جست‌وجوی استاد و پژوهشگر

• Bioinformatics
• Data Analytics
• Machine Learning
• +۳ مورد دیگر

Dr. Zhong-Hui Duan is a Professor in the Department of Computer Science at the University of Akron's College of Engineering and Polymer Science. He joined the university in 2001 and specializes in interdisciplinary research areas including bioinformatics, data analytics, and machine learning. His work spans computational biology, image processing, and algorithm development with applications in healthcare and molecular science. Dr. Duan holds a Ph.D. from Florida State University. His research focuses on developing computational methods for analyzing biological data, such as cancer gene expression networks and protein sequence-function relationships. He also explores machine learning techniques for dynamic scene deblurring and biomedical diagnostics. His publications span diverse domains from computational chemistry to medical informatics, reflecting a career trajectory bridging computer science with life sciences. Despite no explicitly mentioned awards, his work demonstrates consistent contributions to interdisciplinary research. He teaches courses in algorithms, bioinformatics, graph analytics, and machine learning.

• Computer Vision
• Robotics
• Quantum Computing
• +۳ مورد دیگر

Professor Tat-Jun Chin holds the SmartSat CRC Professorial Chair at the University of Adelaide's School of Computer and Mathematical Sciences within the Faculty of Sciences, Engineering and Technology. His research focuses on advanced robotics, computer vision, quantum computing, and space technology applications. He leads the Sentient Satellites Lab, pioneering work in event-based vision systems, satellite pose estimation, and neuromorphic computing. Notable contributions include robust algorithms for spacecraft navigation, quantum-inspired optimization methods, and open datasets for space robotics. His recent publications emphasize interdisciplinary applications, such as event-camera-based star tracking, geometric crater analysis for lunar missions, and quantum computing for robust fitting. He is actively involved in the Asian Conference on Computer Vision (ACCV) and collaborates with industry partners like SmartSat CRC. Awards and grants are not explicitly listed here, but his professorial chair indicates significant research funding. He supervises a dynamic research group advancing autonomous systems and space exploration technologies.

Yezhou Yangمشاهده پروفایل

دانشیار

• Computer Vision
• Artificial Intelligence
• Robotics
• +۴ مورد دیگر

Dr. Yezhou (YZ) Yang is a tenured Associate Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU). He leads the ASU Active Perception Group , focusing on Cognitive Robotics, Computer Vision, and Generative AI . His work integrates vision, deep learning, and AI to interpret human actions and environmental geometry. Education: Ph.D. Computer Science, University of Maryland, College Park (2016) M.S. Computer Science, University of Maryland, College Park (2013) B.E. Computer Science and Engineering, Zhejiang University, China (2010) Research Interests: Dr. Yang’s research emphasizes Visual Recognition with Knowledge (VR-K) , Autonomous Vehicles , and Multimodal Learning . Key projects include: - Developing Secure and Decentralized Generative Models (NSF SaTC Grant) - Enhancing Robustness in Vision-Language Tasks (NSF Robust Intelligence Grant) - Traffic Safety Assessment via Infrastructure Cameras (Institute of Automated Mobility) Publications: Recent work spans Text-to-Image Generation (e.g., RefEdit), Event-based Vision (e.g., SEVD Dataset), and Connected Autonomous Vehicles (e.g., CAROM). His team’s contributions to benchmarks like VL-GLUE and ConceptBed advance multimodal reasoning and model evaluation. Awards & Recognition: NSF CAREER Award (2020) Amazon AWS ML Research Award (2019) Co-founder of ARGOS Vision Inc. Lab & Teams: The ASU Active Perception Group collaborates with industry (e.g., Arizona DOT) and academic partners. Ongoing projects include Robust Multimodal Foundational Models and Socially-Adept Autonomous Vehicles .

Niko Suenderhaufمشاهده پروفایل

استاد

• Robotic Vision
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Professor Niko Suenderhauf is a leading researcher in robotic vision and AI at Queensland University of Technology (QUT). He focuses on enabling robots to understand and interact with their environment through advancements in SLAM, scene understanding, and semantic mapping. His work bridges robotics, computer vision, and machine learning, aiming to create reliable, relatable, and sustainable robotic systems that collaborate with humans to address societal challenges like climate change and aging populations. His key achievements include pioneering graph-based SLAM methods integrated into the ROS ecosystem, developing object-based semantic SLAM systems, and demonstrating the efficacy of deep learning for place recognition. Collaborations with institutions like DeepMind and Amazon reflect his interdisciplinary approach. Awards include recognition for groundbreaking SLAM research and highly cited publications. Research interests emphasize open-world navigation, privacy-preserving vision, and embodied AI, with a focus on practical applications in construction, environmental monitoring, and domestic robotics. His work on uncertainty quantification and robust perception ensures systems operate safely in unpredictable environments. Professor Suenderhauf actively engages in education, mentoring students across undergraduate and postgraduate levels, and emphasizes the societal impact of technology through collaborations with neuroscience, law, and design experts.

