جست‌وجوی استاد و پژوهشگر

• Computer Vision
• Intelligent Robot Control
• 3D Computer Graphics
• +۱۲ مورد دیگر

Dr Jiacheng Tan is a Senior Lecturer in the School of Computing at the University of Portsmouth , where he has been a faculty member since 2002. He is actively involved in research and PhD supervision, with a strong focus on intelligent systems and robotics. Dr Tan earned his BEng in Mechanical Engineering from Jilin University of Technology (1983), an MSc in Mechatronics from Xidian University (1989), and a PhD in Computer Graphics from De Montfort University (2001). Prior to joining Portsmouth, he served as a Visiting Researcher in robotics at the University of Salford (1996–1997) and a Research Fellow in artificial intelligence at the Open University (2000–2002). His research centers on computer vision, intelligent robot control, 3D graphics, and human-robot interaction . He investigates how robots can understand and act upon natural language commands by grounding spatial relations, recognizing objects, and reasoning about tasks in unstructured environments. His work integrates fuzzy logic, knowledge engineering, and machine learning to enable robots to learn from demonstrations and interact meaningfully with humans. The trends in his publications reveal a consistent focus on robotics and intelligent systems , evolving from early work in virtual environments and telerobotics to recent contributions in cloud-based scientific visualization and machine learning applications in astrophysics. His interdisciplinary research spans computer science, control theory, and cognitive systems. Dr Tan has not received any explicitly mentioned scientific awards in the provided text. He serves as a PhD supervisor and has contributed to multiple research projects, including the development of integrated AI frameworks for robotic manipulation. While specific grant details are not listed, his collaborations and publications suggest active involvement in funded research initiatives. He has worked with researchers across institutions on topics ranging from scientific visualization to intelligent interfaces. Dr Tan is affiliated with the Computational Intelligence Research Group at the University of Portsmouth. His lab work involves developing virtual environments, symbolic representations of 3D scenes, and natural language interfaces for robot control, supporting both academic research and practical applications in automation.

Sean Bechhoferمشاهده پروفایل

استاد

• Semantic Web
• Ontologies
• Knowledge Representation
• +۳ مورد دیگر

Sean Bechhofer is a Professor in the Department of Computer Science at the University of Manchester, specializing in teaching and scholarship. His research focuses on Semantic Web technologies, ontologies, knowledge representation, and linked data. He has held roles including Discipline Head of Education for Computer Science (2019–2023) and contributed to projects like the Information Management Group and WF4EVER initiative. Education: BSc Mathematics from the University of Bristol (1988), postgraduate work in Category Theory at the University of Manchester. Research interests include ontology engineering, semantic web languages, and workflow preservation. He co-developed the OWL API and contributed to standards like SKOS and the Research Object Model. Teaching responsibilities include courses on Operating Systems, Computational Thinking, and Algorithms. Key projects include the SCAPE preservation framework and myExperiment workflow repository. His work aligns with UN Sustainable Development Goals through contributions to Digital Futures and data science initiatives.

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Professor Georgios Leontidis is a Professor of Machine Learning at the University of Aberdeen, where he serves as the Interdisciplinary Institute Director for AI & Big Data and co-Director of the £10.9M UKRI AI CDT SUSTAIN. He is a member of the ELLIS Society and a Turing Academic Liaison, actively shaping national and international AI research agendas. His research expertise lies at the intersection of theoretical and applied machine learning, with key interests in capsule networks, domain adaptation, self-supervised learning, federated learning, and generative AI. His work addresses real-world challenges in environmental monitoring, agrifood sustainability, industrial systems, healthcare, and space science. The recent publications highlight a strong trend in developing novel AI architectures (e.g., OmniNet, Masked Capsule Autoencoders), advancing federated and self-supervised learning methods, and applying AI to sustainability (e.g., greenhouse gas accounting, yield forecasting) and scientific discovery (e.g., lunar radar analysis). His work increasingly integrates ethics, policy, and human-AI interaction, as seen in studies on bias amplification and AI copyright. TMLR - Action Editor ICLR 2025 - Area Chair NeurIPS 2024, 2025 - Area Chair Best Area Chair award, NLDL 2025 Shortlisted for Outstanding Contribution to Accessibility and Inclusivity in Blended Learning (2021) Ranked in Top 4% of EPSRC Full Peer Review College NeurIPS top 10% Reviewer (2020, 2023) Best PhD Paper Award at FISA 2019 (supervised student) Leontidis supervises 14 PhD students and manages 4 research fellows. He leads or co-leads major funded projects from UKRI, EPSRC, EU, and industry partners including Siemens and Tesco, with total funding exceeding £10 million. His collaborative network spans institutions in the UK, Sweden, Switzerland, Greece, Spain, and the Czech Republic. He is actively involved in research infrastructure and policy, serving as External Examiner at Cranfield and Hull Universities, member of the EPSRC Full College, and contributor to the UK AI Council’s Data Working Group. He also co-organized BMVC 2023 and chairs the BMVA Summer School.

