Jianping Gao is a Professor in applied mathematical modeling and control systems, with significant contributions to vehicular networks, privacy protection algorithms, and nonlinear dynamical systems. His work spans interdisciplinary domains including intelligent transportation, federated learning, and biomechanical engineering. Core Research Areas : Mathematical modeling of chemotaxis and ecological systems Secure computation offloading in vehicle edge networks Federated learning optimization for Internet of Vehicles Privacy-preserving algorithms in social networked transportation Recent Trends : 2025 publications focus on dynamic gradient compression strategies and 3D radar sensing for autonomous vehicles 2024 work emphasizes blockchain-based privacy methods and Gaussian process vehicle state estimation 2023 studies include comprehensive surveys on social IoV security and contraflow control optimization The most frequent co-authors include Ling Xing (13 collaborations), Honghai Wu (12), Huahong Ma (8), and Kaikai Deng (4), indicating sustained interdisciplinary team efforts.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۷۲۱-۷۳۵ از ۸۷۲ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Konstantinos P. Triantis is the John Lawrence Professor of Industrial and Systems Engineering at Virginia Tech's College of Engineering, based at the Northern Virginia Center. He serves as Director of the Industrial and Systems Engineering (ISE) program in the National Capital Region (NCR) and co-director of the System Performance Lab (SPL). His roles include leadership on the NCR College of Engineering executive committee and former directorship of the Systems Engineering Program. Education: Ph.D., Industrial Engineering and Operations Research, Columbia University (1984) M. Phil., Industrial Engineering and Operations Research, Columbia University (1980) M.S., Industrial and Management Engineering, Columbia University (1977) B.S., Industrial and Management Engineering, Columbia University (1976) Research Interests: Dr. Triantis focuses on Management Systems Engineering , Data Envelopment Analysis (DEA) , and Critical Infrastructure Resilience . His work bridges engineering with social sciences, emphasizing dynamic performance evaluation (DPEM), complex adaptive systems, and efficiency measurement in transportation, healthcare, and disaster response systems. Recent themes include automation reliance in safety-critical roles and policy evaluation for infrastructure sustainability. Grants & Funding: He has led over $6.6M in funded projects from NSF, NASA, DOD, and others. Notable areas include resilience of interdependent infrastructures and traffic congestion mitigation strategies. Awards: John L. Lawrence Professorship (2013) Ralph H. Bogle Professor Fellow (2008) Advising: Advised over 60 graduate students, with six current advisees. Pioneered TV/compressed video course delivery in the NCR. Labs & Outreach: Co-directs the System Performance Lab (SPL), focusing on interdisciplinary solutions for engineered systems. Active in NSF-funded initiatives like the Resilient Infrastructure Processes and Systems (RIPS) program.
- Control Systems
- Electrical Engineering
- Robotics
- +۳ مورد دیگر
Srinivasa M Salapaka is a Professor at the University of Illinois, holding joint appointments in the Department of Mechanical Science and Engineering and the Department of Industrial and Enterprise Systems Engineering. He is also affiliated with the Coordinated Science Lab. His research focuses on control systems, power electronics, optimization algorithms, and robotics applications such as UAV routing and atomic force microscopy. Salapaka has received prestigious awards including the ASME Fellow (2015), IEEE Senior Member (2020), and NSF CAREER Award (2005). His work integrates theoretical advancements in control theory with practical applications in energy systems, including inverters for microgrids and grid stability solutions. Recent research emphasizes scalable UAV path planning, maximum entropy principle frameworks for resource allocation, and robust control designs for dynamic systems. Collaborations span aerospace, mechanical, and electrical engineering disciplines, addressing challenges in renewable energy, autonomous systems, and manufacturing processes. Salapaka's publications highlight contributions to multi-agent systems, optimization under constraints, and real-time control architectures. His lab's activities involve developing novel control frameworks for high-speed microscopy and investigating fouling prediction in industrial heat exchangers using machine learning. Research trends reflect a balance between foundational control methodologies and their deployment in emerging technologies like advanced air mobility and smart grids.
