جستجوی استاد راهنمای دانشگاهی

Yashar Deldjooمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +۷ مورد دیگر

Yashar Deldjoo is an Assistant Professor specializing in artificial intelligence, machine learning, and their applications in healthcare, user modeling, and recommender systems. His research focuses on explainable AI, ethical AI practices, multimodal learning, and brain-computer interfaces. He has contributed to projects like WeBIUM (Wearable Devices and Brain-Computer Interfaces for User Modelling) and ARIEL (emotional support via BCI and LLMs). His work bridges technical advancements with societal impacts, addressing challenges in healthcare diagnostics (e.g., Alzheimer’s disease prediction), environmental sensing (real-time pollution-aware recommendations), and cybersecurity (data poisoning attacks on recommendation systems). He also explores fairness in large language models and the integration of EEG-based emotion recognition systems. Notable contributions include frameworks like BRAINEX (CNN model evaluation for brain age prediction), Ducho (multimodal feature extraction for recommendation systems), and KGUF (knowledge-aware graph-based recommenders). He chairs conferences like RecSys and contributes to industry-relevant standards for trustworthy AI systems. His research emphasizes interdisciplinary collaboration, combining neural networks, ethical considerations, and human-computer interaction. Current projects involve neurorehabilitation tools, quantum security for smart cities, and AI-driven usability testing.

DANIEL CORES COSTAمشاهده پروفایل

استادیار

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

Daniel Cores Costa is an Assistant Professor at the University of Santiago de Compostela, affiliated with the Department of Electronics and Computing under the Higher Technical School of Engineering. His research focuses on advanced computer vision techniques, including spatio-temporal convolutional neural networks, few-shot learning, and generative adversarial networks (GANs) for small object detection. He earned his Doctorate from the same university in 2022 with a thesis titled Spatio-temporal convolutional neural networks for video object detection , advised by Dr. Victor Manuel Brea Sánchez and Dr. Manuel Mucientes Molina. His work spans video object detection, medical imaging applications (e.g., COVID-19 diagnosis), and autonomous systems like UAV navigation. Key areas include improving data efficiency via pseudo-label mining, enhancing GAN-based data augmentation pipelines, and developing spatio-temporal feature aggregation techniques for dynamic scenes. He collaborates with the GSI Group (Intelligent Systems Group) , contributing to interdisciplinary projects at the intersection of AI, robotics, and healthcare. Publications highlight trends in few-shot learning for medical imaging, video-language evaluation benchmarks (TVBench), and real-time UAV-based object detection. Notable contributions address challenges in small object detection in videos and X-ray vision systems. His research emphasizes practical applications in healthcare diagnostics and autonomous systems. Despite no explicit mention of awards or grants, his prolific publication record (2015–2025) underscores sustained academic engagement. Advising details are unavailable, but his involvement in thesis supervision and collaborative groups like GSI indicates active mentorship.

Sharon Diمشاهده پروفایل

دانشیار

• Transportation Systems
• Data Analytics
• Machine Learning
• +۵ مورد دیگر

Sharon Di: Academic Overview Sharon Di is an Associate Professor in the Department of Civil Engineering and Engineering Mechanics at Columbia University. She holds affiliations with the Data Science Institute (DSI), serving as Co-Chair of the Smart Cities initiative. Her research bridges theoretical frameworks with practical applications in transportation systems, leveraging emerging technologies like AI, data analytics, and cyber-physical systems to enhance infrastructure resilience and urban mobility efficiency. Research Focus: Di's work emphasizes travel behavior analysis during disruptions (e.g., natural disasters), optimization of traffic networks, and the integration of autonomous vehicles and ride-sharing services. Her methodologies include game theory, reinforcement learning, and physics-informed deep learning applied to large-scale sensor data. Recent projects explore digital twins for urban planning and causal inference in transportation decision-making. Key Contributions: Di has pioneered frameworks for adaptive traffic signal control, resilient infrastructure design, and multimodal mobility modeling. Her lab, DitectLab ( website ), develops AI-driven solutions for smart cities, with applications in real-time traffic management and safety optimization. She also serves on the Center for Smart Cities committee within Columbia's Data Science Institute.

