جست‌وجوی استاد و پژوهشگر

• Natural Language Processing
• Artificial Intelligence
• Epistemic Reasoning
• +۵ مورد دیگر

Dr. Andrés Occhipinti Liberman is an Assistant Professor at the ICAI School of Engineering , Comillas Pontifical University , in the Department of Telematics and Computer Science . He teaches Natural Language Processing and Foundations of Artificial Intelligence , focusing on improving large language models' reasoning and planning capabilities . His research explores how autonomous agents can develop internal environmental representations for planning, with a foundational interest in planning algorithms and mathematical logic . Education Ph.D. in Artificial Intelligence and Logic, Technical University of Denmark (2020) M.Sc. in Logic, University of Amsterdam M.Sc. in Development and International Aid, Complutense University B.A. in Philosophy, Universidad Autónoma de Madrid His work bridges epistemic logic with machine learning , particularly in dynamic term-modal logic for social network analysis and planning. Recent publications examine partially observable domains , grounded representations , and decidability in epistemic systems . He previously served as a postdoc at Universitat Pompeu Fabra in the EU project RLeap and as an external lecturer at DTU. Key article trends include: (1) epistemic planning using first-order logic , (2) dynamic logic frameworks for social networks, and (3) knowledge grounding in reinforcement learning. No formal scientific awards are listed in the provided text. His current research at IIT (Institute for Research in Technology) involves theory-of-mind agents and symbolic planning representations .

• Educational Technology
• Computer Science Education
• Peer Assessment
• +۳ مورد دیگر

Zuzana Kubincová is a researcher in educational technology and computer science education, contributing to conferences like ICWL, ITHET, and MIS4TEL. Her work focuses on peer assessment, code review tools, and adaptive learning systems. Key collaborations with Martin Homola, Ján Kluka, and Dana Suníková Research spans 2009-2024 with recent emphasis on robotics and computational thinking Research Interests She explores innovative assessment methods including digital badges and rubrics, while developing tools for code review and ontology-driven learning. Her work bridges educational theory with practical technology implementations, particularly in K-12 and higher education contexts. Recent publications analyze robotic education and multi-modal assessment frameworks, showing her commitment to advancing teaching practices through technology. Though specific university affiliation details are unavailable in the provided texts, her consistent publication record indicates active academic engagement.

Kristin Danaمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• Artificial Intelligence
• +۷ مورد دیگر

Professor Kristin Dana is a faculty member in the Department of Electrical and Computer Engineering at Rutgers University, School of Engineering. Her research focuses on computer vision, robotics, artificial intelligence, computational photography, and machine learning. She leads the SOCRATES initiative, a NSF-funded program integrating STEM education with social and behavioral sciences. Dana holds a Ph.D. from Columbia University (1999), an M.S. from MIT (1992), and a B.S. from Cooper Union and NYU (1990). Education: Ph.D., Electrical Engineering, Columbia University, 1999 M.S., Electrical Engineering and Computer Science, MIT, 1992 B.S., Electrical Engineering and Computer Science, Cooper Union/NYU, 1990 Research interests span computer vision applications in agriculture (e.g., cranberry ripening analysis), robotics for socially aware systems, and computational methods for material recognition. Her work bridges optical technologies with practical systems like autonomous bridge inspection robots, earning awards such as the NSF CAREER and Sarnoff Technical Achievement Awards. Recent publications emphasize interdisciplinary innovations: from AI-driven agricultural monitoring to steganography in light fields. Grants include a $3M NSF NRT grant for SOCRATES and robotics-related projects. Honors: 2014 Charles Pankow Award (ASCE) 2011 Rutgers ECE Service Award 2001 NSF CAREER Award 1994 Sarnoff Technical Achievement Award Advising focuses on graduate training through SOCRATES, while lab collaborations include Rutgers Robotics and remote sensing frameworks like Finch. Current work explores vision-based localization systems (ViFi series) and socially cognizant robotic systems.