• Computer Vision
• Machine Learning
• Pattern Recognition
• +۶ مورد دیگر

Xiangyang Xue is a Professor at Fudan University in Shanghai, China, with an extensive research portfolio spanning computer vision, machine learning, and artificial intelligence. His work demonstrates significant contributions to object-centric representation learning, 3D reconstruction, person re-identification, and semantic segmentation. With over two decades of publication history from 1999 to present, he maintains an active research program with numerous collaborations, particularly with researchers like Yanwei Fu, Bin Li, and Yu-Gang Jiang. Professor Xue's research interests focus on advancing computer vision through innovative approaches to object-centric representation learning, 3D scene understanding, and multi-modal learning. His recent work explores the integration of large vision-language models with 3D understanding, diffusion models for data synthesis, and brain-inspired approaches to robotic scene understanding. His research bridges theoretical advances with practical applications in robotics, autonomous systems, and security. Analysis of his recent publications (2023-2026) reveals a strong trend toward multi-modal learning, with increasing integration of vision-language models, 3D understanding, and diffusion-based generation techniques. His work shows a progression from traditional computer vision problems toward more complex, embodied AI challenges that require understanding of both visual scenes and their semantic interpretations. Key themes include object-centric representations, cross-modal alignment, and the application of these techniques to robotics and security domains. Professor Xue has mentored numerous researchers through collaborative projects, with extensive co-authorship indicating a strong advising presence. His work spans multiple funding areas including NSF-supported research in computer vision, AI security, and robotics applications. His publications appear consistently in top venues including CVPR, ICCV, ECCV, AAAI, and IEEE TPAMI. His research group appears to focus on computer vision and machine learning, with particular emphasis on object-centric scene understanding, 3D reconstruction, and person re-identification systems. The team works at the intersection of theoretical computer vision and practical applications, with projects spanning autonomous driving, robotics, security systems, and human-computer interaction. Recent work suggests active exploration of large vision-language models and their integration with 3D scene understanding.

Tuhin Chakrabartyمشاهده پروفایل

استادیار

• Natural Language Processing
• Human-Computer Interaction
• Creativity
• +۶ مورد دیگر

Tuhin Chakrabarty is an Assistant Professor in the Department of Computer Science at Stony Brook University. His research focuses on developing AI systems that address implicature, ambiguity, and human-aligned requirements, with applications in natural language processing and human-AI collaboration. Education: Ph.D. in Computer Science from Columbia University Research Interests: Tuhin explores methods to enhance AI reliability through Long Form Text Generation Evaluation, Benchmarks for Reasoning and Explainability, and Human-AI Collaboration frameworks. His work bridges computational linguistics, machine learning, and creative applications of AI. Scientific Awards: Columbia Center of Artificial Intelligence & Technology (CAIT) Research Support Amazon Science Ph.D. Fellowship Computational Journalism Fellow at NYTimes R&D (2021-2022) Publications Trends: His recent work spans AI safety, creativity support, figurative language understanding, and multimodal systems. Key themes include human-AI alignment, ethical considerations, and empirical studies on generative models. Additional Contributions: Tuhin collaborates with Stony Brook’s Institute for AI-Driven Discovery and Innovation, advancing interdisciplinary research in AI applications.

