جست‌وجوی استاد و پژوهشگر

Cemal Yilmazمشاهده پروفایل

دانشیار

• Software Engineering
• Combinatorial Interaction Testing
• Software Security Testing
• +۴ مورد دیگر

Cemal Yilmaz is an Associate Professor in the Computer Science and Engineering Program at Sabanci University's Faculty of Engineering and Natural Sciences in Istanbul, Turkey. His office is located in FENS G019, and he can be contacted via email at cyilmaz@sabanciuniv.edu or by phone at +90 (216) 483 9532. He directs the SUSOFT (Software Engineering Research Group), focusing on enhancing programmer productivity through practical tools and techniques. Education: Ph.D. in Computer Science, University of Maryland, 2005 M.S. in Computer Science, University of Maryland, 2002 M.S. in Computer Engineering and Information Science, Bilkent University, 1999 B.S. in Computer Engineering and Information Science, Bilkent University, 1997 Research Interests: Yilmaz's research centers on software engineering innovations, particularly combinatorial interaction testing, software security, and runtime failure prediction. He emphasizes experimental approaches with incremental prototyping and empirical analysis. Key domains include: Developing cost-aware testing frameworks for complex systems Hybrid hardware/software instrumentation for security vulnerability detection Systematic testing methodologies for multithreaded applications Real-time side-channel attack detection using hardware counters Publication Trends: His recent articles (2023-2025) demonstrate a strong focus on AI-driven testing automation, cybersecurity vulnerability analysis, and industrial case studies. Predominant themes include model-based test adaptation, privacy compliance in web systems, and deep learning for anomaly detection in telemetry data. Cross-cutting interests in combinatorial optimization and hardware security vulnerabilities are consistently evident. Advising and Grants: Yilmaz actively mentors 6 PhD and 1 MS students while having supervised 9 alumni. Major grants include: TÜBİTAK 1003: Privacy-Preserving Security Operation Center (2017) TÜBİTAK 1001: Cost- and Test Case-Aware Combinatorial Testing (2014) Marie Curie International Reintegration Grant (2009) Laboratory Leadership: As director of SUSOFT, he oversees projects in combinatorial testing, security profiling, and distributed quality assurance. The group collaborates with industry partners like Netas and prioritizes solutions for scalable software reliability.

Kouhyar Tavakolianمشاهده پروفایل

استاد پژوهشی

• Biomedical Signal Processing
• Medical IoT
• Seismocardiography
• +۲ مورد دیگر

Kouhyar Tavakolian is Executive Director of UND's BioInnovation Zone and UND Site Director for NSF's Center to Stream Healthcare in Place. As Research Professor in Biomedical Engineering, his work integrates signal processing, wearable sensors, and clinical applications. Research focuses on noninvasive cardiology methods (seismocardiography, ballistocardiography) and medical IoT systems. He has secured $13M+ funding from NASA, NIH, and DoD. Recent publications emphasize AI-driven food safety systems, space agriculture monitoring, and federated learning for healthcare. Teaches courses in Biomedical Instrumentation, Machine Learning Applications, and Physiology for Engineers. Holds P.Eng licensure and MBA.

Zhaoyan Fanمشاهده پروفایل

استادیار

• Advanced Manufacturing
• Sensor Design
• Energy-Efficient Networks
• +۱ مورد دیگر

Zhaoyan Fan is an Assistant Professor in the College of Engineering at Oregon State University, specializing in Mechanical, Industrial and Manufacturing Engineering since 2016. His research focuses on model-enhanced sensor design for manufacturing processes, energy-efficient sensor networks, and MEMS-enabled sensor miniaturization. Research interests include: In-situ monitoring of manufacturing processes Sensor characterization and optimization Miniaturization of MEMS sensors Energy efficiency in industrial systems Publications primarily focus on advanced manufacturing technologies, particularly developing novel sensing methods for additive manufacturing processes, quality control systems, and industrial automation solutions.

