جست‌وجوی استاد و پژوهشگر

Diana Mateusمشاهده پروفایل

استاد

• Medical Image Analysis
• Machine Learning
• Geometric Computer Vision
• +۵ مورد دیگر

Dr. Diana Mateus is a Full Professor (Professeur des universités) at Ecole Centrale Nantes, France, affiliated with the LS2N laboratory and the Signal Image et Son research team. Her primary research focuses on medical image analysis, machine learning, geometric computer vision, and shape analysis. She leads projects in computational sonography, surgical data science, and vision-language integration in medical contexts. Dr. Mateus has extensive experience in medical imaging applications, including PET-based lesion analysis, ultrasound segmentation, and deep learning for healthcare. Her teaching includes courses on computer-aided medical procedures, medical augmented reality, and surgical robotics at TU Munich (noted in past sections). Research projects span computational surgineering, cross-modality image synthesis, and patient monitoring using wearable sensors. Key contributions include developing IFSS-Net for muscle segmentation in ultrasound, curriculum learning for fracture classification, and graph-based frameworks for treatment response prediction in lymphoma. Her work bridges clinical needs with advanced AI techniques, addressing challenges in low-count PET imaging and weakly supervised learning. Publications highlight innovations in medical imaging modalities (e.g., PET, ultrasound), with a focus on improving diagnostic accuracy and treatment planning. Collaborations include clinical institutions like the Deutsches Herzzentrum München (DHM) and teams at INRIA and MIT.

• Mental Health Services Research
• Substance Use Disorders
• Cannabis Policy and Regulation
• +۳ مورد دیگر

Dr. Moritz Rosenkranz is a researcher at the Clinic and Polyclinic for Psychiatry and Psychotherapy at the University Medical Center Hamburg-Eppendorf (UKE). With a background in sociology, he focuses on substance use research, particularly cannabis policy, mental health services, and stimulant use patterns across European contexts. His research interests span multiple critical areas in public health and addiction medicine, including: Cannabis policy and regulation frameworks Mental health service delivery models Substance use epidemiology and patterns Public health implications of drug policy reforms Digital interventions for substance use disorders Rosenkranz's publication record demonstrates a consistent focus on evidence-based policy development, with particular attention to the German context. His work often employs mixed-methods approaches, including systematic reviews, cross-sectional studies across multiple European countries, and clinical trial evaluations. Recent publications highlight his engagement with the evolving landscape of cannabis legalization in Germany and Europe. Among his notable scientific contributions are: Research on standardized cannabis dosing for policy implementation Analysis of dispensing modalities in regulated cannabis markets Studies on collaborative care models for mental disorders Examination of polysubstance use patterns across European populations His work bridges clinical practice, public health policy, and academic research, contributing to evidence-based approaches for substance use disorders and mental health service delivery. Rosenkranz actively collaborates with European research networks studying substance use patterns and mental health service delivery across different regulatory environments.

Salomon Sickertمشاهده پروفایل

پژوهشگر ارشد

• Formal Methods
• Theoretical Computer Science
• Automata Theory
• +۵ مورد دیگر

Salomon Sickert is a postdoctoral researcher at the Hebrew University of Jerusalem, where he works as a member of Orna Kupferman's research group. He holds a prestigious DFG fellowship and previously completed his doctoral studies at the Technical University of Munich under the supervision of Javier Esparza in 2019. Dr. Sickert's research focuses on the foundations of algorithmic verification with the goal of developing more reliable software and systems. His work sits at the intersection of formal methods and theoretical computer science, with specific interests in: Automata theory and its applications Logic and formal verification techniques Model checking and synthesis of reactive systems Computational complexity theory Theorem provers and verification tools development His publication record shows a strong trajectory in translating theoretical advances into practical verification tools. His recent work demonstrates a consistent focus on improving the efficiency and reliability of formal verification techniques, particularly in the translation between linear temporal logic and various types of automata. This research has direct applications in creating more robust software systems through formal methods. Dr. Sickert has received recognition in the form of a DFG fellowship, supporting his current research endeavors. His work bridges theoretical computer science with practical software verification needs. He has made significant contributions to several important verification tools including Owl (a comprehensive library for ω-words, ω-automata and linear temporal logic), Strix (a reactive synthesis tool), MoChiBA (for probabilistic model checking), and MoTraS (for modal transition systems). These tools have been adopted by the formal methods community and represent practical implementations of his theoretical research.

