جست‌وجوی استاد و پژوهشگر

• Digital Photogrammetry
• Computer Vision
• Geospatial Imaging
• +۱۰ مورد دیگر

Dr. Jackson David Cothren is a Professor in the Department of Geosciences at the University of Arkansas, where he also serves as the Leica Geosystems Chair in Geospatial Imaging. He holds dual leadership roles as Director of the Center for Advanced Spatial Technologies (CAST) and the Arkansas High Performance Computing Center (HPCC). His academic affiliations are deeply rooted in geospatial science, computer vision, and high-performance computing, bridging engineering and environmental applications. Ph.D. in Geodetic Science and Surveying, The Ohio State University M.S. in Geodetic Science and Surveying, The Ohio State University B.S. in Applied Mathematics, United States Air Force Academy Dr. Cothren's research spans digital photogrammetry, computer vision, UAV-based geospatial monitoring, and spatial archaeometry. He investigates non-traditional sensor modeling, feature extraction, surface generation, and integration with enterprise geospatial systems. His work increasingly incorporates deep learning, transformer models, and AI-driven analytics for applications in renewable energy, autonomous systems, and environmental sustainability. His recent publications highlight innovations in solar PV profiling, aerial image segmentation, and fairness-aware domain adaptation. The trends in his recent scholarly output reflect a strong shift toward machine learning and AI in geospatial analysis, particularly using transformer architectures for high-resolution imaging and cross-domain adaptation. His work integrates Lidar, GPS, and InSAR for deformation monitoring and leverages HPC for large-scale data processing. Applications span archaeology, agriculture, transportation, and energy infrastructure. Dr. Cothren has received numerous competitive grants from NSF, NEH, and USDA, supporting interdisciplinary research in geospatial analytics, smart transportation, and cultural heritage. His projects emphasize data-driven decision-making, community engagement, and workforce development in geospatial technologies. Principal Investigator, NSF E-RISE Rll: Arkansas Smart Transportation Research Incubator (2025–2029) Lead, RII Track-1: DART – Data Analytics that are Robust and Trusted (NSF, 2020–2025) Director, OPEN-GATE: Expanding Geospatial Education (NSF, 2016–2020) He mentors a broad interdisciplinary team and leads collaborative research initiatives involving computer vision, environmental science, and archaeology. His labs and research centers—CAST and HPCC—serve as hubs for innovation in spatial technologies, high-performance computing, and data-intensive research across the university and beyond. These centers support large-scale projects in archaeo-geophysics, UAV monitoring, and enterprise GIS integration.

• Algorithms
• Computational Geometry
• Wireless Communication
• +۴ مورد دیگر

Stefan Funke is a researcher at the University of Stuttgart, Germany, with a focus on algorithms and computational geometry. His work spans wireless communication, route planning, and trajectory analysis. Research Interests: Algorithms, Computational Geometry, Wireless Communication, Route Planning, Trajectory Segmentation His recent publications (2024-2025) explore topics like 3D epithelial cell dynamics, graph radius computation, and polyline simplification, emphasizing scalability and efficiency. Earlier works (2017-2019) investigate contraction hierarchies, energy-efficient routing, and trajectory storage systems. Stefan collaborates frequently with Sabine Storandt, Claudius Proissl, and Tobias Rupp. He applies geometric methods to problems in wireless networks, road systems, and data structures, with a recurring emphasis on optimization and robustness.

• Data Mining
• Social Network Analysis
• Machine Learning
• +۳ مورد دیگر

Jaideep Srivastava is a Professor affiliated with Qatar Foundation (Doha, Qatar), University of Minnesota (Minneapolis, USA), and holds a PhD from University of California Berkeley. His research spans data mining, social network analysis, time series modeling, and health informatics. Recent work focuses on clinical deterioration prediction, sleep research, and computational analysis of pandemic behaviors. Key contributions in clustering algorithms and graph neural networks Active in multimodal learning and misinformation detection His publications from 2024-2022 demonstrate expertise in hierarchical clustering , large language model applications , and health data analytics . Articles often integrate machine learning , social network dynamics , and clinical monitoring systems . Current projects involve Covid-19 in-hospital mortality prediction , virtual influencer analysis , and low-light imaging techniques . Collaborations span institutions in Qatar, USA, and India with applications in urban mobility and precision medicine.

