جست‌وجوی استاد و پژوهشگر

Felix Klotzscheمشاهده پروفایل

پژوهشگر

• Neuroscience
• Cognitive Science
• Virtual Reality
• +۳ مورد دیگر

Felix Klotzsche is a Research Scientist at the Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, and an affiliated researcher at the Berlin School of Mind and Brain, Humboldt-Universität zu Berlin. His work focuses on cognitive neuroscience, with a particular emphasis on short-term memory, attention, and virtual reality (VR) applications in clinical and experimental settings. Education: B.Sc. Psychology & B.A. Philosophy (2009–2015), Universität Regensburg M.Sc. 'Mind and Brain' (Track 'Brain', 2015–2017), Berlin School of Mind and Brain | Humboldt-Universität zu Berlin Research Interests: Neural mechanisms of visual short-term memory EEG analysis in VR environments Clinical VR applications for neurological assessment Emotional arousal decoding via neurophysiological signals 3D face perception and deep encoding models Cardiac-phase modulated perception Publications reflect a focus on integrating VR with neuroscience methodologies, particularly in evaluating spatial memory, threat perception, and emotional processing. Key tools include Python, R, Unity3D, and advanced EEG signal processing. No scientific awards explicitly noted. No listed advisees or grants. Active in the Research Group Mind-Body-Emotion, contributing to labs and teams developing VR-based cognitive assessment tools.

• Reinforcement Learning
• Knowledge Representation
• Planning and Decision Making
• +۷ مورد دیگر

Scott Sanner is a Professor in the Department of Mechanical and Industrial Engineering at the University of Toronto's Faculty of Applied Science and Engineering. With an extensive publication record spanning over two decades, his research bridges artificial intelligence, machine learning, and engineering applications. His work demonstrates significant contributions across multiple top-tier conferences including AAAI, ICLR, NeurIPS, and SIGIR. Dr. Sanner's research interests encompass Reinforcement Learning, Knowledge Representation, Planning and Decision Making, Recommender Systems, and Large Language Models. His work shows a consistent focus on bridging symbolic and neural approaches to AI, with particular emphasis on commonsense reasoning, traffic signal control, and conversational recommendation systems. Recent publications demonstrate increasing integration of large language models with traditional AI techniques for complex reasoning tasks. Analysis of his recent publications reveals a strong trend toward leveraging large language models for knowledge representation and reasoning tasks, while maintaining his foundational work in reinforcement learning and planning. His research increasingly focuses on practical applications in transportation systems, recommendation technologies, and commonsense reasoning frameworks that combine neural and symbolic approaches. Dr. Sanner has mentored numerous graduate students who have become active researchers in the field, including Jihwan Jeong, Zheda Mai, Armin Toroghi, and Anton Korikov. His collaborative work spans multiple institutions and demonstrates strong industry and academic partnerships, particularly with researchers from Australian National University and various technology companies. His research group appears to focus on intelligent systems for decision making under uncertainty, with applications ranging from traffic management to personalized recommendation systems. Current projects show significant emphasis on integrating large language models with traditional AI techniques for more robust and explainable systems.

Tayyab Naseerمشاهده پروفایل

پژوهشگر

• Robust Visual Perception
• Outdoor Robotics
• Deep Neural Networks
• +۳ مورد دیگر

Tayyab Naseer is affiliated with the Albert-Ludwigs-Universität Freiburg, specifically within the Technische Fakultät (Faculty of Engineering) and the Autonomous Intelligent Systems Department. His research focuses on robust visual perception for outdoor robots, deep neural networks, and machine learning. He holds a BSc in Electrical Engineering from the University of Engineering and Technology Lahore (2005–2009) and an MSc in Communication Systems Engineering from the Technical University of Munich (2010–2012). He completed his PhD at the Autonomous Intelligent Systems group under Prof. Dr. Wolfram Burgard at Freiburg. His work emphasizes long-term autonomy in robotics, addressing challenges like seasonal changes in visual localization. Notable achievements include the Best PhD-Student Award at ICVSS 2016 . He has contributed to projects like LifeNav and published extensively in robotics conferences (e.g., ICRA, IROS) and journals like IEEE Transactions on Robotics. His research often intersects with computer vision, SLAM, and deep learning for robotic systems. Teaching roles include lab instructor for Image and Video Compression at TU Munich and teaching assistant for Introduction to Mobile Robotics at Freiburg. He has also delivered invited talks at workshops organized by LUMS and TU Kaiserslautern, focusing on field and assistive robotics.

