جست‌وجوی استاد و پژوهشگر

Natalie Hempel de Ibarraمشاهده پروفایل

دانشیار

• Animal vision
• Learning and memory
• Insect behaviour and active sensing
• +۷ مورد دیگر

Dr. Natalie Hempel de Ibarra is an Associate Professor in Neuroethology at the University of Exeter's Psychology Department . She co-directs postgraduate research training and leads the Exeter Bee Lab, which investigates insect cognition, sensory processing, and pollinator-plant interactions . Her work combines behavioral experiments , physiological studies , and spectral data analysis to understand how Apis mellifera and Bombus terrestris navigate complex environments. Academic Leadership: Co-Director of Postgraduate Research for Psychology Research Focus: Sensory ecology, insect navigation, cognitive stressors Collaborations: H. Somanathan (IISER Thiruvananthapuram, India) Research Themes Insect Vision: Modeling photometric data to understand flower perception under natural illuminations Cognitive Stressors: Studying pesticide/pathogen effects on foraging and navigation Active Sensing: Analyzing learning flights and sensory acquisition strategies Media Impact Featured in BBC News , The New York Times , and SciShow for discoveries about floral humidity detection and temperature cues Collaborative work on magpie behavior highlighted in The Guardian and The Washington Post She has held visiting appointments at Sokendai (Japan) and Freie Universität Berlin. Funding sources include BBSRC , Leverhulme Trust , and Royal Society .

• Teleoperation systems
• Human-robot interaction
• AI-based telerobotics
• +۱۰ مورد دیگر

Ziwei Wang is a Lecturer in Robotics at Lancaster University and co-theme lead of the Lancaster Intelligent, Robotic and Autonomous Systems Centre (LIRA). He holds a Ph.D. in Automation from Tsinghua University and previously worked as a Research Associate at Imperial College London's Department of Bioengineering before joining Lancaster. Research Focus : Teleoperation systems, human sensorimotor augmentation, AI-based telerobotics, haptic interaction, and control technologies for extreme environments. His work addresses challenges in remote surgery, space robotics, and nuclear decommissioning through prescribed-performance control, reinforcement learning, and fuzzy modeling. Scientific Contributions : Over 70 publications in journals like IEEE Transactions on Automatic Control and IEEE/ASME Transactions on Mechatronics . Key projects include the Future AI and Robotics for Space (FAIR-SPACE) Hub and RAICo Fellowships. Awards include Best Paper at ICAC 2022 and IROS 2024 New Generation Star recognition. Academic Roles : Associate Editor for IET Journal of Engineering and Biomimetic Intelligence and Robotics , with leadership in IEEE conference committees. Teaches Mechanics Engineering and Machine Learning in Engineering at Lancaster.

Mohamed Kariمشاهده پروفایل

پژوهشگر ارشد

• Human-Computer Interaction
• Mixed Reality
• Computer Graphics
• +۵ مورد دیگر

Mohamed Kari is a Presidential Postdoctoral Fellow at the Department of Computer Science, Princeton University , and an Associate Member at the University of Duisburg-Essen 's Integrated Information Systems research group. His work bridges Mixed Reality , Human-Computer Interaction , and Machine Learning , with a focus on semantically coherent, sensor-rich systems. PhD in Computer Science from University of Duisburg-Essen (with highest distinction) Fulbright Full Grant recipient for studies in the U.S. Research Interests span immersive technologies, sensor integration, and context-aware systems. He investigates how to seamlessly blend virtual actions with physical environments through Scene Responsiveness and develops audio-augmented reality applications like SoundsRide for automotive contexts. Article Trends reveal his focus on environmental manipulation in mixed reality affordance-synchronized user experiences pose-aware object substitution mobile and constrained VR input methods automotive context modeling Scientific Awards include Best Paper Awards at ACM UIST 2021 and 2023 Honorable Mention , as well as recognition for his dissertation with the highest distinction. Thesis Supervision at UDE involves rigorous requirements: English-language theses using LaTeX , original research with structured problem statements, and mandatory video presentations of contributions. He has guided students in topics like Intra-Company Networking Platforms and Automated UI Testing . Laboratory Affiliations include Princeton's Situated Interactions Lab , ETH Zurich's Sensing, Interaction & Perception Lab , and industry labs at Apple , Meta , and Porsche Emerging Tech Research .

