جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Algorithm Optimization
• Neural Networks
• +۴ مورد دیگر

Kyungmi Joanne Lee is a Senior Lecturer at James Cook University (JCU), Australia, specializing in Computer Science and Artificial Intelligence. She holds a PhD in Computer Science from Griffith University (2007) and previously served as a Lecturer at Charles Sturt University (2007–2008). Her research focuses on machine learning, algorithm optimization, neural networks, data mining, and applied AI. Key projects include developing real-world scheduling systems for mining operations, ultrasonic signal classification, and spatio-temporal trajectory analysis. Teaching responsibilities include modules like Database Modelling, where she coordinates and lectures. She currently supervises 5 PhD students, with 3 nearing completion. Her work spans diverse applications, such as emergency management through geospatial data analysis and curriculum development via text mining. With 58 publications across journals and conferences, her contributions highlight interdisciplinary approaches to data-driven solutions.

Daniel Rothمشاهده پروفایل

استاد

• Artificial Intelligence
• Virtual Reality
• Augmented Reality
• +۶ مورد دیگر

Prof. Dr. Daniel Roth is a Professor of Machine Intelligence in Orthopedics at the TUM School of Medicine and Health at Technical University of Munich (TUM), appointed in September 2023. His research focuses on human-machine interfaces in medicine, including AI, virtual/augmented reality (XR) technologies for surgical assistance systems, disease diagnosis, and rehabilitation. Previously, he held a junior professorship in Human-Centered Computing and Extended Reality at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). Education: Bachelor's/Master's in Media and Imaging Technology (TH Köln) PhD in Computer Science (Julius-Maximilians-Universität Würzburg) Research Interests: Roth’s work integrates AI and extended reality to solve healthcare challenges. Key areas include: XR-based surgical training and teleconsultation systems AI-driven analysis of surgical workflows Embodiment in virtual environments for medical applications Accessibility technologies for visually impaired users Publications: Recent work emphasizes immersive medical visualization, telemedicine systems, and user embodiment in VR. Key themes include: XR applications in surgery and patient care 3D teleconsultation for emergency scenarios AI-enhanced anatomy visualization Awards: Best Demo Honorable Mention (IEEE VR, 2021) Best Poster Award (ISMAR, 2020) IEEE TVCG Best Journal Paper (2018) Grants & Teams: Active in interdisciplinary projects at Klinikum rechts der Isar. Collaborates with industry partners on AR/VR healthcare solutions. No formal advisee list is provided, but his work involves multi-disciplinary teams. Labs/Teams: Leads machine intelligence initiatives in TUM’s medical school, focusing on translational research between engineering and clinical practice.

Huiqi Yvonne Luمشاهده پروفایل

مدرس

• AI for Health
• Sensor Informatics
• Digital Health
• +۳ مورد دیگر

Dr. Huiqi Yvonne Lu is a Stipendiary Lecturer in Engineering Science and Researcher in AI for Health and Sensor Informatics at the University of Oxford. She holds positions as co-lead tutor for admissions at Worcester College and a Fulford Junior Research Fellow at Somerville College. Her research focuses on AI-driven health monitoring, wearable devices, and sensor signal processing. She leads projects like SMARTEDGE (EU Horizon) and MIRAGE (Diabetes UK), addressing gestational diabetes and chronic disease management. She is an award-winning researcher with honors including the CAETS High Potential Innovations Prize and the Royal Academy of Engineering Fellowship. Dr. Lu actively contributes to IEEE standards and editorial roles at Nature npj Women’s Health and Frontiers in Signal Processing. Education: DPhil in Mobile Computing from the University of Sussex, funded by UKRI and Sussex GTA scholarships. Post-DPhil work included biomedical research at Oxford John Radcliffe Hospital and Liverpool University, followed by a Daphne Jackson Fellowship in 2019. She earned undergraduate and graduate teaching accolades in engineering and mathematics. Research interests include machine learning for healthcare, physiological modeling, and medical device innovation. She has pioneered work on AI for maternal health, blood glucose monitoring, and LLM-based healthcare worker training in rural India. Her interdisciplinary projects bridge engineering, medicine, and global health equity. Teaching roles span Math, Electronics, and Health Data Science modules at Oxford, Sussex, and Liverpool Universities. She advises on STEM outreach, chairs committees for gender equity (EDI), and promotes public engagement through talks at venues like the Royal Society and IEEE conferences.