Alex Shenfieldمشاهده پروفایل

استاد

• Machine Learning
• Image Processing
• Pattern Recognition
• +۳ مورد دیگر

Professor Alex Shenfield is a Senior Member of the IEEE (SMIEEE) and Fellow of the Higher Education Academy (FHEA) at Sheffield Hallam University , where he leads the Digital Connectivity research theme within the National Centre of Excellence for Food Engineering . He serves as a Professor of Machine Learning in the Department of Engineering and Mathematics under the College of Business, Technology and Engineering . Research Interests: His work focuses on machine learning applications in image processing , pattern recognition , healthcare diagnostics , and Industry 4.0 (particularly food manufacturing). Recent projects include AI for heritage building analysis , smart baking systems , and AI-driven surgical wound monitoring . Article Trends: His research spans machine learning in healthcare (breast cancer biomarkers, sleep apnea detection), food engineering (rice milling, shelf-life prediction), and security systems (IoT encryption, vulnerability detection). Emerging themes include human-in-the-loop AI for archives and digital twins for industrial processes. Scientific Awards: Senior Member of the IEEE (SMIEEE) Fellow of the Higher Education Academy (FHEA) Member of the EPSRC Peer Review College Grants & Collaborations: He has secured over £3.5M in funding from Innovate UK , BBSRC , MRC , and Research England . Collaborators include Innovate UK , STFC , and UKRI . Postgraduate Supervision: Current: Richard Wainwright, Olusogo Popoola, Ryan Lewis, Zeena Al-Tekreeti, Ragab Barika Past: Surapong Kokkrathoke (nonlinear control), Mohammad Musameh (smart grids), Fayad Abdulah (renewable systems), Peter Delves (vehicle dynamics), Shahin Rostami (evolutionary computation) Labs & Teams: He contributes to the National Centre of Excellence for Food Engineering (NCEFE) and Industry and Innovation Research Institute , with projects involving Google and nVidia grants.

Chenhui Shaoمشاهده پروفایل

دانشیار

• Smart manufacturing
• Machine learning
• Statistics
• +۱۵ مورد دیگر

Chenhui Shao is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan, leading the Connected and Intelligent Manufacturing Systems (CIMS) Lab. His work bridges smart manufacturing, machine learning, and data science to advance industrial processes. Ph.D., Mechanical Engineering, University of Michigan (2016) M.A., Statistics, University of Michigan (2013) M.S.E., Industrial and Operations Engineering, University of Michigan (2013) B.E., Automation, University of Science and Technology of China (2009) Chenhui Shao's research focuses on smart manufacturing systems , integrating machine learning , statistics , and cyber-physical infrastructure to address challenges in in-situ process monitoring , quality control , and human-robot collaboration . His methodologies prioritize data efficiency and real-time decision-making for applications in additive manufacturing , ultrasonic welding , and nanomanufacturing . Recent publications highlight trends in edge computing for cost-effective monitoring, hybrid physics-data-driven models for geometric accuracy, and federated learning for cross-industry fault diagnosis. His lab's work spans 3D metrology , anomaly detection , and energy-efficient manufacturing processes . Scientific Awards: IEEE UEMCON Best Paper Award (2023) UIUC Dean’s Excellence in Research (2022) ASME Chao and Trigger Young Manufacturing Engineer Award (2021) NSF CAREER Award (2020) ASME Best Reviewers Award (2021) Chenhui Shao has advised numerous Ph.D. students, many of whom now hold roles at institutions like City University of Hong Kong and companies including Meta , Intel , and C3 AI . His lab collaborates with leading manufacturing entities and research centers such as Oak Ridge National Laboratory and National Center for Supercomputing Applications , developing solutions for cyber-physical infrastructure and agile production systems .