Chao Zhangمشاهده پروفایل

دانشیار

• Dielectrics
• Electrochemistry
• Electrolyte materials
• +۳ مورد دیگر

Chao Zhang is an Associate Professor at the Department of Chemistry-Ångström Laboratory, Uppsala University, specializing in computational electrochemistry and multi-scale modeling of electrolyte materials. His research bridges atomistic simulations with machine learning approaches to address challenges in energy storage and conversion systems. Education: Dr. rer. nat. from RWTH Aachen University (2013); Docent from Uppsala University (2020) Appointments: Postdoctoral researcher at the University of Cambridge (prior to joining Uppsala in 2017) His group develops finite-field methods for computational electrochemistry and investigates electrified solid-liquid interfaces. Recent research trends include neural rendering for underwater SLAM systems (2025), robust path-following control in marine robotics, and event-based localization in LiDAR-integrated environments. Scientific Awards: ERC Starting Grant (2020) Junior Research Fellowship, Wolfson College (2015) Jülich Excellence Prize for Young Scientists (2013)

• Control Systems
• Machine Learning
• Electrical Engineering
• +۴ مورد دیگر

Marko Tanasković (born December 6, 1986) is a researcher at Singidunum University with a PhD in Information Technology and Electrical Engineering from ETH Zurich (2015). His academic background includes master's (ETH Zurich, 2011) and bachelor's (University of Belgrade, 2009) studies in Electrical Engineering. Doctoral studies: Information Technology and Electrical Engineering, ETH Zurich (2011-2015) Master studies: Information Technology and Electrical Engineering, ETH Zurich (2009-2011) Basic studies: Electrical Engineering, University of Belgrade (2005-2009) Tanasković's research focuses on control systems , predictive modeling , and optimization algorithms for mechanical and electrical systems. His work addresses adaptive model predictive control (MPC), sensorless motor positioning, and data-driven approaches for nonlinear systems. Recent publications (2018-2024) demonstrate expertise in: Embedded control systems (rotor polarity detection) Drone forensics and autonomous navigation Industrial automation (LabVIEW applications) Biomedical sensor development ('Smart Anklet') Machine learning optimization (firefly algorithm)

Hu Caoمشاهده پروفایل

پژوهشگر

• Autonomous Driving
• Robotic Grasping
• Medical Image Analysis
• +۵ مورد دیگر

Hu Cao is a postdoctoral research associate at the Chair of Robotics, Artificial Intelligence and Real-Time Systems (Prof. Alois Knoll) at the Technical University of Munich (TUM) . Holding a Ph.D. from TUM, his research bridges autonomous driving , robotic grasping , medical image analysis , and dense prediction (classification, detection, segmentation). Education : Ph.D. from TUM Hu's work explores: Autonomous Driving : Perception under adverse conditions, multi-sensor fusion, and risk-based safety models Robotic Grasping : Vision-language integration for 6D pose estimation Medical Imaging : Transformer-based segmentation techniques (e.g., Swin-Unet) His recent publications include 15+ works at top venues like CVPR , ICCV , IEEE TPAMI , and IEEE TIV , with 6052+ Google Scholar citations . Notably, Swin-Unet ranks among the top 3 most cited ECCV papers in 5 years, and his work on event-based autonomous driving perception was featured in IEEE Xplore Innovation Spotlight . Editorial roles include: Associate Editor for Visual Intelligence and Frontiers in Neurorobotics Editorial Board member of Artificial Intelligence and Autonomous Systems (AIAS) Topic Editor for Frontiers in Robotics and AI and Frontiers in Neuroscience He has reviewed for 20+ top journals (e.g., Nature Computational Science , IEEE TRO ) and served on program committees for NeurIPS , CVPR , ICCV , and MICCAI .