Kristian Kerstingمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning
• Statistical Relational Artificial Intelligence
• +۵ مورد دیگر

Kristian Kersting is an Associate Professor in the Computer Science Department at Technical University of Darmstadt, Germany, having moved from TU Dortmund in 2017. He holds additional affiliations including Adjunct Assistant Professor roles and has contributed to institutions like the University of Bonn and Wake Forest University. His research focuses on data mining, machine learning, and statistical relational AI, with applications in medicine, plant phenotyping, and traffic analysis. He has authored over 150 peer-reviewed publications and received numerous awards, including the ECCAI Dissertation Award 2006 and Best Student Paper Award at ECML 2006. Education includes a Ph.D. from the University of Freiburg and postdoctoral research at MIT. He has held roles such as Assistant Professor at the University of Bonn and co-chaired major conferences like ECML PKDD 2013. His work emphasizes interdisciplinary applications, including collaborations with industries like goedle.io and pflegix.de. Scientific contributions span probabilistic logic learning, lifted inference, and neuro-symbolic systems. His research group's activities include projects on plant disease detection, traffic prediction, and collective attention analysis. Key awards highlight his impact in AI and data science.

• Artificial Intelligence
• Human-Computer Interaction
• Brain-Computer Interfaces
• +۵ مورد دیگر

Dr. Thomas Do is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), where he also serves as Co-Director of the Human-centric AI (HAI) Centre since March 2023. His academic appointments at UTS include Senior Lecturer (November 2023-present), Research Fellow (November 2021-October 2023), and Research Associate (October 2020-October 2021). He is a member of the Faculty of Engineering and Information Technology, the Australian Artificial Intelligence Institute (AAII), and the School of Computer Science at UTS. Dr. Do's educational background includes: PhD in Computer Science from the University of Technology Sydney (2020) Master's degree in Human-Computer Interaction and Robotics from the Korea Institute of Science and Technology (2016) Dr. Do's research spans the intersection of artificial intelligence, neuroscience, and human-computer interaction. His primary focus is on Brain-Computer Interfaces (BCIs), where he investigates how to integrate BCI technology into everyday applications. He explores the convergence of AI, data science, neural engineering, and human-computer interaction to unlock the full potential of BCIs. His current projects include Human-centric AI technologies (funded by GrapheneX), Brain-Robot Interfaces (funded by Australian Defence), and Human-AI teaming (funded by ARC DP). His work often involves EEG analysis, spatial navigation studies, and object recognition systems. Dr. Do's recent publications demonstrate a strong focus on advancing Brain-Computer Interface technology through innovative approaches to signal processing, multimodal integration, and practical applications. His work spans from fundamental neuroscience research on brain connectivity patterns to applied AI systems for real-world problems in healthcare, transportation, and security. A notable trend in his research is the integration of explainable AI methods with neuroscience to create more transparent and trustworthy BCI systems. Dr. Do is actively involved in teaching and supervision at UTS, where he has taught courses including Data System, AI/Analytics Capstone Project, Technology Research Methods, and Interactive Media. His current research is supported by an ARC Discovery Project grant titled "Mind-reading AI to translate silent speech into words" (2025-2027). Dr. Do maintains active collaborations with institutions worldwide, having previously worked with the Technical University of Berlin, Germany, the University of California San Diego, USA, and the US Army Research Lab. His research has practical applications in medical, defense, and consumer technology sectors.

• Business Process Management
• Process Mining
• Applied Data Analytics
• +۴ مورد دیگر

Prof. Jochen De Weerdt is an Associate Professor at the Department of Decision Sciences and Information Management, KU Leuven, affiliated with the Faculty of Economics and Business. He is part of the Information Systems Engineering Research Group (LIRIS) and a member of Leuven.AI. His research focuses on Business Process Management, Process Mining, IoT-integrated analytics, and data-driven decision-making. He teaches courses such as Business Information Systems and chairs the Postgraduate Studies in Big Data and Analytics. His professional activities include organizing international workshops (e.g., BPI'14 and BPI'13) and serving as a reviewer for top journals like Information Systems and Decision Support Systems . Academically, he holds a PhD (2012) and MSc/BSc degrees (2009/2007) in Business Economics from KU Leuven. His work bridges theory and practice, addressing challenges in process optimization, fraud detection, and cross-domain applications of machine learning. He has pioneered methods for IoT-enhanced predictive process monitoring and graph-based analytics in dynamic systems. Research interests span predictive process analytics, adversarial robustness in models, and geospatial data integration. Teaching emphasizes practical skills in business informatics and data science, with a focus on industry-relevant outcomes. He actively collaborates on projects funded by institutions like the Australian Research Council (ARC).

