جست‌وجوی استاد و پژوهشگر

Emily Heathمشاهده پروفایل

دانشیار

• Radiation Therapy
• Medical Physics
• Monte Carlo Simulations
• +۳ مورد دیگر

Dr. Emily Heath is an Associate Professor in the Department of Physics at Carleton University, specializing in improving radiation therapy for cancer treatment through advanced modeling and treatment planning techniques. Her research focuses on Monte Carlo simulations, anatomical changes during therapy, and robust treatment optimization. She leads the Carleton Laboratory for Radiotherapy Physics (CLRP), emphasizing clinical translation of innovations. Research Highlights: Developing pyroelectric dosimeters for thermal calibration and real-time dose monitoring. Advancing 4D Monte Carlo simulations for dose reconstruction in moving/deforming anatomies. Pioneering adaptive frameworks like ALERT-RA for respiratory motion management. Advising & Collaborations: Supervises graduate students in projects involving EPID modeling, pyroelectric calorimeters, and motion-impact analysis. Collaborates with National Research Council and Ottawa Hospital Cancer Centre. Labs & Infrastructure: The CLRP lab integrates experimental verification using deformable phantoms and advanced dosimetry systems, ensuring clinical applicability of theoretical models.

Tong Xuمشاهده پروفایل

استاد

• Medical Physics
• Radiation Therapy
• Medical Imaging
• +۳ مورد دیگر

Dr. Tong Xu is a Professor in the Department of Physics at Carleton University, affiliated with the Faculty of Science. His research focuses on real-time motion tracking for medical procedures, including tumor tracking during radiation therapy and image-guided surgery. He has pioneered the PeTrack technique using positron emission markers and developed the dynamic dual-energy x-ray imaging (dDEXI) method for lung tissue motion assessment. His work aims to improve radiation therapy accuracy, reduce patient radiation exposure, and enhance diagnostic tools. Dr. Xu holds a U.S. Patent (#8447387) for real-time tumor tracking technology and has contributed to advancements in medical imaging and radiation oncology. His research interests span medical physics, radiation therapy, and biomedical engineering, with applications in oncology and cardiology. Key projects include respiratory motion management in PET/CT imaging and optimizing 4D-VMAT radiotherapy frameworks. Dr. Xu collaborates on interdisciplinary projects to translate imaging innovations into clinical practice. Awards : U.S. Patent #8447387 (2013) Key Technologies : PeTrack, dDEXI, motion-compensated imaging Labs/Groups : Medical Imaging and Radiation Physics Research Group at Carleton University

Brent Wardمشاهده پروفایل

استاد

• Quaternary geology
• Sedimentology
• Stratigraphy
• +۶ مورد دیگر

Brent Ward is a Professor in the Department of Earth Sciences at Simon Fraser University and serves as Co-director of the Centre for Natural Hazards Research. His academic career is deeply rooted in Quaternary geology with a focus on understanding glacial history and paleoenvironmental changes in western North America. Ward received his B.Sc. (Hons.) in 1986 and Ph.D. in 1993, both from the University of Alberta. His educational background provided the foundation for his specialization in field-based Quaternary geology. Dr. Ward is a classical, field-based Quaternary geologist whose research interests span sedimentology, stratigraphy, paleoenvironmental reconstructions, surficial mapping, drift prospecting, and landslides. His current research primarily focuses on resolving aspects of the stratigraphy of the Cordilleran Ice Sheet and associated paleoenvironmental reconstructions. This interdisciplinary work requires collaborations with researchers across various fields and combines stratigraphic studies with plant and insect macrofossil and palynological analysis to reconstruct paleoenvironments and paleoclimate. A particularly innovative aspect of his research involves work on raised sea caves—the only such work being done in North America—which provides direct information to solve questions of glacial history and past fauna and climate. His research has significant implications for understanding potential human coastal migrations during the Late Wisconsinan period. Analysis of Ward's recent publications reveals a strong focus on Cordilleran Ice Sheet history, landslide mechanics, and glacial sedimentology. His work bridges traditional field geology with modern analytical techniques including cosmogenic nuclide dating, LiDAR analysis, and geospatial modeling. The research spans multiple geographic areas including British Columbia, Yukon, and the Canadian Arctic, addressing both fundamental scientific questions and practical applications in mineral exploration and hazard assessment. The Westerman Award for Outstanding Achievement in Geoscience EGBC (2023) APEG Fellowship APEGBC (2013) Engineers Canada Fellowship Engineers Canada (2009) Victor Milligan Paper Award Canadian Geotechnical Society (2010) Barlow Memorial Medal Canadian Institute for Mining, Metallurgy and Petroleum (2009) Ward actively supervises graduate students, with recent projects examining the glacial history of the Ruby Range in southwest Yukon and the effects of deglacial meltwater processes on kimberlite indicator mineral concentrations. His research has secured significant funding for field investigations and laboratory analyses, supporting both fundamental research and practical applications in natural hazard assessment and mineral exploration. He has developed strong collaborative relationships with government geological surveys, industry partners, and international research teams. As Co-director of the Centre for Natural Hazards Research, Ward leads interdisciplinary teams investigating geological hazards including landslides, glacial lake outburst floods, and seismic risks. His work integrates field observations with numerical modeling to improve hazard assessment and risk management in mountainous terrain. The Centre maintains strong connections with emergency management agencies and industry partners to ensure research findings translate into practical risk reduction strategies.

