جست‌وجوی استاد و پژوهشگر

Timothy C. Havensمشاهده پروفایل

استاد

• Machine Learning
• Signal Processing
• Sensor Fusion
• +۷ مورد دیگر

Timothy C. Havens is the William and Gloria Jackson Professor in Electrical and Computer Engineering and Computer Science at Michigan Tech University. He serves as Director of the Great Lakes Research Center and Institute of Computing and Cybersystems, overseeing multidisciplinary research in autonomous systems, environmental monitoring, and AI. He also directs the PRIME Lab, focusing on pattern recognition and machine learning. Havens holds a Ph.D. in Electrical and Computer Engineering from the University of Missouri (2010), advised by Jim Keller. His postdoctoral training included an NSF/CRA Computing Innovation Fellowship at Michigan State University under Anil Jain. He has taught extensively at Michigan Tech, including courses like Machine Learning (EE/CS5841), Data Mining (CS4821), and Signals and Systems (ECE3830). His research spans sensor fusion, underwater imaging, autonomous robotics, and explainable AI. Notable projects include reconstructing Great Lakes temperature data via deep learning, developing robust ROS-based autonomous systems, and advancing fuzzy integral techniques for interpretable machine learning. He emphasizes practical applications in environmental science, transportation safety, and defense technology. Havens' work has produced over 150 refereed publications and numerous patents. He prioritizes student training in LaTeX, programming, and publication practices, requiring all advisees to master these tools. His lab collaborates with industry and government agencies on projects involving sensor networks, underwater robotics, and AI security.

Aaron Beckerمشاهده پروفایل

استادیار

• Robotics
• Medical Engineering
• Swarm Intelligence
• +۳ مورد دیگر

Aaron Becker is an Assistant Professor in the Department of Electrical Engineering at the Cullen College of Engineering, University of Houston. His research focuses on robotics, particularly using swarms of robots and magnetic manipulation for biomedical and engineering applications. His lab develops solutions involving particle relocation, surgical assist systems, and magnetic modular robots. He is affiliated with the Hewlett Packard Enterprise Data Science Institute (HPE DSI) and the Research Computing Data Core (RCDC). Research interests include swarm robotics, magnetic control systems, human-robot interfaces, and medical robotics applications such as clot removal and endovascular interventions. His work integrates algorithmic foundations with practical implementations, often validated through experiments and simulations. Recent work emphasizes magnetic rotating swimmers for biomedical uses, collaborative research on modular cubes, and interface design for robotic scope systems. His publications span topics from network optimization to surgical tool development, reflecting interdisciplinary approaches to robotics challenges. No scientific awards are explicitly mentioned in the provided materials. His research activities include grant-funded projects and partnerships with institutions like HPE DSI. The lab's innovations aim to advance programmable matter and medical robotics through novel control strategies and modular systems design.

Sam Anandمشاهده پروفایل

استاد

• Additive Manufacturing
• Manufacturing Systems Optimization
• Industrial Internet of Things (IIoT)
• +۴ مورد دیگر

Sam Anand is a Professor and Director of the Siemens PLM Simulation Technology Center at the University of Cincinnati's College of Engineering, Architecture and Art (CEAS), Mechanical Engineering Department. He holds a PhD in Industrial Engineering from Penn State University (1990), and prior degrees from the Indian Institute of Science and the University of Madras. His research focuses on Additive Manufacturing (AM), Manufacturing Systems Optimization, Industrial IoT (IIoT), and Augmented Reality applications in industry. He has led over 50 grants totaling millions from federal agencies (NSF, DoD), industry partners (Procter & Gamble, Siemens), and state initiatives. Notable projects include topology optimization for AM, digital twin integration, and AI-driven manufacturing processes. Publications (15+ recent articles) emphasize AM distortion prediction, lattice structure design, and IIoT-enabled systems. He advises 18 current doctoral and master's students, emphasizing industry-ready skill development. His lab, the Siemens Center, collaborates with industry to bridge academia and real-world manufacturing challenges.

