جست‌وجوی استاد و پژوهشگر

• Digital Art and Culture
• Video Art and New Media
• Robotics in Cultural Context
• +۷ مورد دیگر

Assoc. Prof. Jana Horáková, Ph.D. serves as Deputy Head of the Department of Musicology at the Faculty of Arts, Masaryk University in Brno, Czech Republic. With a distinguished career spanning over two decades, she has established herself as a leading scholar at the intersection of digital culture, video art, and human-machine interaction. Her academic leadership extends to multiple institutional roles including membership on the Academic Senate of Masaryk University, the Editorial Board of the Faculty of Arts, and the Industry Council for Digital Culture and Creative Industries. Prof. Horáková's research explores the cultural implications of robotics and artificial intelligence in artistic contexts, with particular expertise in video art preservation and digital media. Her work examines robots not merely as technological artifacts but as cultural phenomena with rich historical lineages, tracing concepts from Karel Čapek's RUR through contemporary AI systems. She has made significant contributions to understanding how machines and humans co-create cultural meaning in performance and artistic contexts. Her recent scholarly output reveals a growing emphasis on AI's role in analyzing and preserving video art heritage, particularly through her work with the Vasulka Live Archive. Horáková investigates how machine learning can serve as an epistemological tool for understanding artistic creation while addressing the ethical and practical challenges of preserving digital cultural heritage. Her interdisciplinary approach bridges musicology, media studies, and computer science to develop innovative frameworks for understanding digital artistic practices. Prof. Horáková has been actively involved in organizing exhibitions, conferences, and public lectures that explore the intersection of technology and art. Her curatorial work demonstrates a commitment to making complex technological concepts accessible while maintaining scholarly rigor. She frequently collaborates with international institutions and scholars, contributing to the global discourse on digital culture and its implications for artistic practice and cultural preservation.

Robert Nehmerمشاهده پروفایل

استاد

• Accounting Information Systems
• Auditing
• Blockchain Technology
• +۸ مورد دیگر

Robert Nehmer is a Professor of Accounting at the School of Business Administration, Oakland University. He holds a Ph.D. in Accounting from the University of Illinois at Urbana-Champaign, where he also completed his master's and bachelor's degrees in Accountancy. Dr. Nehmer teaches graduate and undergraduate courses in auditing and accounting information systems. His research explores formal systems, internal controls, software agents, and emerging technologies in accounting. Key focus areas include blockchain applications, drone technology in auditing, cloud-based systems, continuous auditing frameworks, XBRL implementations, and financial ontologies. He collaborates with industry partners including KPMG on drone-enabled auditing research. Publications demonstrate strong emphasis on technology-driven accounting innovations, particularly blockchain systems (2020), drone auditing frameworks (2017), AI in education (2024), and semantic modeling of financial data (2022). His work consistently bridges theoretical accounting concepts with applied technological solutions. Awards & Recognition: University Service Award, Oakland University/SBA (April 2016) 10 Year Service Award, Oakland University (February 2016) He maintains active membership in the American Accounting Association, Information Systems Audit and Control Association, XBRL-US, and the Object Management Group's Finance Domain Task Force.

• Knowledge Representation
• Artificial Intelligence
• Belief Revision
• +۳ مورد دیگر