Natalie Al-Otaibiمشاهده پروفایل

پژوهشگر

• Structural Biology
• Bacterial Macromolecular Assemblies
• Microbiology
• +۳ مورد دیگر

Natalie Al-Otaibi holds the position of Postdoctoral Researcher at the School of Natural Sciences. Her research focuses on structural biology and bacterial macromolecular assemblies, particularly the structure and assembly of bacterial flagella. She earned a PhD in 2021 and an MBiolSci in 2017, both from The University of Sheffield. Her work employs cutting-edge techniques such as cryo-electron microscopy to investigate flagellar biogenesis and bacterial mechanisms. Key research themes include flagellar cap dynamics, filament elongation, and the molecular basis of flagellar motor function. Recent studies explore the impact of genetic factors and environmental conditions on flagellation patterns and biofilm formation. Al-Otaibi’s publications highlight contributions to understanding protein incorporation in flagella, nutrient-dependent biofilm regulation, and structural insights into bacterial systems. She has co-authored discussions on cryo-EM sample preparation and correlative microscopy techniques, underscoring methodological advancements in structural biology.

Hector Santos-Villalobosمشاهده پروفایل

استادیار

• Computer Vision
• Signal Processing
• Machine Learning
• +۷ مورد دیگر

Hector Santos-Villalobos is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. His research focuses on advanced imaging systems, biometric recognition, seismic analysis, and nondestructive evaluation techniques. He holds a PhD in Electrical and Computer Engineering from Purdue University (2010), and MS/BS in Computer Engineering from the University of Puerto Rico Mayagüez (2005/2003). Education: PhD, Electrical and Computer Engineering, Purdue University (2005–2010) MS, Computer Engineering, University of Puerto Rico Mayagüez (2003–2005) BS, Computer Engineering, University of Puerto Rico Mayagüez (1998–2003) Research interests span computer vision applications in biometric systems (e.g., iris recognition, facial screening), seismic imaging for geothermal energy projects (EGS Collab), and ultrasound-based nondestructive evaluation of materials. His work emphasizes privacy-preserving algorithms, iterative reconstruction techniques, and multimodal data fusion. Notable projects include developing the BRIAR dataset for long-distance facial recognition and creating handheld multi-sensor biometric devices. Publications highlight trends in: Biometric security (privacy-preserving facial screening, bias-mitigated face ID) Ultrasound imaging for concrete defect detection Geothermal energy exploration via seismic tomography Deep learning applications in seismic phase picking and image reconstruction Grants and collaborations involve geothermal energy initiatives (e.g., EGS Collab), Department of Energy projects, and National Science Foundation funding. His work bridges computational methods with practical engineering challenges in imaging and material analysis.