Matteo Scandellaمشاهده پروفایل
استادیار
Matteo Scandella is an Assistant Professor at the University of Bergamo , Italy, since February 2024. Previously, he served as a post-doctoral researcher at Imperial College London (2020–2024). He holds a PhD in Control Systems (2019) and advanced degrees in Computer Science Engineering from the University of Bergamo (Bachelor 2014, Master 2016). His research focuses on kernel-based machine learning techniques applied to system identification , nonlinear dynamics , and control systems , with emphasis on aerospace applications and mechatronics. He has developed methods for stable nonlinear system modeling, continuous-time system identification, and data-driven control strategies like SelfMPC. Education: Bachelor Degree in Computer Science Engineering (2014) – University of Bergamo Master Degree in Computer Science Engineering (2016) – University of Bergamo PhD in Control Systems (2019) – University of Bergamo Research interests span kernel methods (e.g., manifold regularization, RKHS), stability analysis of nonlinear systems, and data-driven control . His work bridges theoretical advancements with engineering applications such as health monitoring of aerospace actuators and urban traffic optimization. Recent publications highlight innovations in automated MPC tuning and graph-based system identification techniques. Teaching includes courses like Automatica (6 CFU) and laboratory modules in sustainable industrial systems. His research has been published in top journals like Automatica and conferences such as L4DC and SYSID.
Paris Mastorocostas is a Professor at the University of West Attica, specializing in computational intelligence, signal processing, and algorithmic data mining. His research focuses on neuro-fuzzy systems, deep learning applications, and their integration into domains like energy systems, transportation networks, and industrial automation. He has pioneered methodologies for short-term load forecasting, telecommunications fraud detection, and adaptive noise cancellation in medical signals. Key research themes include: Neuro-fuzzy modeling for dynamic systems Machine learning in energy and transportation Data warehouse development using Python/MySQL UAV-based inventory quantification Graph neural networks for urban metro flow His work demonstrates a strong interdisciplinary approach, combining algorithm design with practical industrial applications. Notable contributions include the ReNFuzz-LF model for electricity load forecasting and TMD-BERT for transportation mode detection. His publications span over 25 years, showing sustained innovation in computational intelligence techniques and their real-world implementation.
Mark Nejadمشاهده پروفایل
دانشیار
Mark Nejad is an Associate Professor at the University of Delaware, focusing on autonomous and connected vehicle systems, sustainable transportation, and interdependent infrastructure optimization. His research integrates blockchain technology, federated learning, and operations research to address challenges in smart transportation and cybersecurity. Education: M.S., Wayne State University Ph.D., Wayne State University Research Interests: Blockchain applications in transportation security and trust management Federated learning for real-time traffic prediction Autonomous vehicle integration in mixed traffic environments Optimization of cloud computing and edge infrastructure for smart cities Game theory-driven resource allocation strategies Key Article Trends (2022-2025): Recent work emphasizes blockchain-based solutions for VANET trust systems, decentralized EHR management, and adaptive federated learning frameworks. His research bridges cybersecurity, AI, and transportation engineering to create resilient infrastructure systems. Awards & Grants: No explicit awards listed. Active in securing grants for blockchain-transportation and cloud federation projects. Advising & Labs: No listed advisees. Engaged in collaborative research teams focusing on smart city technologies and decentralized systems.
Nicholas Rhinehartمشاهده پروفایل
استادیار
- Robotics
- Artificial Intelligence
- Machine Learning
- +۳ مورد دیگر
Nicholas Rhinehart is an Assistant Professor at the University of Toronto, holding primary appointments in the Institute for Aerospace Studies and cross-appointments in the Department of Computer Science and Robotics Institute. He leads the Learning, Embodied Autonomy, and Forecasting (LEAF) Lab, focused on developing autonomous systems that operate safely in complex environments through advancements in reinforcement learning, imitation learning, and deep learning. His research bridges robotics, computer vision, and AI to create efficient algorithms for autonomous decision-making. Education: Ph.D. in Robotics from Carnegie Mellon University (2014–2019), M.S. in Robotics (2013–2014), and B.S. in Engineering and B.A. in Computer Science from Swarthmore College (2008–2012). Prior to academia, he was a Senior Research Scientist at Waymo Research (2022–2024), a Postdoctoral Scholar at UC Berkeley’s RAIL Lab (2019–2022), and held research internships at NEC Labs, Uber Advanced Technologies Group, and Berkeley. Research Interests: His work emphasizes autonomous systems, reinforcement learning, imitation learning, and generative models for 3D scene forecasting. Recent publications explore diverse topics like residual reward models, traffic prediction, and social navigation using deep learning techniques. Labs/Teams: PI of the LEAF Lab, which develops foundational algorithms for embodied autonomy and forecasting in robotics and AI.