Jana Niederمشاهده پروفایل

پژوهشگر

• Bioimaging
• Nanophotonics
• Quantum Photonics
• +۴ مورد دیگر

Jana Nieder is a Research Scientist and Group Leader at the International Iberian Nanotechnology Laboratory (INL), directing the Ultrafast Bio- and Nanophotonics (UBNP) research group and the ISO-certified Nanophotonics and Bioimaging (NBI) core facility. Her leadership spans funding acquisition, student supervision, scientific conference organization, and science outreach initiatives. Institutional roles include Head of Nanophotonics Department (2016-2020) and strategic coordination of the Advanced Materials and Computation cluster (2020-2022). Her educational trajectory features a summa cum laude PhD from Freie Universität Berlin (2011) on "Single molecule spectroscopy on Pigment Protein Complexes," followed by postdoctoral work at ICFO (Spain, 2011-2014) in ultrafast single-molecule spectroscopy and IGC (Portugal, 2014) in super-resolution microscopy. Research centers on light-matter interactions across three pillars: Advanced Bioimaging & Sensing : Nanofunctionalized surfaces with ultrafast lasers for high-resolution cellular thermal mapping and multiparameter imaging Integrated Photonics : Photonic chips for neuromorphic computing and biosensing via two-photon polymerization Quantum Photonics : Single quantum emitters in 2D materials for diamond-based neuronal sensing platforms Her group develops techniques including SyncRGB-FLIM microscopy, graphene-based biosensors, and femtosecond laser micro-nanofabrication. Publication analysis reveals strong biomedical-photonics convergence, with recent work emphasizing graphene biosensors for DNA hybridization tracking, 3D microscaffolds for stem cell growth, and photonic neuromorphic computing. Trends show increasing focus on quantum-enabled health diagnostics and energy-efficient photonic AI hardware. Dr. Nieder has secured multi-million euro funding from EU programs (Marie Skłodowska-Curie, FCT), national agencies, and philanthropic foundations across 15+ projects including Diamond4Brain, QUA-ND-O, and ChipAI. She actively mentors 8 PhD candidates and 22 Master's students through structured training programs and international fellowships. The NBI facility operates as a major open-access hub with 30+ team members, supporting projects like Parkinson's research via diamond photonics and cancer hyperthermia monitoring. Current initiatives integrate quantum sensors with 3D microscaffolds for personalized medicine applications.

• Computer Graphics
• Medical Imaging
• Computer Vision
• +۴ مورد دیگر

Dr. Hammadi Nait-Charif is an Associate Professor in Animation at Bournemouth University. His research spans computer graphics, medical imaging, and neural networks, with a focus on applications in healthcare technology and interactive systems. He has contributed to advancements in motion tracking, 3D reconstruction, and assistive elderly care frameworks like ANGELAH. His work integrates interdisciplinary approaches combining computer vision, biomedical engineering, and machine learning. Research Interests : His key areas include computer animation techniques, medical image analysis (e.g., mammography and vertebra tracking), human motion capture systems, and fault-tolerant neural network architectures. Recent projects involve multi-modal feature fusion for medical report generation and holographic display technologies. Grants & Contributions : He secured a grant for developing a handheld POS system (InnovateUK, 2011). His collaborations span institutions worldwide, addressing challenges in elderly care, surgical training simulators, and spinal imaging. Labs & Teams : His work often involves interdisciplinary teams focusing on medical imaging solutions and animation systems integration.

Isaac O. Afaraمشاهده پروفایل

دانشیار

• Applied spectroscopy
• Computational biophotonics
• Machine learning in biospectroscopy
• +۲ مورد دیگر

Isaac O. Afara is an Associate Professor at the Department of Technical Physics , Faculty of Science, Forestry and Technology , University of Eastern Finland . He leads the Biomedical Spectroscopy Lab (BSL) and the Biophysics of Bone and Cartilage (BBC) research group, focusing on translational spectroscopy and computational biophotonics for tissue diagnostics. Multi-disciplinary team of physicists and engineers Established 2018 (BSL), 1998 (BBC) His research bridges applied spectroscopy , machine learning , and biomechanics to develop optical methods for non-destructive assessment of biological tissues. Key areas include: Visible/NIR/Raman spectroscopy for cartilage/meniscus diagnostics Computational modeling of tissue optical properties Development of AI-driven preprocessing algorithms Characterization of viscoelastic properties in connective tissues Recent work demonstrates machine learning-enhanced spectroscopy for real-time cartilage assessment during arthroscopy, with applications in osteoarthritis detection, tissue engineering, and joint biomechanics. His 15 most recent articles (2025-2017) focus on: Cartilage/meniscus biomechanical properties Spectral differentiation of tissue pathologies Photon path length modeling Multi-sensor data fusion techniques Optical diagnostics for fibrosis and osteoarthritis Development of open-source preprocessing tools He collaborates with international institutions on computational biophotonics and has contributed to open-source Python modules for spectroscopic data analysis.

