جست‌وجوی استاد و پژوهشگر

Ting Leiمشاهده پروفایل

دانشیار

• Geographic Information Science
• Transportation Geography
• Remote Sensing
• +۳ مورد دیگر

Ting Lei is an Associate Professor in the Department of Geography at the University of Kansas, located in Malott Hall #1021. His primary research interests focus on Geographic Information Science (GIS), including algorithmic development, geospatial computational methods, network analysis, location theory, and web GIS. He also explores remote sensing applications and advancements in GIS technology such as data structures, databases, computational geometry, and open-source software. His teaching covers GIS principles, transportation geography, and geo-computational methods. Dr. Lei's publications emphasize spatial data conflation, transportation network vulnerability, and optimization models for facility location. Recent works include studies on optimal spatial data matching, hub center interdiction problems, and unified location-allocation approaches integrating GIS and distributed computing. His research addresses real-world challenges in urban planning, water resources management, and celestial imaging analysis. Key research trends in his articles include computational GIS advancements, transportation infrastructure resilience, and interdisciplinary applications of geospatial technologies. No specific scientific awards are listed, though his extensive publication record highlights scholarly contribution. Advising and grants details are not provided in the text, but his active research in multiple geospatial domains indicates engagement with academic and applied projects.

Golnaz Habibiمشاهده پروفایل

استادیار

• Robotics
• Autonomous Driving
• Computer Vision
• +۴ مورد دیگر

Golnaz Habibi is an Assistant Professor in the School of Computer Science at the University of Oklahoma. She holds a PhD in Computer Science from Rice University and MS/BS degrees in Electrical and Control Engineering from Tarbiat Modares University. Her research focuses on robotics, autonomous driving, computer vision, multi-agent systems, machine learning, and reinforcement learning. She previously served as a Research Scientist at MIT's Department of Aeronautics and Astronautics and was affiliated with the Laboratory for Information and Decision Systems (LIDS). Education: Ph.D., Computer Science, Rice University M.S., Electrical and Control Engineering, Tarbiat Modares University B.S., Electrical and Control Engineering, Tarbiat Modares University Her research emphasizes visual navigation, autonomous control systems, and reliable communication in autonomous agents. Notable contributions include work on multi-agent coordination, neural network robustness, and human-robot interaction. Awards include the K2I Fellowship (2013) and a best student paper nomination at DARS 2012. Scientific Awards: Best Student Paper Nomination (DARS 2012) K2I Fellowship Award (2013) Her work bridges theoretical foundations and practical applications, with a focus on safety-critical systems. She has contributed to open-source robotics education initiatives and serves on the program committees of top conferences like AAAI and RSS. Labs & Teams: Her research group (AIR Lab) at OU develops solutions for autonomous systems, leveraging interdisciplinary approaches in robotics and AI.

• Machine Learning
• Materials Science
• Physics
• +۳ مورد دیگر

Qian Yang is an Assistant Professor in the Computer Science and Engineering Department at the University of Connecticut (Storrs). Her research focuses on computational science applied to physical sciences, particularly machine learning for materials, physics, and chemistry. She holds a Ph.D. in Computational and Mathematical Engineering from Stanford University and a B.A. in Applied Mathematics/Computer Science from Harvard College. Before joining UConn, she was a postdoctoral scholar at Stanford's Materials Computation and Theory Group. Her work emphasizes developing scalable models from molecular dynamics data, model reduction for nonlinear systems, and addressing challenges in scientific datasets (e.g., small, clustered, or imbalanced data). Research Interests : Machine Learning for Materials Science, Data-Driven Modeling, Uncertainty Quantification, Chemical Reaction Networks, and Multiscale Simulation. Awards : Best Instructor Award (Stanford, 2015/2016), Best Student/Postdoc Presentation Award (Materials Research Society, 2015). Teaching : Taught courses on Machine Learning for Physical Sciences, Computer Architecture, and computational methods at UConn and Stanford. Known for interdisciplinary approaches and student engagement. Labs/Teams : Leads research on computational modeling and hosts the Materials and Megabytes podcast, featuring interdisciplinary discussions with leading researchers.

