جست‌وجوی استاد و پژوهشگر

Ásgerdur Arna Pálsdóttirمشاهده پروفایل

پژوهشگر ارشد

• Neurorehabilitation Robotics
• Assistive Technology
• Human-Computer Interaction
• +۳ مورد دیگر

Ásgerdur Arna Pálsdóttir serves as a Postdoctoral Research Fellow at Aalborg University's Department of Health Science and Technology within the Faculty of Medicine. Her work focuses on neurorehabilitation robotics, developing innovative tongue-based control systems for assistive robotic manipulators to enhance mobility for individuals with severe disabilities. As an active participant in the Center for Rehabilitation Robotics and principal investigator for the Tongue Control project, she bridges engineering and clinical applications to translate technical innovations into practical rehabilitation solutions. Her research expertise spans neurorehabilitation robotics , adaptive semi-automation , and tongue-based human-computer interfaces . She investigates control frameworks that combine tongue and brain signals for users with conditions like amyotrophic lateral sclerosis (ALS), emphasizing user-centered design for wheelchair-mounted robotic arms and underactuated grippers. Her work addresses critical challenges in slip detection, visual guidance systems, and real-world implementation of assistive technologies for daily living activities. Recent publications reveal a strong trend toward multimodal control systems that adapt to user capabilities, with increasing focus on hybrid tongue-brain interfaces and safety-critical functions like transparent object manipulation. Her research consistently prioritizes clinical validation with disabled individuals, demonstrating measurable improvements in task completion and user autonomy through adaptive automation. Dr. Pálsdóttir has secured significant research funding through the Independent Research Fund Denmark's MultiRob project (2018-2022) and currently contributes to the active Center for Rehabilitation Robotics. Her grant work fosters cross-disciplinary collaboration between engineers, clinicians, and end-users to develop practical assistive technologies. She actively organizes academic events including the Aalborg Symposium in Advances in Rehabilitation Robotics (2023) and annual Girls' Day in Science initiatives (2020-2023), promoting STEM education and interdisciplinary dialogue within the rehabilitation robotics community.

• Sustainable Supply Chain Management
• Transportation Management
• Operational Research
• +۳ مورد دیگر

Dr. Sadeque Hamdan is a Senior Lecturer in Data Analytics at Bangor Business School, Bangor University, UK. His academic work focuses on optimization problems in sustainable transportation and supply chain management, with applications in both theoretical and practical domains. Education Postgraduate Certificate in Higher Education, University of Kent (2023) PhD in Complex Systems Engineering, University of Paris-Saclay (2020) MSc in Engineering Management, University of Sharjah (2015) BSc in Civil Engineering, University of Sharjah (2013) Research Focus Dr. Hamdan's research spans several interconnected domains: sustainable supply chain operations, transportation management, and operational research applications. His work employs advanced data analytics to solve complex optimization problems in logistics, aviation management, and green transportation systems. Publication Trends Recent publications (2023-2025) demonstrate a strong focus on sustainable transportation optimization, particularly electric vehicle charging systems, maritime logistics, and air traffic management. Methodologically, Dr. Hamdan frequently applies heuristic algorithms, multi-objective optimization, and combinatorial approaches to solve complex supply chain and transportation problems. Projects & Advising Dr. Hamdan leads the ongoing research project 'Sustainable Aviation and Emerging Technologies: Maximizing Operational and Resource Efficiency' (2025-2026). He actively supervises postgraduate students, though specific student names are not listed in available materials.

