جستجوی استاد راهنمای دانشگاهی

Niels Henze is a Professor at the Chair of Media Informatics within the Faculty of Languages, Literature and Cultural Studies at the University of Regensburg, where he has been serving since May 2018. His research centers on human-computer interaction, with a strong focus on predictive models in interactive systems, mobile interaction, augmented and virtual reality, and attention-aware computing. His research interests include: Human-Computer Interaction (HCI) Predictive modeling for runtime adaptation Mobile and wearable interaction Augmented and Virtual Reality (AR/VR) Attention-aware and context-sensitive systems Sociocognitive aspects of interactive technologies The analysis of his recent publications reveals a consistent focus on leveraging user behavior and contextual cues to build intelligent, adaptive interfaces. His work integrates machine learning with interaction design, emphasizing real-time model updates, implicit feedback, and cognitive load awareness to improve usability and user experience across mobile and immersive platforms. Scientific awards: No awards mentioned in the provided text. Niels Henze leads research in adaptive interactive systems and advises students in the field of media informatics. He has previously held a junior professorship at the University of Stuttgart and completed his doctorate at the University of Oldenburg under Susanne Boll. He is actively involved in advancing the theoretical and practical foundations of predictive and attention-aware computing. Grants and funding sources are not specified in the text. He is associated with the Institute for Information and Media, Language and Culture (I:IMSK) at the University of Regensburg, contributing to a multidisciplinary environment that bridges informatics with cultural and linguistic studies.

• Human-Computer Interaction
• Predictive Models in Interactive Systems
• Mobile Human-Computer Interaction
• +۵ مورد دیگر

Dr. Philipp Allgeuer is a Postdoctoral Research Associate at the Knowledge Technology Research Group within the Department of Informatics at the University of Hamburg. His work focuses on humanoid robotics, bipedal locomotion, and sensor fusion. He holds a PhD from the Autonomous Intelligent Systems Group at the University of Bonn, alongside dual bachelor's degrees in Mechatronic Engineering and Mathematical/Computer Sciences (both with First-Class Honors). His research contributions include the development of the igus Humanoid Open Platform and the NimbRo-OP series of humanoid robots, recognized with awards like the RoboCup HARTING Open Source Award (2016) and the Best Humanoid Award (2018). He has authored influential papers on fused angles for robot balance, tilt phase space representations, and neuro-inspired control architectures. Allgeuer's teams have dominated RoboCup competitions, winning titles in AdultSize and TeenSize leagues multiple times. His open-source software frameworks (e.g., rot_conv_lib , attitude_estimator ) and hardware designs are widely used in robotics research. Recent work explores multimodal human-robot interaction and AI-driven robotic task coordination. He is affiliated with the Knowledge Technology Research Group and contributes to projects like the NICOL humanoid robot, bridging social interaction and reliable manipulation. His research spans from low-level control algorithms to high-level behavior planning systems.

• Robotics
• Humanoid Robotics
• Bipedal Locomotion
• +۵ مورد دیگر

Diogo Carbonera Luvizon is a Researcher at the Max-Planck-Institut für Informatik (MPI-INF) in Saarbrücken, Germany, and a member of the Visual Computing and Artificial Intelligence (VIA) Research Center. He holds a PhD in Computer Vision and Machine Learning from CY Cergy Paris University (2019), and Bachelor's and Master's degrees in Engineering and Applied Computing from UTFPR, Brazil. His research focuses on solving complex problems in Computer Vision, Computer Graphics, and Deep Learning, particularly in human modeling and real-time systems. Education: PhD (2019) - CY Cergy Paris University; M.Sc. (2015) - UTFPR; B.Sc. (2011) - UTFPR. Research interests include 3D human pose estimation, action recognition, multitask learning, and novel view synthesis. He has contributed to patents on multiplane image generation (Samsung) and vehicle speed measurement systems. His work has been recognized with awards like the Best Paper Honorable Mention at GCPR-VMV 2022 and Best Presentation Award at ETIS Lab (2018). He has developed open-source tools, including the deephar repository for human action recognition and pose estimation. His current affiliations include MPI-INF and the VIA Research Center, a partnership between MPI-INF and Google.

