جستجوی استاد راهنمای دانشگاهی

Paul L Bendich is an Adjunct Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. from Duke University (2008). His research focuses on adapting topological and geometric methods for data analysis, particularly in topological data analysis (TDA). He has pioneered TDA methodologies for applications in machine learning, sensor fusion, and environmental modeling. Current appointments include leading research initiatives in multi-modal data analysis and reinforcement learning optimization. Key areas of expertise include computational topology, persistent homology, and topological signal processing. He teaches courses on topological data analysis (COMPSCI 434, MATH 412) and has developed educational programs like Data+ at Duke. Grants include NSF-funded projects (BIGDATA: F: DKA: CSD) and Air Force Office of Scientific Research initiatives. Recent work emphasizes topological methods in AI safety (topological parallax), reinforcement learning efficiency, and geophysical feature tracking. Professional activities include conference presentations on TDA applications and editorial work for journals. His research bridges theoretical mathematics with practical data-driven challenges in science and engineering.

• Topological Data Analysis
• Machine Learning
• Computational Topology
• +۴ مورد دیگر

Matti Siekkinen is a Lecturer in the Department of Computer Science at Aalto University. He is also affiliated with the Helsinki Institute for Information Technology (HIIT), the Professorship Di Francesco Mario, and the Computer Science Lecturers group. His research focuses on latency-critical mobile multimedia services, cloud-based virtual reality (VR), and edge computing solutions to optimize network performance and user experience. Education: Doctoral degree in Engineering and Technology (2007) from Université Côte d'Azur Master's degree in Engineering and Technology (2003) from Helsinki University of Technology Research Interests: His work emphasizes improving Quality-of-Experience (QoE) in mobile and cloud gaming, optimizing video streaming for VR applications, and deploying edge computing to reduce latency. Key topics include foveated rendering, dynamic resource allocation, and energy-efficient mobile service delivery. He has led projects such as CloudVR (2019–2020) and Latency-Critical Mobile Multimedia Services (2016–2019), which investigate network architecture and latency reduction strategies. Recent Research Trends: Recent publications highlight advancements in foveated compression techniques, head-pose prediction for remote VR, and neural network-based depth map packing. His work often bridges theoretical models with practical implementations in edge and cloud environments. Grants & Projects: Principal Investigator for CloudVR: High-Quality Mobile VR Using Cloud Acceleration (Business Finland) Principal Investigator for Latency-Critical Mobile Multimedia Services (Academy of Finland) Labs & Groups: He leads the Siekkinen Matti group, focusing on innovative solutions for mobile and cloud-based multimedia systems. Collaborations include Tsinghua University and industry partners like Elisa.

• Video Streaming
• Virtual Reality
• Multi-Access Edge Computing
• +۳ مورد دیگر

Bang LIU is an Associate Professor at the University of Montreal's Faculty of Arts and Science, specifically within the Department of Computer Science and Operations Research. His research focuses on Natural Language Processing (NLP), Deep Learning, and Data Science with applications in healthcare, materials science, and embodied AI systems. He leads multiple research projects funded by organizations like NSERC and MITACS, exploring causal inference, multimodal reasoning, and AI-driven material discovery. Education: PhD in Computer Science (University of Montreal). Research Highlights : Developing causal discovery frameworks (e.g., CausalNET, OCDB) Pioneering materials science applications via AI agents (MatExpert, Honeycomb) Advancing embodied instruction following systems (OPEx) Grants & Projects : NSERC Discovery Grants (2021-2027) MITACS Accelerate Projects (2022-2025) focusing on healthcare LLM trust, CAD generation, and procedural knowledge extraction He supervises Master's students in low-resource NLP tasks and serves as a lead researcher in over 15 active projects. Affiliated with the CRM and RALI labs, his work bridges theoretical AI advancements with practical industrial applications.