Paolo Liberatore is an Associate Professor in the Department of Computer, Control and Management Engineering at Sapienza University of Rome. His research focuses on knowledge representation and reasoning in artificial intelligence, with expertise in belief revision, abduction, computational complexity, and compilability. His educational background includes a Laurea in Computer Engineering (1994) and a PhD in Computer Engineering (1998) from Sapienza University of Rome. Liberatore's research spans several key areas of artificial intelligence. He has made significant contributions to belief revision, including the development of commutative operators for integrating information from multiple sources and efficient algorithms for knowledge revision. His work on abduction addresses diagnostic problems and the minimization of tests for identifying abductive explanations. In computational complexity, he has analyzed the complexity of reasoning about actions, belief update, and model checking. Additionally, he has pioneered work on compilability and compact representations of knowledge, showing how precompilation can reduce the complexity of AI problems. His recent publications (2015-2022) reflect a continued focus on belief merging, integration, and revision, with an emphasis on handling uncertainty and reliability of sources. The work spans theoretical foundations and practical applications in multi-agent systems and knowledge fusion. Liberatore has been recognized with several awards: CNR scholarship for new graduates (awarded twice) ECCAI Award for best European doctoral thesis in Artificial Intelligence (1998) Best Paper Award at the International Conference on Principles of Knowledge Representation and Reasoning (KR 2004) He has led and participated in numerous research projects, including the Italian-French coordinated project "Knowledge fusion and planning in multi-agent decision making" and the university project "Applications of artificial intelligence techniques to web services composition". His project portfolio also includes significant contributions to model checking, satisfiability, mobile robotics, multi-robot systems for emergency response, and verification of web services. Although not explicitly mentioned in the provided text, his extensive involvement in program committees (e.g., KR, ECAI, IJCAI) and editorial boards (e.g., Journal of Artificial Intelligence Research) underscores his active role in the AI research community.

Fabio Patriziمشاهده پروفایل

دانشیار

• Formal Methods
• Knowledge Representation
• Machine Learning
• +۷ مورد دیگر

Fabio Patrizi is an Associate Professor in Computer Science at the Department of Computer, Control and Management Engineering (DIAG) at Sapienza University of Rome. He serves as Coordinator of the Bachelor in Information Engineering program at the Latina site. His academic career includes significant service roles such as Editorial Board Member for the prestigious Artificial Intelligence Journal (AIJ) since 2023, Area Chair for ICSOC 2023, and co-chair for the KR 2025 track on Knowledge Representation & Reasoning and Planning & Scheduling. Patrizi's educational background led to National Scientific Habilitation for Full Professorship for sector SC 09/H1 (now GSD 09/IINF-05). His research spans Formal Methods, Knowledge Representation, Machine Learning, Reasoning about Action, Planning in AI, Service-oriented Computing, and Business Processes. His work focuses on theoretical, methodological, and practical aspects including Behavior and Service composition, Planning programs, MAS Verification, Reasoning About Actions, Infinite-plan synthesis, Data abstraction techniques, and Reinforcement Learning with non-Markovian Rewards. His publication record includes over 70 scientific papers in top-level international journals and conferences. Recent research trends show a strong focus on temporal reasoning in reinforcement learning, planning frameworks, process mining, and verification techniques. His work bridges theoretical foundations with practical applications in AI planning and verification. ICDT Test of Time Award on Automatic verification of data-centric business processes (2009) RMIT Visiting Researcher's Award (2011) ICAPS 2024 Outstanding SPC Award Patrizi has supervised 2 PhD students and actively serves on program committees for major AI conferences including AAAI, IJCAI, ICAPS, AAMAS, KR, and ECAI. His research projects include MARLeN (Principal Investigator, 2023-2025), AIPlan4EU (Task Leader, 2021-2023), DRAPE (Principal Investigator, 2019-2022), WhiteMech (Participant, 2019-2024), TAILOR (Participant, 2020-2023), VerySynCopated (Principal Investigator, 2014-2016), ACSI (Participant, 2010-2013), and SM4All (Participant, 2008-2011). He is a member of the Data Management & Service-Oriented Computing and Artificial Intelligence & Knowledge Representation research groups at DIAG, contributing to collaborative efforts in advanced AI research and applications.