• Computer Vision
• Computer Graphics
• Deep Learning
• +۴ مورد دیگر

Christian Wald is a Post-doctoral researcher at Technical University Berlin working under Professor Gabriele Steidl, focusing on generative modeling, flow matching, and stochastic processes in machine learning. His research bridges theoretical probability with practical medical imaging applications, particularly in MRI reconstruction and analysis. He completed his PhD at Humboldt University of Berlin in 2017 with a thesis on p-adic quantum groups. His academic journey transitioned from pure mathematics to interdisciplinary machine learning research, reflecting his versatile expertise. Wald's primary research explores generative models through the lens of optimal transport and flow matching, with significant contributions to Wasserstein geometry and conditional distance metrics. His work frequently integrates stochastic processes to enhance medical image reconstruction, demonstrating strong cross-disciplinary impact in both theoretical machine learning and clinical applications. Recent publications highlight innovations in sliced MMD flows, Bayesian OT methods, and uncertainty-aware medical image analysis. Analysis of his 15 most recent publications (2019-2025) reveals a consistent trajectory toward unifying geometric probability with deep learning. Key themes include flow-based generative modeling for medical time-series data, optimal transport applications in image reconstruction, and novel kernel methods for distribution matching. His work spans both foundational theory (e.g., Fisher-Rao curves) and high-impact medical applications (e.g., coronary calcium scoring). No specific scientific awards are documented in the provided text, though his publications appear in prestigious venues including ICLR, IEEE TMI, and Physics in Medicine & Biology. Wald maintains extensive collaborations with the medical imaging group at Technical University Berlin, particularly with Andreas Kofler and Gabriele Steidl. His co-authored works demonstrate consistent contributions to MRI reconstruction pipelines and segmentation frameworks, though no formal advising roles or grant leadership are indicated. Current projects focus on uncertainty quantification in active learning for medical image segmentation. He operates within Gabriele Steidl's research group at Technical University Berlin, which specializes in mathematical imaging and machine learning. The team combines expertise in optimization, probability theory, and deep learning to solve medical imaging challenges, with Wald contributing core algorithmic innovations in generative modeling and stochastic reconstruction.

• Generative Modeling
• Flow Matching
• Stochastic Processes
• +۳ مورد دیگر

Prof. Constantin A. Rothkopf is a W3 Professor at the Department of Psychology, Technische Universität Darmstadt, and a secondary member of the Department of Computer Science. He serves as Founding Director of the Centre for Cognitive Science and founding member of the Hessisches Zentrum für Künstliche Intelligenz (hessian.ai). He is also part of the European Laboratory for Learning and Intelligent Systems (ELLIS) and the DAAD Konrad Zuse Schools of Excellence in Artificial Intelligence (ELIZA). His research focuses on the interplay between perception and action, using computational models and experimental studies in humans. Current work includes eye-tracking studies in naturalistic environments, inverse optimal control models, and developing algorithms for virtual agents. Education: Ph.D. in Neuroscience and Informatics from the University of Rochester, followed by postdoctoral research at Frankfurt Institute for Advanced Studies (FIAS). He has held visiting professorships at Central European University (2017) and Columbia University (2023). Awards include an ERC Consolidator Grant (2022) and SCENE Project Funding (2025). Research Interests: Active vision, decision-making under uncertainty, sensorimotor control, and computational modeling. Key themes include how humans use sensory input to form beliefs, make decisions, and act in dynamic environments. Grants/Awards: ERC Consolidator Grant (2022), SCENE Funding (2025) Labs/Teams: Centre for Cognitive Science, hessian.ai, ELLIS Unit Darmstadt

• Perception and Action
• Computational Neuroscience
• Machine Learning
• +۳ مورد دیگر