Tarek Rakhaمشاهده پروفایل

دانشیار

• Sustainable Design
• Urban Modeling
• Building Performance Simulation
• +۷ مورد دیگر

Tarek Rakha is an Associate Professor of Architecture and Director of the High Performance Building Lab (HPBL) at Georgia Tech's College of Design. He also serves as Co-founder and CEO of Lamarr.AI, a startup commercializing his research. His academic journey includes a Ph.D. in Building Technology from MIT (2015) and prior faculty roles at Syracuse University before joining Georgia Tech in 2019. Rakha's work focuses on sustainable design, urban modeling, and building performance simulation, with a strong emphasis on underserved communities. His research, totaling $4M+ in federal/state funding, explores aerial data robotics for building diagnostics, urban energy sensing, and thermal comfort modeling. Education Background: Ph.D. in Building Technology, MIT (2015) M.S. and B.S. in Architecture, Cairo University (2010, 2007) Research Interests: He pioneers interdisciplinary approaches combining AI, drones, and computational methods to address environmental challenges. Key areas include: - Thermal anomaly detection via capsule networks and YOLO models - Inclusive urban energy modeling with socioeconomic data integration - Climate-resilient native architectures and heritage preservation - Energy efficiency in healthcare environments and retrofit programs. Recent Projects: His work spans $7M+ in funded projects from NSF, DOE, ARPA-E, and corporate partners like Coca-Cola. Notable outputs include an open-source decarbonization framework and thermal comfort analysis tools for chemotherapy spaces. His drone-based envelope diagnostic systems have advanced non-destructive testing practices. Awards & Impact: Recognized nationally with IBPSA-USA's Emerging Contributor Award (2023). His teaching excellence earned the 2022 Class of 1934 Award at Georgia Tech. Mentored DOE Solar Decathlon Grand Winner (2022) and led award-winning design competitions (AIA/ACSA 2019). Lab & Outreach: The HPBL develops innovative solutions for energy-efficient urban systems. Rakha co-edited SimAUD conference proceedings as General Chair (2018) and Program Chair (2019). Public engagement includes active social media presence and LinkedIn networking.

Marco Schaerfمشاهده پروفایل

استاد

• Artificial Intelligence
• Nonmonotonic Reasoning
• Computational Complexity
• +۶ مورد دیگر

Marco Schaerf is a Full Professor at Sapienza University of Rome, Department of Computer, Control, and Management Engineering. He previously held roles as Associate Professor at the University of Cagliari (1992–1994) and Sapienza (1994–2000). He chairs the Engineering Information Area in Latina and serves on the editorial boards of Artificial Intelligence Journal and Journal of Artificial Intelligence Research . His research focuses on artificial intelligence, nonmonotonic reasoning, computational complexity, digital libraries, and bibliometrics. Notably, he received the 1997 AI*IA Award for Best Young Researcher in AI. Key projects include coordinating the European MAGICSTER project, FIRB 2001 ASTRO, and COFIN grants (2002/2004). He led Sapienza’s participation in Italy’s VQR research assessment program and contributed to digital library systems like the Sapienza Digital Library. His work bridges AI with cultural heritage, including AI-driven visitor experience analysis in museums. Research interests span AI applications, knowledge representation, and computational methods in cultural heritage. He has published over 60 papers in top journals, with recent work on scientometrics, anomaly detection in infrastructure, and weakly supervised semantic segmentation. His contributions to AI theory, robotics, and bibliometric analysis underscore his interdisciplinary impact.