• Natural Language Processing (NLP)
• Deep Learning
• Data Science
• +۳ مورد دیگر

Eugene Belilovsky is an Associate Professor at the University of Montreal's Department of Computer Science and Operational Research and an Assistant Professor at Concordia University's Department of Computer Science and Software Engineering. He is also an Associate Member of Mila – Quebec Institute for Artificial Intelligence. His research focuses on computer vision, deep learning, and their applications in areas like continual learning and few-shot learning at the intersection of vision and natural language processing. Belilovsky's expertise includes distributed systems, federated learning, and optimization. His work addresses challenges in model generalization, spurious correlations, and efficient training strategies. He has advised numerous graduate students, including Charles-Étienne Joseph, Medric B. Djeafea Sonwa, Gwendolyne Legate, and Irene Tenison. His recent publications highlight contributions to federated learning, continual pre-training, and fairness in AI systems. Notable work includes optimizing distributed learning protocols and mitigating forgetting in evolving data streams. Belilovsky's research also explores clinical applications, such as diagnosing hepatic steatosis using ultrasound imaging through deep learning techniques. He collaborates with institutions like Mila and the DIRO department, advancing interdisciplinary projects in AI-driven healthcare, robotics, and language modeling.

• Computer Vision
• Deep Learning
• Continual Learning
• +۴ مورد دیگر

Prof. Weikuan Yu is a Professor in the Department of Computer Science at Florida State University. His research focuses on computer architecture, high-performance computing (HPC), cloud computing, parallel file systems, and deep learning applications. He holds the role of Chair and can be contacted via yuw@cs.fsu.edu or (850) 644-5442. His expertise includes optimizing storage systems and I/O behaviors in scientific workflows, developing scalable distributed systems, and applying machine learning to improve computational efficiency. Notable projects include work on burst buffer systems (e.g., BurstFS, TRIO), persistent memory management (PHAST), and distributed deep learning frameworks (e.g., compression techniques for time-evolutionary data). Recent research trends emphasize enhancing HPC storage efficiency through novel file systems and I/O emulation, as well as leveraging machine learning for fault tolerance and configuration tuning. His work bridges hardware-software co-design to address challenges in exascale computing and big data analytics. Prof. Yu has contributed to multiple open-source projects and frameworks, including OpenSHMEM-based key-value stores and MapReduce optimizations. His publications highlight advancements in parallel processing, distributed algorithms, and energy-efficient memory architectures.

• Computer Architecture
• High-Performance Computing (HPC)
• Cloud Computing
• +۴ مورد دیگر

Marios Pattichis is a Professor of Electrical and Computer Engineering at the University of New Mexico (UNM), serving as Director of Online Programs since 1999. He leads the Image and Video Processing and Communications Lab (ivPCL) and co-founded Cosmiac. His work focuses on biomedical image analysis, video analytics for clinical decision support, and educational technology initiatives like the AOLME project targeting underrepresented middle-school students. Pattichis holds 10 patents, over 80 journal papers, and $15.9M in research funding from NSF, NIH, and AFRL. Education: Ph.D. and M.S. in Computer/Electrical Engineering from UT Austin (1998, 1993), with dual B.A./B.Sc. in Mathematics and Computer Sciences (1991). Research Interests: Biomedical CAD systems, explainable AI in imaging, dynamically reconfigurable architectures, and bilingual STEM education. His lab develops tools for stroke risk assessment via carotid ultrasound, solar coronal hole segmentation, and classroom activity analysis to measure student participation. Awards: 2022 EAMBES Fellow for contributions to biomedical image analysis. Editorships include IEEE Transactions on Image Processing and IEEE Signal Processing Letters. Grants & Teams: Directed over $15.9M in grants, advised numerous students (though none listed explicitly), and collaborates with educators on AOLME's integrated math/CS curricula. Active in codec comparisons for healthcare video streaming (VVC vs. AV1) and FPGA-based image processing acceleration. Labs/Initiatives: ivPCL (medical imaging/video systems); DRASTIC (adaptive video processing); ESTRELLA (bilingual STEM outreach). Involved in IEEE journal special issues on video analytics and multilingual education.