Brian McClendonمشاهده پروفایل

استاد پژوهشی

• machine learning
• computer vision
• autonomous vehicles
• +۱ مورد دیگر

Brian McClendon is a Research Professor at the University of Kansas School of Engineering's Department of Electrical Engineering and Computer Science. Previously, he served as Vice President at Google, leading the Geo business unit responsible for Google Maps, Street View, and Google Earth. His work earned him the United Nations 'Champions of the Earth' award in 2013 and induction into the National Academy of Engineering in 2015. He holds an honorary doctorate in electrical engineering from KU and has over 30 patents. His research focuses on democratizing machine learning, computer vision, autonomous vehicles, and next-generation mapping technologies. Key areas include intelligent geographic information systems, autonomous vehicle navigation, and privacy-preserving geolocation solutions. His work bridges academia and industry, emphasizing scalable, user-centric systems. Notable contributions include innovations in distributed GIS architecture, encryption for transport systems, and methods for image-based location determination. His patents and publications reflect expertise in geospatial data management, sensor integration, and cybersecurity in transportation. Awards : UN Champions of the Earth (2013) National Academy of Engineering Member (2015) Honorary Doctorate in EE (KU, 2015) Grants/Political Activity : Ran for Kansas Secretary of State (2018, Democratic ticket) His lab collaborations emphasize real-world applications of mapping technology, including disaster zone mapping and indoor navigation systems. Current projects explore AI-driven mapping and ethical geospatial data practices.

David Sinclairمشاهده پروفایل

دانشیار

• Formal Methods
• Distributed Systems
• Real-time Systems
• +۲ مورد دیگر

Dr. David Sinclair is an Associate Professor and Year Head in the School of Computing at Dublin City University, affiliated with the Dependable Systems, Software Engineering, and Security research groups. His research spans formal methods, distributed systems, real-time systems, and AI applications in strategic games. Teaching responsibilities include foundational and advanced courses: CSC1018 Logic CSC1048 Computability & Complexity CSC1098 Compiler Construction CSC1141 Concurrent Programming CSC1049 Third Year Project supervision His publications demonstrate strong focus on real-time systems, motion prediction, and immersive technologies, with recent work exploring AI-driven virtual environments and light field processing. Research consistently addresses computational efficiency and formal verification challenges.

• Robotics
• Control Systems
• Sensor Networks
• +۵ مورد دیگر

Daniele Fontanelli is a Full Professor at the Department of Industrial Engineering , University of Trento, Italy. He holds a Ph.D. in "Automatics, Robotics, and Bioengineering" from the University of Pisa (2006) and a Laurea in Industrial Informatics (2001). He has taught courses such as Internet of Things , Distributed Measurement Systems , and Statistical Estimation at both Trento and Pisa. Research Interests: Robotics, human-robot interaction, signal processing for autonomous systems, UWB localization, distributed sensor networks, and smart grid measurement systems. His work spans mobile robotics, control theory, and embedded systems with applications in medical robotics, warehouse automation, and environmental monitoring. Scientific Contributions: Over 150 publications and leadership roles in European projects like AWARD2.0, SAFESTRIP, and ACANTO. He serves as Associate Editor in Chief for IEEE Transactions on Instrumentation and Measurement. Outstanding Associate Editor (2020) Most Productive Italian Reviewer (2020) Distinguished Service Award (2020)

Junsong Yuanمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Video Analytics
• +۶ مورد دیگر

Junsong Yuan is a Professor and Director of the Visual Computing Lab at the Department of Computer Science and Engineering, University at Buffalo (SUNY). He previously served as an Associate Professor and Nanyang Assistant Professor at Nanyang Technological University (NTU), Singapore. His research focuses on computer vision, pattern recognition, video analytics, and large-scale visual search. He holds a Ph.D. from Northwestern University, M.Eng. from National University of Singapore, and B.Eng. from Huazhong University of Science and Technology. Education : Ph.D., Electrical and Computer Engineering, Northwestern University, 2009 M.Eng., Electrical and Computer Engineering, National University of Singapore, 2005 B.Eng., Communication Engineering, Huazhong University of Science and Technology, 2002 Research Interests : Dr. Yuan's work spans computer vision, video analytics, gesture/action analysis, and multimodal learning. He emphasizes scalable visual search, 3D reconstruction, and federated learning for medical imaging. His lab develops algorithms for autonomous systems, generative media processing, and human-AI interaction. Awards : IEEE Fellow (2021) IAPR Fellow (2018) SUNY Chancellor's Award for Excellence in Scholarship (2022) Advising & Grants : He has advised numerous PhD students, including Sudhir Yarram, Nan Xi, and Tianyu Luan. His research is supported by grants focusing on video generation, medical image analysis, and federated learning. He serves as Editor-in-Chief of JVCI and on editorial boards of T-PAMI, T-IP, and T-CSVT. Labs & Teams : Director of the Visual Computing Lab, leading projects in generative models, 3D scene understanding, and healthcare AI. Collaborates with industry partners like Meta, Amazon, and Accenture on applied vision systems.