• Cybersecurity
• Mechanism Design
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Mingyu Guo is a Senior Lecturer in the Department of Computer Science at the University of Adelaide, within the School of Computer and Mathematical Sciences. His research focuses on interdisciplinary areas including cybersecurity, mechanism design, artificial intelligence, network optimization, and game theory. He is actively involved in supervising students at the Masters and PhD levels, as indicated in his profile. Key research interests include: Cybersecurity defense mechanisms for Active Directory systems using reinforcement learning and graph neural networks Automated mechanism design frameworks with applications in auctions and public projects Optimization of energy systems, particularly battery storage and renewable energy integration Multi-document summarization techniques in natural language processing Evolutionary algorithms for diversity optimization and network analysis Recent publications emphasize innovative solutions for dynamic network security, VCG redistribution mechanisms, and interdisciplinary applications of AI. He maintains an academic homepage at https://mingyuguo.github.io/ .

Richard Palmerمشاهده پروفایل

پژوهشگر

• 3D Facial Analysis
• Rare Disease Diagnosis
• Medical Imaging
• +۳ مورد دیگر

Dr. Richard Palmer is a Researcher at Curtin University, affiliated with the School of Earth and Planetary Sciences (EPS) within the Faculty of Science and Engineering. He holds a portfolio role in the Office of the Provost, demonstrating leadership in academic governance. His research focuses on innovative applications of 3D facial analysis for rare disease diagnosis, treatment monitoring, and public health initiatives. Key collaborations include the CLINIFACE project, which integrates non-rigid registration techniques for phenotypic visualization. Palmer’s work spans clinical research, medical imaging, and interdisciplinary projects such as the Western Australian Undiagnosed Diseases Program. His expertise bridges computer science, genetics, and public health, addressing challenges like autism spectrum disorder analysis and hereditary conditions like Silver Russel syndrome and hereditary angioedema. He has pioneered open-source tools for 3D facial growth curves and mobile-based 3D facial reconstruction. His publications emphasize methodological advancements in 3D modeling, algorithmic comparisons for spatial analysis, and the impact of BMI on facial clustering studies. Collaborators include institutions like the Human Phenotype Ontology consortium and global medical research networks. Palmer actively contributes to precision medicine through both technological innovations and policy-driven healthcare solutions.

Anjany Sekuboyinaمشاهده پروفایل

پژوهشگر

• Medical Image Analysis
• Machine Learning
• Probabilistic Machine Learning
• +۳ مورد دیگر

Anjany Sekuboyina is a Postdoctoral Researcher at ETH Zurich's Department of Quantitative Biomedicine, focusing on medical image analysis using machine learning. Her work emphasizes deployable solutions for hospitals, including probabilistic ML, generative models, and relational ML/graph-based approaches. She co-developed the VerSe dataset, a large-scale CT spine segmentation benchmark, and contributed to projects like MedShapeNet and GenerateCT. Research Interests: Medical Image Segmentation (e.g., vertebrae, spine, and vascular structures) Generative Models for Medical Imaging Synthesis Relational Machine Learning and Graph Neural Networks Automated Clinical Workflow Integration Labs/Teams: Bjoern Menze Team at ETH Zurich. Active contributor to open-source repositories like VerSe (234 stars) , focusing on medical imaging challenges and datasets.

Plinio Moreno Lópezمشاهده پروفایل

پژوهشگر

• Robotics
• Social Robotics
• Deep Learning
• +۶ مورد دیگر

Plinio Moreno López is a Researcher at the Faculty of Engineering , University of Lisbon . His work spans robotics, computer science, and neurotechnology, focusing on human-robot interaction, 3D perception, and EEG-based systems. Active in robotics and artificial intelligence research Specializes in multimodal sensing and action recognition Develops socially interactive robotic systems Research interests center around human-robot interaction , deep learning , and 3D object detection . His recent publications demonstrate expertise in knowledge distillation for autonomous systems, cross-view action recognition, and EEG-based interaction models. Collaborations span both robotics and healthcare domains with emphasis on sensor networks and temporal resolution. Scientific contributions include: Advancing social robotics through engagement models Developing EEG-based anticipation frameworks Optimizing 3D perception via knowledge distillation Creating fall detection systems using wrist sensors Building mutual information metrics for robot adaptation Pioneering exocentric-to-egocentric view transitions