Marian Codreanu is a Senior Associate Professor at Linköping University's Department of Science and Technology (ITN), within the Communications and Transport Systems (KTS) division. He holds a M.Sc. from the University Politehnica of Bucharest (1998) and a Ph.D. from the University of Oulu, Finland (2007), where his thesis was recognized as the best in technical sciences. He has held roles including Marie Skłodowska-Curie Fellow (2019) and Academy of Finland Research Fellow (2013). His research focuses on information freshness optimization, sparse signal processing, and machine learning applications in wireless networking. Dr. Codreanu’s work spans wireless communications, statistical signal processing, and mathematical optimization. He co-founded the Nordic Workshop on System and Network Optimization for Wireless (SNOW) and served as IEEE Vice Chair (2011–2018). His contributions include over 100 journal/conference papers and co-authored books like Compressed Sensing with Applications in Wireless Networks (2019) and Weighted Sum-Rate Maximization in Wireless Networks (2012). His research group, Wireless Optimization and Analytics, develops models and algorithms for wireless network performance and data analytics. Key projects include B-PREPARED (disaster preparedness) and EAN (electric aircraft networks). His work integrates theoretical advancements with practical applications in smart cities, logistics, and air traffic management.
Maria Itoمشاهده پروفایل
استاد مهمان
Maria Ito is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science, focusing on interdisciplinary research at the intersection of computer vision, networking protocols, and adaptive media streaming. Her work emphasizes video stabilization techniques, network performance optimization, and machine learning-driven solutions for real-time media delivery systems. Her research interests span video processing, deep learning applications in computer vision, and the design of efficient adaptive streaming protocols. Notable contributions include frameworks for distortion-free video stabilization and performance evaluations of QUIC vs. TCP protocols. Publications highlight trends in combining control theory with adaptive streaming systems and developing benchmark datasets for deep learning models. While no specific awards or grants are listed, her work demonstrates significant contributions to both theoretical and applied aspects of media streaming technologies. Advising and lab affiliations are not explicitly detailed in the provided materials.
Tan Chee Pinمشاهده پروفایل
دانشیار
- Control Theory
- Fault Detection
- State Estimation
- +۶ مورد دیگر
Tan Chee Pin is an Associate Professor and Head of the Mechatronics Engineering program at Monash University Malaysia. His research focuses on observer design for system state estimation, with applications in robotics, traffic management, and energy systems. He has published extensively in top journals like IEEE Transactions on Automation Science and Engineering, and secured RM1.5m in research funding. His teaching emphasizes student-centric approaches, earning him internal awards and a national educator award nomination. Research interests include model-based observer configurations for minimizing sensor usage, condition monitoring, and predictive maintenance. Applications span soft robotics, traffic congestion reduction, and energy optimization. He collaborates on projects like IoT-based predictive maintenance systems and solar technology for Orang Asli communities. Editorial roles include associate editorships at IEEE Transactions on Cybernetics and the Journal of the Franklin Institute. Awards include finalist status for the 2018 National Outstanding Educator’s Award. Advising includes supervising 7 PhD completions and 13 current students. Projects include smart drone trash monitoring and control systems for soft robots using few-shot learning.
Adam Ścibiorمشاهده پروفایل
استاد مدعو
Adam Ścibior is an Adjunct Professor in the Department of Computer Science at the University of British Columbia (UBC), situated in the Faculty of Science. His research focuses on artificial intelligence, machine learning, and probabilistic programming, with a strong emphasis on autonomous systems, generative models, and reinforcement learning applications in autonomous driving. He contributes to developing simulation benchmarks, diffusion models, and algorithms for multi-agent behavior prediction. His work bridges theoretical advancements in probabilistic inference with practical implementations in robotics and computer vision. Key research areas include autonomous vehicle navigation, scene simulation, and improving synthetic training data realism. He has published extensively on topics like diffusion models for traffic scene generation, reinforcement learning benchmarks for autonomous systems, and conditional permutation-invariant flows. His research often involves collaboration across disciplines, integrating probabilistic programming with real-world applications. Notable contributions include the TorchDriveEnv benchmark for autonomous driving and advancements in diffusion-based generative models for video inpainting and interactive navigation. While no specific awards or grants are explicitly listed, his work reflects a focus on impactful contributions to AI and robotics. No advising relationships or lab affiliations are detailed in the provided materials.
