excitable systems and biological networks. Notable contributions involve analyzing microbial interaction networks, perturbation therapies for neurodegenerative diseases, and the impact of environmental factors on marine ecosystems. Recent studies explore interdisciplinary citation patterns and machine learning applications in network inference. His publications span computational biology, neuroscience, and network theory, with a focus on attractor analysis, node centrality, and systems-level modeling. The most recent work addresses sampling bias in networks and 5G radiation effects on cellular genetics. Despite extensive contributions, no specific awards are listed in the provided texts. Prof. Hütt collaborates with industry on steel production optimization and maintains a research group active in bioinformatics and systems medicine. His lab's work is accessible via his research website .
Prof. Thomas Winter is a Professor at Berlin University of Technology's Department II - Mathematics - Physics - Chemistry. His expertise spans optimization, operations research, revenue management, and mobile communications. He teaches courses in mathematics, numerical methods, and optimization for engineering and data science programs. Research interests focus on optimization in transport logistics, revenue management, and mobile communication networks. His work includes real-time dispatch systems, UMTS network design, and e-commerce customer journey analysis. Over 20 years, he has contributed to 15+ publications, emphasizing algorithmic solutions for complex systems. No scientific awards are listed. He advises students on thesis topics involving optimization algorithms, revenue management models, and network analysis. No specific grants or labs are mentioned in the provided texts.
Giovanni Paolini is an Associate Professor of Mathematics at the University of Bologna (Italy) since 2023. Previously, he worked as an Applied Scientist/Senior Applied Scientist at Amazon Web Services and Caltech (2019–2023), and held a postdoctoral position at the University of Fribourg (2019). He earned his PhD in Mathematics from Scuola Normale Superiore (Pisa, Italy) in 2019, under Prof. Mario Salvetti, with a thesis on topology and combinatorics of affine reflection arrangements. His research spans combinatorics, topology, group theory , and machine learning (deep learning and NLP). Notable contributions include resolving the K(π,1) conjecture for affine Artin groups and developing AI for strategic games like 7 Wonders Duel. He organizes events such as MATH-MIND: Mathematics-Industry Networking Days (2025) and has participated in workshops like Artin Groups and Arrangements at MSRI (2024). Teaching roles include courses on geometry, combinatorial topology, and machine learning at the University of Bologna. His work bridges pure mathematics and applied AI, with publications in journals like Inventiones Mathematicae and conferences like ICLR and ICML. Collaborations include projects with Stefano Soatto (UCLA), Mario Salvetti, and interdisciplinary teams at Amazon.
Ruben Bach serves as a Research Fellow in the Data and Methods Unit at the Mannheim Centre for European Social Research (MZES), University of Mannheim, where he advances computational methodologies in social science research through interdisciplinary collaboration. His research centers on Data Quality as the cornerstone of valid social science inquiry, integrating Survey Methodology with Natural Language Processing and Machine Learning to design questionnaires that optimize human response accuracy while generating algorithm-ready data structures. Bach actively employs transformer-based language models and classification/regression tree algorithms to analyze textual data, with future work targeting audiovisual analysis techniques. He advocates for prioritizing high-quality data over algorithmic complexity, arguing current social science research overemphasizes analysis pipelines at the expense of foundational data integrity. MZES provides Bach's primary research ecosystem as a leading interdisciplinary hub for European social science innovation, fostering methodological advancements across its specialized units. No student advising details or research grants are documented in available sources, reflecting his current focus on independent methodological development within the Data and Methods Unit.
Viridiane Fay is a researcher in the Institute of Modern Languages and Romance Studies at the University of Würzburg , specializing in French language practice and applied computational methods. During lecture periods, she holds office hours every Tuesday from 1 p.m. to 2 p.m., with appointments available during semester breaks. Role: French Editor, Language Practice Location: Am Hubland, 97074 Würzburg Contact: viridiane.fay@uni-wuerzburg.de Her research spans two distinct domains: French language pedagogy and computer vision/AI . While her institutional affiliation focuses on Romance Studies, her publication record reveals expertise in: Medical and underwater image processing Transformer-based architectures Federated learning and network optimization Watermarking and security algorithms Multi-modal AI applications Neuroscience signal analysis Recent work includes lightweight segmentation models (e.g., ContextFormer), drone/aerial imagery detection frameworks (CH-YOLO-Lite), and privacy-preserving AI systems (Find). Despite limited biographical details in the scrape, her 30+ publications since 2017 suggest significant technical contributions.