• Medical Informatics
• E-learning
• Semantic Web
• +۵ مورد دیگر

Biagio Lenzitti is a Researcher at the Department of Mathematics and Computer Science , University of Palermo, School of Basic and Applied Sciences. He has taught courses like Programming and Laboratory , Computer Networks , and Educational Planning of Information Systems since at least 2015. Current Office Hours: Monday 9:00-11:00, Studio 201 Contact: biagio.lenzitti@unipa.it | +3909123891101 His research focuses on medical text simplification , patient empowerment , and interactive educational tools , including the development of systems like: MED-VTD: Multilingual Medical Dictionary SimpleHealth Platform: Medical Text Simplification Tools U-MedSearch: Meta Search Engine for Medical Content ETN-FETCH Project: Future Education in Computing Recent publications address AI-driven health information systems , IoT data sharing , and conversational agents for medical education. Key collaborations include the Anghelos Communication Studies Center and European networks like ETN-FETCH . He has supervised theses on topics spanning semantic web applications , open data , and network security since at least 2005.

Francesco Percassiمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Automated Planning
• Privacy-Preserving Techniques
• +۴ مورد دیگر

Francesco Percassi is a Research Fellow in the Department of Computer Science at the University of Huddersfield, affiliated with the School of Computing and Engineering. He is a member of the Centre for Planning, Autonomy and Representation of Knowledge and the Centre for Autonomous and Intelligent Systems. Percassi holds a Ph.D. from the University of Brescia, Italy, where he was supervised by Alfonso Gerevini. His research focuses on automated planning, privacy-preserving techniques, and AI applications in challenging domains like urban traffic control and renewable energy systems. He has co-authored over 40 peer-reviewed publications and actively contributes to international conferences such as IJCAI and AAAI. Education: Ph.D. in Computer Science (2019, University of Brescia), M.Sc. and B.Sc. in Computer Engineering (University of Brescia). His expertise aligns with UN Sustainable Development Goals related to innovation and infrastructure. Current research emphasizes robustness quantification in planning systems, numeric planning optimizations, and LLM-driven configurations for urban planning. Scientific Contributions: 129 citations, h-index of 8 (Scopus), and significant work on heuristic algorithms and trajectory optimization. Grants & Collaborations: Active in multi-agent systems and privacy-preserving planning, collaborating with institutions globally. Labs/Teams: Part of the PARK research group at the University of Huddersfield, focusing on automated planning and knowledge representation.

Hod Lipsonمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Digital Manufacturing
• +۶ مورد دیگر

Hod Lipson is the James and Sally Scapa Professor of Innovation in the Department of Mechanical Engineering at Columbia Engineering. He directs the Creative Machines Lab, pioneering research in artificial intelligence, robotics, and digital manufacturing. His work explores machine creativity, self-replication, and autonomous systems. His research focuses on artificial intelligence and robotics with emphasis on machine creativity, self-modeling, and generative systems. Lipson investigates fundamental questions about robot self-awareness and autonomous design capabilities. His lab develops novel approaches to soft robotics, metamaterials, and AI-driven scientific discovery, bridging mechanical engineering with computational intelligence. Lipson's recent publications reveal strong trends in self-referential robotics (egocentric self-modeling, robot metabolism) and AI-enhanced scientific methods (diffraction analysis, neural network reconstruction). His work consistently explores the intersection of physical embodiment and machine learning, with growing emphasis on generative models for materials science and sustainable engineering applications. Lipson is recognized as an award-winning researcher and communicator, though specific awards aren't detailed in the source text. His contributions span academic research, public engagement through books and media appearances, and practical applications in digital manufacturing. As director of the Creative Machines Lab, Lipson leads research on autonomous robot design, self-replicating systems, and creative machines. His team develops platforms for soft robotics, self-modeling systems, and AI-driven scientific tools. Current projects include laser-based cooking systems, smart building controls, and fundamental investigations into machine consciousness.