John See Su Yangمشاهده پروفایل

دانشیار

• Computer vision
• Artificial intelligence
• Affective computing
• +۷ مورد دیگر

Dr. John See Su Yang is an Associate Professor at Heriot-Watt University's School of Mathematical and Computer Sciences, Malaysia Campus. He serves as Programme Director for BSc Computing Science and leads the Multimedia Data Analysis (MuDA) Lab. Previously, he was a Senior Lecturer at Multimedia University and founded the Visual Processing (ViPr) Lab, and served as a Visiting Research Fellow at Shanghai Jiao Tong University from 2017-2019. Received Bachelor, Masters, and PhD degrees from Multimedia University Published over 140 articles in top journals and conferences including IEEE T-PAMI, CVPR, and NeurIPS Secured over MYR 3 million in research funding as PI/Co-PI Dr. See's research spans multimedia signal processing and computer vision with particular focus on affect and emotion understanding from images and videos. His work includes facial micro-expression analysis, image aesthetics, visual surveillance tasks, activity recognition, and classical image/video processing algorithms. His research has practical applications in micro-expression detection, human-robot interaction, and multimodal content generation. His recent publications (2023-2025) demonstrate strong activity in wind turbine blade inspection using UAVs, micro-expression analysis networks, drowsiness detection, and human-robot interaction. His work bridges theoretical computer vision advances with practical applications in renewable energy, transportation safety, and human-computer interaction. Best Reviewer Award, IEEE ICIP 2020 Outstanding Reviewer Award, CVPR 2021 Dr. See actively mentors PhD students and serves on editorial boards for Signal Processing (as Subject Editor), IEEE Transactions on Multimedia, and several other prestigious journals. He is a Senior Member of IEEE and has served on technical committees for IEEE Multimedia Systems and Applications and Multimedia Signal Processing. His MuDA Lab collaborates with the Visual Processing Lab at Multimedia University and Shanghai Jiao Tong University, focusing on emotional analysis and multimodal systems for solving real-world problems. Dr. See accepts PhD students for projects on aesthetics and emotion processing in media and new paradigms for understanding human emotions in real-world scenarios.

Matej Kristanمشاهده پروفایل

استاد

• Computer Vision
• Visual Object Tracking
• Detection and Segmentation
• +۳ مورد دیگر

Matej Kristan is a Full Professor at the Faculty of Computer and Information Science , University of Ljubljana, where he serves as Vice Chair of the Department of Artificial Intelligence . He leads the Visual Object Tracking (VOT) Initiative , presides over the IAPR Slovenian Pattern Recognition Society , and acts as Associate Editor for IJCV . PhD from Faculty of Electrical Engineering, UL (2008) Co-organized 13+ workshops/conferences Research Interests focus on: Visual Object Tracking : Long-term re-detection, transparent object tracking, segmentation-based methods Autonomous Boats : Obstacle detection, sensor fusion, maritime navigation Physics-Informed Deep Learning : Sea level forecasting (HIDRA models), climate prediction Surface Defect Detection : Real-time deep architectures for cracks/dents Scientific Excellence : 26 Research Excellence Awards (Slovenian Research Agency) 10 Teaching Excellence Awards (UL/FRI) Best Paper Awards: ISPA2015, BMVC2022, Pattern Recognition Journal 2024 Student Mentorship : Supervised 8 PhD students including Alan Lukežič (summa cum laude 2021), Domen Tabernik , and Peter Uršič . His students have won 23 awards including multiple Prešeren Awards and Uroš Seljak distinctions. Laboratory Involvement : Key member of the Visual Cognitive Systems Laboratory , contributing to multimodal perception systems for maritime robotics, educational robotics platforms, and advanced deep learning architectures.