Weija Shangمشاهده پروفایل

استاد

• Parallel Processing
• Computer Architecture
• Parallelizing Compiler
• +۷ مورد دیگر

Weija Shang is a Professor at the School of Engineering, Santa Clara University, where she has been since 1994. She previously served at the Center for Advanced Computer Studies, University of SW Louisiana (1990–1993). Her research spans parallel processing, computer architecture, parallelizing compilers, algorithm theory, and non-linear optimization. PhD in Computer Engineering, Purdue University (1990) MS in Computer Engineering, Purdue University (1984) BS in Computer Engineering, Changsha Institute of Technology (1982) Her publications focus on parallel computing , GPGPU optimization , FPGA design , and video coding techniques. Key themes include supernode transformations , media distribution algorithms , and stack optimization in recursive programs. Scientific awards include: Clare Boothe Luce Professor (1994–2000) NSF Research Initiation Award (1991) NSF Career Award (1995) She has supervised numerous research projects in parallel programming and compiler design, contributing to high-performance computing and distributed systems through collaborations with institutions like Xilinx and NASA.

• Materials Science
• Condensed Matter Physics
• Nanotechnology
• +۳ مورد دیگر

Roberto Di Capua is an Associate Professor affiliated with the CNR Institute for SuPerconductors, INnovative materials, and devices in Naples, Italy. His research focuses on advanced materials science, including graphene-like materials, oxide interfaces, superconductivity, and nanotechnology. He explores applications in energy materials, biomedical engineering, and environmental chemistry, such as developing biocompatible bio-interfaces and sustainable waste-to-material solutions. His studies on two-dimensional electron systems, ferromagnetic materials, and quantum phenomena contribute to the understanding of electronic and spin properties in novel heterostructures. He also investigates practical applications in sports biomechanics, analyzing gait variability and footwear effects on motor performance. Key research areas include hybrid materials synthesis (e.g., metal-organic frameworks), surface reconstructions in SrTiO3 systems, and the interplay of spin-orbit coupling in magnetic materials. His work bridges fundamental physics with applied technologies, emphasizing interdisciplinary approaches to material innovation.

Gim Hee Leeمشاهده پروفایل

دانشیار

• Computer Vision
• Robotic Perception
• Machine Learning
• +۳ مورد دیگر

Gim Hee Lee is an Associate Professor at the Department of Computer Science, National University of Singapore (NUS) School of Computing. He heads the Computer Vision and Robotic Perception (CVRP) Laboratory and holds affiliations with the NUS Graduate School for Integrative Sciences and Engineering (NGS) and the NUS Institute of Data Science (IDS). Previously, he was a researcher at Mitsubishi Electric Research Laboratories (MERL) in the USA and worked at DSO National Laboratories in Singapore. Education Dr.sc. in Computer Science from ETH Zurich M.Eng. in Mechanical Engineering from National University of Singapore B.Eng. in Mechanical Engineering (1st Class Honors) from National University of Singapore Research Interests Professor Lee's research focuses on Computer Vision , Robotic Perception , and Machine Learning , with specialization in dynamic 3D scene reconstruction, event camera applications, and cross-view geo-localization. His work bridges theoretical advances with real-world applications in autonomous systems and augmented reality, particularly through neural scene representations and geometric computer vision techniques. Scientific Awards Faculty Teaching Excellence Award (AY 2018/19 and AY 2017/18) CVPR 2014 Doctoral Consortium Travel Award Finalist for IROS 2012 Best Paper Award Finalist for IROS 2012 Best Video Award Advising and Service Professor Lee serves as Associate Editor for IJCV and has held Area Chair positions for major conferences including CVPR, ICCV, ECCV, and NeurIPS. He chaired Program Committees for 3DV 2022 and served as Demo Chair for CVPR 2023. His research on the Cross-View Matching Network (CVM-Net) demonstrated real-time ground-to-aerial geo-localization in moving vehicles, establishing a benchmark for subsequent research in this field. Laboratories He leads the Computer Vision and Robotic Perception (CVRP) Laboratory at NUS, which develops cutting-edge solutions for 3D scene understanding, event-based vision, and robotic navigation. The lab's work includes acquiring high-quality datasets for dynamic scene reconstruction and advancing neural representations for real-world applications.