• Biomedical Image Analysis
• Medical Imaging
• Computer-Aided Diagnosis
• +۴ مورد دیگر

Dr. Karen Eguiazarian is a Professor of Signal Processing at the Department of Computing Sciences , Tampere University . He leads the Computational Imaging research group and has served as head of the Signal Processing Research Community (SPRC) at Tampere University of Technology (2016-2018). Education: M.Sc. in Mathematics, Yerevan State University, Armenia (1981) Ph.D. in Physics and Mathematics, Moscow State University, Russia (1986) Doctor of Technology in Signal Processing, Tampere University of Technology, Finland (1994) His research focuses on Computational Imaging , Compressed Sensing , and Efficient Signal Processing Algorithms , with significant contributions to Image/Video Restoration and Compression . Recent work includes AI-driven phase imaging, hyperspectral reconstruction, and noise-robust algorithms for remote sensing and biomedical applications. Scientific Awards: Service Award from the Society for Imaging Science and Technology (IS&T) (2014) Honorary Doctoral Degree from Don State-Technical University, Russia (2015) Dr. Eguiazarian has supervised 25 doctoral theses and published over 650 papers. He serves as Editor-in-Chief of the Journal of Electronic Imaging and associate editor of the IEEE Transactions on Image Processing , while co-founding Noiseless Imaging Oy , a Tampere University spin-off.

• Computational Imaging
• Compressed Sensing
• Signal Processing
• +۵ مورد دیگر

Yu Lu is an Assistant Professor in the Department of Biological & Ecological Engineering at Oregon State University. Their research focuses on video coding, image processing, and algorithm optimization with applications in multimedia systems and computer vision. Current interests include fast video compression algorithms, salient object detection, and error resilience in 3D video coding. Yu Lu has published extensively on topics such as HEVC/SHVC intra/inter coding optimizations, sparse regularization techniques for image restoration, and Bayesian decision models for scalable video coding. Research highlights include developing multi-stage motion estimation algorithms for versatile video coding (VVC), fast intra-coding methods using online learning, and depth map error concealment strategies for 3D video. Their work bridges algorithmic efficiency and practical multimedia applications, with publications spanning both traditional video coding standards (HEVC) and emerging formats like screen content coding. Publications trends show a strong emphasis on optimizing video encoding processes through hierarchical classification, adaptive switching mechanisms, and edge-assisted mode decisions. Earlier work (2013) explored piezoelectric material analysis, indicating interdisciplinary research interests. No awards or grants are explicitly listed in the provided materials.

• Video Coding
• Image Processing
• Machine Learning
• +۳ مورد دیگر

Dr. Maryam Haghighat is a Lecturer at QUT's School of Electrical Engineering & Robotics, specializing in Machine Learning, Computer Vision, and Robotics. She holds a PhD from UNSW's School of Electrical Engineering and Telecommunications, where her thesis earned the 2020 UNSW Award for Outstanding Doctoral Thesis. Her postdoctoral research at the University of Oxford's Big Data Institute (2020–2022) focused on medical image analysis for the PathLAKE project. Currently, she leads AI projects with over $10M in funding, including ARC grants and industry collaborations. Her research spans applications in robotics, healthcare, and remote sensing. Notable contributions include developing machine learning algorithms for histopathology image analysis and robust multi-robot localization systems. Awards include the UNSW Doctoral Thesis Award (2020). She actively supervises PhD students in topics like multimodal fusion and 3D visual understanding. Publications highlight advancements in AI-driven medical imaging, hyperspectral transformers, and illumination-aware video compression. Her work bridges theoretical machine learning with practical applications in healthcare, robotics, and environmental monitoring.