Kede Ma is an Associate Professor in the Department of Computer Science at City University of Hong Kong (CityUHK). He received his B.E. from the University of Science and Technology of China (USTC) in 2012, and MASc and Ph.D. degrees from the University of Waterloo in 2014 and 2017, respectively. From 2018 to 2019, he was a Research Associate with the Howard Hughes Medical Institute and New York University. Prof. Ma has been named to the Highly Cited Researchers list by Clarivate Analytics in 2024 and currently serves on the editorial boards of IEEE Transactions on Image Processing, IEEE Transactions on Information Forensics and Security, and IEEE Signal Processing Letters. Prof. Ma leads the Multimedia Analytics (MA) Laboratory, an interdisciplinary research group focused on computational vision, computational modeling of human visual perception, perceptual multimedia signal processing, quality assessment, and multimedia forensics. His research spans computational photography, high dynamic range imaging and rendering, omnidirectional video analysis, camera processing pipeline design, and artificial intelligence safety in multimedia systems. His work integrates machine learning techniques including reinforcement learning, generative modeling, self-supervised learning, and continual learning for multimedia signal processing applications. His recent publications demonstrate a strong focus on image quality assessment, deep learning for multimedia processing, and multimedia forensics. His work bridges theoretical computer vision principles with practical applications in multimedia systems. The research trends show increasing integration of foundation models with specialized multimedia processing tasks, particularly in quality assessment and security applications. Highly Cited Researchers list by Clarivate Analytics (2024) Best Paper Award at IEEE International Conference on Virtual Reality and Visualization (2021) Best Paper Runner-Up at International Joint Conference on Artificial Intelligence Workshop (2021) Top 10% Award at IEEE International Conference on Image Processing (2015) Finalist for the Governor General's Gold Medal, University of Waterloo (2017) Spotlight presentation at NeurIPS (2022) Highlight paper at ICCV (2025) Oral presentation at ICLR (2025) Prof. Ma advises numerous PhD students and postdoctoral fellows in the MA Laboratory. His research is supported by various grants enabling work in multimedia analytics, image processing, and computer vision. The laboratory maintains active collaborations with researchers at institutions including SUSTech, ZJU, and HIT. Current projects focus on advancing image quality assessment methodologies, developing more robust deep learning techniques for multimedia forensics, and exploring new approaches to HDR imaging and omnidirectional video processing. The Multimedia Analytics Laboratory maintains a strong focus on both theoretical foundations and practical applications of multimedia processing. Current research directions include integrating large language models with image quality assessment, developing more robust deepfake detection methods, and advancing techniques for continual learning in multimedia applications. The lab emphasizes rigorous evaluation methodologies and maintains multiple datasets for multimedia quality assessment research.

• Image Quality Assessment
• Computational Photography
• High Dynamic Range Imaging
• +۷ مورد دیگر

Insa Feinkohl is a Professor at the Chair of Medical Biometry and Epidemiology within the Faculty of Health at the University of Witten/Herdecke . Her research focuses on risk factors for cognitive dysfunction and mental health in older adults, particularly post-surgery, with emphasis on metabolic and cognitive risk factors. Bachelor of Science (BSc) in Psychology (1 st class honors) – University of Dundee (2006-2009) Master of Science (MSc) in Psychology of Individual Differences (with distinction) – University of Edinburgh (2009-2010) PhD in Community Health Sciences – University of Edinburgh (2010-2014) Post Doc in Knowledge Construction Group – Leibniz Institute for Knowledge Media, Tübingen (2014-2015) Postdoc in Molecular Epidemiology Group – Max Delbrück Center, Berlin (2015-2022) Habilitation in Molecular Epidemiology – Charité Universitätsmedizin Berlin (2021) Her research integrates medical biometry and epidemiology to study postoperative cognitive dysfunction (POCD), delirium, and aging-related cognitive decline. Key areas include biomarker validation (e.g., leptin, interleukins), brain connectivity (dopaminergic networks, thalamus), and metabolic risk factors (diabetes, obesity). She contributed to the BioCog project , an EU-funded initiative for personalized risk prediction of postoperative cognitive impairment. Her recent publications highlight trends in perioperative neuroscience, including brain mineralization, cytokine associations with neurocognitive disorders, and structural/functional imaging in delirium. Articles also explore metabolic syndrome, cognitive reserve, and delirium prediction models using machine learning. Insa Feinkohl is affiliated with major academic societies, including the German Society for Epidemiology , German Society for Medical Informatics, Biometry and Epidemiology , and the German University Association .