Amit K. Roy-Chowdhuryمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Robot Autonomy
• +۳ مورد دیگر

Amit K. Roy-Chowdhury is a Professor and UC Presidential Chair at the University of California, Riverside (UCR), where he chairs the Robotics Program and co-directs the UC Riverside AI Research (RAISE) Institute and the Center for Networked Configurable Command, Control, and Communications (NC4). He previously chaired the Department of Electrical and Computer Engineering at UCR. His research focuses on computer vision, machine learning, and robotics, with applications in autonomous systems and AI for science. Education: He holds a PhD in Electrical and Computer Engineering from the University of Maryland, College Park (2000), an MS in Systems Science and Automation from the Indian Institute of Science, Bangalore (1995), and a BEng in Electrical Engineering from Jadavpur University, Kolkata (1993). Research Interests: His work spans foundational areas of computer vision, image processing, and machine learning, with a focus on robot autonomy, statistical signal processing, and AI applications in science. His Vision and Learning Group has published over 250 peer-reviewed papers and leads DoD-funded research in AI and robotics. Recent Research Trends : His articles emphasize AI safety, domain adaptation, 3D scene understanding, and embodied agents. Themes include robustness to occlusion, ethical AI, and cross-modal learning. Awards : IEEE Fellow (2020), NAAI Fellow (2022), IAPR Fellow (2023), UCR Doctoral Mentoring Award (2019), UMD Distinguished Alumni (2020). Grants & Leadership : Leads a DoD Center of Excellence, oversees AI initiatives at RAISE, and collaborates with industry/government on AI, robotics, and medical imaging projects. His work on plant cell tracking and face recognition has gained media attention, including a PBS/National Geographic documentary. Labs & Teams : Directs the Vision and Learning Group (formerly Video Computing Group) and co-leads RAISE and NC4, fostering interdisciplinary research in AI and robotics.

Florian T. Pokornyمشاهده پروفایل

دانشیار

• Robotics
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Florian T. Pokorny is an Associate Professor in Machine Learning at the Division of Robotics, Perception and Learning (RPL), Department of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology. He coordinates research projects such as the Horizon Europe-funded SoftEnable and the WASP-funded Intelligent Cloud Robotics for Real-Time Manipulation at Scale . His research focuses on data-driven methods for robotic manipulation, including transfer learning, cloud robotics, and caging-based manipulation of rigid and deformable objects. Education: PhD in Mathematics, University of Edinburgh (supervisor: Michael Singer) MSc in Advanced Study in Mathematics (Part III), University of Cambridge BSc in Mathematics, University of Edinburgh Research Interests: His work emphasizes scalable robotic manipulation, leveraging deep learning and geometric/topological methods. Key areas include training data requirements, transfer learning, and cloud robotics paradigms for manipulation at scale. Recent projects explore energy margin analysis, caging-guided morphology optimization, and robust policy learning. Publications: His recent work spans topics like caging-based manipulation, federated learning, and cloud robotics infrastructure. Notable contributions include CageCoOpt (manipulation robustness), CloudGripper (open-source testbed), and RealCraft (zero-shot video editing). Awards & Grants: Funded by WASP, Horizon Europe, and the Knut and Alice Wallenberg Foundation. His group hosts the CloudGripper platform and collaborates on benchmarks like DLO@Scale. Advising & Labs: Supervises multiple PhD students and research engineers. Alumni include Robert Gieselmann (Amazon Robotics), Yiannis Karayiannidis (Chalmers University), and Anastasiia Varava (Postdoc at KTH). Active in organizing workshops like RoDGE (IROS 2025) and ICRA 2025 robotics learning events.

David Clausiمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Pattern Recognition
• +۲ مورد دیگر

David Clausi is a Professor in the Department of Systems Design Engineering at the University of Waterloo. His research focuses on computer vision, image processing, and pattern recognition, with applications in SAR imagery and biomedical imaging. He leads the Vision and Image Processing (VIP) Lab, which explores AI, transparent AI systems, and scalable solutions. He has supervised numerous graduate and undergraduate students, contributing to advancements in medical imaging, SAR sea ice classification, and sports analytics. Awards include Teaching Excellence recognitions and a Research Excellence Award. His work spans publications in remote sensing, medical imaging, and computer vision, with a focus on real-world applications like environmental monitoring and healthcare. Education: Ph.D., P.Eng. (details not explicitly provided, inferred from titles). Research Interests include automated image interpretation, SAR and biomedical imagery analysis, and AI ethics. His lab emphasizes scalable AI and transparent AI explanations. Recent articles address sea ice mapping, hockey analytics, and wildlife detection using advanced machine learning techniques. Awards highlight contributions to research and teaching excellence. Advising spans over 30 students, with notable theses in SAR segmentation, medical image analysis, and computer vision. The VIP Lab collaborates on projects like the AI4Arctic initiative and hockey analytics tools like PuckNet and GoalieNet.

• Artificial Intelligence
• Computational Creativity
• Computing Education
• +۴ مورد دیگر

Brian C. O'Neill is an Associate Professor of Computer Science and Associate Dean at Quinnipiac University's School of Computing and Engineering. He holds a PhD and MS in Computer Science from Georgia Institute of Technology and previously served as Department Chair at Western New England University before joining Quinnipiac in 2023. Education: BS, Saint Joseph's University MS, Georgia Institute of Technology PhD, Georgia Institute of Technology Dr. O'Neill's research focuses on Artificial Intelligence and its intersection with Computational Creativity , particularly in storytelling and board games. He has developed a multidisciplinary AI minor at Quinnipiac and investigates AI's role in enhancing human creative processes through emotion-driven narrative generation and collaborative improvisation systems. His recent publications span AI accessibility, game theory, and computing education. While no specific scientific awards are listed, he contributes to computing education as co-Editor-in-Chief of EngageCSEdu, an ACM open-education resource platform.

Kristóf Marussyمشاهده پروفایل

استادیار

• Model-Driven Engineering
• Dependability Analysis
• Logic Solvers
• +۶ مورد دیگر

Kristóf Marussy is an Assistant Professor at the Department of Artificial Intelligence and Systems Engineering , Budapest University of Technology and Economics , Hungary. His work bridges model-driven engineering with dependability analysis and logic solvers , focusing on critical cyber-physical systems in railway, automotive, and aerospace domains. Assistant Professor (2025–) Research Fellow (2023–2024), Assistant Research Fellow (2020–2023) Contributor to Refinery (graph solver for model generation) and Ferdium (open-source messaging suite) Research Interests center on: Formal Verification of critical system designs Automated Reasoning via logic/numerical solvers Graph Generation for testing data-driven systems AI Integration in system modeling Recent Publications demonstrate expertise in: Graph solver infrastructure (Refinery framework) Cyber-physical system architecture synthesis Formal methods for API key security (PKCE workflow) Constraint optimization in model generation Probabilistic analysis for system reliability Awards & Scholarships : 2024 EKÖP Postdoc Fellowship 2023 ÚNKP Researcher Prize 2023 Josef Heim Award for innovation 2021 OOPSLA Artifact Reviewer Recognition Academic Service includes committee roles at OOPSLA 2025, LLM4MDE 2024, ECOOP 2023, and FASE 2025. He actively mentors 7 BSc and 5 MSc students while contributing to European Space Agency's VAMPIR project on AI-enhanced positioning systems.

Xavier Puigمشاهده پروفایل

پژوهشگر

• Embodied AI
• Artificial Intelligence
• Computer Science
• +۴ مورد دیگر

Xavier Puig is a Research Scientist at FAIR, focusing on Embodied AI and human-centered intelligent systems. Previously, he earned his Ph.D. from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), advised by Professor Antonio Torralba. He also holds a double degree in Computer Science and Telecommunications from UPC's CFIS program. Education Ph.D., MIT (CSAIL), 2022 B.S./B.S., UPC (CFIS program), 2016 Research Interests span building agents that collaborate with humans through goal anticipation, task coordination, and realistic behavior modeling in simulation environments. His work integrates Embodied AI , Multi-agent Systems , and 3D Scene Understanding with applications in Human-Robot Collaboration and Interactive Decision-Making . Recent Trends in his publications emphasize Planning in Multi-agent Environments , Language-guided Robotics , Procedural Scene Generation , and Human Foundation Models for embodied tasks. Scientific Awards Best Paper Award, NeurIPS Cooperative AI Workshop (2020) Spotlight Presentation, ICLR (2021)