• Machine Learning
• Computer Vision
• Deep Learning
• +۴ مورد دیگر

Dr. Bin Li is an Assistant Professor in the Department of Management at the Naveen Jindal School of Management, University of Texas at Dallas. He specializes in accounting and teaches courses like 'Introduction to Financial Accounting,' emphasizing financial statement analysis for undergraduate students. Dr. Li holds a Ph.D. in Business Administration from Duke University (2012), an M.S. in Computer Science from Tsinghua University (2007), and a B.S. in Economics from Renmin University (2005). His research focuses on data storage systems, blockchain technology, machine learning applications, and neural network optimization. Key areas include hybrid SSD management, DNA storage innovations, stochastic computing for adversarial defense, and graph processing efficiency. He has pioneered techniques such as encoding schemes to enhance DNA storage density and hybrid learning-based SSD mapping strategies. His recent work explores large language model (LLM) applications for storage system analysis and integrates semantics-aware approaches in blockchain key-value stores. He also investigates energy-efficient storage solutions and sustainable SSD lifecycle management. Dr. Li has received the Best Paper Award from the American Accounting Association’s Financial Accounting and Reporting Section for his collaborative study on FASB standards’ shareholder value impact. His research grants and collaborations involve interdisciplinary projects at the intersection of computer science and accounting, with a focus on real-world applications in storage technologies and financial systems. Dr. Li is affiliated with research groups advancing next-generation storage architectures and participates in academic initiatives promoting sustainable computing practices.

• Data Storage Systems
• Blockchain Technology
• Machine Learning
• +۴ مورد دیگر

Håkon Kvale Stensland is an Associate Professor in the Department of Networks and Distributed Systems at the University of Oslo (UiO), affiliated with Simula Research Laboratory. His work focuses on multimedia systems, machine learning applications in medical imaging and sports analytics, distributed computing, and GPU optimization. He has contributed to projects like HyperKvasir (a gastrointestinal dataset) and SmartIO (PCIe networking for device sharing). Key research areas include real-time video processing, 3D convolutional neural networks for event detection in soccer, and energy-efficient multimedia workloads. He has co-authored over 50 publications in venues like ACM Multimedia, IEEE Transactions, and Nature Communications. His tools and datasets, such as Saga and Bagadus, emphasize collaborative machine learning and real-time sports analytics. Recent work (2024-2025) explores prompt generation for medical segmentation and multi-host device sharing in high-performance clusters. His research bridges theoretical computer science with practical applications in healthcare, sports, and distributed systems.

• Machine Learning
• Multimedia Systems
• Real-Time Processing
• +۵ مورد دیگر

Thomas Goldstein is a Professor of Computer Science at the University of Maryland, with appointments in Applied Mathematics and Electrical and Computer Engineering. He leads the Maryland Center for Machine Learning and focuses on developing responsible AI systems through the integration of optimization, signal processing, and applied mathematics. His work emphasizes efficient hardware utilization and theoretical foundations to improve model training, robustness, and scalability. Goldstein holds a PhD in Mathematics from UCLA and has held research positions at Rice University and Stanford University. He is a recipient of the SIAM DiPrima Prize, a DARPA Young Faculty Award, and a Sloan Fellowship. Education: PhD in Mathematics, UCLA Research Scientist, Rice University Research Scientist, Stanford University Research Interests: Goldstein's research bridges theory and systems, addressing challenges in large-scale machine learning, distributed optimization, and AI safety. His lab develops novel architectures like recurrent depth models for latent reasoning and explores watermarking techniques to protect AI-generated content. Recent work includes the Huginn-3.5B model, which achieves superior performance on GSM8K tasks using latent reasoning without explicit CoT training. Grants & Awards: Sloan Fellowship DARPA Young Faculty Award SIAM DiPrima Prize Labs & Teams: The Maryland Center for Machine Learning (MCML) serves as a hub for interdisciplinary AI research, focusing on ethical AI development and scalable systems. Goldstein collaborates closely with industry partners and maintains an active open-source initiative, releasing tools like the Common Pile dataset and Neftune optimization frameworks.