• Artificial Intelligence for Materials Science
• Alloy Design
• Metallic Glasses
• +۵ مورد دیگر

Dr. Anurag Bajpai is a Research Fellow and Project Group Leader of the "Artificial Intelligence for Materials Science" group at the Max Planck Institute for Sustainable Materials in Düsseldorf, Germany. He holds a Ph.D. and M.Tech in Materials Science and Engineering from the Indian Institute of Technology, Kanpur (2017–2023), and a B.E. in Materials and Metallurgical Engineering from Punjab Engineering College, Chandigarh (2011–2015). His research focuses on integrating AI with experimental materials science to advance sustainable alloy design, particularly in metallic glasses and multicomponent alloys. Research Interests include: Machine learning-driven alloy/process design Thermal and mechanical behavior of metallic glasses Electronic waste recycling and green metallurgy Molecular dynamics simulations Process kinetics optimization His current work emphasizes sustainable materials development using scrap steel and reducing environmental impacts. Key Contributions include pioneering machine learning approaches for glass formation prediction (2022–2023), developing AI models for high-entropy alloy design (2024), and advancing cryomilling techniques for waste beneficiation. His group's work on attention-enhanced variational learning (2025) has pushed boundaries in ultra-hard metallic glass design. Awards include the prestigious Alexander von Humboldt Postdoctoral Fellowship (2024–present). His research has been published in 20+ articles, with notable contributions in AI-driven materials discovery and sustainable waste management.

• Climate Dynamics
• Arctic Sea Ice
• North Atlantic Variability
• +۳ مورد دیگر

Dr. Rohit Ghosh is a Researcher in the Climate Dynamics department at the Alfred Wegener Institute (AWI) in Bremerhaven, Germany. His work focuses on high-resolution climate modeling, Arctic sea ice dynamics, North Atlantic variability, and the impacts of climate change on regional patterns such as the Warm Arctic Cold Eurasia (WACE) phenomenon. He specializes in advanced coupled models like IFS-FESOM and contributes to global storyline simulations for extreme event analysis. His research investigates the role of Arctic sea ice loss, Northern Hemisphere snow cover, and tropical Pacific processes in driving atmospheric circulation changes. He has pioneered kilometer-scale climate simulations to resolve eddies and mesoscale features, enhancing model accuracy for regional projections. Key projects include the MPI Grand Ensemble (MPI-GE) and studies on the Atlantic Multidecadal Variability's influence on European climate. Dr. Ghosh’s publications highlight innovations in coupled ocean-atmosphere modeling, the interplay between ocean gyres and global warming, and the societal implications of wildfire and agricultural risks under climate change. He collaborates on international initiatives such as the Earth’s Future Climate scenarios and EERIE projects, advancing computational methods for large-scale supercomputing simulations.

• Computer Vision
• Medical Imaging
• Artificial Intelligence
• +۶ مورد دیگر

Seong Tae Kim is a Professor at Kyung Hee University's Department of Biomedical Engineering. He previously held affiliations at the Technical University of Munich (Germany) and KAIST's Image and Video Systems Laboratory in South Korea. His research focuses on computer vision, medical imaging, and AI applications in healthcare. Kim's work emphasizes explainable AI, neural network interpretability, and deep learning for medical diagnosis. He has contributed to advancements in knowledge graph-based reasoning, video object segmentation, and generative models for medical data synthesis. His research often bridges computer vision techniques with biomedical applications, such as cancer diagnosis and surgical phase recognition. Over 100 publications since 2003 highlight his expertise in neural networks, federated learning, and robust training methods. Notable projects include developing frameworks for COVID-19 CT analysis and generating realistic biomedical datasets. He collaborates widely with institutions like the Max Planck Institute for Informatics (via Nassir Navab's lab) and Samsung Research. Current research interests include interpretable machine learning models, longitudinal medical image analysis, and AI-driven healthcare solutions. His work frequently appears in top conferences like CVPR, MICCAI, and ECCV.

Gediminas Schüppenhauerمشاهده پروفایل

پژوهشگر

• Sociolinguistics
• Phonetics
• Language Variation
• +۳ مورد دیگر

Gediminas Schüppenhauer is a researcher at the Leibniz-Zentrum Allgemeine Sprachwissenschaft (ZAS), focusing on sociolinguistics and phonetics. He contributes to the Collaborative Research Centre CRC 1412 'Register: Language Users' Knowledge of Situational-Functional Variation', investigating cross-linguistic and cross-cultural aspects of language register. His work emphasizes methodological innovation in analyzing situational language variation and cognitive modeling of register knowledge. Key research areas include phonetic coding of register differences, formal/informal speech contrasts, and experimental studies on speaker variability. He collaborates on projects exploring Namibian German and Berlin German dialects, examining prosodic features, vowel dispersion, and sociolinguistic perception. Publications span register theory, corpus-based analyses, and experimental methodologies. His work integrates interdisciplinary approaches to understand how language users adapt to situational demands, informed by both quantitative and qualitative data.