• Machine Learning
• Deep Learning
• Recommender Systems
• +۷ مورد دیگر

Dr. Ruihai Dong is an Assistant Professor at the School of Computer Science, University College Dublin, affiliated with the Insight Centre for Data Analytics. His research bridges Machine Learning, Deep Learning, and Recommender Systems with significant applications in finance and medical imaging. Develops AI-driven solutions for financial markets and healthcare Collaborates with industry partners like Eagle Alpha and Samsung Funded by Enterprise Ireland for commercialization studies Research focuses on: Recommender systems (news, finance, conversational AI) Graph neural networks for financial risk modeling Medical imaging under limited labeling constraints Algorithmic transparency and ethical AI applications 3D CNNs & transformers for seismic analysis Large language models for multilingual evaluation Recent publications emphasize: Hybrid architectures for multimodal data Explainable AI techniques in financial and news domains Geometric deep learning for microstructure and seismic data Curriculum strategies in active learning Media bias mitigation frameworks Latent space modeling for asset embeddings Scientific contributions recognized through: Outstanding Research Award 2018 by UCD School of Computer Science Industry collaborations include work with: Eagle Alpha (financial analytics) SkillPages (data-driven platforms) Samsung (technology innovation)

• Automatic text analysis
• Information search
• IT project management
• +۲ مورد دیگر

Natalia Sergeevna Belova is an Associate Professor in the Department of Software Engineering at the Faculty of Computer Science, National Research University Higher School of Economics (HSE University) in Moscow. She has been working at HSE since 2012 with 13 years of scientific and teaching experience. Education: 2010: Candidate of Technical Sciences from Moscow State University of Instrument Engineering and Computer Science, specialty 05.13.11 "Mathematical and software support for computing machines, complexes and computer networks" 2009: Postgraduate study from Moscow State University of Instrument Engineering and Computer Science 2005: Engineer qualification in "Computers, complexes, systems and networks" from Moscow State Academy of Instrument Engineering and Computer Science Research Interests: Dr. Belova's research focuses on automatic text analysis, information search, IT project management, embedded databases, and project-based learning in engineering education. Her work bridges theoretical computer science with practical applications in software engineering education and industry. She has made significant contributions to pattern recognition, face identification systems, and the development of computationally efficient methods for intelligent systems processing multimedia information. Publications Trend: Dr. Belova's research has evolved from foundational work on embedded databases to more recent contributions in computer vision, affect recognition, and AI applications. Her early work (2009-2013) focused on database systems reliability and project-based learning methodologies. Since 2015, her research shifted toward computer vision, pattern recognition, and deep learning applications. Her most recent publications (2022-2025) demonstrate expertise in AI applications across various domains including transportation systems and video analysis. Scientific Achievements: Recipient of the Grant of the President of the Russian Federation for young doctors of science MD-306.2017.9 (2017-2018) Gratitude from the Vice-Rector of HSE University (February 2025) Gratitude from the Faculty of Computer Science, HSE University (July 2023) Multiple publication bonuses for journals in List A and international peer-reviewed publications (2016-2025) Best Teacher award for 2016-2017 Member of the High Professional Potential Group (HSE personnel reserve) Teaching and Mentoring: Dr. Belova has supervised over 25 bachelor's theses on diverse software engineering topics including mobile applications, web platforms, biometric identification systems, and educational software tools. She teaches courses on "Group Dynamics and Communications in Professional Software Engineering Practice" and "Economics of Software Engineering." Her commitment to project-based learning methodology is evident in both her research and teaching practice. Professional Development: Dr. Belova actively participates in advanced training programs, including recent courses on modernization of educational programs in software engineering (2025) and problems of training modern specialists in software engineering (2024).

• Machine Learning
• Deep Learning
• Climate Science
• +۴ مورد دیگر

Sebastien Francois Lefevre is a Professor in Machine Learning at the Department of Physics and Technology, UiT The Arctic University of Norway, located in Tromsø. He contributes to interdisciplinary research at the intersection of artificial intelligence and geophysical sciences. His research focuses on: Deep learning for environmental modeling Remote sensing data analysis Feature reduction techniques in predictive models Multimodal object detection systems Seasonal sea ice forecasting applications Recent work explores Gaussian contrastive fusion methods for multimodal detection (2025) and feature reduction impacts in Arctic climate models (2024-2025). He collaborates within the Machine Learning Group at UiT.

