جست‌وجوی استاد و پژوهشگر

Jiju Poovvancheriمشاهده پروفایل

دانشیار

• Computer Graphics
• 3D Vision
• Machine Learning
• +۸ مورد دیگر

Dr. Jiju Poovvancheri is an Associate Professor in the Department of Math & Computing Science at Saint Mary’s University, Halifax, Canada. He holds affiliations with the Graphics & Spatial Computing Lab and previously held postdoctoral positions at the University of Victoria and University of Calgary. His research focuses on computer graphics, 3D vision, and machine learning, with applications in virtual/augmented reality, autonomous robotics, urban planning, and bio-mechanical studies. Key research areas include point cloud processing, semantic surface reconstruction, spatial data structures, and geometric deep learning. Education: PhD from Indian Institute of Technology Madras (2011–2014), supervised by Prof. Ramanathan Muthuganapathy. Postdoctoral work at University of Calgary (EYES-HIGH Fellowship, 2015–2017) and University of Victoria (MITACS Elevate Fellowship, 2018). Research & Awards: Winner of MITACS Elevate Fellowship (2018), EYES-HIGH Fellowship (2015–2017), and multiple grants including NSERC DG (2019–2026). His work has been supported by NVIDIA GPU hardware, NSERC, CFI, and industry partners like Modest Tree Media and Caterpillar. Professional Activities: Associate Editor for IEEE Access , Guest Editor for special issues in Remote Sensing and Sensors , and reviewer for top conferences like CVPR, ICCV, and ECCV. Member of ACM SIGGRAPH, Solid Modeling Association, and IEEE Geoscience & Remote Sensing Society. Lab & Collaborations: Leads the Graphics & Spatial Computing Lab, collaborating with institutions globally. Current projects include "Interaction and navigation in virtual spaces" and industry partnerships with Modest Tree Media for real-time object recognition. Advising: Supervised over 20 graduate and undergraduate students, with notable alumni advancing to roles at ReelData AI, Royal Canadian Air Force, and academic institutions like Dalhousie University.

Sofia Kalakouمشاهده پروفایل

استادیار

• Urban Transport Planning
• Air Transport Management
• Technology Integration in Operations
• +۲ مورد دیگر

Sofia Kalakou is an Assistant Professor in the Department of Marketing, Operations and General Management at ISCTE Business School, where she teaches operations management across undergraduate, graduate, and postgraduate programs. She also serves as Director of the Industrial Management and Logistics degree program. Her research focuses on urban and air transport planning, technology integration in operations, sustainable practices, and performance evaluation. She coordinates four EU-funded projects on operations management and new technologies. Dr. Kalakou holds a doctorate and master’s in Transport Systems from ISCTE, a civil engineering diploma, and prior experience as a transport and mobility consultant. Her work bridges academic research with practical applications, addressing challenges like airport terminal optimization, drone delivery systems, and gender-sensitive mobility policies. She has published extensively on topics ranging from lean six sigma applications in telecom repair to SWOT analysis of Greek seaplane operations. Her funded projects emphasize sustainable transport solutions and capacity-building for local authorities implementing MaaS and smart mobility initiatives. Key contributions include frameworks for setting transport sustainability targets and analyzing passenger behavior in airports through activity-based modeling.

Hong Wangمشاهده پروفایل

استاد

• Numerical Analysis
• Differential Equations
• Fractional Calculus
• +۳ مورد دیگر