• Computer Vision
• Machine Learning
• Remote Sensing
• +۴ مورد دیگر

Min Liu is a Professor at the Institute of Computing Technology, Chinese Academy of Sciences, where they lead research at the intersection of computer vision, machine learning, and remote sensing applications. With an extensive publication record spanning from 1998 to 2025, their work demonstrates significant contributions to multiple scientific domains. Professor Liu's research interests span computer vision, machine learning, remote sensing, image processing, artificial intelligence, and environmental monitoring. Their work uniquely bridges theoretical computer science with practical applications in environmental science, particularly in analyzing climate change impacts, vegetation dynamics, and water resource management. Recent publications reveal a strong focus on applying deep learning techniques to solve complex problems in geospatial analysis and environmental monitoring, with particular attention to Chinese ecosystems and urban environments. Analysis of Professor Liu's recent publications (2023-2025) shows a clear trend toward interdisciplinary research that combines advanced computer vision techniques with environmental science applications. Their work frequently employs deep learning architectures like feature pyramid networks, graph convolutional networks, and self-attention mechanisms to address challenges in remote sensing image analysis, object detection, and environmental monitoring. A significant portion of their research focuses on China-specific environmental challenges, including urbanization impacts on vegetation, salt marsh carbon stock estimation in the Yangtze River estuary, and lake water body extraction from satellite imagery. Professor Liu has made significant contributions across multiple domains including: Environmental monitoring using remote sensing data Deep learning applications for geospatial analysis Computer vision algorithms for object detection and image segmentation Climate change impact assessment through data-driven approaches Ecosystem modeling and analysis Their research demonstrates strong methodological innovation while maintaining practical relevance to pressing environmental challenges, particularly those facing China's rapidly urbanizing landscape and changing ecosystems.

• Educational Data Mining
• Artificial Intelligence in Education
• Learning Analytics
• +۷ مورد دیگر

Steven Moore is a prolific researcher in educational data mining, AI in education, and learning analytics. He has co-authored 43 indexed works from 2011 to 2025, collaborating extensively with scholars such as John C. Stamper and Huy Anh Nguyen. His research addresses crowdsourcing in education, automated question generation, knowledge component extraction, and the application of generative AI to instructional design. Research Interests: Educational Data Mining & Learning Analytics AI-driven Assessment & Question Generation Crowdsourcing & Learnersourcing at Scale Generative AI for Curriculum and Content Design Human-Computer Interaction in Learning Systems Across his recent publications, Moore explores how large language models and automated toolkits can enhance the quality and scalability of educational content. His work increasingly integrates advanced NLP techniques—such as GPT-4-based evaluation—with rule-based systems to support both formative and summative assessment processes.

• Computer Vision
• Machine Learning
• Object Detection
• +۵ مورد دیگر

Shanshan Zhang is an academic affiliated with the School of Computer Science and Engineering at Nanjing University of Science and Technology. Previously, she held positions at the Max Planck Institute for Informatics (2015-2016) and completed her PhD at the University of Bonn in 2015. Her research focuses on computer vision, particularly pedestrian detection, object detection, and domain adaptation. She has contributed to datasets like CityPersons and pioneered methods for occluded pedestrian detection, crowd counting, and cross-domain learning. Her work emphasizes practical applications in urban traffic scenes and adaptive systems for diverse environments. Key research areas include: Deep learning architectures for vision tasks Occlusion handling in re-identification systems Domain adaptation for robust object detection Feature alignment techniques Adaptive learning for cross-domain scenarios Publications span top venues like CVPR, ECCV, and IEEE TPAMI, reflecting contributions to both theoretical advances and real-world system development.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۸ مورد دیگر

Yong Cheng is a prolific researcher with significant contributions to computer science, artificial intelligence, and mathematical logic. His work spans biomedical image segmentation, federated learning, robotics, and formal logic, reflecting a multidisciplinary approach to solving complex technical challenges. Key Research Areas: Machine Learning, Natural Language Processing, Computer Vision, Remote Sensing, Robotics, Privacy-Preserving Techniques. Publication Trends show a focus on deep learning architectures (e.g., Transformer, U-Net), adversarial training, and applications in healthcare, autonomous systems, and geospatial analysis. His 2025 work on biomedical imaging and logic theorems highlights ongoing interests in theoretical and applied domains. Scientific Contributions include foundational work in Gödel's incompleteness theorems and practical innovations in edge computing and fusion robotics. While no formal honors are listed, his collaborations with institutions like IEEE and ACM suggest industry-wide recognition.