Berk Calliمشاهده پروفایل

دانشیار

• Robotic Manipulation
• Robot Vision
• Machine Learning
• +۲ مورد دیگر

Berk Calli is an Associate Professor in the Department of Robotics Engineering at Worcester Polytechnic Institute (WPI), where he leads the Manipulation and Environmental Robotics Laboratory (MER Lab). He holds a PhD from Delft University of Technology and completed postdoctoral research at Yale University. His research focuses on advancing robotic capabilities for unstructured environments through innovations in manipulation, computer vision, and machine learning. Calli's research spans robotic manipulation, robot vision, machine learning, dexterous manipulation, and environmental robotics. His MER Lab develops multi-modal manipulation strategies using advanced control methods, active vision frameworks, and intelligent mechanical design to address uncertainties in real-world applications like recycling and industrial automation. His publication trends demonstrate consistent contributions to robotic grasping, vision-based control, and industrial applications, with recent work emphasizing benchmark development and sustainable technologies. The articles explore themes of adaptive control systems, recycling automation, and performance evaluation frameworks. Scientific Awards: Prestigious NSF CAREER Award ($599,559) for enhancing robotic object manipulation capabilities Early-Career Faculty Research Award recognizing innovative contributions to environmental robotics Calli leads multiple NSF-funded projects, including initiatives to establish environmental robotics tracks for undergraduates. He advises graduate researchers in the MER Lab and founded the Yale-CMU-Berkeley Object and Model Set project, a globally used benchmarking resource. As head of MER Lab, he directs research on fundamental manipulation problems and environmental sustainability projects including waste sorting and metal scrap recovery. His patented robotic technology is being developed for industrial applications like ship dismantling and metal cutting.

Yawei Liمشاهده پروفایل

مدرس

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +۶ مورد دیگر

Yawei Li is a Lecturer at ETH Zürich, affiliated with the Integrated Systems Institute. His research focuses on efficient deep learning and AI systems for vision, language, and biosignals. Collaborating with Professors Luca Benini and Luc Van Gool, he explores topics like foundation models for biosignals, efficient neural network design, and deployment on RISC-V cores. He has organized workshops such as the NTIRE Challenge on Efficient Super-Resolution and served as a Senior Program Committee member for AAAI and IJCAI. He has supervised numerous PhD and Master students, with open positions available in foundational model development and bio-signal analysis. Key achievements include the Best Poster Presentation at ICVSS 2019 and contributions to projects like TinyYOLO for smart glasses. His work emphasizes practical applications of AI on embedded systems and low-power devices. Teaching includes courses like 'Deep Learning for Image Manipulation' and involvement in Computer Vision Lab initiatives. His research spans image restoration, neural network compression, and cross-modal analysis, with publications in top venues like CVPR, NeurIPS, and ECCV.

• Computer Science
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Hong Liu is a Professor at Shandong University's School of Computer Science and Technology, with an extensive publication record spanning multiple disciplines within computer science and engineering. His research spans computer vision, machine learning, medical imaging, robotics, and signal processing, with over 500 publications listed in the dblp database from 1993 to 2025. Dr. Liu's research interests focus on the intersection of artificial intelligence and practical applications across various domains. His recent work demonstrates expertise in deep learning architectures, medical image analysis, robotics navigation, and computer vision systems. He has made significant contributions to medical imaging techniques, particularly in segmentation methods using quaternion mathematics and self-attention mechanisms, as well as applications in liver disease diagnosis and pediatric fracture detection. The publication trends show increasing activity in recent years, with substantial contributions in 2023-2025 across IEEE journals and conferences. His work frequently appears in high-impact venues including IEEE Transactions on Medical Imaging, IEEE Access, and Pattern Recognition, demonstrating both theoretical rigor and practical applications. His research bridges theoretical computer science with real-world engineering and medical challenges, particularly in China's healthcare and technology sectors. Dr. Liu has collaborated extensively with researchers across Chinese institutions including Shandong University, Harbin Institute of Technology, and Huazhong University of Science and Technology, as well as international partners. His work shows strong interdisciplinary connections between computer science, electrical engineering, and medical applications.

• Deep Learning for Graphs
• Continual Learning
• Neural Networks
• +۱۵ مورد دیگر

Davide Bacciu is a Professor at the University of Pisa, associated with the Department of Computer Science within the School of Engineering. His research focuses on advancing artificial intelligence, particularly in deep learning for graphs, continual learning, and neural networks. He leads projects such as TEACHING (Trustworthy autonomous cyber-physical applications) and CLaaS (Continual-Learning-as-a-Service), emphasizing human-centered AI and edge computing solutions. His work bridges theoretical advancements with practical applications in robotics, healthcare, and smart systems. Key research interests include graph representation learning, federated learning, and generative models. He has contributed to frameworks like PyDGN for graph deep learning and Avalanche for continual learning. His interdisciplinary projects involve collaborations with institutions like the University of Trento and the Institute for Bioengineering of Catalonia. Publications reflect his expertise in domains like molecular property optimization, causal inference, and swarm intelligence. He has advised on grants related to edge computing, autonomous systems, and human-AI collaboration. His work is disseminated through top venues such as AAAI, ICLR, and IEEE Transactions.