• Medical Biometry
• Epidemiology
• Cognitive Dysfunction
• +۴ مورد دیگر

Matthias Lang is an Associate Professor of Economics at Ludwig Maximilian University (LMU) Munich, specializing in applied microeconomic theory. He holds a PhD from the Bonn Graduate School of Economics and is Principal Investigator of the Collaborative Research Centre/Transregio 190 (CRC TRR 190) on 'Rationality and Competition.' He is also a CESifo Fellow. His research focuses on uncertainty, contract design, and behavioral economics, with applications to insurance fraud, tax evasion, and political competition. Lang has served as an Assistant Professor at Freie Universität Berlin and Humboldt University Berlin, and was a Senior Research Fellow at the Max Planck Institute for Research on Collective Goods. His work bridges game theory, mechanism design, and decision theory, addressing strategic uncertainty and contractual solutions to mitigate its effects. He has been awarded the Walther-Rathenau Award for Best Paper (2015) for his work on communicating subjective evaluations. His recent research explores ambiguous product information dynamics, narrative-based mechanisms, and the value of transparency in campaign promises. Lang actively contributes to academic communities through numerous presentations at international conferences and serves on editorial review boards for top journals.

• Microeconomic Theory
• Contract Theory
• Behavioral Economics
• +۴ مورد دیگر

Dr. Leif Langsdorf is a Researcher at the Department of Psychology within the Faculty of Philosophy I at Martin Luther University Halle-Wittenberg. His work focuses on cognitive processes related to multitasking, motivation, and cognitive control, utilizing both behavioral methods and electrophysiological techniques including EEG. Langsdorf's research interests center on understanding how motivation affects multitasking abilities, particularly examining how reward processing influences cognitive control in dual-task situations. His work investigates whether motivated individuals show faster reaction times in simple response tasks and explores the underlying cognitive processes that enable this enhanced performance. His research employs a range of methodologies from cognitive psychology and EEG to examine the physiological processes during multitasking. His publication record shows a consistent focus on reward effects in dual-task paradigms, with recent work examining the neural correlates of task switching, temporal dynamics of reward utilization, and the application of neuromodulation techniques like tDCS to influence task coordination. His research has appeared in journals including Journal of Cognitive Neuroscience, Attention Perception & Psychophysics, and Acta Psychologica. Langsdorf actively supervises thesis work on multitasking and motivation, guiding students through research on how motivation impacts multitasking capabilities. His work has been presented at multiple conferences including TeaP (Experimental Psychology conference) and HexKop. He collaborates extensively with Prof. Torsten Schubert and other researchers at the university, including Sebastian Kübler, Daniel Darnstaedt, and Maryam Talarposhti, forming part of a research group focused on cognitive control and task switching phenomena.

