جستجوی استاد راهنمای دانشگاهی

Lauro Snidaro is an Associate Professor at the Università degli Studi di Udine , Department of Mathematics and Computer Science. His research focuses on Information Fusion , Data Fusion , Autonomous Vehicles , and Context-Aware AI Systems . He co-organized the 2024 International Conference on Information Fusion (FUSION 2024) in Venice and has led NATO-funded research projects (e.g., IST-165 RTG on High-level Fusion). Research highlights include the Context-Enhanced Information Fusion book (Springer, 2016) and a widely-downloaded paper on Context-based Information Fusion . He actively collaborates with industries for autonomous vehicle data fusion and defect detection systems . Teaching includes the Intelligent Multimedia Systems course, emphasizing openFrameworks and C++ for creative programming. He advises PhD candidates and research fellows in Computer Vision and Machine Learning domains. Key contributions span multi-sensor fusion for surveillance, context-aware tracking, and AI applications in crisis computing and healthcare.

• Data Fusion
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Marcella ASTRID is a Researcher at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), specifically within the CVI2 department. Her research focuses on advanced machine learning techniques applied to cybersecurity and computer vision challenges, particularly in the detection of synthetic media (deepfakes) and anomaly detection systems. She specializes in developing robust models that address vulnerabilities in adversarial scenarios and improve generalizability across diverse datasets. Key research areas include deepfake detection through spatio-temporal analysis, anomaly detection via novel training paradigms, and efficient neural network compression for resource-constrained environments. Her work bridges theoretical advancements in machine learning with practical applications in surveillance systems, autonomous robotics, and cybersecurity infrastructure. Recent contributions emphasize leveraging autoencoder weaknesses, pseudo anomaly generation, and localized attention mechanisms to enhance detection accuracy and model robustness. Marcella’s research also explores cross-modal learning (audio-visual synchronization) and semi-supervised methodologies to tackle data scarcity issues in critical domains like battery thermal imaging and multi-camera traffic classification. She actively publishes in top-tier venues and collaborates on interdisciplinary projects addressing real-world security and reliability challenges.

• Computer Vision
• Deep Learning
• Cybersecurity
• +۳ مورد دیگر

Professor Sandeep Singh is an Assistant Professor in the Mechanical, Aerospace and Nuclear Engineering Department at Rensselaer Polytechnic Institute (RPI). He joined RPI in 2022 after completing his Ph.D. at Texas A&M University, where his research focused on manifold-based trajectory optimization for space missions. Prior to his doctoral studies, he worked at the Indian Space Research Organization (ISRO) as a scientist and program manager for the ASTROSAT mission's mechanical design. His research interests include astrodynamics, low-thrust trajectory design, autonomous navigation, and space mission design leveraging manifold theory and machine learning. Education: Ph.D., Texas A&M University, 2022 B. Tech., Indian Institute of Space Science and Technology, 2013 Research Interests: Optimal low-thrust trajectory design Manifold theory applications in space mission planning Autonomous navigation using CNN frameworks and probabilistic regression Orbit maintenance and exploration in celestial mechanics Spacecraft guidance and control systems Scientific Recognition: John V. Breakwell Student Award (American Astronautical Society) Multiple university fellowships during doctoral studies Professional Roles: RPI Representative to Universities Space Research Association Member of the AIAA Astrodynamics Technical Committee Referee for international journals/conferences Labs/Teams: Active involvement with the Aerospace Systems and Controls Lab (ASCLAB) at RPI.

• Aerospace Engineering
• Astrodynamics
• Space Trajectory Optimization
• +۶ مورد دیگر

Vincenzo Pasquadibisceglie is an Assistant Professor (non tenure) at the Department of Computer Science, University of Bari Aldo Moro. He holds a PhD and is affiliated with the Knowledge Discovery and Data Engineering research group within the LACAM Laboratory. His research focuses on Data Mining, Process Mining, Deep Learning, and their applications in predictive analytics and business process optimization. Key research areas include: Predictive process monitoring using machine learning and computer vision techniques Adaptive models for concept drift in streaming data Explainable AI approaches for customer behavior prediction Integration of LLMs with process event logs Publications emphasize innovative methods like GANDALF (LLM-based environmental analysis), PROPHET (graph neural networks for process prediction), and DARWIN (online deep learning for process drift). Recent work addresses retail analytics (CENTAURO, TSUNAMI) and healthcare applications (contactless vital sign monitoring). His contributions bridge theoretical advancements with practical implementations, including the STARDUST process mining framework and the FOX neuro-fuzzy model. Current work explores LLM applications in healthcare and environmental monitoring.