Christian Heineمشاهده پروفایل

پژوهشگر

• Data Visualization
• Topology-Based Methods
• Scientific Computing
• +۳ مورد دیگر

Christian Heine is a researcher at the Institute of Computer Science , University of Leipzig. His work focuses on advanced data visualization techniques, particularly those grounded in topological and geometric analysis of scalar fields, ensemble data, and high-dimensional datasets. Key Research Areas: Topological visualization, scalar field analysis, medical imaging, and uncertainty quantification. Methodologies: Bayesian inference, fiber trajectories, volume rendering, and dynamic workflows. Applications: Meteorological data analysis, medical diagnostics, and interactive visualization systems. He has published extensively on these topics, with recent work addressing spatio-temporal trends in climate data and noise-robust visualization techniques. His research often integrates interdisciplinary approaches, bridging computer science and applied sciences.

Jan von der Assenمشاهده پروفایل

پژوهشگر

• Holistic Cybersecurity
• Threat and Asset Management
• Security Economics
• +۶ مورد دیگر

Jan von der Assen is a doctoral student at the Communication Systems Group (CSG) within the Department of Informatics at the University of Zurich . His research focuses on Holistic Cybersecurity , spanning Threat and Asset Management , Security Economics , and AI-powered Malware Mitigation . Education : MSc in Software Systems from University of Zurich (2021) Research Affiliations : Involved in 12+ national/EU projects (2021–2025) including CheeseChain , CONCORDIA , and DSI Cybersecurity . His work emphasizes Moving Target Defense (MTD) for IoT security, Decentralized Federated Learning with blockchain-based reputation systems, and Ransomware Detection using hypervisor-level system call monitoring. Recent publications include: GuardFS (JISA 2025) for Linux ransomware mitigation HyperDtct (IEEE CSR 2025) on hypervisor-based detection ThreatFinderAI (CNSM 2024) for LLM threat modeling

• Industrial Artificial Intelligence
• Digital Twin Technology
• Condition Monitoring
• +۷ مورد دیگر

Pradeep Kundu serves as an Assistant Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Technology (Bruges Campus), where he leads the M-Group Asset Performance Management subdivision. He is an active member of Leuven.AI Institute for Artificial Intelligence and holds governance roles in the Mechanical Engineering Department Council and Faculty Council. His research centers on Industrial Artificial Intelligence integrated with Digital Twin Technology, focusing on three core domains: rotating machinery condition monitoring (fault diagnosis/prognosis), manufacturing quality control (tool wear/surface monitoring), and production process optimization. Key methodological contributions address data scarcity through synthetic data generation (Digital Twin/Generative AI) and enhance model robustness via Physics-Informed Machine Learning, Hybrid Modeling, and advanced Statistical Regression. Analysis of his 15 most recent publications (2024-2025) reveals a dominant focus on predictive maintenance for mechanical systems, utilizing diffusion models for damage imaging, entropy-based domain adaptation for bearing failure prediction, and sensor fusion techniques. His work consistently bridges physics-based modeling with deep learning across rotating machinery, structural health monitoring, and smart manufacturing applications. Dr. Kundu currently leads multiple funded projects including 'Intelligent Prognosis of Rotating Machines in Industry 4.0 using Generative AI' (2024-2025) and 'Digital Twin Framework for Fleet-Level Feed Drive Systems Health Assessment' (2023-2027), addressing critical challenges in data-limited industrial AI deployment. His research directly supports Industry 4.0 transformation through maintenance optimization and quality control innovations. He actively contributes to academic governance as member of the OC Smart Operations and Maintenance in Industry committee and the Mechanical Engineering Department Council, while his research group within the Mecha(tro)nic System Dynamics unit develops practical AI solutions for industrial asset performance management.