Hong Wang is a Professor in the Department of Mathematics at the University of South Carolina, part of the McCausland College of Arts and Sciences. He specializes in numerical analysis and differential equations, with a focus on numerical methods for fractional and variable-order equations. His work addresses complex boundary conditions, optimal control, and scientific computing challenges in advection-diffusion systems. Education Ph.D. in Mathematics, University of Wyoming (1992) Research Interests His research emphasizes numerical approximation techniques for differential/integral equations, particularly fractional diffusion-wave equations, variable-order models, and stochastic systems. Key areas include finite element methods, spectral methods, and fast algorithms for solving high-dimensional and time-dependent problems. He explores applications in optimal control, viscoelasticity, and multi-scale modeling. Recent Work Trends Recent publications highlight advancements in fractional calculus applications, including variable-exponent diffusion, distributed-order equations, and stochastic fractional differential equations. His work often combines theoretical analysis with computational efficiency, addressing challenges like nonsmooth parameters and singular density functions. Grants and Advising No specific grants or advisees are listed, but his research collaborations span computational mathematics, applied physics, and engineering systems. Labs/Teams No dedicated lab or team is explicitly mentioned, though his research aligns with computational and applied mathematics groups at the University of South Carolina.

• Data Quality
• Database Systems
• Data Profiling
• +۷ مورد دیگر

Felix Naumann is a Professor at the Hasso Plattner Institute in Potsdam, Germany, specializing in data management and database systems. His research focuses on data quality, data profiling, entity resolution, functional dependencies, and database optimization. He has authored over 300 publications in top-tier conferences and journals, including VLDB, SIGMOD, and ICDE. His work bridges theoretical foundations with practical systems, such as TASHEEH for data cleaning and PRISMA for privacy-preserving schema matching. He serves as an editor for the ACM Journal of Data and Information Quality and has led initiatives on hybrid conference formats and inclusive computing. Key contributions include: Data Quality: Pioneered studies on data quality's impact on machine learning and developed tools like AutoTSAD for anomaly detection. Data Dependency Discovery: Advanced functional and inclusion dependency mining algorithms, including Hitting Set Enumeration methods. Schema Matching & Integration: Created systems like BrewER and Frost for entity resolution and schema alignment. Educational Impact: Led massive open courses in data engineering with over 10,000 participants. His work emphasizes practical applications in cultural heritage data (ReCLAIM), Wikipedia table analysis, and cross-platform data systems (RHEEMix). He collaborates extensively with industry and academia, addressing challenges in dynamic datasets and data governance.

Jean Ponceمشاهده پروفایل

استاد

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

Jean Ponce is a Professor of Computer Science at Ecole Normale Superieure (ENS) in Paris and a Part-Time Global Distinguished Professor at New York University's Courant Institute of Mathematical Sciences and Center for Data Science (CDS). He previously served as Director of the ENS Computer Science Department (2011-2017) and held positions at Inria (2017-2022), University of Illinois at Urbana-Champaign (1998-2006), MIT, Stanford, and Inria (1982-1985). Academic Leadership: Scientific Director of PRAIRIE Interdisciplinary AI Research Institute in Paris Startup Involvement: Co-founder and CEO of Enhance Lab (2022) Editorial Roles: Senior Editor-in-Chief of International Journal of Computer Vision (2019-2022) Conference Leadership: Chair of IEEE CVPR (1997,2000), ECCV (2008), and upcoming ICCV (2023) Research Focus: Computer vision, machine learning, robotics, and AI with applications in exoplanet imaging, 3D reconstruction, and image quality assessment. His work bridges statistical learning and deep learning approaches. Awards: IEEE Fellow (2003) ELLIS Fellow (2019) ERC Advanced Grant (2011) IEEE CVPR Longuet-Higgins Prizes (2016,2020) ICML Test-of-Time Award (2019) Patents & Publications: Co-author of influential textbook Computer Vision: A Modern Approach (translated into Chinese, Japanese, Russian). Holds two US patents and one pending French patent. Google Scholar h-index of 78 with over 55,000 citations.