• Computer Graphics
• 3D Computer Vision
• User Interaction
• +۵ مورد دیگر

Chi-Wing FU, Philip is a Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong (CUHK). He holds dual roles in research and education, including Associate Editor-in-Chief of IEEE Computer Graphics and Applications. His research focuses on computer graphics, 3D vision, and human-computer interaction, with over 100 publications in top venues like SIGGRAPH, CVPR, and IEEE Visualization. Education: B.Sc. (1st Hons), Computer Science & Engineering, CUHK M.Phil., Computer Science & Engineering, CUHK PhD, Indiana University, Bloomington Research Interests: Dr. Fu's work spans 3D shape generation, computational LEGO design, AR visualization, and robotic interaction. He has pioneered projects like Make-A-Shape (large-scale 3D modeling) and DreamStone (text-driven 3D creation). His team also develops tools for medical data visualization and hand-object pose estimation. Recent Trends in Articles: Recent work emphasizes AI-driven creativity (e.g., LEGO art, text-to-3D systems) and real-time AR applications. His publications often bridge theory (e.g., generative models) with practical systems (e.g., user interfaces for design). Awards: Postgraduate Research Output Award (2023) MSRA Fellowship Nomination (2022) Best Associate Editor (IEEE CG&A) Outstanding Reviewer (ICCV 2021, CCF CAD/CG 2023) Advising & Grants: Supervised over 40 PhD/Master students and postdocs. Active in securing grants for projects like computational LEGO design (with Autodesk), medical AR visualization, and 3D generative AI. Collaborates with industry partners like Adobe and Huawei. Labs & Teams: Leads the Computational Design and Visualization Lab, focusing on 3D systems, robotics, and creative AI. Key projects include the LEGO Sketch Art toolchain and the HandShadowPoser AR system.

Olov Anderssonمشاهده پروفایل

استادیار

• Autonomous Systems
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

Olov Andersson is an Assistant Professor and WASP Fellow in AI for Autonomous Systems at KTH Royal Institute of Technology, leading the Division of Robotics, Perception and Learning. His research focuses on Embodied AI for autonomous robots and vehicles, combining advancements in Vision-Language Models (VLM), Large Language Models (LLM), and real-world navigation challenges. Key projects include the DARPA SubT Challenge-winning team CERBERUS and the EU H2020 Heron project for robotic road repair. He supervises multiple PhD students and postdocs, including Timon Homberger, Finn Lukas Busch, and Jesper Eriksson. Research interests emphasize full-stack autonomy in dynamic environments, including planning, mapping, and navigation. Notable contributions include the OneMap real-time open-vocabulary mapping system and self-supervised scene flow methods like Seflow. He has been recognized for technical leadership in autonomous systems through awards like the WASP Fellowship. Professional activities include co-chairing the 2024 IROS workshop on robot perception in dynamic environments and advising the Swedish Prime Minister’s AI initiative. Teaching roles span multiple graduate courses in machine learning, robotics, and systems engineering at KTH.

• Urban Policy
• Sustainable Development
• Governance
• +۳ مورد دیگر

Ilari Karppi is a University Lecturer at Tampere University’s Faculty of Management and Business, specializing in Administrative Studies. His research focuses on urban policy, governance, and sustainable development, with particular emphasis on urban safety, event management, and the integration of technology in urban environments. He has been actively contributing to interdisciplinary studies addressing challenges in urban planning, crisis management, and public-private collaboration. Key research interests include smart urban safety frameworks, the role of technology in public spaces, and the complexities of multi-actor governance systems. His work frequently explores how urban design and policy can enhance safety, equity, and sustainability in rapidly evolving cities. Recent studies have examined perceptions of security in mass events, machine learning applications in crisis simulations, and boundary objects for sustainable urban development. Key Themes: Urban security, governance innovation, sustainable urban planning, technology adoption in public spaces, event management, and collaborative governance models. Notable Contributions: Analysis of simulated trust in security exercises, integration of eye-tracking systems in urban planning, and evaluation of transit equity in urban mobility. His publications span over two decades, reflecting a deep engagement with regional development, policy push dynamics, and institutional stability in globalized contexts. Though no formal awards or grants are listed, his extensive academic output underscores his influence in urban studies and administrative sciences.

Yang Huaمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Visual Recognition
• +۳ مورد دیگر

Dr. Yang Hua is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on computer vision, machine learning, and their practical applications, including visual recognition, robust learning with limited data, and hardware-aware systems. He has supervised multiple PhD students in areas like generative adversarial networks and domain adaptation. Affiliations: Institute of Electronics, Communications & Information Technology; Co-Investigator in multiple research projects (e.g., R5159ECS, R2733CPH). Teaching: Courses include 'How to do Research' and 'Database and Programming Fundamentals' for MSc and PhD students. Research Interests: Dr. Hua's work addresses challenges in visual recognition, domain adaptation, and federated learning. He emphasizes real-world applications and has contributed to competitions like PASCAL VOC Challenge and VOT-TIR2015. Awards: Recognized with Queen's University STAR award (2019), VOT-TIR2015 tracker win, and multiple PASCAL VOC Challenge wins. His research has been funded by Microsoft Research and European conferences. Grants & Projects: Includes 'Artificial Intelligence for Vision-Based Navigation' and collaborations on medical imaging (e.g., AMD diagnosis). He leads projects on federated learning and domain adaptation. Labs/Teams: Active in the Institute of Electronics, Communications & Information Technology, contributing to distributed AI and biomedical applications.