Xiaodong Jiaمشاهده پروفایل

استادیار

• Smart Manufacturing
• Advanced Process Control
• Machine Learning
• +۳ مورد دیگر

Xiaodong Jia is an Assistant Professor (ISE-Data Science Focus) at the University of Cincinnati's Department of Industrial & Systems Engineering, affiliated with the College of Engineering, Architecture and Art. He leads the Lab for Intelligent Metrology Systems and has held roles including Post-Doc Research Fellow at the Center for Intelligent Maintenance Systems and Research Assistant Professor at UC. His research focuses on smart manufacturing, advanced process control, and machine learning applications in industrial systems. Education includes a Ph.D. in Industrial Big Data and Prognostics from UC (2018), M.S. in Turbomachinery from Shanghai Jiaotong University (2014), and a B.S. in Mechanical Engineering from Central South University (2008). Research interests include data-driven modeling, prognostics and health management (PHM), and digital twin technologies. His work addresses challenges in semiconductor manufacturing, robotics, and predictive maintenance. Recent projects involve closed-loop defect identification in roll-to-part systems and AI-driven yield enhancement for semiconductors. Grants include $498K from the Department of Army for defect remediation in manufacturing, $325K from NIST for semiconductor yield optimization, and multiple industry partnerships with GM, Applied Materials, and Hitachi. Awards include top placements in global industrial AI and PHM competitions. His lab collaborates with industry leaders like General Motors, Mitsubishi, and TSMC, focusing on applied machine learning solutions for manufacturing systems. Ongoing projects emphasize transfer learning, data anonymization, and digital twin applications.

Oscar De Silvaمشاهده پروفایل

استادیار

• Robotics
• Control Systems
• Sensor Design
• +۶ مورد دیگر

Dr. Oscar De Silva is an Assistant Professor in the Department of Mechanical Engineering at Memorial University of Newfoundland. He holds a B.Sc. from the University of Moratuwa, Sri Lanka, and a PhD from Memorial University. His expertise spans instrumentation, controls, mechatronics, and robotics, with a focus on sensor design, state estimation, and navigation systems. Dr. De Silva's research interests include state estimation, control systems, nonlinear dynamics, navigation systems, sensor design, localization, mapping, and intelligent prosthetics. He has contributed to projects such as ice detection systems for marine safety and computer vision for robotics applications. Before his current role, he worked as a research fellow at the American Bureau of Shipping-Harsh Environment Technology Centre and taught at Memorial University as a sessional instructor. His work emphasizes practical applications in autonomous systems, industrial inspection, and environmental monitoring. Notable achievements include the IMechE UK Award for Outstanding Achievement and a Gold Medal in Mechanical Engineering. His research trends focus on integrating AI, LiDAR, and radar technologies for navigation and safety systems in robotics and marine environments. Awards: IMechE UK Award for Outstanding Achievement Gold Medal in Mechanical Engineering (University of Moratuwa) Fellow of Graduate Studies (Memorial University) Advising and Grants: While no specific student names are listed, his role as an Assistant Professor involves academic supervision. His grants and collaborations likely align with his research in robotics and sensor systems, though specific details are not provided here. Labs: He is associated with the Intelligent Systems Lab at Memorial University and previously contributed to the ABS HETC. His work often involves multidisciplinary teams focused on real-world engineering challenges.

Julien Leclercمشاهده پروفایل

استاد پژوهشی

• Robotic magnetic manipulation
• Underwater robots localization
• Motion planning for teams of robots

Dr. Julien Leclerc is a Research Assistant Professor in the Electrical and Computer Engineering department at the University of Houston's Cullen College of Engineering. His research focuses on robotic magnetic manipulation for medical applications, underwater robots localization and mapping, and motion planning for teams of robots. He holds a Ph.D. in Electrical Engineering from the University of Lorraine, France. Research interests include: Medical robotics for thrombus fragmentation Underwater pipe detection using magnetic imaging CFD simulation of magnetic swimmers 3D navigation systems for medical devices Network optimization for multi-robot systems Recent publications demonstrate strong focus on experimental validation of magnetic manipulation systems, with applications ranging from medical procedures to underwater infrastructure inspection.