Shivanjali Khareمشاهده پروفایل

استادیار

• Internet of Things (IoT) Data Security
• Hybrid Cryptography
• Wireless Sensor Technologies
• +۳ مورد دیگر

Dr. Shivanjali Khare is an Assistant Professor in the Computer Science department at the University of New Haven, affiliated with the Tagliatela College of Engineering. She leads the SARI (Security and ARtificial Intelligence) Research Lab and teaches courses in Artificial Intelligence and Data Mining. Her research focuses on enhancing cybersecurity through hybrid cryptography, IoT data security, machine learning, and wireless sensor technologies to empower user protection in the digital age. Education: Ph.D. and M.S. in Computer Science from the University of Louisiana at Lafayette (2021 and 2016, respectively). Research Interests: IoT Data Security Hybrid Cryptography Systems Wireless Sensor Networks Big Data Sharing Mechanisms AI-Driven Cybersecurity Solutions Publications highlight contributions in IoT security protocols, cryptographic algorithm optimization, and machine learning applications. Notable work includes energy-efficient secure IoT drone systems (ESIoD) and ensemble learning for anomaly detection in smart homes. Recent outreach includes conducting cybersecurity seminars for senior citizens through the New Haven Free Public Library, demonstrating her commitment to societal impact and public education.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Li Wei is a distinguished academic affiliated with Tsinghua University, with a focus on interdisciplinary research spanning artificial intelligence, machine learning, and computer vision. His work often intersects with medical informatics, remote sensing, and signal processing, demonstrating a commitment to advancing technological solutions in healthcare, environmental monitoring, and engineering systems. Research interests include deep learning applications in clinical diagnostics, satellite data analysis for climate modeling, and optimization of energy storage systems. He has contributed to innovative solutions in areas such as UAV-enabled edge computing, privacy-preserving blockchain protocols, and thermal-based surveillance systems. His collaborative projects often involve multidisciplinary teams across institutions. Publications reflect a strong emphasis on practical applications, such as mobile health tools for tumor recognition, transformer-based super-resolution techniques for oceanography, and AI-driven risk classification models for respiratory diseases. While no specific awards or grants are listed, his prolific output across top-tier journals indicates sustained research impact. Professional activities include contributions to conferences like RecSys, MICCAI, and AAAI, and editorial roles are implied through his extensive publication record. Collaborations with industry partners (e.g., in energy systems and medical imaging) suggest engagement with real-world problem-solving.

Dongmei Chenمشاهده پروفایل

استاد

• Geomatics
• Environmental Science
• GIS Applications
• +۴ مورد دیگر

Dr. Dongmei Chen is a Professor in the Department of Geography and Planning at Queen's University, affiliated with the Faculty of Arts and Science. She holds cross appointments in the School of Environmental Studies and the Beaty Water Research Centre. Her research focuses on understanding human-environment interactions through GIS and remote sensing, with specialties in land use modeling, climate change impacts, and spatial data analysis. Education: PhD in Geography (2001, San Diego State University/UCSB); M.Sc. in GIS/Remote Sensing (Chinese Academy of Sciences); B.Sc. in Geography (Peking University). Pre-Queen's career included work as a GIS product specialist at ESRI. Research interests include geomatics, environmental science, and applying machine learning to remote sensing challenges. Recent work addresses flood susceptibility modeling, urban sprawl analysis, and spatial-temporal disease risk. Her interdisciplinary approach bridges geospatial technologies with environmental and social science. Active in research centers like LaGISA, she explores topics like air pollution monitoring and spatial data privacy. Grants and advising are integral to her role, though specific details are not detailed here. Collaborations span global contexts, including Brazil, Thailand, and China.

Luca Mottolaمشاهده پروفایل

استاد

• Battery-less IoT
• Mobile Space Robotics
• Smart Cities
• +۳ مورد دیگر

Luca Mottola is a Full Professor at Politecnico di Milano and Senior Researcher at RI.SE Sweden. He leads the Networked Embedded Software Lab (NESLab), focusing on next-generation IoT, battery-less systems, and mobile robotics. His work bridges embedded computing, wireless networks, and cyber-physical systems, with applications in smart cities and space robotics. Education: No explicit details provided in texts. Research interests include energy-harvesting IoT, drone communication, and real-time systems. Notable awards include the ACM SENSYS Test of Time Award (2022) and multiple Best Paper Awards at top conferences like MOBICOM and RTCSA. Grants/Projects: Overseen projects on battery-less IoT, drone localization, and energy-efficient systems. Editorial roles include IEEE Transactions on Mobile Computing and ACM Transactions on Sensor Networks. Labs/Teams: NESLab, FlyZone Testbed for aerial drones, and collaborations with NXP Technologies, TU Graz, and USC.