Alisha Pradhanمشاهده پروفایل

استادیار

• Assistive Technology
• Human-Computer Interaction (HCI)
• Aging in Place
• +۵ مورد دیگر

Alisha Pradhan is an Assistant Professor in the Informatics department at the New Jersey Institute of Technology (NJIT). Her research focuses on human-centered design of technologies for older adults and people with disabilities, emphasizing accessibility, inclusivity, and ethical considerations. She holds a Ph.D. in Information Studies from the University of Maryland-College Park (2022), an M.S. in Human-Computer Interaction (2018), and a B.Tech. in Industrial Design from National Institute of Technology (2015). Research Interests: Dr. Pradhan's work spans assistive technologies, voice interaction design for aging populations, inclusive AI systems, and participatory design methodologies with marginalized communities. Her current projects investigate the use of smart objects and voice assistants to support cognitive aging, while addressing challenges in technology adoption and ethical implications. She also explores sustainable makerspace models for older adults and co-design approaches with people living with dementia. Publications: Her recent work addresses voice assistant personalization for seniors, accessibility of smart home technologies, and frameworks for equitable AI development. Key topics include: ontological categorization of voice agents, multimodal interaction for memory support, and posthumanist perspectives in technology design. Grants & Awards: Active research funding includes NSF-supported projects exploring future-proofing smart technologies for age-related cognitive changes. She has developed curriculum modules integrating accessibility principles into design education. Labs & Collaborations: Maintains an active research website (https://www.alishapradhan.net/) documenting her work. Her lab collaborates with geriatric care organizations and technology firms to translate research into practical solutions for aging-in-place scenarios.

• Distributed Machine Intelligence
• Low-Power AI for IoT
• Indoor Localization
• +۳ مورد دیگر

Anuradha Ravi is a Research Assistant Professor in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC). Previously, she served as a Research Scientist at the Living Analytics Research Center, Singapore Management University (2018-2023), and as an Assistant Professor at Shiv Nadar University (2016-2018). She holds a Ph.D. in Computer Science from the Indian Institute of Technology Roorkee, where her doctoral research focused on optimizing energy efficiency and latency reduction for mobile devices through intelligent offloading strategies in heterogeneous networks. Her research interests span multiple cutting-edge domains in computing and networking: Distributed machine intelligence at the edge Low-power AI systems for IoT devices Indoor localization and occupancy sensing Wireless networking protocols Mobile and ubiquitous computing Smart building management systems Analysis of her recent publications (2015-2025) reveals a consistent focus on efficiency optimization in constrained environments, with key themes including autonomous robotics, AI-driven compression techniques, multi-sensor localization, and energy-aware mobile systems. Her work increasingly emphasizes real-world applications in contested or resource-limited settings, leveraging machine learning for adaptive solutions. She actively mentors graduate students at UMBC in projects related to perception systems for unmanned vehicles, compression-aware federated learning, and network middleware development. During her tenure at Shiv Nadar University, she supervised undergraduate research on cooperative IoT strategies and 5G edge computing. She previously contributed to the Living Analytics Research Center's initiatives in smart building occupancy detection and collaborative machine intelligence frameworks for IoT devices.