Bo Xiaoمشاهده پروفایل

استادیار

• Construction Informatics
• Computer Vision
• Construction Robotics
• +۳ مورد دیگر

Bo Xiao is an Assistant Professor in Construction Management at Michigan Technological University, with joint appointments in Civil Engineering and the College of Business. His research develops AI and robotics solutions for construction challenges. His work focuses on: Computer vision for construction site monitoring and productivity analysis Natural language processing for automated documentation Blockchain applications in contract management Cybersecurity for construction data Robotics and automation in building processes His publications demonstrate innovations in vision-based monitoring, including nighttime equipment tracking, safety hazard detection, and automated report generation. Recent articles integrate deep learning with construction management applications. He received the 2022 Outstanding Reviewer Award from ASCE and third place in ASCE's Best Data Competition. He leads NSF-funded projects on cyber-training platforms and geodynamic modeling frameworks. He directs the Computing and Robotic Construction (CaRC) Lab, advancing automation in construction through computer vision, robotics, and AI technologies.

Ankit Agrawalمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Cyber-Physical Systems
• Software Engineering

Ankit Agrawal is Assistant Professor of Computer Science at Saint Louis University's School of Science and Engineering, where he directs the Human Computation Lab. His research focuses on human-computer interaction for autonomous systems, particularly human-drone collaboration frameworks. Education: Ph.D. in Computer Science, University of Notre Dame M.S. in Software Engineering, National Institute of Technology Rourkela Research explores human-AI collaboration paradigms for autonomous systems, with emphasis on safety validation, explainable autonomy, and simulation frameworks for cyber-physical systems. Recent work integrates large language models for automated UAV testing and augmented reality for emergency response. Publications demonstrate consistent focus on drone autonomy validation, with thematic emphasis on simulation-to-reality testing, human interaction patterns, and safety assurance for collaborative UAV operations. Awards: Honorable Mention, CHI 2020 Best Paper Award, SPLC 2020 Leads multiple projects funded by NASA and NSF investigating UAV testing frameworks. Supervises 12+ graduate students across drone simulation, human-AI collaboration, and autonomous systems validation projects. Directs the Human Computation Lab which develops interactive systems for human-machine teaming in critical applications including search-and-rescue and infrastructure inspection.

Riyadh Hindiمشاهده پروفایل

استاد

• Structural Engineering
• Earthquake Engineering
• Bridge Engineering
• +۳ مورد دیگر

Dr. Riyadh Hindi is a Professor of Civil Engineering at Saint Louis University's School of Science and Engineering. He holds a Ph.D. in Structural and Earthquake Engineering from the University of British Columbia, M.Sc. in Structural Engineering from University of Baghdad, and B.Sc. in Civil Engineering (First Class Honors) from University of Baghdad. His expertise encompasses non-linear behavior of concrete structures, seismic performance of bridges, and experimental analysis of structural components. Dr. Hindi's research focuses on structural response under extreme loads, including cyclic behavior of reinforced concrete, confinement techniques for bridge columns, and damage modeling. He directs the Concrete and Materials Laboratory featuring 500,000 lbs capacity testing equipment. His work integrates experimental testing with analytical modeling to improve infrastructure resilience. Recent publications demonstrate strong focus on construction-phase structural behavior (girder rotation mitigation), advanced materials (GFRP composites, high-strength concrete), and computational methods (deep learning applications). His 2025 publications indicate emerging emphasis on AI-based structural health monitoring. Honors include Fellowship in the Structural Engineering Institute (F.SEI) and professional memberships in ACI, ASCE, and IABMAS. He chairs ACI-343A subcommittee on concrete bridge design and serves as associate dean for graduate education. Dr. Hindi has supervised over 20 graduate theses and led research projects funded by NSF, Caltrans, and state DOTs. His team addresses practical construction challenges through field monitoring and develops retrofit solutions for aging infrastructure.

Kolios Panayiotisمشاهده پروفایل

استادیار

• Emergency Management
• Artificial Intelligence
• Autonomous Unmanned Aerial Systems
• +۳ مورد دیگر