• space-metric associations
• SNARC effect
• language comprehension
• +۶ مورد دیگر

Elena Albu is a postdoctoral researcher at the University of Tübingen, working within the Faculty of Science, Department of Psychology. She is affiliated with the Diagnostics and Cognitive Neuropsychology Research Group and contributes to the DFG Research Unit FOR 2718: Modal and Amodal Cognition: Functions and Interactions, focusing on the interplay between modal and amodal encodings underlying space-metric associations. Her research centers on the nature of SNARC (Spatial-Numerical Association of Response Codes) and SNARC-like effects, investigating whether these phenomena stem from continuous magnitude processing or require discrete serial ordering. With a strong background in linguistics, she equally explores language comprehension and negation processing through rigorous behavioral studies. Her work bridges cognitive psychology and linguistic theory to understand fundamental cognitive mechanisms. Analysis of her publication trajectory reveals a clear evolution from early work analyzing negation in political discourse and social media (particularly during the 2014 European Elections) to more recent investigations of the cognitive architecture supporting negation processing. Her research consistently integrates experimental techniques with corpus analysis, providing both controlled laboratory findings and real-world usage patterns across cognitive psychology, linguistics, and political communication. Her notable recognition includes: Humboldt Research Fellowship for postdoctoral researchers Dr. Albu actively supervises student research projects investigating fundamental questions about negation processing, including whether negative sentences can express events and what mental representations people form when reading negative information. Her current work within the DFG Research Unit examines how different cognitive systems interact in processing spatial and numerical information. As a member of the Diagnostics and Cognitive Neuropsychology Research Group, she collaborates with colleagues including Maristella Lunardon, Katharina Sautter, Mine Avcil, Christina Artemenko, and others on projects related to cognitive processing, neuropsychological assessment, and the cognitive underpinnings of language comprehension.

• Haptics
• Tactile Technology
• Robotics
• +۳ مورد دیگر

Vincent Hayward is a researcher specializing in haptics , tactile technology , and human-computer interaction . His work bridges robotics , biomechanics , and sensory feedback systems , focusing on advanced tactile interfaces, texture synthesis, and multimodal communication devices. Collaborations span institutions studying deafblind communication and microscale interactions .

Weynants, Melanieمشاهده پروفایل

پژوهشگر

• Soil Hydropedology
• Soil Hydraulic Properties
• Pedotransfer Functions
• +۵ مورد دیگر

Melanie Weynants is a Researcher at the Max Planck Institute for Biogeochemistry, affiliated with the Department Biogeochemical Integration. She contributes to two research groups: Global Diagnostic Modelling (led by Dr. Martin Jung) and Scalable Spatiotemporal Data Structures and Analytics (SSDSA) (led by Dr. Fabian Gans). Her work is centered on environmental data modeling, soil science, and geospatial analysis. Her research focuses on soil hydropedology, pedotransfer functions, land degradation, and spatiotemporal modeling of Earth systems. She has made significant contributions to the development of pedotransfer functions for soil hydraulic properties and is actively involved in creating data-driven models for assessing climate-related disasters and compound environmental extremes. Her work bridges soil science, remote sensing, and data science to understand and model complex environmental processes. The recent publications highlight a strong trend in leveraging large-scale spatiotemporal datasets and machine learning for environmental forecasting. Her research spans from fundamental soil property estimation to applied modeling of climate impacts on vegetation and compound extreme events, demonstrating a consistent focus on data-intensive environmental science. Her work has been part of significant collaborative efforts, including contributions to the World Atlas of Desertification and the European Hydropedological Data Inventory (EU-HYDI). She has collaborated with major European research institutions such as UCL and the European Commission Joint Research Centre on projects related to soil mapping and land degradation assessment. Melanie Weynants is an active researcher with no indication of part-time status, retirement, or former staff status.