Evangelos Boukasمشاهده پروفایل

دانشیار

• Deep Learning
• Unmanned Aerial Vehicles
• Computer Vision
• +۵ مورد دیگر

Evangelos Boukas is an Associate Professor in the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU), Faculty of Engineering. His research focuses on autonomous robotic systems for marine vessel inspection, confined space navigation, and man overboard detection using UAVs and deep learning techniques. His core research interests include Deep Learning , Unmanned Aerial Vehicles , Computer Vision , and Robotics , applied to solve challenges in marine robotics and autonomous inspection. He develops uncertainty-aware navigation systems, probabilistic segmentation methods, and reinforcement learning frameworks for real-world deployment in complex environments. Recent publications demonstrate a strong trend toward GPU-accelerated deep learning for confined space inspection, with emphasis on uncertainty estimation, domain adaptation, and real-time processing. Key application areas include marine vessel classification, ballast tank inspection, and search-and-rescue operations, often leveraging multimodal sensor fusion. Dr. Boukas actively supervises five PhD students across major research projects: Probabilistic Deep Learning for Autonomous Aerial Inspection (2025-2028) Aerial Robots Design for Confined Spaces Inspection (2024-2027) Active Semantic Segmentation for Confined Spaces using Aerial Robots (2024-2027) Autonomous Aerial Robotics for Man Overboard Incidents (2023-2026) Visual and Multimodal Perception for Construction Robotics (2023-2026) He leads the "Perception and Cognition for Autonomous Systems" research group at DTU, which develops advanced perception algorithms for robots operating in challenging environments like ship ballast tanks and marine vessels, contributing to UN Sustainable Development Goals for industry innovation and marine conservation.

Ning Wangمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Human-Machine Interaction
• +۵ مورد دیگر

Dr. Ning Wang is an Associate Professor in Robotics and AI at Sheffield Hallam University's Department of Computing. She holds a PhD in Electronics Engineering from The Chinese University of Hong Kong (2011). Her research focuses on human-machine interaction (HMI), embodied AI, and healthcare robotics applications. She has led projects funded by EU FP7, ASTUTE 2020, and the Royal Society. Key achievements include advancements in robotic grasping, teleoperation systems, and autonomous driving algorithms. Awards include Best Paper at ICAC’24 and DISA’23. Education: PhD in Electronics Engineering (2011, The Chinese University of Hong Kong) Affiliations: Communication and Computing Research Centre; Industry and Innovation Research Institute Professional Roles: Associate Editor for International Journal of Advanced Robotic Systems and Frontiers in Computer Science Research interests span robotics, AI, and healthcare integration. Notable contributions include multimodal human-robot interaction frameworks and hybrid force/motion learning systems. Recent work on vision-guided grasping (2025) and impedance control (2024) highlights her impact in dexterous robotics. Awards include 7 major recognitions across conferences like ICAC, DISA, and IEEE forums. She actively participates in organizing international conferences (e.g., ICAC’22, ICIT’24) and serves as a grant reviewer for UKRI and NWO. Labs/Teams: Leads a robotics team focusing on embodied AI, teleoperation, and medical robotics applications. Collaborates with industry partners on autonomous systems and smart manufacturing solutions.

Eleni Boumpaمشاهده پروفایل

پژوهشگر

• Smart Environments
• Healthcare Services
• Cyber-Physical Systems
• +۳ مورد دیگر

Eleni Boumpa is a Researcher and Ph.D. candidate at the Department of Computer Science and Biomedical Informatics, University of Thessaly. She leads the AuDi-o-Mentia research team and is affiliated with the Intelligent Systems Laboratory. Her work focuses on assistive technologies for people with neurodegenerative diseases, smart environments, cyber-physical systems, and healthcare in consumer electronics. Education: BSc in Computer Science & Biomedical Informatics (2018), University of Thessaly Current Role: Ph.D. candidate since 2018, researching 'Ambient Assistive Systems in Smart Environments' Her research interests include developing smart systems for elderly care, gas leakage detection via TinyML, logistics optimization, and security in healthcare wearables. Her work is funded by the EPICS in IEEE program and has led to innovations like home assistive systems for dementia patients. Awards: 1st place in IEEE Mind the Gap 2017 (dementia assistive system) Finalist in Microsoft Imagine Cup 2017 3rd place at ECESCON 2017 (best business idea) Advising & Grants: As team leader of AuDi-o-Mentia, she coordinates interdisciplinary research on memory aids for neurodegenerative diseases. Her projects integrate acoustic-based systems, tinyML, and sensor networks into assistive technologies. Labs & Teams: Member of the Intelligent Systems Laboratory and founder of the AuDi-o-Mentia team, focusing on dementia care technologies.