Peter Hallمشاهده پروفایل

استاد

• Computer Vision
• Computer Graphics
• Medical Imaging
• +۵ مورد دیگر

Professor Peter Hall is a faculty member in the Department of Computer Science at the University of Bath, affiliated with the Centre for Digital Entertainment (CDE), Centre for the Analysis of Motion, Entertainment Research & Applications (CAMERA), Visual Computing, and the Bath Institute for the Augmented Human. His research focuses on computer vision applications in computer graphics, including style transfer, tactile interfaces for visual impairment, and medical imaging techniques. He has led and collaborated on projects funded by EPSRC and EU Horizon 2020, such as the CAMERA initiative and studies on multisensory interaction. His expertise spans computer vision fundamentals like image segmentation, object recognition, and generative adversarial networks (GANs), alongside interdisciplinary work in medical imaging, robotics, and accessibility technologies. Notable contributions include deep learning-based MRI reconstruction, geometric style transfer algorithms, and tactile image generation for visually impaired users. Education details not explicitly stated in provided texts Projects include IAA tactile display systems, CAMERA motion analysis, and generative adversarial network research Advising and grants involve leadership roles in EPSRC-funded projects like 'Acquiring Complete and Editable Outdoor Models' and EU initiatives. His work aligns with UN SDGs related to health innovation and accessibility. Current research emphasizes domain adaptation techniques and cross-depiction problem solutions in computer vision.

• Computer Vision
• Biomedical Image Analysis
• Machine Learning
• +۳ مورد دیگر

Kevin Smith is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Division of Computational Science and Technology. His research focuses on applying machine learning and computer vision to biomedical problems, particularly in healthcare and medical imaging. Previously, he worked at ETH Zürich and EPFL. His work bridges AI, biology, and medicine, addressing challenges like disease diagnosis, risk prediction, and treatment optimization. Research Interests: Smith’s research spans computer vision, machine learning, and their applications in healthcare. Key areas include medical image analysis, AI-driven diagnostic tools, and privacy-preserving medical data processing. He explores topics such as tumor detection in ovarian ultrasound, breast cancer risk modeling, and MRI privacy through autoencoders. Publications & Trends: Recent work emphasizes AI-human collaboration frameworks, privacy in medical imaging, and domain adaptation for high-content data. His articles highlight advancements in transformer models for medical tasks and multicenter AI validation in breast imaging. Teaching & Roles: Smith teaches courses like Foundations of Machine Learning (DD1420) and oversees the Applied Programming and Computer Science program. He mentors MSc students in topics like medical image segmentation and risk prediction. Affiliations: He is part of the Science for Life Laboratory and contributes to KTH’s collaborative research ecosystem, including the VAI-B platform for AI validation in breast imaging.

• Artificial Intelligence
• Wireless Communications
• Machine Learning
• +۵ مورد دیگر

Giovanni Geraci is a Tenured Professor at the Department of Information and Communications Technologies, Universitat Pompeu Fabra. Specializing in applying AI/ML to wireless communications, he focuses on GenAI for data augmentation and LLMs for telecom applications. His expertise spans 5G/6G cellular and Wi-Fi (IEEE 802.11be/bn) technologies, informed by roles at Nokia Standards, Telefónica, and Bell Labs. He holds the IEEE Communications Theory Early Achievement Award (2023) and the IEEE Outstanding Young Researcher Award (2018). Research interests include AI-driven network design, UAV communications, and next-generation wireless standards. He has authored over 100 publications, including Best Paper Awards at IEEE ICC (2025), Globecom (2022), and PIMRC (2019). Geraci has led academic roles like Head of Telecommunications Engineering and is an IEEE Distinguished Lecturer, delivering over 100 keynotes globally. He created the IEEE course 'Fundamentals of Wi-Fi' and teaches Signals and Systems, praised for his 'fantastic, passionate, and supportive' teaching style. Geraci’s work bridges academia and industry, addressing challenges in cellular networks for UAV corridors, millimeter-wave channel modeling, and integrated terrestrial/non-terrestrial networks. His recent research explores AI/ML applications for 6G, Wi-Fi 7/8, and data-driven optimization of telecom infrastructure.