Panayiotis Kolios is an Assistant Professor at the Department of Computer Science, University of Cyprus. He holds a PhD and BEng in Telecommunications Engineering from King's College London. His research focuses on networked intelligent systems, including autonomous UAVs, cyber-physical systems, and AI-driven emergency management. Key roles include coordinating the KIOS CoE's security and emergency response group, developing the SafeCY emergency app, and leading the AIDERS project for real-time disaster response using drones and machine learning. Education: BEng (King's College London, 2008), PhD (King's College London, 2012) Affiliations: KIOS Research and Innovation Centre of Excellence, University of Cyprus Projects: AIDERS, SWIFTERS, PREDICATE (EU-funded), SafeCY app, Cyprus Civil Defence Aerial Observation Unit Grants: Over 40M€ in EU and industrial funding Research emphasizes AI algorithms for emergency response, including drone-based situational awareness, wildfire monitoring, and rogue UAV detection. He co-organized the Exchange of Experts training program and led the team that won the Cooperative Aerial Robots Inspection Challenge (CDC 2023). Notable Achievements: ETEK Engineering Award Honorable Mention (2021) First Prize in CDC 2023 Cooperative Aerial Robots Challenge Development of the Cyprus Civil Defence Aerial Observation Unit Active in both academic and applied research, Kolios bridges theoretical advancements with real-world emergency management solutions.

Yanlin Guoمشاهده پروفایل

دانشیار

• Structural Engineering
• Wind Hazards
• Structural Health Monitoring
• +۲ مورد دیگر

Yanlin Guo is an Associate Professor in the Department of Civil and Environmental Engineering at Colorado State University and Director of the Center for Sustainable and Intelligent Transportation Systems. Her research focuses on mitigating wind and compound hazards through structural health monitoring, physics-integrated simulations, and remote sensing technologies to enhance infrastructure resilience. Education: B.S. Civil Engineering, Southeast University (2007) M.S. Civil Engineering, Hong Kong Polytechnic University (2010) Ph.D. Civil Engineering, University of Notre Dame (2015) Her work integrates natural hazard engineering with data science to evaluate buildings, transportation, and energy infrastructure. Key innovations include real-time monitoring systems for emergency response and climate adaptation strategies for coastal communities. Recent publications focus on hurricane resilience, autonomous inspection systems, and AI-driven structural assessment. Trends show strong emphasis on machine learning applications in wind engineering and UAV-based monitoring technologies. Awards: NSF CAREER Award (2024) DOE RACER Grant (2023) NSF Wind Hazard Grant (2022) Best Paper Award, Journal of Structural Engineering (2020) Dr. Guo leads multiple DOE and NSF projects on solar tracker resilience and urban wind risks. She advises PhD students in structural monitoring and climate adaptation research, and directs a center developing sustainable transportation solutions. The Guo Research Group specializes in full-scale structural testing and collaborates with national labs on field deployments. Current work includes developing cloud-based risk assessment platforms for engineers.

Noel O'Dowdمشاهده پروفایل

استاد

• Computational Mechanics
• Fracture Mechanics
• Materials Behaviour
• +۴ مورد دیگر

Noel O'Dowd is a Professor at the University of Limerick, holding dual affiliations with the Bernal Institute and the School of Engineering. His research focuses on Computational Mechanics, Fracture Mechanics, Materials Behaviour, and Structural Integrity, with a particular emphasis on composite materials, metallic alloys, and advanced manufacturing processes. He has contributed extensively to understanding material behavior under extreme conditions, including thermal aging, mechanical fatigue, and microstructural evolution. Prof. O'Dowd’s expertise spans experimental and computational methods, including finite element analysis (FEA), machine learning for material modeling, and in-situ microscopy techniques. His work addresses challenges in aerospace, energy, and biomedical engineering sectors, aligning with UN Sustainable Development Goals related to infrastructure and industry innovation. Recent research trends include optimizing manufacturing processes via neural networks, analyzing interfacial properties of bio-based composites, and investigating phase transformations in high-performance steels. He collaborates internationally, with studies often involving advanced materials characterization and multiscale modeling. Prof. O'Dowd has authored over 210 publications and serves on editorial boards of materials science journals. His lab, affiliated with the Bernal Institute, focuses on bridging computational and experimental approaches to advance structural integrity and material innovation.