• AI Ethics
• Network Analysis
• Machine Learning
• +۳ مورد دیگر

Dr. Doina Bucur is a prominent academic in the Electrical Engineering, Mathematics and Computer Science (EEMCS) faculty at the University of Twente. Her work bridges interdisciplinary research in AI ethics, network science, and computational sustainability. She leads the DECIDE project, which focuses on transparent AI decision-making systems impacting public services, healthcare, and insurance sectors. Her research spans machine learning applications in ecological networks, fault detection in sensor systems, and multi-objective optimization in social networks. She has pioneered methods for analyzing microbial community structures (e.g., gFlora), automated fault tree learning, and bias reduction in AI systems. Grants & Collaborations: Recipient of NWA ORC funding (2024) for the DECIDE initiative. Collaborates with over 50 researchers across disciplines including computer science, philosophy, law, and psychology. Active in developing evolutionary algorithms for network vulnerability analysis and multi-objective optimization. Labs & Teams: Core member of the Twente AI Ethics Lab and the Network Science Research Group. Leads interdisciplinary teams addressing real-world challenges through computational methods.

• Machine Learning
• Compressed Sensing
• Privacy-Preserving Techniques
• +۴ مورد دیگر

Jia Liang is a researcher at Henan Polytechnic University's School of Electrical Engineering and Automation, with a focus on Machine Learning , Compressed Sensing , and Privacy-Preserving Techniques . His work bridges Computer Science and Signal Processing , particularly in Radar Imaging and Medical Image Analysis . Key Collaborations: Di Xiao, Ying Luo, Qun Zhang, Hui Huang Technical Expertise: Federated Learning, SAR Imaging, Compressive Sensing, Adversarial Learning His research emphasizes secure data processing in IoT and cloud environments, with recent innovations in cross-disciplinary applications like biosignal analysis for cysticercosis diagnosis . Publications span top venues including IEEE Transactions on Aerospace Systems and Remote Sensing . Notable trends include privacy-preserving machine learning for federated systems and 3D radar imaging of rotating targets, alongside medical imaging solutions for chest radiographs and optical coherence tomography .

• Machine Learning
• Biomedical Engineering
• Signal Processing
• +۳ مورد دیگر

Yanbo Zhang is a researcher affiliated with Nanyang Technological University , holding a PhD in Wireless Communication and Sensing (2022). His work spans machine learning , biomedical engineering , and signal processing with applications in healthcare, communication systems, and computer vision. Education: PhD from Nanyang Technological University, Singapore (2022) Research areas include: Machine learning for medical diagnosis (predicting infections, cardiovascular events, and cancer) Advanced analog-digital converter designs for precision electronics Diffusion models and evolutionary algorithms in AI Federated learning for imbalanced data and cybersecurity Recent publications focus on multi-label learning for disease prediction, secure communication architectures , and deep learning applications in medical imaging. Collaborations include institutions in Singapore, China, and international teams.

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

Shengkai Zhang is an active researcher with 26 publications and 444 citations spanning engineering, computer science, and environmental disciplines. His work demonstrates strong interdisciplinary collaboration through co-authorship with researchers like Kezhong Liu and Mozi Chen across multiple high-impact venues including IEEE conferences, arXiv, and specialized journals. His research interests center on Machine Learning applications in maritime systems , with significant contributions to Large Language Model integration for ship navigation, wireless sensing for bridge officer monitoring, and sensor fusion techniques. Additional expertise spans robotics perception (visual-inertial systems, mmWave radar enhancement), environmental modeling (urban energy systems, climate studies), and biomedical applications of traditional medicine. Recent work shows increasing focus on AI foundation models and their security implications. Zhang's publication trajectory reveals consistent output with accelerating impact since 2023, featuring 15+ papers in 2024 alone. His research clusters around three core themes: Maritime AI Systems (LLM navigation, track association, watchkeeping monitoring) Advanced Sensing Technologies (mmWave radar, Wi-Fi sensing, GNSS fusion) Environmental & Biomedical Applications (urban energy modeling, gut microbiome studies) These areas demonstrate both technical depth in signal processing/computer vision and practical focus on real-world engineering challenges.