Dr. Christian Claudel is an Associate Professor in the Department of Civil, Architectural and Environmental Engineering at UT Austin, holding the William J. Murray, Jr. Fellowship in Engineering. His research develops control and estimation methods for distributed parameter systems with applications in traffic monitoring, environmental sensing, and infrastructure systems. Research focuses on cyberphysical systems for transportation, including traffic state estimation algorithms, connected vehicle applications, and unmanned aerial vehicle monitoring. Recent work develops scalable particle filters, traffic control systems for autonomous vehicles, and privacy-preserving monitoring frameworks. Methodologies integrate PDE-based modeling, optimal control theory, and wireless sensor networks. Applications include air pollution monitoring, traffic congestion mitigation, highway deflection measurement, and winter road operations.
Kavi Bhallaمشاهده پروفایل
دانشیار
- Global Health
- Injury Prevention
- Road Safety
- +۳ مورد دیگر
Kavi Bhalla is an Associate Professor in the Department of Public Health Sciences at the University of Chicago, affiliated with the Harris School of Public Policy. His research focuses on road safety in low- and middle-income countries, emphasizing analytical tools for injury estimation and policy development. He co-led the injury expert group for the 2010 Global Burden of Disease Project and applies engineering principles to injury biomechanics and vehicle safety. Education: PhD in Theoretical and Applied Mechanics, Cornell University (2001) BTech in Mechanical Engineering, Indian Institute of Technology Delhi (1995) Ellison Fellow, Harvard University (2007) Bell Fellow, Harvard University (2005) Research Interests: Design of safe transport systems, road safety policy, injury epidemiology, and data-driven approaches to public health challenges. His work bridges mechanical engineering and public health, addressing global disparities in road safety outcomes. Publications: Recent articles focus on road traffic fatalities in India, bicycle safety using mobile data, and the effectiveness of road safety interventions. His work highlights gaps in injury data systems and promotes evidence-based policymaking. Awards/Grants: None explicitly listed. His contributions include foundational work on global injury burden estimation frameworks. Labs/Teams: Collaborates with global networks like the Global Burden of Disease Injury Expert Group and engages in multidisciplinary projects with institutions in Asia, Africa, and Latin America.
Paul Hargreavesمشاهده پروفایل
پژوهشگر ارشد
Paul Hargreaves is a Research Fellow at Scotland's Rural College (SRUC) within the Agriculture & Business Management department. His work focuses on soil health, grassland management, and greenhouse gas emissions. He leads projects on controlled traffic farming, methane detection, and sustainable agriculture practices. Hargreaves collaborates on initiatives like Dairy 4 Future and Climate Smart Cattle Farming, addressing emissions and soil quality. He supervises PhD and undergraduate research on soil microbiology and compaction. Research Interests: Maintaining soil quality through precision agriculture, reducing emissions via legume integration, and developing tools for soil health assessment. Key projects include RESAS-funded work on controlled traffic systems and GHG mitigation strategies. Collaborations span European initiatives, focusing on intercropping, soil biodiversity, and carbon recovery. His recent work emphasizes laser methane detection and nitrification inhibitors to curb emissions. Hargreaves also contributes to policy through soil management guidelines and participatory knowledge exchange programs.
Vitaly Schetininمشاهده پروفایل
مدرس ارشد
Dr Vitaly Schetinin is a Senior Lecturer in Computing and Information Systems at the University of Bedfordshire's School of Computer Science and Technology. He holds a PhD in Computer Science (Computational Intelligence) from 1997 and has held post-doctoral roles at the Universities of Jena (Germany) and Exeter before joining Bedfordshire in 2005. Research: His work spans Computational Intelligence, Machine Learning, Bayesian Modelling, and applications in medical informatics, risk assessment, and pattern recognition. Notable projects include Bayesian models for trauma survival prediction and EEG-based newborn brain maturity assessment funded by the Leverhulme Trust and EPSRC. Teaching: He teaches modules in Computational Intelligence, Data Mining, Robotics, and Statistics across undergraduate and postgraduate programs. He actively supervises research projects at all academic levels. Grants: Awarded £82,500 from the Leverhulme Trust (2009–2012) for newborn brain maturity research and EPSRC funding for radio wave propagation studies (2008–2009). Publications: Over 50 peer-reviewed articles focus on machine learning applications in healthcare, finance, and engineering. Recent work includes adaptive Bayesian learning frameworks and deep learning for medical imaging. Professional Engagement: Reviews for IEEE journals (Pattern Analysis, Neural Networks, Biomedical Engineering) and serves on technical committees for international conferences including IEA-AIE and KES.