• Robotics
• Simultaneous Localization and Mapping (SLAM)
• Sensor Fusion
• +۵ مورد دیگر

Maurice Fallon is a Professor of Engineering Science at the University of Oxford and a Royal Society University Research Fellow, leading the Dynamic Robot Systems Group (Perception) at the Oxford Robotics Institute. His research focuses on robust probabilistic methods for localization and mapping in challenging environments through advanced sensor fusion. Education: Electronic Engineering, University College Dublin PhD in Acoustic Source Tracking, University of Cambridge Research Interests: Dr. Fallon specializes in probabilistic state estimation , legged robot navigation , and dynamic motion planning for autonomous systems operating in vision-denied or complex natural environments. His work emphasizes robustness through multi-sensor integration , with applications spanning disaster response, forestry, and industrial inspection. Key innovations include terrain-aware locomotion and long-term autonomy frameworks. Publication Trends: Recent work (2024-2025) demonstrates a strategic shift toward forest robotics and long-term industrial inspection , leveraging legged and aerial platforms. There is strong emphasis on vision foundation models for place recognition, scalable 3D reconstruction using neural radiance fields, and open-vocabulary scene understanding . The research consistently addresses real-world challenges like lighting variations, sensor dropout, and environmental dynamics. Scientific Awards: Royal Society University Research Fellowship 4x Best Paper Awards at ICRA Nominations at Intelligent Vehicles, AAAI, and Humanoids conferences Advising and Grants: Dr. Fallon has secured major funding as PI/Co-I for EU/UK projects including ORCA, RAIN, THING, MEMMO, and the DARPA SubT-winning CERBERUS team. Current initiatives include the Horizon Europe DigiForest project and UKAEA collaborations. He mentors PhD students and postdocs in robotics systems development, though specific advisees aren't listed in source materials. Labs and Teams: He directs the Dynamic Robot Systems Group, which achieved global recognition through DARPA Robotics Challenge participation and SubT Challenge victory. The team operates specialized facilities for legged robot testing and maintains partnerships with nuclear energy and forestry sectors for field deployment.

• Trustworthy machine learning
• Explainable AI
• Interpretability
• +۵ مورد دیگر

Damien Garreau is Professor for the Theory of Machine Learning at Julius-Maximilians-Universität Würzburg , Germany. Until March 2024 he served as Associate Professor in the Probability and Statistics team of the J. A. Dieudonné laboratory at Université Côte d'Azur and was a member of the Inria Maasai team in Sophia-Antipolis. Earlier positions include post-doctoral research at the Max Planck Institute for Intelligent Systems in Tübingen and PhD studies in the Inria Sierra team in Paris. Education & Career Path PhD, Inria Sierra team, Paris – advisors Sylvain Arlot & Gérard Biau Post-doc, Max Planck Institute for Intelligent Systems, Tübingen – mentor Ulrike von Luxburg Associate Professor, Université Côte d’Azur / Inria Maasai (until March 2024) Professor for Theory of Machine Learning, Julius-Maximilians-Universität Würzburg (since 2024) Research Focus Garreau’s research centers on trustworthy machine learning . He investigates how to explain, audit, and robustify modern AI systems, with particular emphasis on post-hoc interpretability , statistical guarantees of explanation methods, fairness , and causality . Representative contributions include theoretical analyses of LIME and Anchors, novel explanation methods such as SMACE and GLEAMS, and practical tools for vision and NLP that remain faithful under adversarial or out-of-distribution settings. Across computer vision, natural-language processing, and healthcare applications, his work bridges rigorous theory with impactful algorithms, advancing the societal goal of deploying AI systems whose decisions can be trusted and understood by humans. Scientific Awards & Recognition Best Paper Award , ECML 2024 Area Chair , ICML 2025 ANR JCJC Grant NIM-ML (2021–2025) Université franco-allemande support for Winter School on Causality and Explainable AI Advising, Grants & Collaborative Projects Garreau has successfully supervised or co-supervised a growing cohort of doctoral and master’s students, including Gianluigi Lopardo, Kensuke Mitsuzawa, Martin Charachon, Jonas Wacker, Samuel, Antonio, Magamed, Arthur Assad, Charbel Yahchouchi, and Mariana Chaves. He is the PI of the ANR JCJC project NIM-ML , whose goal is to develop next-generation interpretability methods endowed with statistical guarantees. He co-organizes the annual Winter School on Causality and Explainable AI , fostering Franco-German academic exchange. Labs & Teams Since 2024 he leads the Professorship for the Theory of Machine Learning at Julius-Maximilians-Universität Würzburg. Previously he was a core member of the Maasai Inria team on the Sophia-Antipolis campus, and an active collaborator of the J. A. Dieudonné mathematics laboratory. He maintains strong ties with the TML group at the Max Planck Institute for Intelligent Systems and regularly hosts joint visitors and workshops.