Lin SHAOمشاهده پروفایل

استادیار

• Robotics
• Artificial Intelligence
• Decision Making & Planning
• +۵ مورد دیگر

Lin SHAO is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His research focuses on developing algorithms and systems for robotic perception and manipulation, aiming to create general-purpose robotic systems capable of performing diverse tasks in varied environments. He holds a Ph.D. and M.S. from Stanford University, and a B.S. in Geochemistry from Nanjing University. His educational background includes: Ph.D. (Stanford University, 2021) M.S. (Stanford University, 2017) B.S. in Geochemistry (Nanjing University, 2014) Key research areas include robotic perception and manipulation, reinforcement learning, differentiable physics simulation, and large-scale robotic learning systems. His work emphasizes enabling robots to autonomously perceive and interact with their environment through advanced algorithms and simulation techniques. Notable contributions include the SAM-RL framework for model-based reinforcement learning and the SAGCI-System for sample-efficient robot learning. He has received the Best System Paper Award Finalist at RSS 2023 and serves as a co-chair of the IEEE Robotics and Automation Society's Technical Committee on Robot Learning. Courses taught include CS2040S (Data Structures and Algorithms), CS4278/CS5478 (Intelligent Robots: Algorithms and Systems), and Knowledge@Computing workshops. His research group actively explores topics such as robotic grasping, motion planning, and human-robot interaction, with applications in both industrial and service robotics domains.

Nidhi Seethapathiمشاهده پروفایل

استادیار

• Human Movement Science
• Computational Neuroscience
• Biomechanics
• +۳ مورد دیگر

Nidhi Seethapathi is the Frederick A. (1971) and Carole J. Middleton Career Development Assistant Professor of Brain and Cognitive Sciences and Electrical Engineering and Computer Science at MIT. She holds a shared affiliation with the Schwarzman College of Computing. Her research focuses on building predictive computational models to understand human movement, integrating theory, modeling, and experimental approaches. Key areas include analyzing computational objectives in movement decisions (e.g., energy, stability, time), studying internal variables guiding locomotion control, and developing theories of adaptive motor learning. Her lab emphasizes practical applications in neuromotor rehabilitation and explores strategies to enhance learning efficiency. They employ a 'nails-not-hammers' approach, letting scientific questions drive methodological choices rather than vice versa. Current projects include markerless motion capture for gait analysis and developing therapeutic tools for Parkinson's disease using magnetoelectric nanodiscs. Seethapathi is actively recruiting graduate students, postdoctoral researchers, and undergraduates. The lab collaborates across disciplines, combining neuroscience, engineering, and computer science to advance translational research in movement science.

Michael Bruenigمشاهده پروفایل

استاد

• Robotics
• Cybersecurity
• Data Innovation
• +۶ مورد دیگر

Professor Michael Bruenig is the Head of School for the School of Electrical Engineering and Computer Science (EECS) at The University of Queensland (UQ), a position he has held since 2016, with an interim role as Head of UQ’s Business School from 2019–2021. He previously led the CSIRO’s $140m National Research Flagship on Digital Productivity and co-founded Data61. With a PhD from RWTH Aachen University, his career spans automotive R&D in Germany, Silicon Valley, and Australia. He holds a Master’s and PhD in Science from RWTH Aachen University, Germany. His research focuses on strategic innovation, entrepreneurship, and translating research into industry impact through ventures like UQ Cyber, the National Industry 4.0 Energy Testlab, and the QLD Government AI Hub. He pioneered UQ’s Bachelor of Computer Science, Master of Cyber Security (based on US NICE Framework), and online Master of Business Analytics. His work spans robotics, sensor networks, and terahertz technology, with recent articles addressing lidar-based navigation, terahertz imaging, and cybersecurity. He advises startups and sits on boards, driving commercialization and spin-offs. Key initiatives include UQ Innovate and UQ Ventures, fostering student and faculty entrepreneurship. Labs and teams include collaborations on AI, Industry 4.0, and energy systems. His leadership emphasizes cross-disciplinary projects and curriculum innovation in tech and data fields.