• Multitasking
• Motivation
• Cognitive Control
• +۴ مورد دیگر

Alfonso Emilio Gerevini is a prominent researcher in artificial intelligence with over 30 years of continuous academic contributions. His work spans theoretical foundations of automated planning to practical healthcare applications, with recent publications demonstrating significant impact in both traditional AI domains and emerging interdisciplinary areas. His research interests focus on automated planning systems , temporal reasoning , and multi-agent coordination , with recent expansion into healthcare applications using machine learning techniques. Gerevini has made fundamental contributions to planning algorithms, particularly in width-based search, case-based planning, and privacy-preserving multi-agent planning. His work on PDDL (Planning Domain Definition Language) has been influential in standardizing planning representations. Analysis of his 15 most recent publications reveals a strategic evolution from core planning research toward impactful healthcare applications, particularly during the COVID-19 pandemic. While maintaining his expertise in planning algorithms, he has successfully integrated machine learning techniques to address real-world medical challenges including radiology report analysis, prognosis prediction, and lab test interpretation. His work demonstrates exceptional versatility across both theoretical and applied domains of artificial intelligence. Gerevini maintains a robust collaborative network, primarily with Italian researchers including Ivan Serina, Alessandro Saetti, and Luca Putelli. His publications appear consistently in top-tier AI venues including Artificial Intelligence journal, Journal of Artificial Intelligence Research, and AAAI/ICAPS conferences. The collaborative patterns suggest he leads a significant research group focused on advancing planning systems while applying them to critical real-world problems.

• Artificial Intelligence
• Automated Planning
• Temporal Reasoning
• +۴ مورد دیگر

Marian Sauter is a researcher at Ulm University's Institute for Psychology in Germany, specializing in the intersection of psychology, human-computer interaction, and educational technology. Her work primarily focuses on eye-tracking methodologies and gaze analysis to improve online teaching and learning environments. She frequently collaborates with colleagues from the Institute of Media Informatics at Ulm University, indicating strong interdisciplinary connections between psychology and computer science departments. Her research interests center on understanding visual attention patterns in educational contexts, particularly how gaze behavior correlates with learning outcomes in digital environments. Dr. Sauter investigates how eye movement data can be visualized and utilized to optimize instructional design, with specific attention to gaze-laser pointer relationships, gaze synchronicity among students, and attention monitoring in virtual classrooms. Her work bridges cognitive psychology theories with practical applications for e-learning platforms and remote education systems. Analysis of her recent publications reveals a strong trajectory in applying eye-tracking technology to solve real-world educational challenges, particularly those emerging during and after the pandemic-driven shift to online learning. Her research spans both theoretical investigations of visual attention mechanisms and practical implementations for educational technology. The consistent focus on webcam-based eye-tracking demonstrates her commitment to developing accessible methods that don't require specialized hardware, making her research particularly relevant for widespread educational applications. Best Paper Award at ETRA '22 Symposium on Eye Tracking Research and Applications Berlin Health Award (2023) Dr. Sauter's collaborative approach is evident in her frequent co-authorship with Tobias Wagner from Ulm University's Institute of Media Informatics and Anke Huckauf from General Psychology. Her research appears to be supported by institutional resources at Ulm University, though specific external grants are not mentioned in the available information. She appears to be actively mentoring students given her involvement in educational psychology research and student-focused studies. Her work suggests involvement with research groups focused on human-computer interaction, educational technology, and cognitive psychology at Ulm University, though specific lab names are not provided in the available information. The interdisciplinary nature of her publications indicates collaboration across psychology and media informatics departments.

• Eye tracking
• Online teaching
• Human-computer interaction
• +۵ مورد دیگر

Selim Başoğlu is a Researcher and doctoral candidate affiliated with the Center for Media, Communication, and Information Research (ZeMKI) at the University of Bremen since April 2024. He concurrently serves as a visiting scholar at the University of Oslo's Psychological Institute (since June 2023) and teaches introductory courses at the University of Bremen. His research focuses on mediated time, time studies, and media psychology, particularly examining the intersection of media consumption and temporal experiences. Education: Bachelor's degrees in New Media and Psychology (2021) from Kadir Has University, with a minor in Political Science and Public Administration Master's in Digital Media and Society (University of Bremen), thesis: 'Media Multitasking and the Experience/Construction of Time' His sole listed publication (2023) explores socio-economic factors influencing relationship satisfaction through mediation analysis. While currently no awards or grants are explicitly mentioned, he has participated in multiple research projects and held roles as a student assistant at ZeMKI and other labs. He contributes to interdisciplinary teams investigating digital media's societal impacts.