Riccardo Renzulliمشاهده پروفایل

پژوهشگر

• Object-Centric Representation Learning
• Medical Image Analysis
• Computer Vision
• +۵ مورد دیگر

Riccardo Renzulli is a Researcher at the Department of Computer Science, University of Turin, focusing on object-centric representation learning, medical image analysis, and AI-based computer vision applications. His research emphasizes capsule networks, deep learning models for hierarchical relationships, and applications in healthcare and aerial/satellite imagery. Education: MSc and BSc in Computer Science from University of Turin (2018 and 2015). Previous research with Prof. Valentina Gliozzi explored description logics and non-monotonic reasoning. Professional experience includes a 2022 post at Aalto University (supervised by Prof. Ville Kyrki and Francesco Verdoja) and roles at Addfor and Machine Learning Reply as a deep learning scientist. Research interests span concept learning, few-shot learning, interpretability, and medical imaging. Notable work includes visual localization systems for UAVs, AI-assisted diagnosis for COVID-19 via CXR analysis, and lung nodule segmentation using DeepHealth Toolkit. He contributed to the UniToChest dataset for cancerous nodule detection. His recent publications (2022-2025) address efficient neural architectures, medical imaging applications, and 3D scene modeling. Collaborations include EIDOSLAB, with research emphasizing scalable compression, entropy-based pruning, and ensemble methods for neural networks.

Gunnar Teegeمشاهده پروفایل

استاد

• Formal Verification
• Virtualization
• Distributed Systems
• +۷ مورد دیگر

Prof. Dr. Gunnar Teege is a faculty member at the Institute for Computer Engineering within the Faculty of Computer Science at Bundeswehr University Munich. His career spans over two decades, focusing on distributed systems, virtualization, and formal verification of operating system components. He has led numerous research projects, including collaborations with institutions like the Runder Tisch GIS eV and the Bavarian State Office for Surveying and Geoinformation. Education: Diploma in Computer Science from Technical University of Munich (1985), Doctorate in Computer Science from TUM (1991) Affiliations: Member of GI (Gesellschaft für Informatik) and ACM (Association for Computing Machinery) Research interests include formal verification of software components for secure systems, virtualization frameworks, distributed systems, military communications, geospatial data interoperability via OpenGIS standards, computer-supported cooperative work (CSCW), and E-Learning platforms. His work often bridges theoretical and applied domains, particularly in defense IT systems and education technology. He has contributed to projects like Targeteam (XML-based adaptable teaching materials), HoBIT (high-security embedded systems), and MiKscHA (microkernel applications for high-security environments). His publications emphasize modularity, tailorability, and security in software design. Teaching activities at Bundeswehr University Munich include courses on operating systems, distributed systems, virtualization, and cybersecurity. Previously, he taught problem-based learning in computer science at TU Munich and developed virtual lectures for the Virtual University of Bavaria (vhb) from 2001–2005.

Bidhan L. Parmarمشاهده پروفایل

استاد

• Decision-Making
• Stakeholder Management
• Business Ethics
• +۳ مورد دیگر

Bidhan L. Parmar, also known as Bobby Parmar, is the Associate Dean for Faculty Development and the Shannon G. Smith Bicentennial Professor of Business Administration at the Darden School of Business, University of Virginia. He teaches First Year Ethics and Second Year electives on collaboration, creative thinking, and critical thinking. Education B.A., MBA, Ph.D., University of Virginia His research focuses on decision-making under uncertainty, stakeholder management, and strategies for creating stakeholder value. His work helps executives navigate ambiguity and ethical challenges in organizational settings. Parmar has published in top-tier journals such as Organization Science , Journal of Business Ethics , and Business & Society , and co-authored books on stakeholder theory. His recent publications address topics like brand activism, ethical product design, and regulatory uncertainty in innovation. His work bridges theoretical frameworks with practical applications, including case studies on technology, pharmaceuticals, and global supply chains. Scientific Awards Top 40 business school professors under 40 globally Fellow, Olsson Center for Applied Ethics Fellow, Safra Center for Ethics at Harvard University Teaching and Research Awards As Co-Director of the Darden Experiential Leadership Development Lab, Parmar oversees programs where students practice leadership skills in high-stress scenarios. He collaborates with executives across industries on decision-making, stakeholder management, and leadership.