Marco Pavoneمشاهده پروفایل

دانشیار

• Autonomous Systems
• Robotics
• Control Systems
• +۶ مورد دیگر

Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University, directing the Autonomous Systems Laboratory and the Center for Automotive Research at Stanford (CARS). He is also a Distinguished Research Scientist at NVIDIA leading autonomous vehicle research. He holds courtesy appointments in Electrical Engineering and Computer Science, and affiliations with HAI and ICME. His research focuses on autonomous systems, including self-driving cars, aerospace vehicles, and mobility systems, emphasizing control methodologies and system design. Education: Ph.D. in Aeronautics and Astronautics from MIT (2010). Former roles include Research Technologist at NASA JPL and participation in the National Academy of Engineering’s Frontiers program. He teaches courses on optimal control, robotics, and autonomous systems. Research Interests: Development of methodologies for analysis/design/control of autonomous systems, with emphasis on self-driving cars, aerospace vehicles, future mobility systems, and integration with energy networks. His work spans robotics, AI, control theory, and transportation systems. Publications reflect expertise in autonomous vehicle coordination, energy systems optimization, real-time perception, and multimodal decision-making. Recent work explores generative models for scenario analysis, transformer-based control, and safety-critical systems. Awards: PECASE (2017), ONR YIP (2017), NSF CAREER (2015), NASA Early Career (2012), Hellman Scholar (2012) Labs: Autonomous Systems Laboratory (ASL) and CARS Grants: Extensive funding from NSF, ONR, NASA, and industry partnerships Students: Mentors over 30 graduate students and postdocs, focusing on robotics, control, and AI

• Artificial Intelligence
• Machine Learning
• Pattern Recognition
• +۶ مورد دیگر

Professor Jean-Christophe Nebel is a Professor of Computer Science at Kingston University , Faculty of Science, Engineering and Computing, Computing and Information Systems Department. He holds a PhD in Computing Science from the University of St-Etienne (1997), an MSc in Computing Science (University of Lyon), an MScEng in Electronics & Signal Processing (CPE Lyon), and a Postgraduate Certificate in Higher Education Learning & Teaching. His research focuses on Artificial Intelligence, Machine Learning, Pattern Recognition, Computer Vision , and Bioinformatics , with applications in renewable energy , ecology , and genomics-inspired surveillance . PhD in Computing Science, University of St-Etienne MSc in Computing Science, University of Lyon MScEng in Electronics & Signal Processing, CPE Lyon Postgraduate Certificate in Learning & Teaching, Kingston University His work bridges AI and interdisciplinary domains , including protein structure prediction, human action recognition, and AI-driven environmental monitoring. Key projects include a stochastic context-free grammar framework for proteomics, manifold-based modeling of multivariate sequences, and game-theoretic optimization for smart grids and PPE management during the pandemic. Recent articles explore deepfake detection , air pollution analytics , and honeybee health monitoring via acoustic signals. Notable scientific awards include the IEE Reeve Premium (2004) , Senior Fellow of the Higher Education Academy (SFHEA) , and Senior Member of the IEEE . He led an Innovate UK-funded Knowledge Transfer Partnership (KTP) with Instinet Global Services, achieving the highest "Outstanding" grade. At REF2014, he contributed to a 3* Impact Case Study on pedestrian tracking technology commercialized by Ipsotek and BAe Systems, benefiting entities like the London Eye and the Australian Government. As Director of Kingston University’s Knowledge Exchange & Research Institute for Cyber, Engineering & Digital Technologies , he coordinated the REF2021 submission for Computer Science & Informatics, which achieved 73% world-leading/internationally excellent research. He serves on editorial boards of BMC Bioinformatics , PLOS ONE , and Pattern Analysis & Applications , and evaluates proposals for the EPSRC , NSERC , and international councils.