• Game Theory
• Economics
• Political Strategy
• +۴ مورد دیگر

Eran Shmaya is an Associate Professor in the Managerial Economics and Decision Sciences department at Northwestern University's Kellogg School of Management. His academic work focuses on game theory, economics, and decision sciences, with particular expertise in political applications of game theory and stochastic optimization problems. His research interests span Game Theory, Economics, Political Strategy, Stochastic Games, Decision Theory, Dynamic Programming, and Operations Research. Shmaya's work often examines strategic behavior in political contexts, as evidenced by his analyses of Israeli electoral politics and coalition formation. He has made significant contributions to understanding the Gittins Index Theorem and multi-armed bandit problems through accessible explanations of complex theoretical concepts. His recent publications reveal a growing interest in the intersection of artificial intelligence and economics, with articles on algorithmic collusion, robot obedience, and market homogenization reflecting contemporary concerns about technology's impact on economic behavior. His blog, The Leisure of the Theory Class, serves as an important platform for disseminating complex game theory concepts to broader academic audiences. Shmaya's academic contributions include insightful commentary on foundational game theory concepts, statistical methodology debates, and tributes to influential scholars like Lloyd Shapley and John Nash. His writing demonstrates both technical mastery and an ability to communicate sophisticated ideas clearly.

• Artificial Intelligence
• Case-Based Reasoning
• Experience-Based Learning Systems
• +۱۲ مورد دیگر

Professor Ralph Bergmann is a distinguished academic at the University of Trier, where he holds the position of Professor of Business Information Systems since 2004. He also serves as the research group leader for Experience-Based Learning Systems (EBLS) at the German Research Center for Artificial Intelligence (DFKI) Trier branch since 2020. His academic career spans multiple institutions with appointments at the University of Hildesheim (2001-2004) and the University of Kaiserslautern (1997-2001). Dr. Bergmann's research focuses on the integration of data-oriented AI methods with semantic technologies to create hybrid AI systems. His work centers on experience-based learning systems that combine machine learning, case-based reasoning, ontologies, and knowledge graphs for modeling explicit knowledge. This research enables the redesign, adaptation, and flexible execution of processes with applications across diverse domains including Industry 4.0, construction, crisis management, service, medicine, cooking, and political argumentation. His recent publications demonstrate a strong trend toward integrating traditional case-based reasoning with modern AI techniques like large language models and knowledge graphs. The research shows increasing application in healthcare (particularly oncology and emergency response), IoT-enabled process monitoring, and argumentation analysis. His work bridges theoretical AI foundations with practical implementations across multiple industries. Professor Bergmann has led approximately 35 research projects funded by prestigious organizations including the European Union, German Research Foundation (DFG), Federal Ministry of Education and Research (BMBF), and the State of Rhineland-Palatinate. With an h-index of 40, he has authored more than 200 scientific publications including four books and 13 conference proceedings. He actively contributes to the academic community through service on editorial boards including Engineering Applications of AI and the International Journal of Intelligent Information Technologies. His leadership extends to program committees for major conferences in artificial intelligence and case-based reasoning. Professor Bergmann heads the Artificial Intelligence and Intelligent Information Systems research group at the University of Trier and leads the Experience-Based Learning Systems research group at DFKI. His teams work on multiple projects including AI-AIM (AI-based anonymization in medicine), KIAFlex (interactive AI assistance for predictive and flexible control in discharge and transition management), DZW (Digital Twin Water Management), myRPA (experience-based Robotic Process Automation), and SPELL (Semantic Platform for Intelligent Decision and Operational Support in Control Centers).

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۹ مورد دیگر

Han Chen is an active academic researcher in the fields of artificial intelligence, machine learning, computer vision, and biomedical informatics, with extensive publications in top-tier venues such as CVPR, ICLR, IEEE Transactions, and Bioinformatics. The research spans diverse applications including medical image analysis, deepfake detection, federated learning, and multimodal affective computing. The research interests of Han Chen include artificial intelligence, machine learning, computer vision, medical image analysis, graph neural networks, and cybersecurity. The work emphasizes deep learning architectures, multimodal fusion, and robustness in AI systems, particularly in healthcare and security applications. Key themes include disentangled representation learning, contrastive learning, and transformer-based models. The recent articles demonstrate a strong trend toward multimodal AI, with applications in medical diagnostics (e.g., mammography, nasopharyngeal imaging), trustworthy AI (e.g., deepfake detection, backdoor defense), and efficient learning paradigms (e.g., federated learning, weak supervision). There is a consistent focus on enhancing model interpretability, robustness, and real-world applicability across domains. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: There is no explicit information about students or advising. However, the high volume of collaborative research suggests active involvement in research teams and likely supervision of graduate students. Funding sources are not mentioned, but the scope of work implies support from national or institutional research grants in AI and health informatics. Labs and Teams: While no specific lab or team is named, the collaborative nature of the work—especially with researchers in medical imaging and federated learning—suggests membership in a multidisciplinary AI research group, possibly affiliated with a medical school or engineering institute.