Sasan Matinfarمشاهده پروفایل

پژوهشگر

• Medical Sonification
• Sonic Interaction Design
• Extended Reality (XR)
• +۴ مورد دیگر

Sasan Matinfar is a research scientist at the Technical University of Munich (TUM), affiliated with the Chair of Computer Aided Medical Procedures (Prof. Navab) and the Munich Center for Machine Learning (MCML). He serves as scientific staff at Rechts der Isar Hospital, developing XR and sonification systems for surgical environments since 2020. His educational background includes: Master’s and Bachelor’s in Computer Science, Ludwig Maximilian University of Munich (LMU) Musicology, Franz Liszt University of Music, Weimar Piano Interpretation, Art University of Tehran Matinfar pioneers medical sonification and multisensory XR, creating auditory interfaces that convert tissue properties into sound for surgical guidance. His work in user-centered design produces clinically viable tools like the Ocular Stethoscope for retinal procedures and physics-based BioSonix frameworks, merging computer vision with perceptual audio engineering to enhance intraoperative precision without visual overload. Analysis of his 12 recent publications (2017-2025) reveals an evolving research arc from foundational surgical soundtracks to sophisticated context-aware sonification. Current work integrates generative AI with real-time tissue deformation modeling, focusing on multimodal frameworks where auditory feedback complements visual navigation in complex surgeries like cardiac interventions and retinal peeling. Key recognitions include: The Data Sonification Award (2025) MICCAI 2023 Best Paper Nominee (top 3% of submissions) MICCAI Young Scientist Award (2017, top 2% of papers) As an educator, Matinfar mentors students through TUM courses including Medical Augmented Reality (WS 2025/26) and Surgical Robotics, while co-organizing the Medical Augmented Reality Summer School and IEEE ISMAR 2025’s MIX Workshop. His patented technologies emerge from collaborations with Politecnico di Milano, TU Dresden’s CeTI, and Balgrist Hospital Zurich, securing interdisciplinary grants in surgical data science. Matinfar operates within TUM’s NARVIS Lab for medical image analysis and RobUSt for robotics-ultrasound integration, leveraging the German Heart Center Munich (DHM) infrastructure to validate XR systems in live surgical workflows and advance vision-language models for intraoperative decision support.

Jin Woo Songمشاهده پروفایل

دانشیار

• Navigation and Sensor Systems
• Control Theory
• Estimation Theory
• +۷ مورد دیگر

Jin Woo Song is an Associate Professor in the Department of Artificial Intelligence and Robotics at Sejong University's School of Intelligent Mechatronics Engineering. He has held academic positions at Seoul National University and Hoseo University prior to joining Sejong University in 2017. His professional experience also includes serving as CTO/COO at Microinfinity Co., Ltd. from 2004-2014. Currently, he holds multiple leadership roles including Director of Sejong Enterprise Support Foundation, Director of SW-AI Convergence Center, and Provost of Sejong University. Dr. Song received his B.S., M.S., and Ph.D. in Control and Instrumentation Engineering/Electrical Engineering from Seoul National University. His educational background laid the foundation for his expertise in navigation systems and control theory. Dr. Song's research focuses on navigation and sensor systems, control theory, and estimation theory with applications in unmanned systems. His work spans MEMS inertial sensor technologies, Kalman filtering techniques, optimal and robust control theories, and fault detection algorithms. His research has significant applications in unmanned aircraft systems, autonomous vehicles, robotics, and military intelligence systems. His recent publications (2023-2025) demonstrate continued innovation in navigation systems, with emphasis on fault tolerance, sensor fusion, and improved positioning accuracy in challenging environments. His work shows strong trends toward integrating multiple sensor modalities and developing robust algorithms for real-world navigation challenges. Best student paper award, NAECON2000 (IEEE, National Aerospace and Electronics Conference), USA, 2000 Ministry Award, 'IT R&D Engineer of the Year', Ministry of Information and Communication, Republic of Korea, 2005 Best Presentation Award, ICASS 2015, 2015 Best Paper Award, KIEE Conference, 2019 Distinguished Teaching Award, Sejong University, 2021 Distinguished Research Award, Sejong University, 2022 Distinguished Professor, Sejong University, 2024 Dr. Song has served as Editor-in-Chief for ICROS journal and holds editorial positions for multiple prestigious journals including the International Journal of Aeronautical and Space Sciences. He has been actively involved in numerous research grants and industry collaborations, including a recent academic-industrial partnership with Hanwha Systems. As Vice-President of The Institute of Control, Robotics and Systems (ICROS) and various other professional organizations, he plays a significant role in shaping research directions in his field. He leads the Intelligent Navigation and Control Systems Lab at Sejong University, where his team develops cutting-edge navigation sensors and systems, optimal controllers for autonomous systems, and embedded systems for reliable navigation and control. The lab's work has direct applications in unmanned aircraft systems, autonomous vehicles, robotics, and military intelligence systems.

