جست‌وجوی استاد و پژوهشگر

• Robotics
• Computer Vision
• Simultaneous Localization and Mapping (SLAM)
• +۶ مورد دیگر

Niko Sünderhauf is a Professor in the School of Electrical Engineering and Robotics at Queensland University of Technology (QUT), specializing in robotics, computer vision, and autonomous systems. His research spans visual place recognition, simultaneous localization and mapping (SLAM), neural radiance fields, and out-of-distribution detection for robotics applications. His research interests focus on enabling robots to understand and navigate complex environments through advanced computer vision techniques. He has made significant contributions to visual place recognition, particularly for handling viewpoint and environmental changes. His work on switchable constraints has improved robustness in SLAM systems, while his more recent research explores neural radiance fields, Gaussian splatting, and integrating large language models with robotic perception. Prof. Sünderhauf's publication record demonstrates consistent research productivity with 115 publications spanning from 2005 to 2025. His recent work shows a clear progression from traditional SLAM techniques toward more advanced neural scene representations and integration with large language models. His research has been published in top venues including IEEE ICRA, CVPR, IROS, and the International Journal of Robotics Research. Among his scientific contributions are novel approaches to visual place recognition that work across seasons and viewpoints, robust SLAM techniques that handle outliers and non-linearities, and recent innovations in neural scene representations for robotics. His work on switchable constraints has been particularly influential in the robotics community. Prof. Sünderhauf actively supervises PhD students including Dimity Miller, Krishan Rana, and Jad Abou-Chakra, who frequently appear as first authors on publications. His collaborative network includes researchers from QUT and international institutions, demonstrating strong research leadership in the robotics community.

Torben Petersمشاهده پروفایل

مدرس

• 3D Computer Vision
• Deep Learning
• Generative Models
• +۲ مورد دیگر

Torben Peters is a Lecturer in the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. His research focuses on 3D computer vision, deep learning, and generative models applied to geospatial analysis and photogrammetry. Research Focus: Peters develops computational tools for processing LiDAR point clouds, aerial imagery, and satellite data. His work enables automated environmental monitoring (e.g., forest inventories and avalanche mapping) and urban modeling through advanced segmentation and 3D reconstruction techniques. Generative models like TetraDiffusion expand capabilities in geometric deep learning. Publication Trends: Recent articles emphasize scalable geospatial AI, including war damage assessment in Ukraine, global biomass datasets, and self-supervised shape completion. Methodological innovations center on reducing annotation dependencies and improving geometric accuracy. Teaching: Leads courses on image-based mapping and geodetic data processing at ETH Zürich.

• Computer Vision
• Human Activity Forecasting
• First-Person Vision
• +۹ مورد دیگر

Kris M. Kitani is an Associate Research Professor at the Robotics Institute of Carnegie Mellon University and a Research Scientist at Meta's Artificial Intelligence division. He co-directs the Extended Reality Technology Center (XRTC) at CMU and holds courtesy appointments in the Electrical and Computer Engineering Department at CMU and as a Guest Professor at the University of Tokyo's Institute of Industrial Science. Education : BS in Electrical Engineering (University of Southern California), MS and PhD in Computer Science (University of Tokyo) Labs : Cognitive Assistance Laboratory His research focuses on computer vision , human activity forecasting , and inverse reinforcement learning with applications in human-centered robotics and assistive technologies . Recent work explores 3D scene understanding , multi-modal sensing , and generative models for physical simulation . Publications analyze pedestrian interaction modeling , human pose estimation , and zero-shot learning for robotic applications. Scientific Awards : Marr Prize Honorable Mention, ICCV 2017 Best Paper Honorable Mentions at CHI 2017/2020 Best Papers at W4A 2017/2019 Best Application Paper, ACCV 2014 Kitani has advised 8 current PhD students and 18 past PhD/MSc students. He has taught graduate-level courses in Computer Vision and Statistical Techniques in Robotics since 2013. The lab develops autonomous systems for real-world perception and interactive decision-making.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Dr. Chenyang Zhao is an Assistant Professor in the Department of Computer Science and Engineering at Shanghai Jiao Tong University's School of Electronic Information and Electrical Engineering. His research spans multiple domains at the intersection of computer vision, machine learning, and engineering applications. Dr. Zhao's primary research interests include: Computer Vision and Deep Learning Robotics and Autonomous Systems Medical Imaging and Healthcare Applications Hardware Acceleration for AI Precision Measurement and Instrumentation His recent work demonstrates a strong focus on practical applications of AI across diverse domains, from medical imaging to infrastructure inspection. Dr. Zhao has developed innovative approaches in areas such as sewer inspection systems using evidential deep learning, RGB-D semantic SLAM for robotics, and lightweight computing-in-memory architectures for edge AI applications. His research shows a consistent pattern of addressing real-world engineering challenges with cutting-edge machine learning techniques, particularly emphasizing trustworthiness, uncertainty quantification, and practical implementation constraints. Dr. Zhao's laboratory focuses on developing trustworthy AI systems with applications in: Infrastructure monitoring and maintenance Medical diagnostics and imaging Autonomous robotic systems Energy-efficient AI hardware