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Logic Programming
• +۵ مورد دیگر

Thomas Eiter is a Full Professor at the Vienna University of Technology (TU Wien) in the Department of Knowledge-Based Systems, Faculty of Informatics. His research focuses on artificial intelligence, knowledge representation and reasoning, logic programming, computational logic, and neurosymbolic AI integration. He leads projects in declarative problem-solving, intelligent agent systems, and stream reasoning frameworks like LARS. Eiter has contributed to foundational work in answer set programming (ASP), algebraic reasoning, and their applications in scheduling, robotics, and real-time data processing. He has extensive international collaborations, including EU-funded projects like HumanE-AI-Net and the Austrian Science Fund (FWF) initiatives. His work emphasizes bridging symbolic AI with modern machine learning techniques, particularly in visual question answering and neural-symbolic systems. Eiter has supervised numerous PhD students and maintains active roles in academic leadership, including editorial boards of journals like Theory and Practice of Logic Programming . Key contributions include development of the DLVHEX system for hybrid knowledge representation, optimization frameworks for ASP, and methodologies for stream reasoning in dynamic environments. His research also addresses ethical AI through projects like the TAIGER initiative, focusing on training AI agents with ethical rules.

Joshua Tenenbaumمشاهده پروفایل

استاد

• Computational Cognitive Science
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Joshua Tenenbaum is a Professor of Computational Cognitive Science in the Department of Brain and Cognitive Sciences at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds and Machines (CBMM). His research bridges human cognition and machine learning, focusing on perception, learning, and reasoning. He develops computational models inspired by Bayesian statistics and probability theory to explain human inductive reasoning, including causal inference, object perception, and language acquisition. Key projects include 3D object modeling from single images, neuro-symbolic AI frameworks, and theory-of-mind reasoning systems. His algorithms are widely used globally in science and engineering. Tenenbaum leads the Computational Cognitive Science Group at MIT, emphasizing human-like intelligence in machines and bidirectional insights between cognitive science and AI. His work spans visual computing, embodied intelligence, and cognitive AI, with applications in robotics, healthcare, and education. Recent research advances include understanding intuitive physics in humans and machines, moral judgment modeling, and multi-agent reasoning systems.

Kenneth Goldbergمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Automation
• +۹ مورد دیگر