Xiao Zhangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Statistical Machine Learning
• Convex Optimization
• +۴ مورد دیگر

Xiao Zhang is a tenure-track faculty member at the CISPA Helmholtz Center for Information Security and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). He obtained his Ph.D. in Computer Science from the University of Virginia in 2022 under Prof. David Evans, following an M.S. in Statistics from the same institution and a B.S. in Mathematics from Tsinghua University. Research Focus Zhang's research spans foundational and applied aspects of trustworthy machine learning, with emphasis on: Security vulnerabilities in ML systems (adversarial attacks, data poisoning) Robustness guarantees for learning algorithms Optimization methods for deep learning Privacy-preserving techniques Multimodal and physical-world applications His recent work focuses on developing theoretically-grounded defenses against emerging threats to AI systems. Publication Trends Zhang's 15 most recent publications (2019-2025) demonstrate consistent focus on adversarial machine learning, with growing emphasis on: 1) Physical-world attacks/defenses, 2) Multimodal model security, and 3) Certified robustness guarantees. The work consistently appears at top-tier venues (NeurIPS, ICLR, CCS). Academic Activities He actively recruits students and collaborators for trustworthy ML research, with open positions for PhD candidates and research assistants. No awards or specific grant information is documented in the provided materials.

Renjie Liaoمشاهده پروفایل

استادیار

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

Renjie Liao is an Assistant Professor (tenure-track) in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), with an associated appointment in the Department of Computer Science. He is also a Faculty Member at the Vector Institute and a Canada CIFAR AI Chair. Prior to UBC, Dr. Liao was a Visiting Faculty Researcher at Google Brain and held a Senior Research Scientist position at Uber Advanced Technologies Group during his PhD. He earned his B.Eng. (Automation) from Beihang University, M.Phil. (CS) from the Chinese University of Hong Kong, and PhD (CS) from the University of Toronto. His research focuses on probabilistic and geometric deep learning , with key contributions in deep generative models, geometric deep learning, neural algorithmic reasoning, and generalization bounds. Notable areas include 3D point cloud analysis, self-driving systems, and healthcare applications using graph neural networks. His work bridges theoretical foundations (e.g., PAC-Bayes bounds) with practical applications like motion forecasting and medical imaging. Education: B.Eng. in Automation, Beihang University (2011) M.Phil. in Computer Science, CUHK (2015) PhD in Computer Science, UofT (2021) Dr. Liao has received awards such as the RBC Graduate Fellowship and Connaught International Scholarship. His lab (Deep Structured Learning Lab) emphasizes principled mathematical approaches to solving complex problems. He advises students in machine learning, computer vision, and robotics, encouraging applications from those with strong coding/mathematical backgrounds. Labs/Teams: Deep Structured Learning Lab (UBC) Vector Institute Collaboration

Ronald Garciaمشاهده پروفایل

دانشیار

• Programming Languages
• Gradual Typing
• Type Systems
• +۶ مورد دیگر

Ronald Garcia is an Associate Professor in the Department of Computer Science at the University of British Columbia, affiliated with the Faculty of Science and the Software Practices Lab. His research focuses on programming language theory, emphasizing gradual typing, type systems, and end-user programming. He teaches courses such as Programming Language Principles (CPSC 509) and Compiler Construction (CPSC 411). Education details are not explicitly stated in the provided texts, but his extensive academic contributions span over two decades. Research interests include formal semantics of programming languages, typestate systems, dependent types, and improving software practices for end-users. He has advised numerous Ph.D. and Master's students, contributing to over 30 academic publications. Recent work explores hybrid programming environments for end-users, runtime type slicing for debugging, and foundational frameworks for gradual dependent types. Garcia actively participates in academic conferences as a PC member and keynote speaker, notably in PLDI, POPL, and ICFP. His lab and collaborations focus on advancing theoretical foundations while addressing practical challenges in software development and education.

Steven Weijsمشاهده پروفایل

دانشیار

• Water Resources Management
• Mountain Hydrology
• Catchment Hydrology
• +۴ مورد دیگر

Dr. Steven Weijs is an Associate Professor and Associate Head (Graduate Programs) in the Department of Civil Engineering at the University of British Columbia (UBC), part of the Faculty of Applied Science. He leads research in water resources management, hydrology, and information theory applications in environmental systems. His work explores optimizing information flows from observations to decision-making in complex water systems. Education: PhD (2011) and M.Sc. (2004) in Water Resources Management from TU Delft, Netherlands. Postdoctoral Research: AXA Research Fund fellowship (2011–2013) at EPFL, Switzerland, followed by roles in environmental fluid mechanics and cryospheric science groups. Research Interests: Mountain hydrology, catchment dynamics, flood forecasting, uncertainty quantification, and information theory-based monitoring strategies. He teaches CIVL 418 Engineering Hydrology and contributes to UBC’s Master of Engineering Leadership in Integrated Water Management. Publications focus on sensor networks, data-driven hydrology, flood analysis, and environmental decision-making. Notable work includes novel methods for streamflow prediction in ungauged basins and optimal monitoring network design. Awards: Leading Scholar (Green College, 2016–2018), AXA Research Fund Postdoctoral Fellowship. He collaborates on projects like WeSenseIt, a citizen observatory initiative, and develops low-cost sensing technologies for hydrological monitoring. His research bridges theoretical frameworks with practical applications in water resource management.