Joseph P. Havlicekمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Video Processing
• +۷ مورد دیگر

Dr. Joseph P. Havlicek is the Gerald Tuma Presidential Professor in the Gallogly College of Engineering at the University of Oklahoma, where he has served since 1997. He directs the OU Center for Intelligent Transportation Systems and is a full member of the OU Institute for Biomedical Engineering, Science, and Technology. His extensive career spans both academic and professional realms, with significant contributions to signal processing, image analysis, and transportation systems. His educational background includes: BS in Electrical Engineering from Virginia Tech (1986) MS in Electrical Engineering from Virginia Tech (1988) PhD in Electrical and Computer Engineering from the University of Texas at Austin (1996) Dr. Havlicek's research spans multiple domains including signal, image, and video processing; modulation domain signal processing; target tracking; medical imaging; and intelligent transportation systems. His work bridges theoretical signal processing with practical applications in healthcare and transportation. He has pioneered techniques in AM-FM signal and image modeling, uncertainty measures, and developed innovative approaches for medical image analysis and infrared target tracking. Analysis of his recent publications reveals a strong interdisciplinary trend, with significant work at the intersection of signal processing and medical imaging, particularly in bone marrow assessment and cancer treatment monitoring using PET/CT imaging. His computer vision research focuses on infrared object detection and tracking, with recent work on dual-band infrared systems improving small object detection. The publications also demonstrate his continued theoretical contributions to signal processing, particularly in modulation domain techniques and entropy-based uncertainty measures. His scientific achievements have been recognized with numerous awards: University of Oklahoma Outstanding Faculty Advisor Award (2006) University of Oklahoma College of Engineering Brandon H. Griffith Faculty Award (2003) University of Oklahoma IEEE Favorite Instructor Award (1998, 2000) Department of the Navy Award of Merit for Group Achievement (1990) University of Texas Engineering Foundation Award for Exemplary Engineering Teaching (1992) Dr. Havlicek has been exceptionally successful in securing research funding, serving as PI or co-PI on more than 100 externally funded grants and contracts totaling over $25 million. His professional service includes editorial positions with IEEE Transactions on Image Processing and IEEE Transactions on Industrial Informatics, as well as leadership roles in major conferences including ICIP and ICASSP. He has chaired both graduate and undergraduate studies committees in the ECE department. He leads the OU Center for Intelligent Transportation Systems, which develops advanced technologies for traffic management and traveler information systems. His biomedical engineering work through the OU Institute focuses on medical image analysis, particularly for cancer treatment monitoring and bone marrow assessment. His research teams regularly collaborate with medical institutions to translate signal processing techniques into clinical applications.

• Machine Learning
• Cybersecurity
• Internet of Things (IoT)
• +۸ مورد دیگر

Robert Atkinson is a Professor in the Department of Electronic and Electrical Engineering at the University of Strathclyde, affiliated with StrathCyber. He holds a PhD in Mobile Communications Systems (2003), MSc in Communications Control and Digital Signal Processing (1996), and BEng in Electronic and Electrical Engineering (1993), all from the University of Strathclyde. He is actively involved in supervising PhD students and has contributed to over 139 research outputs. Research Interests: Dr. Atkinson's work focuses on machine learning applications in industry, cybersecurity, and IoT. His expertise includes explainable AI, hyperparameter optimization, data processing pipelines, and collaboration with industry on topics like partial discharge localization and predictive maintenance. He has pioneered projects such as DelugeAI (flood forecasting using AI) and EPSRC-funded submarine cable data analysis. Key Projects: DelugeAI: Scottish Government-funded project exploring AI for flood forecasting. EPSRC IAA: Subsea cable data analysis for underwater infrastructure monitoring. Publications: His recent work spans cybersecurity deception systems, neural networks for image processing, and IoT intrusion detection. Notable contributions include the MQTT-IoT-IDS2020 dataset for intrusion detection. Awards: DASC 2015 Best Paper Award Finalist in 2022 Herald Higher Education Awards for Outstanding Business Engagement Advising & Grants: Supervised four students and contributed to over 36 research projects. His work aligns with UN SDGs, particularly in sustainable infrastructure and innovation. Labs & Affiliations: Active in StrathCyber, collaborating on AI-driven solutions for cyber resilience and industrial IoT challenges.