Ken Goldberg is a distinguished Professor at the University of California, Berkeley, holding the William S. Floyd Jr. Distinguished Chair in Engineering. He serves as Chair of the Industrial Engineering and Operations Research Department, Director of the AUTOLAB and CITRIS 'People and Robots' Initiative, and is a Founding Member of the Berkeley AI Research (BAIR) Lab. His academic appointments span multiple disciplines, including EECS in the College of Engineering, Art Practice, School of Information at UC Berkeley, and Radiation Oncology at UC San Francisco. As Chief Scientist for Ambi Robotics and Jacobi Robotics, he bridges academic research with industry applications. Goldberg's research spans robotics, artificial intelligence, and automation with significant contributions to robot grasping, cloud robotics, and medical robotics. His work integrates theoretical foundations with practical applications across manufacturing, healthcare, and artistic domains. He has pioneered concepts like Fog Robotics, which distributes computation between cloud and edge devices, and developed the Dexterity Network (Dex-Net) for robust grasping. His research consistently explores the intersection of technology and human experience, including collaborative human-robot systems and the societal implications of automation. His recent publications reveal a strong focus on robust robot learning, particularly addressing the 'reality gap' between simulation and real-world performance. Goldberg emphasizes hybrid approaches combining data-driven methods with traditional engineering principles, as seen in his work on 'Large Robot Action Models.' His research increasingly addresses medical applications, including surgical robotics and precision healthcare delivery, while maintaining strong connections to fundamental robotics challenges like part orientation and grasping. Goldberg has founded and leads several significant research initiatives including the AUTOLAB, the Center for Automation and Learning for Medical Robotics (Cal-MR), and the Robot Learning Foundation which organizes the Conference on Robot Learning (CoRL). He previously served as Editor-in-Chief of IEEE Transactions on Automation Science and Engineering and currently sits on the editorial board of Science Robotics. His teaching portfolio includes courses on database systems, robotics, and the intersection of art and technology. As Founding Director of the Art, Technology, and Culture Colloquium, he has fostered interdisciplinary dialogue across campus. His research has been supported by major funding from NSF, NIH, and industry partners, with practical applications emerging in manufacturing, healthcare, and agricultural domains.

Anne Hsuمشاهده پروفایل

مدرس

• Machine Learning
• Cognitive Psychology
• Human-Computer Interaction
• +۵ مورد دیگر

Anne Hsu serves as a Lecturer in the School of Electronic Engineering and Computer Science at Queen Mary University of London. Her academic work bridges computer science and psychology, focusing on human-computer interaction, cognitive psychology, and behavioral change applications. Dr. Hsu's research spans multiple interdisciplinary domains including machine learning, cognitive psychology, interaction design, behavior change, and legal reasoning. Her work combines computer science and psychology principles to address challenges in reasoning, wellbeing, health, behavioral change, and gamification. This interdisciplinary approach enables innovative solutions that translate psychological insights into practical technological applications. Analysis of Dr. Hsu's recent publications reveals a clear trajectory toward health-focused digital interventions, particularly in mental wellbeing and pediatric healthcare. Her research demonstrates consistent integration of cognitive psychology principles with advanced computing techniques, with growing emphasis on practical healthcare applications as evidenced by projects like the GrowthMonitor smartphone application and gratitude-promoting mobile tools for young adults. Dr. Hsu actively supervises PhD research, currently guiding students working on causal modeling for healthcare decision support, video games for prejudice reduction, and generative AI for financial trading. Her grant portfolio includes the Merck-funded project 'Design and test the feasibility of a novel smartphone growth monitoring app' (2019-2022), which received £104,886 and involved collaboration with medical researchers Helen Storr, Leo Dunkel, and James Chapple. As a member of the Centre for Multimodal AI, Dr. Hsu contributes to a research environment focused on integrating multiple data modalities to create more natural and effective human-AI interactions. Her work within this center emphasizes practical applications that address real-world challenges through the thoughtful combination of psychological insights and technological innovation.

Ruohan Gaoمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Multisensory Machine Intelligence
• +۳ مورد دیگر

Ruohan Gao is an Assistant Professor in the Department of Computer Science at University of Maryland, College Park, leading the UMD Multisensory Machine Intelligence Lab. He holds affiliations with UMIACS, MRC, and AIM institutes. His research focuses on Computer vision and machine learning Multisensory machine intelligence (sight, sound, touch) Robotic manipulation through sensory fusion Audio-visual spatialization Sim2Real transfer methods His recent publications (2025-2022) span ICCV (5 papers) CVPR (6 papers) CoRL (4 papers) ECCV (2 papers) BVMC (1 paper) covering topics like differentiable acoustic rendering, audio-visual navigation, and embodied AI systems. Scientific recognition includes Michael H. Granof Award (UT Austin Top Dissertation) BMVC Best Paper Runner-Up CVPR Highlight Paper CVPR Best Paper Finalist He actively mentors students and collaborates with institutions including Stanford University, UT Austin, and Northwestern University (future affiliation).