• Information Technology
• Communication Engineering
• Image Processing
• +۳ مورد دیگر

Hiroshi Watanabe is a Professor at Waseda University 's Department of Communications and Computer Engineering , School of Fundamental Science and Engineering. With a Doctor of Engineering from Hokkaido University (1985), his career spans NTT Human Interface Laboratories (1985-2000) and academic leadership at Waseda since 2000, including chairing ISO/IEC JTC 1/SC 29 (1999-2006). A Fellow of IEEE , IEICE , and other institutions, he focuses on image/video coding , machine vision , and multimedia distribution . Education: BE, ME, Ph.D. in Electronic Engineering (Hokkaido University) Professional Affiliations: IEEE, IEICE, IPSJ, ITE, IIEEJ His research bridges image/video processing with deep learning , emphasizing machine-centric coding , real-time detection , and multimodal analysis . Key recent work includes: Novel image coding frameworks combining edge learning and diffusion models Super-resolution techniques for QR codes and medical imaging Advanced object detection using non-local modules and attention networks 3D pose estimation and point cloud analysis for robotics and healthcare He has received prestigious awards including the 2005 Information Processing Society of Japan Standardization Contribution Award and multiple earlier honors. His recent publications (2024-2025) demonstrate leadership in machine vision coding , real-time medical detection , and multimodal signal processing , often integrating stable diffusion , feature fusion , and parameter-efficient models .

• Supply Chain Logistics
• National Security
• Large-Scale Algorithm Design
• +۴ مورد دیگر

Dr. Chase Rainwater is a Professor and Department Head of Industrial Engineering at the University of Arkansas, where he has served since 2009. He co-directs the Arkansas Security Research and Education Institute and maintains active collaborations across engineering, food safety, and cybersecurity domains. Education: Ph.D. in Industrial and Systems Engineering from University of Florida, B.S. in Industrial Engineering from University of Arkansas Research Interests include: Supply chain optimization and logistics National security applications via network interdiction and Markov decision processes Machine learning for image processing and domain adaptation Food safety risk assessment and public health modeling Large-scale algorithm design for complex systems His work has been supported by over $20M in grants from NSF, USDA, Walmart Foundation, and Department of Defense. He supervises numerous graduate theses and has advised six Ph.D. dissertations. Scientific Awards include: Multiple Outstanding Teaching Awards (2012-2022) Outstanding Researcher (2019) Collaborative Faculty Award (2018) Fellow, University of Arkansas Teaching Academy (2022) He actively contributes to community outreach through FIRST robotics mentorship and professional service in IISE and INFORMS committees.

• Machine Learning
• Robotics
• Biomedical Engineering
• +۴ مورد دیگر

Alejandro Gonzalez Alzate is a contracted professor at the La Salle School of Engineering (Universitat Ramon Llull) within the Department of Engineering . He leads the Human-Environment Research group, focusing on interdisciplinary applications of machine learning and robotics. Research Areas 3D Biomarker Detection in Psychiatry Robot Perception Systems Medical Imaging Analysis Virtual Simulation for Robotics Multi-Omics Data Integration Projects BeNeXT : Machine Learning for Turner Syndrome Phenotyping DISTRESS: Stress Biomarker Identification in Education Face3DBiomark: Low-Cost Facial Biomarker Detection Technical Expertise 3D Reconstruction Pipelines Multimodal Data Fusion Image Classification Systems Point Cloud Processing Publication Trends show consistent contributions in robotics (100% coverage in tags) and biomedical applications (face-brain correlations in psychiatric disorders). His work combines Computer Science (91% tag coverage) with Engineering (100% departmental affiliation). Collaboration Network spans 14 projects (2017-2027) with teams across Spain and international partners. Key tools developed include MyROOT 2.0 for plant root analysis and Face3DBiomark for facial disorder detection.

Friedrich Neubarthمشاهده پروفایل

مدرس

• Speech Synthesis
• Computational Linguistics
• Syntax-Semantics Interfaces
• +۶ مورد دیگر

Friedrich Neubarth is a lecturer at the University of Vienna, affiliated with the Department of German Studies and the Department of Philosophy. His work bridges theoretical linguistics and computational approaches to language processing. Research Focus: Speech synthesis for dialectal varieties, computational modeling of morphology/syntax, phonological representation primitives, and prosodic correlates of information structure. Key Projects: VSDS (Viennese Dialect Synthesis), MLT4MLV (Machine Learning for Language Varieties), LEGO Audio & Braille Building Instructions, and MAGNIFICENT (Multifaceted News Analysis). Academic Contributions: 15-year publication record (2008-2023) spanning speech technology, dialectal machine translation, crossmodal word learning, and formal syntactic theory. Notable collaborations with Brigitte Krenn, Harald Trost, and Michael Pucher. Teaching: Delivers courses in linguistics and grammar at the University of Vienna, including exercise courses and lecture+exercise combinations since at least 2003. Technical Expertise: Development of spoken dialogue systems incorporating Viennese dialect synthesis, creation of multimodal language learning frameworks, and construction of annotated linguistic corpora for research.