Steffen Schneiderمشاهده پروفایل

دانشیار

• Dynamical systems
• Representation learning
• Neural dynamics
• +۳ مورد دیگر

Dr. Steffen Schneider is an Associate Professor at the Ludwig Maximilian University of Munich , affiliated with the Graduate School of Systemic Neuroscience (GSN) and the Helmholtz Center Munich, Computational Health Center . His research focuses on developing machine learning algorithms for representation learning, nonlinear system dynamics, and neural interpretability in biological datasets. His work spans dynamical systems , self-supervised learning , and identifiability theory , with a strong emphasis on compressing multi-modal data into foundation models and studying their mechanistic interpretability . Recent publications demonstrate applications in neural dynamics , biomedical modeling , and speech recognition . His team at Helmholtz Center Munich explores system identification and neural embeddings , with collaborations across neuroscience and computational health. Contact: steffen.schneider@helmholtz-munich.de | Research Website

Jessica Freiherrمشاهده پروفایل

استاد

• Neuroscience
• Cognitive Neuroscience
• Sensory Perception
• +۳ مورد دیگر

Prof. Dr. Jessica Freiherr serves as a full faculty member of the Graduate School of Systemic Neurosciences (GSN) at Ludwig Maximilian University of Munich and leads the MultiSense research group at the Fraunhofer Institute for Process Technology and Packaging IVV. Her work bridges academic neuroscience with applied sensory analytics in industrial contexts. Her research centers on Behavioral & Cognitive Neuroscience with deep specialization in human chemical senses (olfaction, gustation, trigeminal perception). She investigates multisensory integration mechanisms using multimodal stimulation and advanced neuroimaging techniques (EEG/ERP, fMRI), examining how chemosensory stimuli interact with other sensory systems to form unified percepts. Her work spans fundamental neural processing in healthy subjects to clinical applications in psychiatric and neurological patient populations, with particular interest in food-related odors, warning signals, and emotion-communicating body odors. Analysis of her recent publications reveals consistent focus on multisensory integration dynamics, especially chemosensory-visual/auditory interactions. Her research demonstrates methodological sophistication through graph theoretical network analysis, superadditive/subadditive neural processing models, and cross-modal cognitive load paradigms, establishing significant contributions to understanding cortical odor processing and social chemosignaling. The MultiSense group under her direction integrates behavioral experiments, psychophysiological measurements, and functional neuroimaging to decode sensory perception mechanisms, with applications spanning food science, clinical diagnostics, and human-computer interaction.

Felix Binkowskiمشاهده پروفایل

پژوهشگر

• Nanophotonics
• Quantum Technologies
• Computational Modeling
• +۴ مورد دیگر

Felix Binkowski is a researcher at the Zuse Institute Berlin within the Modeling and Simulation of Complex Processes department and Computational Nano Optics group. His work focuses on computational methods for photonic systems and quantum technologies. Position: Researcher Email: binkowski@zib.de Research Interests include: modal analysis of nanophotonic devices, resonance phenomena in non-Hermitian systems, Purcell effect optimization for quantum emitters, and application of Riesz projections to eigenvalue problems. His projects span from theoretical developments to experimental validation of optical materials. Key methodologies: AAA rational approximation, Riesz projections, Gaussian process optimization Application areas: photovoltaics, nanolasers, plasmonic systems Recent Publications (2024-2025) demonstrate expertise in: computational resonance extraction, pole-zero analysis of photonic systems, and uncertainty-guided design optimization. Notable works include software frameworks for resonance expansion (RPExpand) and studies on Purcell enhancement in 2D material-based nanoresonators. Education includes a doctoral degree (2023) from Freie Universität Berlin under Christof Schütte, and a Master's (2017) from Technische Universität Berlin with advisors Jörg Liesen and Martin Weiser.

• Medical Imaging
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Jiliu Zhou is an active academic researcher with a focus on medical imaging, artificial intelligence, and biomedical engineering. His work spans topics such as PET image reconstruction, radiotherapy dose prediction, and domain adaptation, often employing advanced deep learning techniques like GANs, Transformers, and diffusion models. Collaborations with experts like Yan Wang, Xi Wu, and Dinggang Shen highlight his interdisciplinary approach. Research interests include Medical Imaging: Developing algorithms for PET and MRI reconstruction. Deep Learning: Applying GANs, Transformers, and diffusion models to medical data. Domain Adaptation: Enhancing robustness in multi-source environments. Image Processing: Innovating segmentation and super-resolution methods. His recent publications demonstrate trends in semi-supervised learning , contrastive strategies , and multi-task frameworks for medical applications. Despite no explicit awards or grants mentioned, his prolific output in top journals and conferences underscores significant contributions to biomedical image analysis.