Yalong Yangمشاهده پروفایل

استادیار

• Immersive Analytics
• Virtual Reality
• Data Visualization
• +۳ مورد دیگر

Yalong Yang is an Assistant Professor at the School of Interactive Computing at Georgia Institute of Technology. His research focuses on immersive analytics, virtual reality (VR), and augmented reality (AR) interfaces, with a particular emphasis on spatial interaction, hybrid user interfaces, and data visualization techniques. He explores how embodied interactions and immersive environments enhance understanding in domains like education, sports analytics, and collaborative decision-making. Key research themes include hybrid immersive systems (combining VR/AR with physical devices), asymmetric collaboration in mixed environments, and AI-driven tools for programming education. His work spans both theoretical frameworks and applied systems, such as SPHERE for scalable personalized feedback in coding classrooms and VizGroup for collaborative learning analytics. Yang’s recent publications highlight trends in spatial hybrid interfaces, navigation in immersive environments, and the integration of generative AI in educational technologies. His projects often involve evaluating interaction techniques (e.g., label placement in AR) and comparing performance across desktop and VR platforms. Notable contributions include the SportsXR initiative for immersive analytics in sports, and systems like CompositingVis for creating complex visualizations in 3D spaces. His work addresses challenges in situated analytics, wearable technologies for outdoor activities, and audience analysis in VR exhibitions.

Samuel Wongمشاهده پروفایل

دانشیار

• Protein Structure Prediction
• Bayesian Inference
• Stochastic Systems
• +۲ مورد دیگر

Dr. Samuel Wong is Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. His research develops statistical methods for complex data science problems in protein structure analysis, dynamic systems inference, and materials reliability. Education includes PhD from Harvard Statistics Department (2013). Research addresses challenges in conformational sampling for protein folding, inference for differential equation models, and uncertainty quantification in materials science. Leads development of MAGI software for manifold-constrained Gaussian processes. Publications showcase innovations in Sequential Monte Carlo methods, spatial data fusion, and Bayesian approaches to industrial problems. Recent work focuses on protein structure variability and COVID-19 transmission modeling. Supervises graduate students in Bayesian analysis and computational statistics. Teaches courses including Analysis of Spatial Data and Applied Linear Models.

Martin Ostoja-Starzewskiمشاهده پروفایل

استاد

• Continuum Mechanics
• Stochastic Mechanics
• Fractal Media
• +۷ مورد دیگر

Martin Ostoja-Starzewski , Ph.D. (McGill), is a Professor of Mechanical Science & Engineering at the University of Illinois at Urbana-Champaign , with affiliate appointments at the Beckman Institute and National Center for Supercomputing Applications . His research spans continuum mechanics , stochastic wave propagation , and fractal media , focusing on scaling laws and the statistical-to-representative volume element transition. 2006–Present: Professor, UIUC 2001–2005: Canada Research Chair, McGill University 2023: Rothschild Distinguished Visiting Fellow, University of Cambridge 2022: Member, European Academy of Sciences and Arts 2024: Academia Europaea Foreign Member His work pioneered continuum mechanics with spontaneous second law violations and tensor random fields for stochastic PDEs. He authored four foundational books, including Tensor-Valued Random Fields for Continuum Physics (2019), and edited 16 special issues. Recent 2025–2023 articles explore odd elasticity , fractal thermodynamics , and stochastic wavefronts , bridging non-equilibrium thermodynamics to granular Couette systems . Scientific accolades include the Worcester Reed Warner Medal (2018) and ASME , AAM , SES fellowships. As part-time faculty at Beckman Institute (2008–Present), he integrates bioimaging with mechanics across electrosurgery and traumatic brain injury modeling.