• Cybersecurity
• Internet of Things (IoT)
• Machine Learning
• +۳ مورد دیگر

Dr. Thomas Tan is an Associate Professor at the School of Computing Engineering and the Built Environment , Edinburgh Napier University . Specializing in Cybersecurity , Internet of Things , and Machine Learning , he leads cutting-edge research in secure distributed systems and edge computing. Focus on federated learning security, malware classification, and vehicular network trust models Recipient of National Research Award 2017 (Oman) and SICSA Supervisor of the Year Honourable Mention Supervises PhD/MSc students in topics ranging from cyberbullying detection to blockchain authentication His 117+ research outputs include IEEE journal articles on UAV task scheduling, graph neural network security, and privacy-preserving IoT frameworks. Active grants cover federated unlearning evaluation (Carnegie Trust £73,564) and AI-driven cybersecurity collaboration (SICSA £2,000).

• Artificial Intelligence
• Deep Learning
• Medical Imaging
• +۵ مورد دیگر

Mohamed F. Tolba is a Professor at Ain Shams University in Cairo, Egypt, affiliated with the Department of Scientific Computing within the Faculty of Science. He also maintains a research affiliation with Nile University's NISC Research Center in Giza, Egypt. His career spans over two decades of active research and publication in computational intelligence and its applications. Dr. Tolba's research interests focus on the intersection of artificial intelligence and practical applications, particularly in medical imaging, bioinformatics, and computer vision. His work demonstrates expertise in deep learning architectures, neural networks, and image processing techniques applied to medical diagnostics and biological data analysis. He has made significant contributions to cancer detection systems, EEG signal processing, and hyperspectral image classification. His recent publications (2023-2025) reveal a strong trend toward developing specialized deep learning models for medical applications, including cancer classification, Parkinson's disease diagnosis, and medical image analysis. He frequently collaborates with researchers in Egypt, particularly with Howida A. Shedeed, Hala Mousher Ebeid, and Aboul Ella Hassanien, producing numerous high-impact publications in international conferences and journals. Dr. Tolba has been instrumental in organizing and contributing to major conferences in his field, including the International Conference on Advanced Intelligent Systems and Informatics (AISI), where he has served as editor and contributor to conference proceedings.

• Computer Science
• Artificial Intelligence
• Operations Research
• +۵ مورد دیگر

Wei Cui is an active researcher with affiliations to multiple institutions, including Beijing Institute of Technology, Shandong University of Science and Technology, and others. Their work spans diverse domains such as artificial intelligence , robotics , quantum computing , and environmental engineering . Research Interests include multimodal fake news detection, vehicle routing optimization, biomedical signal processing, and remote sensing applications. Recent publications focus on machine learning for urban infrastructure , humanoid robotics , and quantum key distribution . Article Trends reveal a strong emphasis on interdisciplinary AI (e.g., healthcare waste logistics, 3D perception) and data-driven optimization . Collaborations include experts in control systems , quantum physics , and environmental science .

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

Kelsey Allenمشاهده پروفایل

استادیار

• Cognitive Science
• Machine Learning
• Robotics
• +۴ مورد دیگر

Kelsey Allen is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Research Scientist at DeepMind. Her research bridges cognitive science, machine learning, and robotics, focusing on understanding and replicating human-like problem-solving, tool use, and physical reasoning. She holds a PhD from MIT (2016) under Josh Tenenbaum and a B.Sc. in Physics from UBC (2010). Research Interests : Allen investigates computational mechanisms underlying human complex behaviors, particularly tool use and design. Her work emphasizes endowing machines with flexible problem-solving abilities. Key themes include lifelong learning, embodied cognition, and integrating symbolic and neural approaches. Awards & Recognition Best Paper Award at Robotics: Science and Systems (RSS) 2018 Oral Presentation at Cognitive Science Society 2019 Spotlight at NeurIPS 2018 and ICLR 2019 Key Projects : Includes developing graph network simulators for rigid body dynamics, tools for physical design optimization, and studies on human tool-use learning. Her work often combines empirical experiments with machine learning models to bridge human and artificial intelligence.