Gianluca Demartiniمشاهده پروفایل

استاد

• Information Retrieval
• Semantic Web
• Human Computation
• +۹ مورد دیگر

Professor Gianluca Demartini is a Professor in Data Science and an ARC Future Fellow at the School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology at the University of Queensland, Australia. He also serves as an affiliate of the Centre for Enterprise AI. His research focuses on human-in-the-loop artificial intelligence systems with applications for public good, bridging structured knowledge graphs and unstructured text analytics to address societal challenges. Dr. Demartini earned his Ph.D. in Computer Science from Leibniz University of Hannover in Germany in 2011, with a focus on Semantic Search. His academic journey includes positions as a Lecturer at the University of Sheffield (UK), post-doctoral researcher at the eXascale Infolab at the University of Fribourg (Switzerland), visiting researcher at UC Berkeley, junior researcher at the L3S Research Center (Germany), and intern at Yahoo! Research (Spain). His research interests span four major interconnected domains: Misinformation (studying human interaction with misinformation and AI-based mitigation strategies), Crowdsourcing and Human Computation (improving efficiency of human-in-the-loop systems), Big Data Analytics (designing scalable algorithms for large datasets), and AI for Public Good (applying AI for societal and environmental benefits). His work consistently addresses real-world challenges in information quality, human-AI collaboration, and ethical technology deployment. Analysis of Professor Demartini's recent publications reveals a clear trajectory toward addressing misinformation through sophisticated human-AI collaboration frameworks, with increasing emphasis on cognitive aspects of fact-checking, data bias management, and strategic application of large language models. His research bridges theoretical advances in information retrieval with practical applications for societal challenges, particularly in media literacy, online safety, democratic discourse, and environmental conservation. Professor Demartini has received numerous prestigious awards recognizing the quality and impact of his work: Best Paper Award at ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR) in 2023 Best Paper Award at AAAI Conference on Human Computation and Crowdsourcing (HCOMP) in 2018 Best Paper Awards at European Conference on Information Retrieval (ECIR) in 2016 and 2020 Best Demo award at International Semantic Web Conference (ISWC) in 2011 Honorable Mention Award at CSCW 2020 (Top 2% of submissions) As an active supervisor, Professor Demartini currently guides PhD students working on cutting-edge topics including Retrieval Augmented Generation, Human-in-the-Loop Decision Systems for Online Safety, Human-Centred Artificial Intelligence for Democracy, and Bias in Data Pipelines. His research program is generously funded through multiple major grants: ARC Future Fellowships (2025-2028): PBIAS - A Principled Approach to Data Bias Management Swiss National Science Foundation (2022-2025): Large-Scale Political Participation: Issue Identification, Deliberation, and Co-creation ARC Training Centre for Information Resilience (2021-2026) Previous funding from Wikimedia Foundation, Meta, Google, and Facebook for projects on misinformation detection and human-AI collaboration Professor Demartini's work sits at the critical intersection of human computation, information retrieval, and AI ethics. Through extensive collaborations with industry partners including Facebook, Google, Microsoft, Yahoo!, IBM, SAP, and The National Archives (UK), he has developed practical systems that address real-world challenges in misinformation detection, data quality, and human-AI collaboration. His research group actively explores how to make AI systems more transparent, accountable, and beneficial for society through principled human-in-the-loop approaches that leverage both machine intelligence and human expertise.

