جستجوی استاد راهنمای دانشگاهی

Srinjoy Das is an Assistant Professor in Data Science at the School of Mathematical and Data Sciences, part of the Eberly College of Arts and Sciences at West Virginia University (WVU). He holds a Ph.D. and M.S. in Electrical Engineering and Statistics from the University of California, San Diego (2018), followed by postdoctoral research at UCSD’s Department of Mathematics until 2021. His research focuses on algorithms for predictive inference on time series, generative models, and efficient implementation of deep learning on edge computing devices. Education: Ph.D. in Electrical Engineering, UCSD (2018) M.S. in Statistics, UCSD (2018) Postdoctoral Researcher, UCSD Mathematics (2018–2021) Research Interests: Algorithms for real-time inference on generative neural networks Efficient implementation of deep learning on FPGAs and edge devices Time series analysis and nonparametric prediction Data-efficient learning in additive manufacturing and healthcare Recent Research Trends: His recent work emphasizes machine learning applications in manufacturing defect detection (e.g., melt pool characterization in 3D printing), geospatial analysis, and healthcare prediction systems. He also explores hybrid optimization strategies and bandwidth-efficient video processing techniques. Mentoring & Collaboration: Advised students on projects including FPGA-based neural network design (Xinyu Zhang), generative model evaluation (Ojash Neopane), and statistical inference for random fields (Ivy Zhang). Collaborates with industry partners like Qualcomm and Microsoft Research, focusing on edge computing and hardware optimization. Labs/Teams: His research group focuses on interdisciplinary projects at the intersection of data science, signal processing, and applied mathematics, with active collaborations in both academia and industry.

• Machine Learning
• Time Series Analysis
• Generative Models
• +۶ مورد دیگر

Qiusheng Wu is an Associate Professor and Director of Graduate Studies in the Department of Geography & Sustainability at the University of Tennessee, Knoxville. He also serves as an Amazon Scholar and Amazon Visiting Academic. His research focuses on geospatial data science, open-source software development, and leveraging big geospatial data/cloud computing to study environmental changes, particularly surface water and wetland dynamics. He created packages like geemap and leafmap, and his work is widely recognized through awards like the 2024 Graduate Teaching Award and Google Open Source Peer Bonus Award. Education: Ph.D., Geography, University of Cincinnati; M.A., Geography, University of Cincinnati; B.S., GIS, Sun Yat-sen University. Research Interests: Geographic Information Science, Remote Sensing, Environmental Modeling, LiDAR, Spatial Data Science, Cloud Computing. His work emphasizes open-source contributions, including Python packages for geospatial analysis, and has been published in journals like Remote Sensing of Environment and International Journal of Applied Earth Observation and Geoinformation. Dr. Wu teaches courses such as GEOG 111 (Our Digital Earth) and GEOG 510 (Geographic Software Design). He actively reviews for over 40 journals and serves as an Associate Editor for Wetlands and International Journal of Applied Earth Observation and Geoinformation.

• Geographic Information Science
• Remote Sensing
• Environmental Modeling
• +۴ 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

Schuyler Laparle is a Lecturer at Tilburg University's Tilburg School of Humanities and Digital Sciences (TSHD), affiliated with the Department of Communication and Cognition. His research focuses on gesture studies, cognitive linguistics, metaphor analysis, and discourse analysis. He teaches courses such as Nonverbal Communication and Academic English. His research explores how gestures shape conversation coherence, metaphor use in climate communication, and semiotic complexity in multimodal interaction. Recent publications analyze gestural discourse markers, multimodal AI challenges, and frame-based gesture semantics. He has received the LOT Grotevragenprijs (2023) for collaborative work on linguistic AI questions. Laparle actively participates in academic activities, including organizing conferences like 'Communicating Climate Hope' (2024) and presenting at international forums on gesture analysis and metaphor studies. His work bridges theoretical linguistics with practical applications in discourse and multimodal communication.

• Cognitive Linguistics
• Gesture Studies
• Metaphor Analysis
• +۳ مورد دیگر

Muhammad Mahbubur Rahman is a tenure-track Assistant Professor in Pediatrics and Biostatistics/Bioinformatics at Children's National Hospital. He holds a PhD in Computer Science from University of Maryland, Baltimore County, with postdoctoral training at NIH and Johns Hopkins University in AI/ML and NLP. His research integrates artificial intelligence, natural language processing, and machine learning to address challenges in child mental health, including ADHD prediction, misinformation detection, and clinical decision support. His work applies advanced computational techniques to healthcare data and social media analysis. Recent publications demonstrate applications in wearables for ADHD monitoring, critical event prediction in pediatrics, and AI ethics in mental health. His research leverages large-scale datasets to develop innovative tools for child well-being.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۲ مورد دیگر