• Data Mining
• Knowledge Discovery
• Process Mining
• +۳ مورد دیگر

Baocheng Geng is an Assistant Professor in the Department of Computer Science at The University of Alabama at Birmingham (UAB), within the College of Arts and Sciences. His research focuses on human decision making, distributed detection, and machine learning applications in IoT and autonomous systems. He holds a Ph.D. from Syracuse University and a B.S. from Shanghai Jiao Tong University. Research Interests: His work bridges artificial intelligence, behavioral psychology, and human-technology interactions. Key areas include decision fusion in human-machine inference networks (HMIN), cognitive biases in human decision making, and robust algorithms for distributed systems under adversarial conditions. Publications Trends: Recent work emphasizes adversarial machine learning, federated learning, and interpretable neural networks for remote sensing and cybersecurity. He has explored applications in mental health prediction, cognitive radar, and resilient sensor networks. Affiliations: Previously affiliated with Syracuse University's Sensor Fusion Lab under Prof. Pramod K. Varshney. Current projects involve interdisciplinary collaborations in AI ethics, autonomous systems, and data-driven healthcare solutions. Labs: Involved in advancing the Sensor Fusion Lab's legacy at UAB, focusing on human-in-the-loop decision systems and strategic defense mechanisms against Byzantine attacks.

• Human Decision Making
• Distributed Detection
• Information Fusion
• +۷ مورد دیگر

Dr. Alioune Ngom is a Professor and Graduate Program Chair at the School of Computer Science, University of Windsor. He holds a Ph.D. in Computer Science from the University of Ottawa with additional graduate degrees in the field. Dr. Ngom founded Genesis Genomics Inc. and maintains affiliations with this organization. His research spans multiple interdisciplinary domains with primary focus areas including: Bioinformatics and computational biology (DNA microarray analysis, mitochondrial genomics) Machine learning applications (clustering, classification, neural networks) Network optimization (wireless mobile networks, scheduling) Computational methods in biomedicine (protein structure prediction, drug discovery) Recent publications demonstrate strong focus on AI integration in life sciences, particularly graph neural networks for drug discovery and multi-modal learning frameworks. Publication trends show increasing emphasis on transformer architectures, trustworthy AI systems, and clinical applications of deep learning. Dr. Ngom mentors graduate students as evidenced by his role as Graduate Program Chair.

• Bioinformatics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Richard Yu is a Professor at the Carleton School of Information Technology, Carleton University. His research focuses on cross-layer design in wireless systems, security in wireless networks, green information technologies, and multimedia over wireless networks. He holds a Ph.D. from the University of British Columbia and is a Professional Engineer (P.Eng). His work integrates machine learning, edge computing, and reinforcement learning to address challenges in vehicular networks, autonomous systems, and IoT. Education Ph.D., University of British Columbia Research Interests Professor Yu’s research spans wireless communication systems , edge computing , and AI-driven optimization . He explores novel paradigms such as federated learning for industrial IoT, secure UAV-assisted satellite networks, and generative AI for video understanding. His work emphasizes practical applications in autonomous vehicles, smart infrastructure, and industry 4.0 systems. Recent Trends in Publications His recent articles highlight advancements in reinforcement learning frameworks for resource allocation, safety-critical AI in video generation, and cross-modal fusion for embodied agents. He also investigates hybrid NOMA/OMA systems and quantum-inspired algorithms for next-generation networks. Advising & Grants No specific advisees are listed, but his research is supported by grants focusing on edge intelligence, vehicular networks, and secure computing. Labs & Teams Associated with Carleton’s School of Information Technology research groups, focusing on wireless systems and AI for industrial applications.