• Machine Learning
• Cybersecurity
• Malware Analysis
• +۱۲ مورد دیگر

Mark S Stamp is a tenured Professor in the Department of Computer Science at San José State University. Previously, he held roles at the National Security Agency (NSA) and the startup MediaSnap, Inc. His academic journey began with a B.S. in Computer Science and History from Morningside College, followed by a Ph.D. in Mathematics from Texas Tech University. Current role: Professor, Computer Science, San José State University Prior roles: NSA analyst, MediaSnap entrepreneur Research Interests Professor Stamp's work focuses on Machine Learning and Deep Learning applications in Cybersecurity , particularly Malware Analysis , Adversarial AI , and Steganographic Capacity . His research spans Hidden Markov Models , Federated Learning , and Android Malware Detection . Recent Publications Trend His 2025–2024 publications emphasize malware classification using hybrid models (e.g., HMM-CNN), adversarial attack analysis in federated learning, and steganographic techniques in transformer models. Additional work explores chatbot detection, energy-efficient neural networks, and cybersecurity applications of QR/Aztec code analysis and keystroke dynamics . Advising & Collaborations While specific advisees are not listed, his research engages with collaborators in AI security , mobile threat detection , and sequence modeling . His career bridges academia, intelligence, and startup innovation.

Giuseppe Orioloمشاهده پروفایل

استاد

• Robotics
• Control Systems
• Autonomous Mobile Robots
• +۳ مورد دیگر

Giuseppe Oriolo is a Full Professor of Automatic Control and Robotics at the Department of Computer, Control and Management Engineering (DIAG) of Sapienza University of Rome, where he has been since 1994. He coordinates the DIAG Robotics Lab and has held teaching positions at the University of Siena, University of Cassino, and Roma Tre. Oriolo was a Visiting Scholar at the University of California, Santa Barbara, and serves on various editorial and program committees, including IEEE Transactions on Robotics and international conferences like ICRA and IROS. Education : Ph.D. in Systems Engineering (1992), Sapienza University of Rome His research focuses on robotics and control theory, particularly in robot control, motion planning, trajectory optimization, redundant robotic systems, control of underactuated systems, sensor-based localization, navigation for mobile robots, humanoid robots, and visual servoing. He has over 230 publications and contributes to advanced robotics frameworks. Recent publications highlight his work on Model Predictive Control (MPC) for humanoids and mobile robots, emphasizing robustness, singularity-free trajectories, and decentralized cooperation. His research addresses dynamic constraints, feasibility, and real-time adaptation in complex environments. Scientific Awards : 2017 IEEE Fellow, 2020 IEEE Robotics and Automation Magazine Best Paper Award, 2016 ANTS Best Paper Award, 2024 Human-Friendly Robotics Best Paper Award Oriolo's teaching includes Control Systems at Sapienza, with past roles in courses like Adaptive Systems, Nonholonomic Control, and Underactuated Robots. He has mentored numerous advisees through publications and lab activities. He leads the DIAG Robotics Lab, fostering innovation in humanoid robots, autonomous systems, and control theory.