Prof. Nizar Bouguila is a Professor at the Concordia Institute for Information Systems Engineering (CIISE), Concordia University, and holds the Concordia University Research Chair in Applied Artificial Intelligence. His research focuses on machine learning, data clustering, smart building systems, and energy management. He actively supervises graduate students in Computer Science, Electrical and Computer Engineering, and Information Systems Security programs. Dr. Bouguila's work integrates probabilistic models, deep learning, and domain adaptation techniques. He has pioneered methods in energy disaggregation, load forecasting, and anomaly detection in smart infrastructure. His recent projects address challenges in occupant behavior prediction, speech emotion recognition, and fake news detection using Arabic datasets. Key research themes include: Probabilistic clustering with Bayesian nonparametric mixtures Smart building analytics via IoT and sensor data Domain adaptation for cross-domain learning Explainable AI in energy systems His 2023-2025 publications emphasize: Advanced transformer networks for load forecasting Graph neural networks in transportation Multimodal data fusion for anomaly detection Topic modeling for short texts Dr. Bouguila's advising spans multiple engineering and computer science disciplines, reflecting his interdisciplinary research profile.

• Artificial Intelligence
• Machine Learning
• Data Clustering
• +۷ مورد دیگر

Thomas G. Fevens is a Professor in the Department of Computer Science and Software Engineering at Concordia University, and an Adjunct Professor in the Department of Surgery at McGill University. He holds a PhD in Computing and Information Science from Queen's University (1999), with prior degrees in Astrophysics and Physics. His research focuses on medical imaging applications of deep learning, computational geometry, and surgical software innovation. Notable work includes computer-aided diagnosis systems for breast cancer using cytological images, 3D surgical planning algorithms, and motion tracking frameworks for ACL injury assessment. Education: PhD in Computing and Information Science, Queen's University (1999) MSc in Computing and Information Science, Queen's University (1994) MSc in Physics (General Relativity), Queen's University (1993) BSc in Astrophysics (Honours), Queen's University (1990) Research emphasizes interdisciplinary applications of AI in healthcare, including: Medical image segmentation using U-Net architectures Deep learning for tumor localization Kinect-based motion analysis systems Generative adversarial networks (GANs) for medical imaging Publications demonstrate contributions to both core computer science (mobile ad hoc networks, computational geometry) and clinical applications (surgical planning, cancer diagnosis). Active in industry collaboration through the ML-MVP lab, focusing on translating AI research into clinical tools.

• Deep Learning
• Medical Image Processing
• Computer-Aided Medical Diagnosis
• +۵ مورد دیگر

Jinbo Bi is the Frederick H. Leonhardt Professor of Computer Science and Associate Head in the Department of Computer Science & Engineering at the University of Connecticut's School of Engineering. He holds additional affiliations with the Department of Community Medicine and Health Care at UConn Health Center, Department of Mathematics, Department of Statistics in the College of Liberal Arts and Sciences, and the Department of Biomedical Engineering. Bi earned his Ph.D. from Rensselaer Polytechnic Institute and M.Sc. from Beijing Institute of Technology. His research spans artificial intelligence, machine learning, data mining, pattern recognition, computer vision, bioinformatics, medical informatics, and drug discovery. He directs two laboratories: the Laboratory of Machine Learning & Health Informatics and the Laboratory of Artificial Intelligence & Drug Discovery. His recent publications demonstrate a strong focus on applying machine learning to healthcare challenges, particularly in mental health monitoring through smartphone data, depression prediction, substance use disorder analysis, and drug discovery. His work combines theoretical advances in AI with practical healthcare applications, showing particular strength in multi-view learning, graph neural networks, and optimization techniques. Bi has received recognition including a best paper award at BIBM 2014 workshop and two highly accessed articles in BMC Genomics and BMC Genetics. His research is supported by numerous grants from NSF, NIH, VA, and industry partners including Travelers Insurance and Pfizer. As an educator, Bi teaches advanced courses in machine learning and reinforcement learning. His laboratory currently has multiple postdoctoral fellowship openings, reflecting his active research program and mentorship of numerous students who appear as co-authors on his publications.

• Artificial Intelligence
• Machine learning
• Data mining
• +۷ مورد دیگر

George Matsopoulos is Professor in Biomedical Engineering at the National Technical University of Athens, heading the Biomedical Engineering Laboratory. His research focuses on medical image processing, diagnostic support systems, and health information technologies. With over 300 publications, he develops computational methods for medical data analysis and leads the BIOMIG research group specializing in medical informatics and neuroimaging applications. Primary research areas include multidimensional medical data fusion, digital biosignal processing, telemedicine systems, and visualization techniques. His laboratory utilizes advanced computational tools including ITK, VTK, and CUDA to address clinical challenges in medical imaging and diagnostics.