Nils-Christian Deteringمشاهده پروفایل

دانشیار

• Mathematical Finance
• Probability Theory
• Energy Markets
• +۴ مورد دیگر

Nils-Christian Detering is an Associate Professor in the Department of Statistics & Applied Probability at the University of California, Santa Barbara (UCSB). He also serves as the Undergraduate Diversity, Equity, and Inclusion Officer. His research focuses on financial mathematics, probability theory, and their applications in systemic risk analysis, energy markets, and machine learning. Detering has contributed to understanding default contagion in financial systems, stochastic processes in energy derivatives, and neural network applications in functional data analysis. Research Interests : Financial systemic risk: Analyzing default contagion using random graphs and systemic stability metrics Infinite-dimensional stochastic analysis: Modeling energy markets via stochastic PDEs and forward curves Machine learning: Developing neural network frameworks for functional spaces and financial applications Teaching includes courses on stochastic processes, mathematical finance, and probability theory at both undergraduate and graduate levels. His work has been recognized with awards such as the Best Paper Award at the ACM International Conference on AI in Finance (2023). Key Publications address topics like reinforcement learning in banking networks, neural network calibration of energy curves, and integrated fire sales models. His research bridges theoretical probability with applied financial engineering challenges.

• Sound Design
• Audiovisual Art
• Multisensory Experience
• +۷ مورد دیگر

Associate Professor Darrin Verhagen is an award-winning sound designer and composer at RMIT University's School of Design, where he teaches in the Sound Design specialization within the Digital Media Program and directs the Audiokinetic Experiments (AkE) Lab. His academic work bridges creative practice with scientific inquiry into multisensory perception. Verhagen's research centers on the neurobiology of aesthetic experience, particularly exploring how sound interacts with vision, movement, and vibration. His work in the AkE Lab utilizes motion simulators, 4D cinema seating, and light to create installations that investigate the relationship between hearing, vision, movement and vibration. His academic background includes postgraduate research on musical extremes - lowercase sound for his Masters and Noise for his Doctorate. His creative output spans sound design for contemporary dance, theatre, installation, film, television and computer games. Verhagen has released over 20 albums internationally and performs audiovisual live shows globally. He was founder and curator of Dorobo records, which showcased Australian sound art for 15 years. Industry collaborations include work with Melbourne Theatre Company, Malthouse, Chunky Move, Australian Dance Theatre, and others. Notable awards include multiple Green Room Awards for Sound Design, Australian Academy of Cinema & Television Arts award finalist status, APRA Art Music awards finalist for experimental music, RMIT Research Award for technical innovation, and RMIT Teaching Award for excellence in teaching practice. Verhagen's supervision projects reveal a trajectory toward increasingly sophisticated explorations of sound in relation to space, perception, and human experience, with recent work focusing on transdisciplinary design, spatial sound applications, distributed audio systems, and vocal ecology. His work consistently bridges artistic practice with technological innovation and scientific inquiry into perceptual phenomena.

Eli Hawkinsمشاهده پروفایل

مدرس ارشد

• Noncommutative Geometry
• Mathematical Physics
• Quantum Gravity
• +۴ مورد دیگر

Dr. Eli Hawkins is a Senior Lecturer in the Department of Mathematics at the University of York. His work bridges geometry and noncommutative algebras, with a focus on quantization, noncommutative geometry, and quantum gravity. Education: PhD in Physics and Mathematics from Penn State University Research Interests: His research integrates advanced mathematical frameworks like C*-algebras, Poisson geometry, and Lie groupoids to explore quantization and quantum gravity, with applications in quantum field theory and differential geometry. Recent Publications: His recent work includes studies on Hochschild cohomology, deformation quantization, and noncommutative regularization, reflecting his expertise in algebraic and geometric methods.