• Robotics
• Multi-robot Systems
• Control Systems
• +۴ مورد دیگر

Lorenzo Sabattini is an Associate Professor at the Department of Engineering Sciences and Methods (DISMI) at the University of Modena and Reggio Emilia, Italy. His research focuses on multi-robot systems, decentralized estimation/control, and human-robot interaction. He holds a Ph.D. in Control Systems and Operational Research from the University of Bologna (2012), and has been a Visiting Researcher at the University of Maryland (2010). He served as a founding co-chair of the IEEE RAS Technical Committee on Multi-Robot Systems from 2014–2021. His work has been recognized with awards such as the Fabrizio Flacco Young Author Best Paper Award (2018) and the IEEE RAS Most Active Technical Committee Award (2018). Research interests include multi-robot coordination, coverage control, human-robot collaboration, and Industry 5.0 applications. Notable projects involve digital twins for operator well-being, AGV fleet management, and socially acceptable robotics. He has organized workshops at IEEE ICRA, RSS, and ERF, and serves as an Associate Editor for International Journal of Robotics Research (2023–2024) and IEEE Robotics and Automation Letters (2015–2018). Publications span topics like connectivity maintenance in robot networks, multimodal stress assessment in HRC, and resilient multi-robot systems. He actively collaborates on industrial automation projects, such as the INCLUSIVE system for adaptive HMIs and autonomous depalletizing systems. His research bridges theory and practice, addressing challenges in logistics, manufacturing, and human-centric robotics.

Khaled Salehمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Autonomous Systems
• +۴ مورد دیگر

Dr. Khaled Saleh is a Lecturer in Computing and Information Technology at the University of Newcastle's School of Information and Physical Sciences. He holds a PhD in Computer Science from Deakin University (2019). Prior to his current role, he served as a Postdoc Research Fellow at the University of Technology Sydney (2020–2022) and a Research Fellow at Deakin University (2018–2019). Research Focus: AI, Machine Learning, Autonomous Systems, Computer Vision, and Intelligent Transportation Systems Grants: Secured over $683K in funding, including projects on traffic safety and AI in education Editorial Roles: Topic Editor at MDPI Sensors and reviewer for top journals/conferences (e.g., IEEE T-ITS, ICRA) Research interests span autonomous systems, traffic safety, and AI-driven solutions for transportation and education. He has published over 30 papers in top venues and actively contributes to grants and industry collaborations. Teaching: Coordinates courses like Programming Languages and Business Intelligence.

Suchendra M. Bhandarkarمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۴ مورد دیگر

Suchendra M. Bhandarkar is a Professor in the School of Computing at the University of Georgia, affiliated with the Franklin College of Arts & Sciences. He holds courtesy faculty positions in the College of Engineering and the School of Environmental, Civil, Agricultural, and Mechanical Engineering. His research focuses on Artificial Intelligence, Computer Vision, Pattern Recognition, and Computational Biology. He has advised PhD students such as Anirban Mukhopadhyay and has been actively involved in interdisciplinary projects spanning robotics, coral reef mapping, and food safety automation. Education: Ph.D. and M.S. in Computer Engineering from Syracuse University (1985-1989), and B.S. in Electrical and Electronics Engineering from Indian Institute of Technology Bombay (1983). His funded projects include advanced GeoAI terrain analytics, underwater robotics, and AI-driven food safety systems. Notable grants include support from USDA, NIH, and the US Army. Research highlights include 3D coral reef mapping via SLAM, deep learning for food safety, and zero-shot learning algorithms. Key collaborations involve the USDA Agricultural Research Service, Sony Corporation, and international networks like CapsNet for medical imaging. His labs and teams focus on applying AI to environmental, biomedical, and agricultural challenges.

Dietrich Paulusمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Medical Imaging
• +۲ مورد دیگر

Prof. Dr.-Ing. Dietrich Paulus heads the Active Vision Group at the University of Koblenz's Institute of Computational Visualistics. His research spans computer vision, robotic perception, medical image analysis, and 3D reconstruction techniques. Current projects include automated spine modeling for medical applications, hyperspectral imaging for autonomous driving, agricultural robotics, and sensor calibration methods. Recent work demonstrates strong applications in medical imaging with projects on 3D vertebrae measurement, spinal ligament detection, and cochlear image segmentation. The group also develops educational robotics frameworks and participates in robotic challenges as pedagogical tools. Methodological advances include generative adversarial networks for synthetic data generation, point-cloud change detection, and online calibration of LIDAR systems.