Chua Tat Sengمشاهده پروفایل

استاد

• Multimedia Information Retrieval
• Social Media Analytics
• Unstructured Multimodal Data Analytics
• +۴ مورد دیگر

Professor Chua Tat Seng is a distinguished academic at the National University of Singapore's School of Computing, serving as KITHCT Chair Professor and Director of the NUS-Tsinghua Extreme Search Center (NExT). He also holds Distinguished Visiting Professorships at Tsinghua and Zhejiang Universities in China. PhD in Computer Science (University of Leeds, 1983) Founding Dean of School of Computing (1998-2000) Co-founded ViSenze and 6Estates technology startups His research focuses on unstructured multimodal data analytics, with particular emphasis on multimedia information retrieval, social media analytics, recommendation systems, and trustworthy AI. He has pioneered work in computational wellness and fintech applications, establishing the Lab for Media Search and leading NExT++ research initiatives. Over 300 publications in leading venues (CVPR, SIGIR, WWW, AAAI) Recipient of ACM SIGMM Technical Achievement Award (2015) Supervised 37 PhD students since 2004 Editorial leadership in ACM Transactions and IEEE Multimedia Recent work explores multimodal LLMs, knowledge editing techniques (AlphaEdit), and 3D generation frameworks, reflecting his commitment to advancing web intelligence and user empowerment.

Pong C Yuenمشاهده پروفایل

استاد

• Video Surveillance
• Human Face Recognition
• Biometric Security and Privacy
• +۳ مورد دیگر

Pong C Yuen is a Professor in the Department of Computer Science and Associate Dean of Science Faculty at Hong Kong Baptist University (HKBU). He earned his B.Sc. (1989) from City Polytechnic of Hong Kong and Ph.D. (1993) from The University of Hong Kong. His academic career at HKBU spans since 1993, including a six-year term as Department Head (2011–2017). Dr. Yuen's research focuses on video surveillance , human face recognition , and biometric security and privacy . His work bridges theoretical advancements in deep learning , sparse representation , and domain adaptation with practical applications in medical imaging and human-computer interaction . Recent publications included in this summary demonstrate expertise in unsupervised learning for person re-identification, adversarial domain adaptation for anti-spoofing, and physiological signal analysis for biometric security. These works span venues like IEEE Transactions on Image Processing (TIP) , CVPR , and AAAI . Scientific awards include: University Fellowship (1996) Outstanding Editorial Board Service Award (2018) Guangdong Province First-prize Natural Science Award Ministry of Education China Second-prize Natural Science Award Fellow of IAPR As an educator, Dr. Yuen has taught courses ranging from fundamental programming to graduate-level medical image processing , with a focus on interdisciplinary applications. He has served as Editorial Board Member for journals like Pattern Recognition and SPIE Journal of Electronic Imaging , and as Vice President (Technical Activities) of the IEEE Biometrics Council.