James Caverleeمشاهده پروفایل

استاد

• Machine Learning
• Recommender Systems
• Information Retrieval
• +۷ مورد دیگر

James Caverlee is a Professor in the Department of Computer Science and Engineering at Texas A&M University, and a Visiting Researcher at Google DeepMind. He holds a Ph.D. from Georgia Institute of Technology (2007), and degrees from Stanford University and Duke University. His research focuses on trustworthy, efficient, and personalized machine learning systems, including Recommender Systems, Large Language Models (LLMs), and bias mitigation. Recent work emphasizes fairness, efficiency, and transparency in AI, with applications in conversational systems, speech, and medical QA. He has been recognized with an NSF CAREER Award, AFOSR Young Investigator Award, and DARPA Young Faculty Award. His group has published extensively at top venues like KDD, EMNLP, WWW, SIGIR, and WSDM. Education: Ph.D., Computer Science, Georgia Tech, 2007 M.S., Computer Science & Engineering-Economic Systems, Stanford University, 2001–2000 B.A., Economics, Duke University, 1996 Research Interests: Machine Learning, Recommender Systems, NLP, LLMs Bias Mitigation, Efficiency, Personalization Social Computing, Information Retrieval Awards: NSF CAREER Award (2012) AFOSR Young Investigator (2012) SIGIR Test of Time Honorable Mention (2022) Grants: Supported by NSF, AFOSR, DARPA, and industry (Google, Amazon). Labs/Teams: InfLab research group at Texas A&M, collaborating with Google DeepMind. His publications address topics like adversarial attacks on LMs, gender bias in podcasts, and efficient recommendation algorithms. Recent trends show focus on generative AI for personalization and ethical AI challenges.

• Computer Networks
• Edge Computing
• Internet of Things (IoT)
• +۵ مورد دیگر

Paolo Bellavista is a Full Professor of Distributed and Mobile Systems at the Department of Computer Science - Science and Engineering, Alma Mater Studiorum – University of Bologna. His research focuses on edge computing, federated learning, IoT, digital twins, and blockchain applications. With over 140 journal articles and 200+ conference papers, his work appears in top venues such as ACM Computing Surveys and IEEE Transactions. He serves as Editor-in-Chief of the MDPI Computers Journal and holds editorial roles in IEEE Communications Surveys&Tutorials and ACM Computing Surveys. His contributions span network optimization, cybersecurity, and smart cities, with notable achievements in citation metrics (h-index 46 on Google Scholar). Research interests include distributed systems, vehicular networks, AI-driven security, and QoS-aware edge infrastructures. He has organized major conferences like IEEE Mobile Cloud and ACM ICDCN. Current projects explore federated learning unlearning, digital twin entanglement, and blockchain-based data spaces. His work bridges theoretical foundations with real-world deployments in smart manufacturing and healthcare. Editorial Roles : MDPI Computers (Editor-in-Chief), IEEE Communications Surveys&Tutorials (Associate Editor), ACM Computing Surveys (Editor) Conference Leadership : Technical Program Chair for IEEE Mobile Cloud 2015, General Co-Chair for SCNS 2018 Key Contributions : Over 140 journal papers, 15 special issues as guest editor, and 200+ conference publications His lab develops middleware for edge-cloud continuum applications and collaborates internationally on projects like InAbled Cities. Office hours are held weekly at the University of Bologna’s DISI department.

Francois Chaumetteمشاهده پروفایل

استاد پژوهش

• Robotics
• Computer Vision
• Visual Servoing
• +۱۷ مورد دیگر

François Chaumette is a Senior Research Scientist (Directeur de recherche) at Inria, affiliated with IRISA and the Centre Inria de l'Université de Rennes. He has been a key researcher in robotics and computer vision since 1990 and led the Lagadic research team from 2004 to 2017. His research interests are centered on robot vision, particularly visual servoing and active perception . He has made foundational contributions to image-based and position-based visual servoing, and his work integrates control theory, computer vision, and robotics. His research spans applications in mobile robotics, aerial systems, medical robotics, space robotics, and soft object manipulation. The recent publications highlight a consistent focus on visual servoing under complex constraints—such as motion blur, occlusions, and deformations—applied to drones, cable-driven robots, and space systems. There is a strong emphasis on robustness , stability analysis , and hybrid sensing (e.g., vision + proximity, vision + force). His work with the RemoveDebris mission demonstrates real-world impact in space robotics. AFCET/CNRS Prize for best Ph.D. in Automatic Control Best paper awards at RFIA 1996 & 2004 Best paper in IEEE T-RA (2002) Best paper in IEEE RA-L (2019) Best paper in IEEE RAM (2020) IEEE Fellow (2013) He has advised over 30 Ph.D. students, many of whom have become active researchers in robotics. He has served in editorial roles for top journals including IEEE Transactions on Robotics , IEEE Robotics and Automation Letters , and the International Journal of Robotics Research . He was elected to the IEEE RAS Administrative Committee (2016–2018) and served on ERC grant panels for robotics. Chaumette is the main developer of ViSP (Visual Servoing Platform), a widely used C++ library for visual tracking and servoing. His leadership in both theoretical advances and software tools has significantly shaped the visual servoing community.