• Second Language Acquisition
• Documentary Studies
• Computer Assisted Language Learning (CALL)
• +۵ مورد دیگر

Dr. Maria Loftus is an Assistant Professor in French at the School of Applied Languages and Intercultural Studies, Dublin City University. She holds degrees from French and Irish universities, including a PhD in Sub-Saharan Documentary Cinema. Her research focuses on documentary cinema, CALL (Computer Assisted Language Learning), student-created multimedia content, social empathy, and anti-racism. Education: B.A. in Irish and French; M.A./PhD in French Literature, Discourse and Representations, Applied Linguistics, and Sub-Saharan Documentary Cinema (French/Irish institutions). Research Interests : Protest and documentary cinema, visual representations of colonization, CALL innovations, student multimedia creation in SLA, telecollaboration, and anti-racism through creative outputs. She co-supervises PhDs on blended mobilities, language learning creativity, and modal verbs for second-language learners. Awards : Co-recipient of Research Ireland’s New Foundations funding (2023-2024) for projects on social empathy and antiracism. Active in funded initiatives like Ordinary Treasures: Objects from Home , fostering solidarity through material culture. Grants & Projects : Lead roles in projects like Students as Creators of Interactive Video Content (2020) and Ordinary Treasures (2024). Collaborates with Jawaharlal Nehru University on cross-cultural educational research. Labs/Teams : Co-leads the Ordinary Treasures initiative, exploring antiracism through youth culture. Engages in interdisciplinary teams addressing forced migration, volunteer-led education, and refugee integration networks.

Lucía Gómez Álvarezمشاهده پروفایل

پژوهشگر

• Computational Logic
• Ontology Engineering
• Multi-Perspective Reasoning
• +۳ مورد دیگر

Lucía Gómez Álvarez was a Researcher at the International Center for Computational Logic (ICCL) within the Faculty of Computer Science at Technische Universität Dresden. Her primary role was as a former Research Associate in the Computational Logic Research Group. She contributed to advancing multi-perspective reasoning frameworks and ontology engineering, focusing on integrating standpoint logics with description logics (e.g., SHIQ, EL). Her work bridges formal ontology, knowledge representation, and spatial semantics, addressing challenges in semantic variability and scalable ontology management. Research Interests: Her research spans computational logic, multi-perspective reasoning systems, ontology engineering, and spatial semantics. Notable contributions include developing standpoint-enhanced logics for managing conflicting ontological perspectives and modeling polysemous spatial terms in natural language processing. She has also explored vagueness in predicates and objects, applying formal semantics to environmental and spatial domains. Teaching: She taught Advanced Problem Solving and Search courses from 2021–2023, focusing on artificial intelligence problem-solving techniques. Labs & Projects: Affiliated with the ScaDS.AI Center for Scalable Data Analytics and Artificial Intelligence, her work emphasizes practical applications of formal methods in AI systems. Collaborations include developing tools like Standpoint-OWL 2 for automated reasoning and KARaML for hybrid knowledge-machine learning systems.

• Statistical Shape Analysis
• Medical Imaging and Brain Structure Analysis
• Machine Learning
• +۴ مورد دیگر

Anuj Srivastava is a Professor in the Department of Statistics at Florida State University. His research focuses on statistical shape analysis, computational geometry, and their applications in medical imaging, computer vision, and machine learning. He develops novel methods for analyzing complex shapes, trajectories, and functional data, with particular emphasis on elastic geometry and manifold-based techniques. Key research themes include shape analysis of 3D objects, brain subcortical structures, and tree-like anatomical networks. His work integrates statistical theory with computational tools to address challenges in biomedical imaging, motion tracking, and data-driven modeling. Notable contributions include frameworks for longitudinal elastic shape analysis (LESA), graph-based mobility modeling, and Bayesian emulation of human motion. Recent projects involve NSF-funded collaborations on stochastic shape processes, pandemic simulation (RAW-ALPS), and statistical analysis of chromosome conformations. His methods are applied to diverse domains such as neuroimaging, cell morphology analysis, and electrical load profiling. The research emphasizes interdisciplinary applications with clinical, environmental, and engineering relevance.