• 3D Integrated Circuits
• Hardware Security
• AI Accelerators
• +۴ مورد دیگر

Krishnendu Chakrabarty is the Fulton Professor of Microelectronics in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU). He also serves as CTO of the SWAP Hub for the Department of Defense Microelectronics Commons and Director of the ASU Center for Semiconductor Microelectronics (ACME). Previously, he was the John Cocke Distinguished Professor and Chair of Electrical and Computer Engineering at Duke University. Education: B.Tech from IIT Kharagpur (1990), M.S.E. and Ph.D. from the University of Michigan (1992, 1995). His research focuses on 3D integrated circuits, hardware security, AI accelerators, and microfluidic biochips. He has authored 27 books, 930 peer-reviewed papers, and holds 24 patents. His work has been supported by NSF, DARPA, NIH, and industry partners like Intel and NVIDIA. Key awards include NSF CAREER Award, IEEE Technical Achievement Awards, Humboldt Research Award, and Distinguished Alumnus from IIT Kharagpur. He advises 44 PhD graduates and collaborates with industry leaders such as Synopsys and Cisco. His leadership roles include Editor-in-Chief of IEEE Transactions and Fellowships in ACM, IEEE, and AAAS. Research highlights include pioneering test methodologies for 3D ICs, developing AI-driven biochip solutions, and securing hardware against cyber-physical threats. His labs and teams focus on advancing semiconductor technology and healthcare applications through interdisciplinary innovation.

David Hallمشاهده پروفایل

پژوهشگر ارشد

• Robotic Vision
• Neural Fields
• Machine Learning
• +۳ مورد دیگر

Dr. David Hall is a Research Fellow at Queensland University of Technology (QUT) and a Research Scientist at CSIRO Data 61. He holds a PhD and a Bachelor of Engineering in Infomechatronics from QUT. His research focuses on advancing robotic vision systems, including probabilistic object detection (PrOD), scene understanding, and implicit neural field representations. He has contributed to defining evaluation metrics like PDQ and designing challenges for robotic vision. Previously, he developed adaptable systems for agricultural robotics, particularly in weed species recognition. Affiliations: Current: Commonwealth Scientific and Industrial Research Organisation (CSIRO) Data 61 Past: QUT Centre for Robotics, Australian Centre for Robotic Vision (ACRV) Research Interests: Robotic Vision Challenges Probabilistic Detection Implicit Representations for Semantic Maps Simulation Tools (e.g., BenchBot) Recent Contributions: Developed the Reg-NF framework for implicit surface registration in neural fields (2024). Co-organized the Robotic Vision Scene Understanding Challenge (2020–present).

• GIS and Information Technologies in Planning
• Remote Sensing
• Photogrammetry
• +۷ مورد دیگر

Prof. Elif Sertel is a Professor of Geomatics Engineering at Istanbul Technical University's Faculty of Civil Engineering, specializing in GIS, Remote Sensing, and Satellite Technologies. She holds a PhD from ITU (2008) and has conducted groundbreaking research on land use analysis, urbanization impacts, and geospatial AI applications. Her work integrates historical aerial photography with modern satellite data to model environmental and demographic changes. Notable projects include the GeoAI_LULC_Seg ERC-funded initiative analyzing rural depopulation and agricultural land abandonment in Turkey and Bulgaria since 1940. She has led over 30 research projects totaling €6M+, with notable funding from TÜBA, ERC, and the Ministry of Development. Awards include the 2024 Istanbul Technical University Academic Performance Award and the 2017 TÜBA Outstanding Young Scientist Award. Her teaching includes GIS in Engineering courses. Research focuses on: 1) AI-driven land cover segmentation using deep learning, 2) drought monitoring via satellite data, 3) urban heat island analysis, and 4) landscape metric correlations with satellite resolution. Recent work emphasizes historical data integration, with projects like the 1958-2020 Bursa land use study. Her team developed benchmark datasets (VHRTrees, HRPlanes) and tools like the GeoAI-based mobile drought app. Grants and projects: Over 30 completed projects including €150k ERC GeoAI_LULC_Seg (2022-2024), €5M ITU Satellite Ground Terminal Renewal (2016-2020), and TÜBA-funded city mapping initiatives. Collaborations with institutions like NASA, ESA, and European Research Council have produced over 50 peer-reviewed publications. Awards highlight sustained excellence: 2023-2024 Academic Performance Awards, 2024 Broadcasting Achievement Award, and 2017 TÜBA recognition. Her work bridges geomatics engineering with environmental policy through interdisciplinary approaches.