• Wireless Networks
• Edge Computing
• Federated Learning
• +۷ مورد دیگر

Shonda Bernadin, Ph.D., is an Associate Professor in the Department of Electrical & Computer Engineering at the FAMU-FSU College of Engineering, a joint institution of Florida Agricultural and Mechanical University and Florida State University. She holds a B.S. from Florida A&M University, an M.S. from the University of Florida, and a Ph.D. from Florida State University. Her research spans digital signal processing, speech recognition, artificial intelligence, autonomous vehicles, and engineering education. She directs the Speech Processing and Data Analysis Laboratory (SPADAL) and focuses on broadening STEM participation through K-12 outreach, including ACEE summer camps and CROP-ENG programs. Dr. Bernadin’s work emphasizes engineering education innovation, including evidence-centered assessment design and strategies to improve retention and diversity in engineering. She has authored numerous peer-reviewed publications on topics like neural networks, sensor-based defect detection, and cybersecurity threats in AI systems. Her teaching philosophy prioritizes creating inclusive, effective learning environments through engaging methods and technology integration. Professional activities include leadership in engineering education reform and collaborations with organizations such as ASTERIX to enhance STEM workforce diversity. Her lab’s research often intersects technology development with societal impact, addressing challenges in healthcare, autonomous systems, and additive manufacturing quality control.

• Digital Signal Processing
• Speech Recognition
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Chen Chen is a Lecturer in Computer Vision at the University of Sheffield's Department of Computer Science, with affiliations at Oxford BioMedIA and Imperial College London. She holds a Ph.D. in Advanced Computing from Imperial College London, where she specialized in medical imaging and artificial intelligence. Her research develops robust machine learning algorithms for medical data analysis, focusing on cardiac, brain, and prostate imaging. She has received multiple recognitions including IEEE TMI Gold-level Distinguished Reviewer and MICCAI Outstanding Reviewer awards. Dr. Chen leads projects on multi-modal data integration for cardiac care and has published extensively on adversarial data augmentation, domain generalization, and medical vision-language models. She serves as Associate Editor for the Journal of Visual Communication and Image Representation and organizes workshops on data augmentation in medical imaging.

• Medical Image Analysis
• Artificial Intelligence
• Machine Learning
• +۱ مورد دیگر

Sean Kugele is an Assistant Professor of Computer Science at Rhodes College. His research focuses on artificial intelligence, cognitive modeling, and neuro-symbolic systems, aiming to bridge biological principles with software systems. He holds a PhD in Computer Science from the University of Memphis, complemented by undergraduate degrees in Computer Science, Mathematics, and Anthropology. His research interests emphasize embodied cognition, cognitive architectures (particularly the LIDA model), and the integration of narrative and embodied self-models in autonomous agents. He explores how these systems can adapt and learn through simulation-based approaches, informed by biological and anthropological insights. Dr. Kugele's publications since 2016 have consistently addressed advancements in the LIDA cognitive architecture, embodied intelligence frameworks, and challenges in generalizing AI between simulated and real-world environments. His work often intersects with ethics and human-centered design in autonomous systems. His professional background includes over a decade as a software engineer and architect, bringing practical technical expertise to his academic pursuits. Current projects involve refining hybrid cognitive systems and exploring the dynamics of long-term intention formation in autonomous agents.

• Artificial Intelligence
• Cognitive Modeling
• Neuro-Symbolic Systems
• +۳ مورد دیگر

Kevin Weiner is an Associate Professor at the University of California, Berkeley, joining the department in January 2018. He leads the Cognitive Neuroanatomy Lab and focuses on the structural and functional basis of visual perception, particularly face processing in high-level visual cortex. His research employs multi-modal methodologies combining functional measurements (high-resolution fMRI, electrocorticography) with anatomical analyses in vivo (diffusion imaging, cortical folding) and post-mortem (cytoarchitecture, myeloarchitecture). Core interests include visual perception mechanisms, domain-specific brain regions, comparative neuroanatomy across species, developmental neuroscience, and translational applications for neurological patient populations. Analysis of his publication record reveals consistent expertise in fusiform gyrus organization and face-processing networks. His work demonstrates increasing integration of histological validation with neuroimaging, alongside growing emphasis on open science practices and translational clinical applications in recent years. Dr. Weiner mentors graduate students and directs the Cognitive Neuroanatomy Lab, which prioritizes collaborative research, data sharing initiatives, and science communication efforts while advancing mechanistic models of brain structure-function relationships.