Michael Kuhlمشاهده پروفایل

استاد

• Simulation
• Operations Research
• Healthcare Systems
• +۴ مورد دیگر

Michael Kuhl is a Professor in the Department of Industrial and Systems Engineering at Rochester Institute of Technology's Kate Gleason College of Engineering. His research focuses on simulation methodologies applied to autonomous mobile robots, healthcare systems, and logistics optimization. With appointments spanning teaching, research, and industry collaboration, he directs multiple projects in smart warehouse systems and stochastic input modeling. His educational background includes a Ph.D. and M.S. in Industrial Engineering from North Carolina State University and a B.S. in Industrial Engineering from Bradley University. His research interests center on simulation , operations research , and optimization with applications across healthcare, manufacturing, and logistics domains. Analysis of his recent publications reveals strong trends in autonomous material handling systems (62% of recent works), simulation optimization (28%), and educational pedagogy (10%). His work increasingly integrates machine learning with traditional simulation methods, particularly in indoor localization and warehouse robotics. Healthcare applications remain a consistent thread throughout his career, evolving from hospital supply chain modeling to modern surgical suite optimization. His academic leadership includes directing the REVS K-12 STEM outreach program and developing simulation-based educational modules for assembly systems engineering. Current teaching responsibilities include Systems Simulation (ISEE-510), Simulation Analysis (ISEE-708), and Engineering Value Creation (ISEE-773). Recent industry collaborations include partnerships with Simcona on smart warehouse field experiments, as highlighted in March 2022 Rochester Beacon coverage. His laboratory work focuses on developing intelligent materials handling systems where autonomous robots negotiate right-of-way in warehouse environments using advanced path planning algorithms.

• Computer Vision
• Machine Learning
• Visual Reasoning
• +۵ مورد دیگر

Justin Johnson is an Assistant Professor at the University of Michigan and a Research Scientist at Facebook AI Research (FAIR) . His work spans computer vision and machine learning , with a focus on visual reasoning, vision-and-language integration, image generation, and 3D reasoning through deep neural networks. Education : PhD from Stanford University under Fei-Fei Li His research combines visual reasoning and 3D reconstruction with applications in image generation and neural rendering . He has contributed to advancing style transfer , super-resolution , and 3D deep learning through frameworks like PyTorch3D . Recent publications emphasize scalable 3D modeling , neural fields , and vision-language grounding . He advises PhD students including Karan Desai , Mohamed El Banani , and Chris Rockwell (co-advised with David Fouhey). He has taught courses like EECS 498/598: Deep Learning for Computer Vision at Michigan and CS 231N: Convolutional Neural Networks for Visual Recognition at Stanford.

• Computer Vision
• Pattern Recognition
• Human Behavior Understanding
• +۵ مورد دیگر

Anis KACEM is a Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, part of the Signal Processing and Satellite Communications (SIGCOM) research group. His work focuses on Computer Vision and Pattern Recognition, particularly in Human Behavior Understanding from visual data. He received his PhD in Computer Science from the University of Lille (France) in 2018. Research interests include advanced topics such as Earth Observation via multi-modal autoencoders, domain adaptation for image classification, vulnerability-aware deepfake detection, and CAD system reverse engineering. His contributions span neural network pruning, 3D shape analysis, and generative models for medical imaging. Publications highlight innovations in spatio-temporal learning for deepfake detection, hybrid attention mechanisms for pedestrian detection, and tool-augmented CAD task solvers. His work bridges theoretical advances with practical applications in autonomous systems and space technology. Notably, he has contributed to challenges like the SHARP 2023 Challenge on CAD history recovery and developed frameworks like Picasso for CAD sketch inference using self-supervised learning.