Aditya Guptaمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Neural Networks
• Image and Signal Processing
• +۵ مورد دیگر

Aditya Gupta is a Postdoctoral Research Fellow at the University of Agder's Department of Information and Communication Technology. His research focuses on Machine Learning, Deep Neural Networks, Smart Water Management, and IoT applications. He has held adjunct faculty positions at the College of Engineering Pune (2019-2021) and temporary faculty roles at National Institute of Technology Raipur (2018-2019). His work integrates AI with domains such as water resource optimization, video surveillance systems, and healthcare monitoring. Recent projects include AI-driven solutions for road quality analysis, fall detection, and aquatic health management. Key areas: Smart Water Systems, Video Compression, Autonomous Driving, Healthcare AI Publications span journals like Energies , IEEE Access , and Applied Sciences , with a focus on applying deep learning to real-world challenges such as water distribution optimization and fish health diagnostics. Collaborations include projects on IoT security and biomedical signal processing.

• Robotics
• Human-Robot Interaction (HRI)
• Assistive Robotics
• +۵ مورد دیگر

Guillem Alenyà Ribas is a Researcher and Director at the Institut de Robòtica i Informàtica Industrial (IRI), a joint center of the Spanish Scientific Research Council (CSIC) and the Polytechnic University of Catalonia (UPC). He holds a PhD in Robotics from UPC (2007) with research focusing on mobile robot navigation. His current roles include leading multiple international projects in assistive robotics, deformable object manipulation, and human-robot interaction (HRI). Key affiliations include coordination of the ARISE (EU-DIGITAL-EMERGING), ROMANDIC (EU-WIDERA), and TRAIL (MSCA-DN) projects. Research interests span robotic manipulation of deformable objects, assistive robotics for elderly care, and HRI personalization/explainability. Notable achievements include the 2023 Georges Giralt PhD Award, 2024 Special Doctoral Award UPC, and 2022 AIHUB.CSIC Prize. He advises six PhD students focusing on topics like robotic frailty assessment and cloth manipulation benchmarks. Major projects include developing robotic systems for cotton harvesting (DEMETER 5.0), frailty monitoring (FRAILWATCH), and socially aware robotics (SWEET). His lab, the Perception and Manipulation Group, pioneers work in ontology-based robotic explanations and benchmarking tools for cloth manipulation. Over 150 peer-reviewed publications reflect contributions to computer vision, AI planning, and robotics hardware/software integration. Awards and grants include leadership in EU Horizon projects (€10M+ funding), and contributions to both academic (ERC Advanced Grant) and industrial collaborations (e.g., with PAL Robotics). His work bridges fundamental research with practical applications in healthcare, agriculture, and service robotics.

• Computer Vision
• Machine Learning
• 3D Shape Estimation
• +۵ مورد دیگر

Dr. Anthony Dick is an Associate Professor in the School of Computer and Mathematical Sciences at the University of Adelaide. He is affiliated with the Australian Institute for Machine Learning and the Australian Centre for Visual Technologies. His research focuses on computer vision, particularly visual tracking and 3D shape estimation. He explores applications in sports analytics (e.g., AFL player tracking), medical imaging, and visual question answering leveraging external knowledge sources. He is eligible to supervise PhD and Masters students in these areas. Research interests include: 3D shape analysis, visual tracking algorithms, machine learning integration for imagery, and multimodal reasoning (combining vision and text). His work often addresses challenges in large-scale surveillance networks and automated image interpretation. Publications span topics like neural network-based tracking, deep learning for set prediction, and benchmarking generative models. He has contributed to datasets such as Tenniset for event recognition in sports videos. Affiliated with interdisciplinary teams like the Australian Centre for Visual Technologies, his research bridges academic and applied domains, with potential industrial applications in automated video analysis and medical diagnostics.