• Media Psychology
• Time Studies
• Media Multitasking
• +۳ مورد دیگر

Kristina Mach is a researcher at the Chair of Computer Science Applications in Medicine at the Technische Universität München (TUM) . She focuses on interdisciplinary projects combining computer science and medical applications, particularly in imaging and robotic assistance. Research area: Medical imaging, machine learning, and surgical robotics Key technologies: Deep learning, image registration, and generative adversarial networks Applications: Ophthalmic surgery, radiology reporting, and intraoperative OCT Her recent work includes SpecstatOR for iOCT segmentation, Multitask Weakly Supervised Networks for MR-US registration, and Flexr for few-shot chest X-ray classification. These projects emphasize interoperability between imaging modalities and structured reporting in clinical workflows.

• Medical Imaging
• Computer Science Applications in Medicine
• Machine Learning for Medical Diagnosis
• +۲ مورد دیگر

Dr. Silvia Achtzehn is a researcher at the German Sport University Cologne, affiliated with the Institute of Training Science and Sports Informatics and the Department of Performance Physiology. She is also associated with The German Research Center for Competitive Sports, where she conducts research on performance and training physiology in elite athletes. Her research focuses on exercise physiology , athletic training , biomarkers , and hormonal responses (such as cortisol and testosterone) to physical stress. She investigates recovery modalities , training load monitoring , and point-of-care diagnostics (POCT) in sports settings. Her work also extends to cognitive control during multitasking and the effects of hypoxic training on angiogenic markers. The recent articles highlight her focus on the intersection of cognitive neuroscience and sports performance , particularly through vagus nerve stimulation, as well as physiological responses to exhaustive exercise and training camps in youth athletes . Her research integrates both laboratory and field-based approaches to optimize athlete health and performance. Scientific Awards: POCT: Athletennahe Sofortdiagnostik - Möglichkeiten und Anwendungsbereiche (2017) Young Investigator Award for Poster Presentation (2007) Dr. Achtzehn has been actively involved in multiple research projects, including those on stress management via smartphone apps , physiological responses to recovery , and health management in elite soccer . She has served as a peer reviewer for journals like BMC Sports Science, Medicine and Rehabilitation and has presented at national and international conferences. She collaborates extensively with researchers such as Joachim Mester, Patrick Wahl, and Markus de Marées, indicating a strong network in sports science research. She is involved in research teams and labs focused on performance physiology , training science , and competitive sports medicine , contributing to both fundamental and applied research in elite athlete support.

• Exercise Physiology
• Performance Physiology
• Sports Science
• +۹ مورد دیگر

Dr. Yomna Abdelrahman is a Professor of Usable Security and Privacy at the Bundeswehr University Munich . Her research focuses on thermal imaging for security and privacy , virtual reality usability, eye tracking , and biometric authentication . She collaborates with researchers like Florian Alt and Albrecht Schmidt on projects exploring how thermal sensing can enhance user identification and privacy awareness . Key Research Areas : Thermal Imaging, Human-Computer Interaction, Virtual Reality, Password Security, Biometric Authentication, Eye Tracking, Privacy Her recent publications address VR emotion detection , password usability , and privacy implications of thermal imaging . She has contributed to conferences like CHI, MUM, and INTERACT, often examining security-privacy trade-offs and non-invasive biometric systems . Notably, her work on thermal attacks reveals vulnerabilities in mobile authentication through thermal residue. She holds a PhD in Computer Science from the University of Stuttgart (2018), where her thesis, "Thermal Imaging for Amplifying Human Perception," laid the foundation for her later work on thermal-based interaction and cognitive load estimation . Her collaborations span diverse domains, from smart home notifications to industrial worker assistance , reflecting her interdisciplinary approach to usable security and interactive systems .

• Human-Computer Interaction
• Thermal Imaging
• Usable Security
• +۶ مورد دیگر