Florian Heckerمشاهده پروفایل

استاد

• Sound Art
• Synthetic Sound
• Auditory Experience
• +۷ مورد دیگر

Florian Hecker is a Professor at the Academy of Fine Arts Munich (AdBK Munich), where he leads the Sound and Experiment program, a cross-class initiative supported by the Bavarian Top Professorship Program. His work bridges sound art, digital synthesis, and auditory philosophy, with a focus on non-traditional sonic experiences and experimental listening. His research interests include synthetic sound, algorithmic composition, auditory perception, and the philosophical dimensions of sound. Hecker investigates sound as both abstract structure and sensory phenomenon, often working at the intersection of technology, art, and cognition. His practice emphasizes off-the-grid software environments, critical listening, and the materiality of sound media. The recent body of work and publications reflects a strong trend in machine-generated sound, formal systems, and the deconstruction of auditory norms. His projects often explore how sound shapes space, perception, and meaning beyond linguistic frameworks, utilizing experimental synthesis and spatial audio techniques. Florian Hecker has presented his work internationally at venues such as Galerie Neu (Berlin), Roy and Edna Disney CalArts Theater (Los Angeles), and Simian (Copenhagen), establishing himself as a leading figure in contemporary sound art. He has an extensive discography with labels including Editions Mego, Etat, GRM, and Pan, reflecting both artistic and research-oriented outputs. Notable works include Syn As Text [AC] , Statistique Synthétique , and Inspection II , which demonstrate rigorous engagement with formal and synthetic sound processes. He advises students interested in experimental sound practices through the Sound and Experiment platform, encouraging cross-disciplinary collaboration and independent research. The program hosts lectures and performances by prominent figures in sound and media theory, fostering a vibrant intellectual and artistic environment. Hecker's work is deeply embedded in a lab-like studio practice, where sound is treated as a medium for conceptual and sensory experimentation. The Sound and Experiment program functions as a dynamic research platform, integrating artistic production, philosophical inquiry, and technological innovation.

• Database Systems
• Stream Processing
• Internet of Things (IoT)
• +۷ مورد دیگر

Steffen Zeuch is a researcher at Humboldt University of Berlin, Germany, with a strong research focus on database systems, stream processing, and Internet of Things (IoT) data management. He is a key contributor to the NebulaStream platform, an extensible, high-performance system for multi-modal edge applications. His work spans query optimization, GPU acceleration, fault tolerance, and distributed state management in stream processing environments. Research interests include database systems, stream processing, IoT data management, query optimization, GPU computing, and hardware-aware execution. His research addresses challenges in real-time analytics, efficient data placement, adaptive compilation, and complex event processing in distributed and edge environments. The publication trends in his recent articles highlight a consistent focus on stream processing systems—especially NebulaStream—with increasing attention to GPU acceleration, adaptive optimization, fault tolerance, and complex event processing. His work bridges theoretical query optimization with practical system implementation, emphasizing performance, scalability, and deployment in real-world IoT and edge infrastructures. Steffen Zeuch has made significant contributions to top-tier venues such as VLDB, SIGMOD, EDBT, and DEBS. His collaborative work, primarily with Volker Markl and other members of the database group at Humboldt University, demonstrates strong research leadership and technical depth in data-intensive systems. He has led and contributed to projects involving system design, performance benchmarking, and real-world deployment of stream processing platforms. His work on tutorials and system demonstrations indicates active engagement in community education and dissemination. While no specific lab or team name is mentioned, his research is centered around the NebulaStream project, a comprehensive platform for managing and analyzing data across fog, edge, and cloud environments. This platform supports complex analytics beyond traditional cloud boundaries, enabling scalable and efficient IoT applications.