• Cognitive Neuroscience
• Visual Perception
• Face Processing
• +۵ مورد دیگر

Prof. Marco Hutter is a Full Professor at the Department of Mechanical and Process Engineering at ETH Zürich, and Deputy Head of the Institute for Robotics and Intelligent Systems. His research focuses on advanced robotics, including autonomous legged systems, robotic perception, and control strategies for challenging environments. He leads projects in areas such as forest inventory using legged robots, space exploration robotics, and mobile manipulation systems. His work emphasizes real-world deployment, with contributions to robotic navigation, sensor integration, and policy optimization. Prof. Hutter collaborates on large-scale datasets and frameworks like ROS-LLM for embodied AI, and explores applications in logistics, construction, and planetary exploration. His team develops both hardware (e.g., LEVA logistic vehicles) and software solutions (e.g., risk-guided diffusion models for safety-critical systems). Key projects include ETHcavation (construction environment understanding) and TartanGround (robot perception datasets). He pioneers work in agile locomotion policies, magnetic climbing robots, and multi-agent robotic teams for parcel delivery. His research bridges theoretical advancements with practical implementations, addressing challenges in terrain adaptation, energy efficiency, and robustness in autonomous systems.

• Robotics
• Intelligent Systems
• Autonomous Navigation
• +۴ مورد دیگر

Associate Professor Jen Jen Chung holds a position in Mechatronics at the School of Electrical Engineering and Computer Science, The University of Queensland. Her research focuses on robotic perception, planning, and learning, particularly in mobile manipulation, navigation through human crowds, and adaptive sampling. Prior to UQ, she was a Senior Researcher at ETH Zurich (2018–2022) and a Postdoctoral Scholar at Oregon State University (2014–2017). Her PhD from the University of Sydney (2014) centered on exploration strategies for autonomous systems. She also holds a B.E. in Engineering from the University of Sydney (2010). Education: PhD in Robotics, University of Sydney (2014) Bachelor of Engineering, University of Sydney (2010) Research interests span robotic manipulation , navigational algorithms , and learning-based systems . Current projects include developing robots that understand human-centric environments and optimizing path planning in dynamic settings. Her work integrates machine learning and perception to solve real-world robotics challenges. Grants and collaborations involve multi-agent systems and autonomous navigation. She leads the Robotic Perception, Planning, and Learning (RPPL) Lab , advancing research in manipulation and environmental interaction. Her contributions include over 60 peer-reviewed publications, with recent work on soft robotics and environmental impact-aware path planning.

• Robotics
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Lian Xu is a Research Fellow in the Department of Computer Science and Software Engineering at the University of Western Australia (UWA). She holds a PhD from UWA (2021) and specializes in computer vision, deep learning, and annotation-efficient methodologies. Her work focuses on weakly supervised learning, multi-modal data fusion, and applications in healthcare, agriculture, and maritime surveillance. Education: PhD in Computer Science, University of Western Australia (2021) Research Interests: Weakly supervised semantic segmentation Multi-modal learning with vision and language Computer vision applications in healthcare and agriculture Efficient learning strategies for limited labeled data Her research output emphasizes techniques such as auxiliary task integration, token-based transformers, and spectral analysis for hyperspectral imaging. Recent work includes advancements in small object detection for maritime surveillance and crop stress classification using hyperspectral data. Awards: Recipient of the Award for Growth in Innovation and Entrepreneurship (2021) for contributions to applied research. Teaching & Engagement: Facilitated courses on computer vision (CITS4402) and visualization (CITS2401). Co-organizer of workshops at DICTA 2024, ACCV 2022, and VCIP 2021 on small object detection and deep learning limits. Grants: Co-investigator on the Empowering Robots with 3D Vision and Reasoning for Enhanced Home Assistance project (2024–2025).

• Computer Vision
• Deep Learning
• Annotation-efficient learning
• +۵ مورد دیگر

Dr Muhammad Awais is a Senior Lecturer in Trustworthy and Responsible AI at the University of Surrey. He leads research on foundation models and self-supervised learning, with affiliations to the Surrey Institute for People-Centred Artificial Intelligence (PAI) and the Centre for Vision, Speech and Signal Processing (CVSSP). His expertise spans AI ethics, audio processing, medical AI, and biometric systems. Education: PhD in AI, MSc in AI, BSc Computer Engineering, BSc Mathematics and Physics. His research focuses on advancing AI through self-supervised learning techniques, with applications in healthcare (e.g., Parkinson’s Disease rehabilitation), transportation, and security systems. He has published extensively on topics like masked autoencoders, audio event classification, and age-invariant face recognition. Key contributions include DailyMAE (fast autoencoder pretraining), ASiT (audio-spectrogram transformers), and AiCareGaitRehabilitation (AI-driven gait rehabilitation). His work emphasizes ethical AI deployment and cross-modal learning.

• Artificial Intelligence
• Trustworthy AI
• Self-Supervised Learning
• +۵ مورد دیگر