Jerod Weinmanمشاهده پروفایل

دانشیار

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

Jerod Weinman is an Associate Professor in the Department of Computer Science at Grinnell College, with affiliations in Neuroscience. He holds a Ph.D. in Computer Science from the University of Massachusetts Amherst, specializing in Computer Vision, Machine Learning, and AI. His research focuses on visual learning, historical map analysis, and AI education. He has led NSF-funded projects on AI curriculum expansion and geospatial map processing. Notable awards include Best Paper (ICDAR 2017), Best Presentation (ICVS 2023), and an NSF grant for AI education. Education: Ph.D. (UMass Amherst, 2008), M.S. (UMass Amherst, 2005), B.S. (Rose-Hulman, 2001). Collaborators include institutions like U. Copenhagen and Smith College. He advises undergraduates in projects like historical map OCR and self-supervised depth prediction. Recent work includes organizing ICDAR 2025 competitions and developing the PIPS instruction set for teaching computer architecture. Key research areas span historical map text detection, multimodal learning, and AI applications. His lab focuses on integrating geospatial data with OCR, producing tools like MapTD and CatCorr. He has authored 40+ publications and is a Senior Member of IEEE/ACM.

Aishwarya Agrawalمشاهده پروفایل

استادیار

• Computer Vision
• Natural Language Processing
• Deep Learning
• +۷ مورد دیگر

Aishwarya Agrawal is an Assistant Professor in the Department of Computer Science and Operations Research at the University of Montreal and a core academic member of Mila – Quebec AI Institute, where she holds the Canada CIFAR AI Chair. She also works as a Research Scientist at Google DeepMind, spending one day per week there. Her research sits at the intersection of computer vision, natural language processing, and deep learning, aiming to build AI systems that can both 'see' and 'talk'. Her research interests include vision-language reasoning, data-efficient pretraining, robust evaluation, improving model reliability and explainability, and aligning AI systems with diverse cultural values. She has made significant contributions to visual question answering, particularly in addressing dataset bias through work like the VQA-CP dataset. The recent publications show a strong focus on evaluating and improving vision-language models, especially in areas like cultural alignment, consistency, reliability, and robustness. There is a clear trend toward developing better evaluation methodologies and enhancing model behavior in complex, real-world scenarios. Her group actively publishes in top-tier conferences including CVPR, NeurIPS, ICML, and ACL. Canada CIFAR AI Chair Award Georgia Tech 2020 Sigma Xi Best Ph.D. Thesis Award Georgia Tech 2020 College of Computing Dissertation Award Runner-up, 2019 AAAI/ACM SIGAI Dissertation Award NVIDIA Graduate Fellowship 2018-2019 Rising Stars in EECS 2018 Aishwarya advises several PhD and MSc students, including Rabiul Awal, Qian Yang, Le Zhang, and Oscar Mañas. She has received research grants, such as one from Samsung for compositional reasoning in multimodal transformers. She is actively recruiting graduate students and postdocs through Mila’s supervision request process. She co-organized the annual VQA Challenge and Workshop from 2016 to 2021 and is involved in broader AI community initiatives, including panels and talks on leadership and AI research trends. Her lab, part of Mila, fosters a collaborative environment focused on advancing multimodal AI.

Boris Čuleمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Dr. Boris Čule is an Assistant Professor at Tilburg University's Department of Cognitive Science and Artificial Intelligence within the Tilburg School of Humanities and Digital Sciences. He holds a Ph.D. in Computer Science from the University of Antwerp, followed by post-doctoral research there. His work focuses on data mining, machine learning, and AI, particularly sequential/temporal data, including pattern mining, anomaly detection, and recommender systems. Education: Ph.D. in Computer Science, University of Antwerp Research Interests: Sequential pattern mining, session-based recommendations, time series analysis, subspace clustering, spatial pattern discovery, and applications in environmental and economic forecasting. His contributions include novel algorithms for evaluating sequential patterns, sequence classification, and anomaly detection in dynamic systems. Publications Trends: Recent work emphasizes real-world applications like UAV path planning optimization (2024), news recommendation systems (2023-2024), and employment market forecasting (2023). He explores interdisciplinary challenges such as facial action analysis in gaming (2022) and wind farm response to storms (2021). Advising/Grants: While specific grant details are not listed, his prolific publication record suggests active research projects. Courses taught include Big Data analysis and cognitive science modules.