• Natural Language Processing
• Biomedical Informatics
• Medical Text Simplification
• +۴ مورد دیگر

Byron C. Wallace is a prominent researcher in Natural Language Processing with a focus on biomedical and clinical applications . His work spans model distillation, factuality evaluation, and medical text simplification. Key contributions include developing methods for tracing teacher models in distillation and creating benchmarks like FactPICO and RedHOT. Research interests include LLM interpretability , medical evidence synthesis , and cross-modal alignment in clinical NLP His recent publications analyze syntactic template repetition in LLMs, token erasure effects , and definition-augmented biomedical NER Wallace's work has advanced zero-shot summarization techniques, pharmacovigilance automation , and patient data privacy analysis in clinical models. Current projects explore chain-of-thought distillation , multilingual medical simplification , and interpretable feature extraction from EHR data. Key collaborators include researchers from institutions like Allen Institute for AI, MIT, and various biomedical NLP teams. His methodological innovations in instance attribution , data augmentation , and counterfactual reasoning have influenced modern NLP research paradigms.

• Theoretical Computer Science
• Coalgebras
• Category Theory
• +۱۲ مورد دیگر

Stefan Milius is a Senior Lecturer (Akademischer Direktor) at the Chair of Theoretical Computer Science (Computer Science 8) within the Faculty of Engineering at Friedrich-Alexander University Erlangen-Nuremberg (FAU). He is actively involved in research, teaching, and academic leadership, with a strong emphasis on theoretical foundations of computer science. His research interests are centered on coalgebras, category theory, universal algebra, formal verification, semantics of iteration and recursion, and logic in computer science . He investigates algebraic and categorical methods for modeling and reasoning about computational systems, particularly through the lens of fixed points, automata, and logical semantics. His recent publications (2022–2025) span top-tier venues such as LICS, CALCO, ICALP, MFPS, POPL, and CONCUR, showcasing a consistent focus on algebraic language theory with effects, nominal automata, graded semantics, bialgebraic reasoning, and coalgebraic algorithms . The work often involves deep categorical constructions and has applications in program equivalence, formal verification, and automata minimization. Stefan Milius has received several prestigious awards, including: Ackermann Award (2006) for his PhD thesis Best Theory Paper at FM 2019 EATCS Best Paper Award at MFCS 2017 CALCO 2015 Best Paper Award Braunschweig Prize for Outstanding Academic Achievements (2000) He plays a significant role in the academic community as Editor-in-Chief of Logical Methods in Computer Science (since 2020), member of the advisory board of TheoretiCS , and editorial board member of Applied Categorical Structures . He has served on numerous program and steering committees, including FoSSaCS, LICS, MFPS, CALCO, and CMCS. He has also supervised student projects and thesis topics in theoretical computer science, though specific student names are not listed. He has been involved in externally funded research, including the BMBF project VerSyKo at TU Braunschweig (2011–2012), focusing on formal verification of synchronous software components. His current work continues to advance foundational methods in theoretical computer science with broad applicability.

Fabio Papacchiniمشاهده پروفایل

مدرس ارشد

• Non-classical Logics
• Automated Theorem Proving
• Knowledge Representation and Reasoning
• +۱۲ مورد دیگر

Dr. Fabio Papacchini is a Senior Lecturer in Computer Science at Lancaster University Leipzig, affiliated with the School of Computing and Communications and research groups Security Lancaster (Systems and Software Security). He holds a PhD from the University of Manchester, where he was awarded a Doctoral Prize Fellowship, and previously conducted research at the University of Liverpool on projects involving robotics, artificial intelligence, and software systems for nuclear and offshore energy applications. His research centers on non-classical logics in computer science, with key interests in model theory, automated theorem proving, knowledge representation and reasoning, ontological reasoning, and formal verification. He has made significant contributions to modal logic, spatial logics, and ontology-mediated querying, particularly through reduction techniques and finite model analysis. The recent publications highlight a strong trend in logical foundations for AI and formal methods, focusing on efficient translations in modal logic, runtime verification of multi-agent systems, and integration of formal techniques with robotics and autonomous systems. His work bridges theoretical logic with practical applications in safety-critical domains. Doctoral Prize Fellowship, University of Manchester Dr. Papacchini has collaborated extensively with researchers such as Ullrich Hustadt, Clare Dixon, Cláudia Nalon, Michael Fisher, and Alessandro Ferrando. While no direct mention of student advising or grants is found, his active publication record and leadership in research projects suggest significant involvement in mentoring and collaborative research. He is associated with formal verification of autonomous systems and has explored applications in robot inspection and battery health management. He has contributed to the development of declarative languages for agent platforms and has participated in the Multi-Agent Programming Contest, showcasing applied work in automated planning and agent coordination.