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

Assoc. Prof. Andrew Busch is the Head of the School of Engineering and Built Environment at Griffith University since 2024. With over two decades of academic experience, he specializes in machine vision for agricultural applications , medical imaging , human movement analysis , and marine science engineering . His research has secured over $14M in funding (including $6M from ARC and $7M from industry partners) and spans collaborations across disciplines. Education: PhD, Queensland University of Technology (2004) BEng (Electrical and Electronic), Queensland University of Technology (1998) Bachelor of Information Technology, Queensland University of Technology (1998) Andrew's research bridges computer vision and artificial intelligence with applications in smart farming , medical device development , and marine ecosystem monitoring . Recent works focus on sugarcane quality automation, retinal vessel delineation, and AI-driven energy systems for buildings. His 15 most recent publications (2025-2002) highlight trends in: Agricultural engineering (sugarcane billet analysis, strawberry grading) Medical imaging (retinal vasculature, speech enhancement) Energy systems (smart buildings, digital twins) Education innovation (industry placements, curriculum design) Scientific Awards: 2021: Excellence in Teaching - Group Educational Leadership Award (Highly Commended) Andrew has supervised 13 doctoral students across projects in aquaculture automation, sugarcane AI systems, and medical diagnostics. He chairs conferences like AAEE 2023 and leads programs such as the Bachelor of Advanced Engineering (Honours) . Labs & Teams: He collaborates with the Institute for Integrated and Intelligent Systems (2018-2024) and directs projects funded by Sugar Research Australia , ARC , and DFAT .

Sohaib Kianiمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Causal Machine Learning
• Adversarial Machine Learning
• +۳ مورد دیگر

Sohaib Kiani is an Assistant Professor of Math and Computer Science at Beloit College, USA. He holds a B.Sc. in Communication Engineering from NU-FAST Islamabad, an M.Sc. in Communication Engineering & IT from RWTH Aachen University, and a Ph.D. in Computer Science from the University of Kansas. His research focuses on Trustworthy Machine Learning, emphasizing explainability, fairness, privacy, causality, and robustness. Key areas include causal ML in decision-making systems, multi-view data applications, and democratizing ML through Tiny ML. Recent work includes a 2023 ECAI publication on counterfactual prediction and a 2021 ACSAC distinguished paper on adversarial example detection. He teaches courses like Object-Oriented Programming and Information Security. Awards include the Distinguished Paper Award at ACSAC 2021. Research opportunities are available via a dedicated sign-up link.

Duygu Ceylanمشاهده پروفایل

پژوهشگر

• Computer Vision
• Computer Graphics
• Machine Learning
• +۳ مورد دیگر

Duygu Ceylan is a Principal Research Scientist at Adobe Research London. She holds a PhD from École Polytechnique Fédérale de Lausanne (EPFL), where she worked under Prof. Mark Pauly, and a Master’s degree in Computer Science from Bilkent University. She received her undergraduate degree in Computer Engineering from Middle East Technical University (METU). Prior to Adobe, she worked as a software engineer at Aselsan Inc. Her research focuses on bridging computer vision and graphics, particularly using machine learning for 3D content generation and analysis from images/videos. Key interests include generative models, 3D reconstruction, and interactive editing of 2D/3D assets. Notable awards include the Eurographics PhD Award (2015) and Eurographics Young Researcher Award (2020). Her work spans over 50 publications in top-tier conferences like SIGGRAPH, CVPR, and ECCV, covering topics such as neural rendering, cloth dynamics, and video generation. She leads research initiatives at Adobe, mentoring interns and driving advancements in generative AI for visual content creation. Her group’s projects include tools for video editing, 3D stylization, and dynamic human performance modeling.