Qiben Yanمشاهده پروفایل

دانشیار

• AI Security
• Blockchain System Security
• Mobile and IoT Security
• +۵ مورد دیگر

Qiben Yan is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University (MSU), leading the Secure and Intelligent Things (SEIT) Lab. He holds a Ph.D. from Virginia Tech and has prior experience as an Assistant Professor at the University of Nebraska-Lincoln and as a security researcher at Shape Security, where he contributed to the development of the pioneering 'botwall.' His research focuses on AI Security, Blockchain, IoT/Mobile Security, and Wireless Communication Security. He serves as an Associate Editor for IEEE Transactions on Information Forensics and Security (TIFS) and has been recognized with prestigious awards such as the NSF CRII Award (2016), ACM SenSys Best Paper Award (2021), and IEEE SECON Best Paper Award (2020). Educations: Ph.D. in Computer Science, Virginia Tech His research interests span adversarial machine learning, AI-driven cybersecurity solutions, blockchain system integrity, and privacy-preserving technologies. Recent work includes frameworks for decentralized carbon footprint tracking, defenses against adversarial attacks in autonomous systems, and LLM customization for moving target defense. His research has been supported by NSF (CPS, SaTC, NeTS, SpecEES), DoE, Ford, and Cisco. Awards: ACM CCS Best Paper Honorable Mention (2022) ACM SenSys Best Paper Award (2021) IEEE SECON Best Paper Award (2020) NSF CRII Award (2016) UNL Teaching Awards (2015, 2017) Grants and Labs: Lead researcher at SEIT Lab Funding from NSF, DoE, Industry Partners (Ford, Cisco) His lab explores cutting-edge topics like AI security, IoT privacy, and cyber-physical system defenses, with findings featured in BBC Radio, Scientific American, and Forbes.

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +۳ مورد دیگر

Michael G. Rabbat is a researcher at Facebook AI Research in Montreal, Canada, affiliated with the College of Engineering. His work spans machine learning, artificial intelligence, and distributed computing. Key research interests: Federated learning, reinforcement learning, self-supervised learning, and optimization algorithms. Recent publications focus on neural network training acceleration, physics understanding in AI, and efficient distributed systems. His articles from 2025–2022 emphasize robust visual features (DINOv2), communication-efficient training (SlowMo), and privacy-aware federated systems. Collaborative projects include V-JEPA for video modeling and IntPhys for physics benchmarks.

• Biophysics
• Bioimaging
• Optical Microscopy
• +۶ مورد دیگر

Andreas E Vasdekis serves as an Associate Professor in the Department of Physics at the College of Science, University of Idaho, with cross-affiliated roles as Research Faculty at the Institute for Health in the Human Ecosystem, Member of the Initiative for Bioinformatics and Evolutionary Studies, and Participating Faculty at the Institute for Modeling Collaboration and Innovation. His academic foundation includes a PhD in Physics (2008) and MSc in Optics and Photonics (2003) from the University of St Andrews, United Kingdom, and a BSc in Physics (2002) from the University of Athens, Greece. Dr. Vasdekis pioneers biophysical imaging methodologies, specializing in label-free microscopy and single-cell quantitative analysis . His research integrates optofluidics , deep learning , and Raman spectroscopy to decode cellular heterogeneity, metabolic noise, and nongenetic plasticity. Key innovations include photon-sparse bioimaging, Airy light-sheet microscopy, and gradient retardance optical techniques for tissue and microbial systems. Recent publications reveal a dominant trend toward computational bioimaging , where deep learning reconstructs high-fidelity images from near-zero photon data, and multimodal light-sheet platforms enabling 3D analysis of plant-microbe symbiosis, malaria parasite dynamics, and multicellular gradient responses—all unified by a physics-driven approach to biological complexity. He actively collaborates through the Initiative for Bioinformatics and Evolutionary Studies and the Institute for Modeling Collaboration and Innovation, driving interdisciplinary convergence in biophysics, data science, and biological discovery.