Anders Faarbæk Mikkelstrupمشاهده پروفایل

مدرس پاره‌وقت

• Industrial Automation
• Machine Vision
• Laser Processing
• +۳ مورد دیگر

Anders Faarbæk Mikkelstrup is a Part-time Lecturer at the Department of Materials and Production , part of Aalborg University 's The Faculty of Engineering and Science . His research focuses on advancing machine vision , automation , and laser processing in industrial manufacturing. He holds a PhD from the same department, specializing in applied machine vision for quality assurance. His work contributes to UN Sustainable Development Goals related to Industry and Innovation . Key projects include FLIP (Flexible Laser Integrated Production) and INTERLASE , which integrate laser processes for manufacturing efficiency. He has collaborated with the University of California, San Diego and contributed to impactful research on fatigue life prediction for offshore structures and automated post-weld treatment systems. Education: PhD (2023), M.Sc. in Manufacturing Technology, B.Sc. in Mechanical Engineering Research: Prioritizes laser-based manufacturing, digital reconstruction of welded components, and fatigue life improvement Publications: 12 articles since 2019, including work on 3D scanning and automated quality systems His lab, Production Visual Analysis and Perception , develops vision-based solutions for industrial automation. No scientific awards explicitly mentioned, but his contributions have economic impacts through improved manufacturing processes.

• Transportation Engineering
• Operations Research
• Data Science
• +۶ مورد دیگر

Nikola Markovic is an Associate Professor in the Civil & Environmental Engineering Department at the University of Utah, specializing in applications of operations research and data science to transportation problems. His research spans paratransit optimization, work zone safety, airport operations, and disaster response logistics. Dr. Markovic earned his educational qualifications in Transportation Engineering, including a BS from the University of Belgrade, and both MS and PhD degrees from the University of Maryland. His research focuses on applying advanced computational methods to solve practical transportation challenges. Key areas include optimizing paratransit services for individuals with disabilities, improving snowplow routing in Utah, enhancing work zone safety through computer vision, and developing systems for airport operations monitoring at non-towered airports. His work often involves collaboration with transportation agencies and industry partners to ensure practical applicability. Analysis of his recent publications reveals a strong trend toward integrating machine learning techniques with traditional transportation engineering problems. His work frequently employs advanced methods like generative adversarial networks, deep learning, and optimization algorithms to address complex transportation challenges across multiple domains including paratransit, work zones, airports, and disaster response. Nominated for the Early Career Teaching award (2023 and 2021) Ranked among top 15% instructors in the College of Engineering (2022) Winner of Transportation Science and Logistics Best Paper Award from INFORMS (2022) Winner of National ACRP student design competition (2021) Dr. Markovic has advised numerous graduate students through thesis research courses in transportation engineering. His grant portfolio includes significant funding from the Utah Department of Transportation, Utah Transit Authority, and National Science Foundation, supporting research in accessibility, work zone safety, and paratransit service improvement. Current projects include enhancing accessibility for individuals with limited mobility using AI and cycling data, measuring roadside feature impacts on crashes, and developing technology integration for disadvantaged communities. His laboratory work focuses on transportation data analytics, with emphasis on computer vision applications for infrastructure monitoring and optimization algorithms for transportation operations. Current team projects involve developing systems for non-towered airport monitoring, improving paratransit services through technology integration, and optimizing emergency response after seismic events.

Krishna Pattipatiمشاهده پروفایل

استاد

• Systems Engineering
• Battery Management
• Fault Diagnosis
• +۵ مورد دیگر

Krishna Pattipati is a Board of Trustees Distinguished Professor and UTC Professor of Systems Engineering in the Department of Electrical and Computer Engineering at the University of Connecticut. His research spans systems engineering, battery management, fault diagnostics, and machine learning applications in complex engineering systems. Professor Pattipati's research interests focus on developing advanced methodologies for system health management, including: Battery state estimation and management algorithms for energy storage systems Fault diagnosis and prognosis in power electronics and manufacturing systems Machine learning techniques for predictive maintenance Multi-objective optimization for decision-making under uncertainty Probabilistic inference methods for autonomous systems Analysis of his recent publications (2021-2025) reveals dominant research themes in battery management systems (state estimation, OCV modeling, internal resistance), tool health monitoring in manufacturing (wear classification, condition monitoring), and probabilistic path planning algorithms. His work consistently integrates theoretical frameworks with practical applications in energy, manufacturing, and robotics. Significant scientific recognitions include: Board of Trustees Distinguished Professor honor (University of Connecticut's highest faculty award) Endowed UTC Professorship in Systems Engineering