• Machine Learning
• Optimization
• Distributed Computing
• +۳ مورد دیگر

Dr. Yang Hua is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on computer vision, machine learning, and their practical applications, including visual recognition, robust learning with limited data, and hardware-aware systems. He has supervised multiple PhD students in areas like generative adversarial networks and domain adaptation. Affiliations: Institute of Electronics, Communications & Information Technology; Co-Investigator in multiple research projects (e.g., R5159ECS, R2733CPH). Teaching: Courses include 'How to do Research' and 'Database and Programming Fundamentals' for MSc and PhD students. Research Interests: Dr. Hua's work addresses challenges in visual recognition, domain adaptation, and federated learning. He emphasizes real-world applications and has contributed to competitions like PASCAL VOC Challenge and VOT-TIR2015. Awards: Recognized with Queen's University STAR award (2019), VOT-TIR2015 tracker win, and multiple PASCAL VOC Challenge wins. His research has been funded by Microsoft Research and European conferences. Grants & Projects: Includes 'Artificial Intelligence for Vision-Based Navigation' and collaborations on medical imaging (e.g., AMD diagnosis). He leads projects on federated learning and domain adaptation. Labs/Teams: Active in the Institute of Electronics, Communications & Information Technology, contributing to distributed AI and biomedical applications.

• Computer Vision
• Machine Learning
• Visual Recognition
• +۳ مورد دیگر

Ivor Spence is a Professor in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His academic role includes research and teaching in distributed and cloud computing, machine learning on heterogeneous systems, and benchmarks for satisfiability problems. He is actively involved in supervising PhD students, including Konstantin Bakanov and Ian McRitchie, focusing on topics such as heterogeneous computing and generative programming. His research interests span energy-efficient computing architectures, edge and federated learning systems, and algorithm optimization for heterogeneous hardware. He has contributed to projects like Energy-efficient Heterogeneous Computing at exaSCALE and FUSION/Numa Engineering , emphasizing practical applications in distributed systems and real-time analytics. Dr. Spence collaborates with industry partners through Knowledge Transfer Partnerships (KTPs), addressing challenges in edge computing and hardware-software co-design. His work has led to numerous publications in top-tier journals and conferences, including contributions to federated learning frameworks, DNN pruning systems, and video retrieval algorithms. He is affiliated with the Centre for Intelligent Sustainable Computing and has served on editorial boards and peer-review panels for IEEE Transactions and other venues. His research bridges theoretical computer science with practical innovations in high-performance and sustainable computing.

• Distributed and Cloud Computing
• Machine Learning
• Heterogeneous Systems
• +۴ مورد دیگر

Professor Paul Benjamin is affiliated with the Seidenberg School of CSIS at Pace University , serving as the Director of the Ph.D. program in Computer Science and founder of the Robotics Lab. His research spans Robotics, Artificial Intelligence, Cybersecurity, Data Mining, and applications of semigroup theory. PhD in Computer Science, NYU (1985) MS in Computer Science, NYU (1982) BFA in Music, Carnegie Mellon University (1976) MS in Mathematics, Carnegie Mellon University (1975) BS in Mathematics, Carnegie Mellon University (1973) His research integrates AI into network security, data mining, and robotic systems, emphasizing cognitive vision and algorithmic innovation. Recent publications focus on synthetic video analysis, behavioral prediction in mobile robots, and cognitive vision systems, reflecting interdisciplinary efforts in AI and robotics. Scientific awards include two U.S. patents for intrusion detection and database compression technologies. He leads an NSF-funded grant (2021-2026) to strengthen cybersecurity pathways at Pace University and has pending federal grants for humanoid robot development.

• Robotics
• Artificial Intelligence
• Cybersecurity
• +۲ مورد دیگر