• Machine Learning
• Object-Centric Learning
• Deep Learning
• +۵ مورد دیگر

Rongzhen Zhao is a Doctoral Researcher at Aalto University's Department of Electrical Engineering and Automation, affiliated with the Robot Learning research group. Their work focuses on machine learning and object-centric learning, with recent publications in top-tier conferences like ECML and ICLR. Research interests include: Object-Centric Learning Slot Attention Mechanisms Spatiotemporal Processing Neural Network Architecture Design Brain-inspired AI IoT Middleware Development Recent trends in their publications highlight advancements in attention mechanisms, discrete representations for object recognition, and multi-scale fusion techniques. They also explore bio-inspired approaches for temporal data analysis and IoT systems. Research groups: Robot Learning Contact: Email: rongzhen.zhao@aalto.fi

• Robot Learning
• Object-Centric Learning
• Machine Learning
• +۳ مورد دیگر

Vivienne Huiling Wang is a Postdoctoral Researcher at Aalto University's Department of Electrical Engineering and Automation, specializing in Machine Learning and Robotics. Her research focuses on object-centric learning, hierarchical reinforcement learning, and probabilistic modeling for robotic systems. Robot Learning Object-Centric Learning Probabilistic Subgoal Representations Her recent work (2023-2025) spans object representation techniques, adversarial subgoal generation, and processor performance estimation using machine learning. She contributes to the Robot Learning research group and collaborates with experts in computer vision and AI planning. Current affiliations: Aalto University Department of Electrical Engineering and Automation

Chi-Chun Chouمشاهده پروفایل

استاد

• Accounting Information Systems (AIS)
• Blockchain Technology
• Machine Learning
• +۷ مورد دیگر

Chi-Chun Chou is a Professor of Accounting in the School of Business at California State University, Monterey Bay (CSUMB). He holds a Doctorate from National Cheng-Chi University in Taiwan and has a unique interdisciplinary background in computer science and accounting. His research focuses on advancing Accounting Information Systems (AIS), particularly in areas such as blockchain, machine learning, data analytics, and XBRL standards. He has published five top-tier peer-reviewed articles in the last five years, including in Journal of Information Systems and Computers & Industrial Engineering , with four ranked as A journals by the ABDC. Dr. Chou has served as an Editorial Board member of the Journal of Information Systems (2023–2025) and as Domain Editor of the Journal of Emerging Technologies in Accounting . He has guest-edited special issues on blockchain (2019–2020) and AI/ChatGPT in accounting (2023–2024). With over 60 manuscript reviews in five years, his contributions validate his leadership in AIS research. He teaches courses such as Auditing, Intermediate Accounting, and Business Analytics, integrating cutting-edge technologies like Python and large language models into his curriculum. Education: Doctorate in Accounting, National Cheng-Chi University, Taiwan. Research Focus: Blockchain applications, smart contracts, continuous auditing, XBRL, and AI-driven accounting processes. Editorial Roles: JIS Editorial Board (2023–2025), JETA Domain Editor, and Guest Editor for multiple special issues. His work bridges computer science and accounting, emphasizing transparency, automation, and innovation in financial reporting systems. Collaborations with professionals and international scholars further highlight his commitment to advancing AIS methodologies.

Nada Sanadمشاهده پروفایل

پژوهشگر

• data-driven decision-making
• human-machine collaborative rationality
• hybrid intelligence
• +۹ مورد دیگر

Nada Sanad is a postdoctoral researcher at the Empirical Software Engineering in Software, Systems, and Services (M3S) research unit at the University of Oulu's Faculty of Information Technology and Electrical Engineering. She earned her Ph.D. (2024) in Information Processing Science from the University of Oulu, focusing on data-driven decisions and human-machine collaboration. She holds a B.Sc. (2011) and M.Sc. (2013) in Business Informatics from the German University in Cairo, Egypt, with a focus on data mining and big data analytics. Her research interests span data-driven decision-making, hybrid intelligence, human-machine collaboration, AI/ML for decision support, big data analytics, and business intelligence. Her work emphasizes ethical and practical applications of AI in decision-making processes, particularly in autonomous systems and manufacturing contexts. Recent publications (2023–2025) highlight trends in evaluating AI-driven decisions, hybrid intelligence frameworks, and data governance in industry. She contributes to projects like OXILATE, integrating AI insights into actionable expertise. Nada Sanad has not been listed as having formal advisees but collaborates actively in interdisciplinary teams at the M3S unit. She maintains profiles on ORCID, SCOPUS, Google Scholar, and ResearchGate.