• Phonetics
• Speech Production
• Language Acquisition
• +۴ مورد دیگر

Dr. Francesco Burroni is a Researcher at the Institute of Phonetics and Speech Processing at Ludwig Maximilian University of Munich, specializing in the Spoken Language Processing Group. His work focuses on articulatory phonetics, speech production, and computational linguistics with a particular interest in tone languages, geminate consonants, and endangered languages. He employs machine learning techniques to analyze phonetic patterns and has contributed to studies on Thai, Japanese, Khmer, and Moklen, among others. Research interests include the timing of lexical tones relative to articulatory gestures, the role of cognitive constraints in speech rhythm, and the phonetic realization of geminate consonants. His studies integrate acoustic and articulatory data to explore phonological typology and language universals, often bridging experimental and computational methodologies. Recent work highlights adaptive planning of fundamental frequency trajectories under speaking rate changes, dissimilatory effects in tonal languages, and the application of deep learning to endangered language documentation. Burroni’s research also addresses issues like prominence clash in Italian and the functional load of vowel length in Thai. He serves as a reviewer for the Journal of the International Phonetic Association and has published extensively on topics such as syllabic prominence, coda consonant effects, and the dynamics of F0 planning. His work often involves cross-linguistic comparisons and collaborations with researchers in computational modeling and phonological theory.

Maximilian Starkمشاهده پروفایل

مدرس

• Communication Systems
• Machine Learning
• Information Theory
• +۳ مورد دیگر

Dr. Maximilian Stark is a Lecturer at the Institute of Communications, TU Hamburg. His research focuses on machine learning-driven advancements in communication systems, particularly applying the information bottleneck method to decoding algorithms, signal processing, and quantization techniques. His work bridges theoretical information theory with practical implementation challenges in coding and channel design. Key research areas include LDPC and polar codes optimization, low-bitwidth decoding architectures, and distributed signal processing frameworks. Recent publications emphasize adaptive learning systems for error resilience under quantization constraints and hardware-efficient receiver design. Dr. Stark's academic contributions span over 20 peer-reviewed articles since 2016, with a strong emphasis on integrating machine learning principles into traditional communication engineering problems. Notable themes include neural decoding paradigms, resource-constrained quantization strategies, and distributed information compression methods. No scientific awards explicitly listed. Academic advising details and lab affiliations are not provided in source materials.

Russell Barlowمشاهده پروفایل

پژوهشگر

• Language Documentation
• Historical Linguistics
• Linguistic Typology
• +۵ مورد دیگر

Russell Barlow is a Postdoctoral Researcher at the Department of Linguistic and Cultural Evolution within the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany. His work focuses on endangered languages of Papua New Guinea, particularly within the Keram language family. His educational background includes: PhD in Linguistics (2018), University of Hawai‘i at Mānoa MA in Linguistics (2016), University of Hawai‘i at Mānoa BA in Indo-European Studies (2013), CUNY Baccalaureate BA in Film Studies (2006), Wesleyan University Barlow's research centers on language documentation, historical linguistics, and linguistic typology with a geographical focus on the Pacific region. His fieldwork-intensive approach emphasizes preserving endangered Keram languages like Ulwa, Pondi, and Tomoip. He investigates numeral systems, language contact phenomena between Papuan and Austronesian languages, and morphosyntactic structures including antipassive constructions. His methodology combines traditional fieldwork with computational approaches to language evolution. His publication record shows consistent output in top linguistics journals with recent work examining numeral colexification patterns, areal loanwords in New Britain, and detailed grammatical descriptions of endangered languages. The publications demonstrate interdisciplinary connections between linguistic typology, cultural evolution, and cognitive science. As an active member of the Comparative Oceanic Linguistics (CoOL) research group, Barlow has secured significant research funding through Fulbright and DAAD programs. His fieldwork spans Indonesia (Gorontalo State University), Papua New Guinea, and Germany (MPI-SHH in Jena prior to Leipzig). His research infrastructure includes extensive language archives at SOAS, PARADISEC, and Kaipuleohone, ensuring long-term preservation of endangered language materials. Current projects involve analyzing esoteric numeral systems and cross-linguistic patterns in infix preservation.

Pol van Rijnمشاهده پروفایل

پژوهشگر

• Prosody Perception
• Dynamic Measures of Prosody
• Acoustic Features Analysis
• +۳ مورد دیگر

Pol van Rijn is a Researcher and PhD student at the Max-Planck Institute for Empirical Aesthetics in Frankfurt since 2019. His research focuses on prosody perception, dynamic acoustic analyses, and computational models of vocalizations. He holds a Master of Education in German/Dutch language studies and computer science (2019) and a Bachelor of Arts in literature (2016), both from Carl von Ossietzky University in Oldenburg. His work bridges speech synthesis, machine learning, and auditory perception, with notable contributions to TTS latent space analysis and Gibbs sampling methodologies. He has received awards including the DAAD-Prize for academic excellence and the Deutschlandstipendium. Prior roles included laboratory technician at the Speech and Music Lab (2016–2019) and research assistant in literature studies (2015–2018). His collaborations involve interdisciplinary projects with institutions like Hearing 4 All and the IEMA, addressing speech classification and human-computer interaction.