Shishir K. Shahمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Statistical Image Analysis
• +۵ مورد دیگر

Shishir K. Shah serves as Professor and Chair of the Department of Computer Science within the College of Natural Sciences and Mathematics at the University of Houston. His leadership extends to university-wide committees including Chair of the Undergraduate Committee of the Faculty Senate. Education: B.S. in Mechanical Engineering, The University of Texas at Austin M.S. & Ph.D. in Electrical and Computer Engineering, The University of Texas at Austin Professor Shah's research centers on computer vision fundamentals , specializing in machine learning and statistical methods for image/data analysis. His work addresses critical challenges in human behavior analysis, person re-identification under occlusion and clothing changes, video analytics, biometrics, and microscope image analysis. The Quantitative Imaging Laboratory he directs develops solutions for real-world surveillance, educational technology, and medical imaging applications. His recent publications reveal strong focus trends in occlusion-robust person re-identification (addressing both clothing changes and visual obstructions) and educational video analytics . These works demonstrate innovative approaches using graph attention networks, cross-modality learning, and transformer architectures to solve persistent challenges in visual recognition. Scientific Awards: John C. Butler Teaching Excellence Award (2011) Department of Computer Science Academic Excellence Award (2010, 2016) Student Vuong Dustin Nguyen received Dan E. Wells Outstanding Dissertation Award Professor Shah actively mentors graduate students, with documented success in dissertation supervision. His research program includes significant grant activity supporting the Quantitative Imaging Laboratory. He serves as Associate Editor for Image and Vision Computing, IEEE Journal of Translational Engineering on Health and Medicine, and Pattern Recognition, demonstrating strong community engagement. The Quantitative Imaging Laboratory operates at the intersection of theoretical computer vision research and practical applications, with projects spanning educational technology (Videopoints lecture video portal), surveillance systems, and biomedical imaging. Current work emphasizes robustness to real-world challenges like clothing changes, occlusions, and viewpoint variations in visual recognition systems.

Zois Boukouvalasمشاهده پروفایل

استادیار

• Machine Learning
• Interpretable Models
• Multimodal Data Analysis
• +۶ مورد دیگر

Zois Boukouvalas is an Assistant Professor in the Mathematics and Statistics Department at American University. His research focuses on interpretable machine learning models for analyzing multi-modal data, integrating information geometry, statistics, and optimization. He applies these methods to biomedical imaging, social/linguistic trends, and chemical data for drug discovery. Boukouvalas holds a PhD in Applied Mathematics from UMBC (2017) and has received grants from the Energetics Technology Center for projects on machine learning in energetic materials and misinformation detection. Education: BS in Mathematics (University of Patras, Greece), MS in Applied and Computational Mathematics (Rochester Institute of Technology), MS and PhD in Applied Mathematics (UMBC). His work bridges theoretical foundations with practical applications, emphasizing ethical AI and underrepresented student inclusion. Recent contributions include panel discussions on AI in energetic materials and co-organizing conferences on latent variable methods in machine learning. Research interests span misinformation detection, multimodal learning, and algorithmic transparency. He has published extensively on topics like hate speech detection, stock market prediction via news data, and data fusion techniques for high-impact events. Awards highlight his innovation in human-assisted machine learning and materials discovery.

Le Hai Vuمشاهده پروفایل

استاد

• Intelligent Transport Systems
• Transport Systems Planning
• Autonomous Vehicles
• +۴ مورد دیگر

Le Hai Vu is a Professor in the Department of Civil & Environmental Engineering at Monash University, leading research in Intelligent Transport Systems (ITS). He holds an ARC Future Fellowship (2012-2016) and received the ITS Australia National Research Award (2019). Previously, he led the ITS Lab at Swinburne University and the University of Melbourne. His work focuses on autonomous vehicles, traffic systems optimization, and sustainable urban mobility. Affiliations: Monash University (2016–present), University of Melbourne (2011–2016), Swinburne University (2000–2011) Key projects: Building 4.0 CRC, SPARC Hub for Smart Transport Pavements, Sustainable City Size Research Research interests include autonomous vehicle impacts, traffic signal control, bikeability assessment, and multimodal transport integration. Over 200 publications and 17 research projects highlight his expertise in bridging AI and transportation systems. He actively supervises PhD students and collaborates globally on urban infrastructure challenges.