• Computational Display
• Virtual Reality
• Augmented Reality
• +۸ مورد دیگر

Bin Chen is a Lecturer at the School of Computing and Information Systems, University of Melbourne, where he conducts research at the intersection of computer graphics, computational imaging, and human perception. He was previously a postdoctoral researcher at the Max-Planck-Institut für Informatik and a visiting scholar at the University of Cambridge. Research Interests: His work spans Computational Display , focusing on glass-free 3D and VR/AR systems; Perception , studying how humans perceive virtual materials and gloss; and Computational Imaging , developing AI-driven methods for HDR, deblurring, and depth synthesis. He uses both optical hardware and software rendering to enhance visual fidelity. Recent Research Trends: His recent publications emphasize deep learning for image restoration (e.g., deblurring, tone mapping), neural representations for image stacks, and perceptual validation of material rendering. The integration of light field displays and self-supervised learning is a key theme across his recent work. Scientific Awards: CVPR Best Paper Award Finalist (Top 0.4%) – 2022 Service and Mentoring: Bin Chen has served on the Technical Paper Committees for SIGGRAPH, SIGGRAPH Asia, CVPR, and AAAI. He actively mentors PhD students and invites self-motivated candidates to join his research group. He has advised students such as Tao Huang, Lingyan Ruan, Chao Wang, and Jizhou Li. Laboratory and Teams: While no formal lab name is specified, his research group at the University of Melbourne focuses on visual computing, with strong collaborations extending from City University of Hong Kong to Max-Planck-Institut and the University of Cambridge.

Patricia Wolfمشاهده پروفایل

استاد

• Innovation Management
• Foresight and Future Studies
• Artificial Intelligence in Society
• +۷ مورد دیگر

Patricia Wolf is a Professor in the Department of Business & Management at the University of Southern Denmark (SDU), Faculty of Business and Social Sciences. She is affiliated with multiple interdisciplinary research centers, including the Centre for Integrative Innovation Management (C*I2M), the SDU SCC Elite Center PACA, and the SDU Climate Cluster, reflecting her broad impact across innovation, climate futures, and societal transformation. Her research focuses on innovation management, foresight methodologies, artificial intelligence in social contexts, knowledge management, and climate futures. She explores how organizations and societies can envision and enact sustainable futures, particularly through participatory and creative methods such as scenario development, flash fiction, and citizen science. The 15 most recent publications reveal a strong trend toward integrating AI, climate imagination, and participatory futures, with increasing emphasis on emotional dimensions like climate anxiety and hopeful envisioning. Her work bridges theory and practice, often involving collaborations with SMEs, educational institutions, and environmental organizations to co-create actionable strategies. Innovative Teaching Award 2024 John Bessant Best Paper Award (2023 and 2021) SCC Fast track - uddelingsrunde november 2024 Patricia Wolf has secured significant grant funding for projects such as FUSION, PACA, and CFF, where she serves as Principal Investigator or Co-PI. These projects focus on mobilizing youth, educators, and communities to engage in climate future fiction and responsible innovation. She has supervised PhD students and contributed to curriculum development in innovation and foresight education. Her teaching includes courses on innovation management, knowledge management, and organizational processes. She leads and participates in interdisciplinary teams across SDU, including collaborations with the Department of Sports Science, the Climate Cluster, and external partners in Denmark and Germany. Her labs and research groups emphasize co-creation, narrative methods, and future-oriented design, fostering a culture of integrative and socially responsible innovation.

• Computer Vision
• Machine Learning
• 4D Vision
• +۹ مورد دیگر

Adrian Hilton is a Professor of AI and Computer Vision at the University of Surrey, UK. He serves as the Founding Director of the Surrey Institute for People-Centred AI and Director of the Centre for Vision, Speech and Signal Processing. His research focuses on advanced fields such as 4D Vision, Computer Graphics, and AI-driven Digital Doubles. He holds prestigious fellowships including Fellow of the Royal Academy of Engineering, IET Fellow, and IAPR Fellow. His work spans interdisciplinary areas like Virtual/Augmented Reality, Light Fields, and Audio-Visual Signal Processing. No specific grants, students, or recent publications are detailed in the provided text, but his leadership roles highlight significant contributions to AI and computer vision research.