• Biomedical Engineering
• Medical Image Processing
• Health Information Systems
• +۳ مورد دیگر

Associate Professor Rob Pensalfini holds dual appointments in the School of Communication and Arts and School of Languages and Cultures at the University of Queensland. He is an Affiliate of the Centre for Critical and Creative Writing. His academic career began with a PhD in Theoretical Linguistics from MIT (1997), followed by Visiting Assistant Professorship at the University of Chicago before joining UQ in 1999. Promoted to Senior Lecturer in 2003 and Associate Professor in 2016, his work bridges linguistics and drama, particularly focusing on Indigenous Australian languages (Jingulu/Mudburra) and innovative prison-based Shakespeare performances. Education: PhD in Theoretical Linguistics, MIT, 1997 Fieldwork-based research in Northern Territory's Barkly Tableland Research interests: Documentation of endangered Indigenous languages Language contact phenomena between Jingulu/Mudburra Shakespearean performance in correctional facilities Phonological typology and morphosyntactic theory Interdisciplinary links between linguistics and theatre Leadership roles: Founder of Australia's longest-running Prison Shakespeare program Artistic Director of Queensland Shakespeare Ensemble Co-lead on biocultural knowledge projects with Indigenous communities Labs/Initiatives: Queensland Shakespeare Ensemble Prison Shakespeare Project (since 2008) Collaborations with Batchelor Institute on Indigenous language preservation

• Theoretical Linguistics
• Indigenous Australian Languages (Jingulu/Mudburra)
• Shakespeare Performance Studies
• +۳ مورد دیگر

Jean-Marc Odobez is a Senior Scientist at the IDIAP Research Institute and Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), where he is affiliated with the School of Engineering and serves on the Electrical Engineering Doctoral committee (EDEE). He leads the Perception & Activity Understanding Group at Idiap and has extensive teaching responsibilities across multiple departments. Dr. Odobez received his PhD in Computer Science from Rennes University in 1994. His research focuses on multimodal perception systems combining computer vision, statistical machine learning, and deep learning for activity recognition, behavior understanding, and human-robot interaction. His work spans diverse application domains including human health assessment, social robotics, and media content analysis. His recent research shows strong trends in gaze estimation, human activity recognition, and multimodal processing. His team has developed innovative solutions for gaze tracking, head pose estimation, and activity recognition using depth sensors and neural networks. His work increasingly bridges computer vision with digital humanities, particularly in the analysis of ancient Maya glyphs. IEEE member Associate Editor of Machine Vision and Applications journal Dr. Odobez has supervised numerous PhD students and serves as a committee member for the Electrical Engineering Doctoral program. He has been principal investigator for over 16 European and Swiss research projects and has worked on 10 technology transfer projects with SMEs. He co-founded Klewel SA and Eyeware SA, focusing on eye tracking and attention modeling technologies. His research group actively collaborates with industry partners and maintains strong connections with the computer vision and human-computer interaction research communities.

• Computer Vision
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Dr. Zijian Wang is a Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on improving vision model transferability, domain adaptation, and deep learning applications in computer vision and remote sensing. He holds a PhD in Computer Science from The University of Queensland (2023). Research interests include domain adaptation techniques, neural network generalization, unsupervised learning, and applications in satellite imagery analysis, vegetation segmentation, and disaster damage assessment. His work emphasizes practical solutions for open-world domain shifts and efficient data utilization. Recent publications explore transfer learning via feature space perturbation, neural network-based satellite image delivery (MIDNet), and benchmark datasets for model generalization (CIFAR-10-Warehouse). He collaborates on platforms like FluMA for influenza monitoring and RoadAtlas for infrastructure management. Currently advises PhD candidates in vegetation segmentation for Australian wheat data and crop health assessment. Active in top conferences (CVPR, ICCV, NeurIPS) and journals (IEEE TPAMI, TMM).

• Computer Vision
• Deep Learning
• Domain Adaptation
• +۵ 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

Iaroslav Melekhov is a Postdoctoral Researcher at Aalto University's Department of Computer Science, affiliated with the Professorship Kannala Juho. His work focuses on advancing computer vision techniques, particularly in camera relocalization, 3D reconstruction, semantic segmentation, and autonomous systems. He has contributed to high-impact datasets like ECLAIR and developed methods for memory-efficient visual localization and medical image analysis. Melekhov holds a Nokia Foundation Scholarship (2018) and has engaged in collaborative research with institutions such as Wayve Technologies and ETH Zurich. Research Interests: 3D Reconstruction and Gaussian Splatting Camera Relocalization and Visual Localization Semantic Segmentation and Dataset Development Deep Learning for Autonomous Systems Self-Supervised Learning and Feature Descriptors Key Contributions: Co-developed the ECLAIR aerial LiDAR dataset for semantic segmentation. Advanced memory-efficient camera relocalization via differentiable product quantization. Explored medical image segmentation using SAM-generated annotations. Grants & Awards: Nokia Foundation Scholarship, 2018 Labs & Collaborations: Associated with the imedslab research group, contributing to open-source tools like SOLT. Collaborative efforts include projects with Wayve Technologies and ETH Zurich, focusing on autonomous driving and robotics.

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +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