• Cloud Computing
• Computer Networking
• Machine Learning
• +۱ مورد دیگر

Xiaoqian (Tiffany) Zhang is an Assistant Professor in the Department of Computer Science at the University of Nebraska at Omaha (UNO) College of Information Science & Technology. Her work focuses on cloud computing, computer networking, and cybersecurity. Education: Ph.D. in Computer Science (University of Massachusetts Boston, 2023), MS in Mathematics (New York University, 2014), BA in Mathematics and Economics (Skidmore College, 2012) Her research explores cloud computing and networking with emphasis on machine learning and cybersecurity . Recent work addresses challenges in disaggregated storage systems , edge computing , and augmented reality . She received the DOE NNSA Joule Award (2022) for her scholarship. Her publications span topics including network congestion control , distributed file systems , and audio noise filtering . She teaches courses in cloud computing and computer networking .

Tom Fincham Hainesمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Computer Vision
• Computer Graphics
• +۱۵ مورد دیگر

Tom Fincham Haines is a Senior Lecturer in the Department of Computer Science at the University of Bath. He is affiliated with the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent AI, the Centre for Mathematics and Algorithms for Data (MAD), and multiple interdisciplinary institutes including Visual Computing, IAAPS, the Bath Institute for the Augmented Human, and the Institute for Digital Security and Behaviour (IDSB). He is actively accepting doctoral students and maintains a strong research presence in machine learning and its applications. Research Interests: Dr. Haines applies machine learning to computer vision and graphics, with a focus on graphical models, Bayesian non-parametric models, directional statistics, and active learning. His current interests include developing tools for artists, machine learning for education, online/real-time machine learning, causality, and scaling non-parametric methods for big data. His work contributes to several UN Sustainable Development Goals, particularly in health and security domains. Recent Research Trends: His recent publications (2024–2025) reflect a strong interdisciplinary focus, combining machine learning with applications in sonar imaging, drug detection, and creative media. He leverages deep learning and hybrid spectroscopic techniques for portable forensic devices and explores tangible interfaces for virtual film production, demonstrating a blend of technical innovation and societal impact. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Dr. Haines supervises doctoral students and has contributed to 8 supervised works. He is actively involved in multiple funded research projects, including as Principal Investigator (PI) on a Defence Science and Technology Laboratory project for synthetic opioid detection, and as Co-Investigator on EPSRC, MRC, Innovate UK, and National Police Chiefs' Council projects related to healthcare, chronic pain, and radicalization. His grant portfolio reflects strong collaboration across engineering, health, and security sectors. Labs and Teams: He is embedded in several research centers at the University of Bath, including the Centre for Mathematics and Algorithms for Data (MAD), the Institute for the Augmented Human, and the Institute for Digital Security and Behaviour (IDSB), where he contributes to AI, machine learning, and visual computing initiatives. His work spans collaborative teams in healthcare technology, digital forensics, and creative computing.

Fernando E. Casadoمشاهده پروفایل

پژوهشگر

• Machine Learning
• Robotics
• Human-Robot Interaction
• +۵ مورد دیگر

Dr. Fernando E. Casado is a Researcher at the Personal Robotics Lab (PRL) within the Department of Electrical and Electronic Engineering at Imperial College London since March 2023. He holds a BSc in Computer Science (2017, University of Santiago de Compostela) with awards for academic excellence, an MSc in Artificial Intelligence Research (2018, Menéndez Pelayo International University), and a PhD in Computer Science (2022, USC) with Cum Laude distinction for his work on continual federated learning strategies. His research focuses on multi-robot and multi-user machine learning to enhance trustworthy human-robot interaction, addressing challenges such as concept drift, non-stationary data, and personalized robotic behavior. Key contributions include federated learning frameworks for assistive robotics, adaptive algorithms for heterogeneous data, and eye-gaze tracking for trust assessment in HRI. Publications span topics like federated learning, continual learning, and human-robot trust, with applications in assistive devices and smart environments. Awards include the Best Academic Record and Thesis Award (BSc) and the highest honors in his PhD. Casado’s work bridges theoretical machine learning with practical robotics, emphasizing privacy-aware systems and user-centered design. He collaborates internationally, including visits to PRL in 2021, and contributes to advancing adaptive algorithms for real-world robotic applications.