Pierre Payeurمشاهده پروفایل

استاد

• Machine Vision
• 3D Sensing and Modeling
• Human Motion Capture
• +۴ مورد دیگر

Pierre Payeur is a Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a PhD from Université Laval (1999) and has been a faculty member since then. He leads the SMART Research Group and co-founded the VIVA Research Laboratory. His roles include Graduate Programs Coordinator for the Ottawa-Carleton Institute for Electrical and Computer Engineering and faculty advisor for the ARISE student robotics club. Education: Ph.D. in Electrical Engineering from Université Laval (1999) Research interests focus on machine vision, 3D sensing, human motion capture, tactile sensing, robotic navigation, and computational intelligence. His work integrates industry collaboration and has been funded by major granting agencies. Recent articles explore domain adaptation in human action recognition, multi-agent robotics, and soil temperature prediction using AI. His contributions include over 150 technical papers and supervision of 70+ researchers. Awards: Best Paper Awards at IROS 2020 and ISVC 2021 Advising and grants: Over 70 supervised researchers, grants from major agencies. Active in professional roles like co-chair of IEEE TC-28 and organizer of the ROSE conference series. Labs: SMART Group and VIVA Lab, focusing on robotics, sensing, and autonomous systems.

Yunlong Tangمشاهده پروفایل

مدرس

• Additive Manufacturing
• Smart Manufacturing
• Digital Twin Technology
• +۵ مورد دیگر

Dr. Yunlong Tang is a Lecturer jointly appointed by the Department of Mechanical and Aerospace Engineering and the Department of Materials Science and Engineering at Monash University. He also serves as Assistant Director of the Monash Centre for Additive Manufacturing (MCAM). His research focuses on digital additive manufacturing, AI-driven design tools, and smart manufacturing systems. He holds a Ph.D. from McGill University, a Master’s from Beihang University, and a Bachelor’s from Harbin Institute of Technology. Education: Ph.D. in Mechanical Engineering, McGill University, 2018 M.Sc. in Aerospace Manufacturing Engineering, Beihang University, 2013 B.Eng. in Mechanical Engineering, Harbin Institute of Technology, 2010 Research Interests: Design for Additive Manufacturing (DfAM) and open-source tools like IntraLattice AI and computational design for material-process-structure integration Digital twin-enabled smart manufacturing and process optimization Hybrid materials design and digital manufacturing processes Advising & Grants: He supervises PhD students in topics such as AI-aided DfAM, digital twin process planning, and bio-inspired composites. He leads projects like 'Smart In-Process Quality Inspection' and collaborates on waste management decision analysis. Labs & Teams: He directs the Intelligent Digital Manufacturing and Design (I-DMD) Lab , a cross-departmental team advancing AI and digital twin technologies in manufacturing. The lab focuses on sustainable, adaptive, and personalized manufacturing solutions.

• Nonlinear Control
• Machine Learning
• Robotics
• +۵ مورد دیگر

Olav Egeland is a Professor at the Department of Mechanical and Industrial Engineering, NTNU. He holds a siv.ing (1984) and dr.ing (1987) in Electrical Engineering from NTNU. Previously, he served as Professor of Robotics at the Department of Engineering Cybernetics (1989–2004), and led Marine Cybernetics AS as CTO/CEO (2004–2011). His academic roles include Head of Department of Engineering Cybernetics (1996–1998), Vice Dean of Faculty of Electrical Engineering and Telecommunications, and Research Committee member at NTNU. He contributed to strategic programs like SFI Offshore Mechatronics and the Centre of Ships and Ocean Structures. His research focuses on nonlinear control, machine learning for mechanical systems, robotics, and offshore applications. Notable awards include the Automatica Prize Paper Award (1996) and IEEE Outstanding Paper Award (2000). He has supervised 85 MSc and 30 PhD students, with recent advisees exploring topics like robotic welding, Hamiltonian dynamics learning, and generative AI in robotics. Key projects include vision systems for offshore drilling, laser scanning for aluminum welding, and adaptive control algorithms. His work spans publications in IEEE Transactions, European Journal of Control, and Springer monographs, emphasizing modeling, simulation, and advanced control techniques. Education: Siv.ing (Electrical Engineering), NTNU, 1984 Dr.ing (Electrical Engineering), NTNU, 1987 Research Themes: Nonlinear control and adaptive systems Machine learning applications in robotics and offshore engineering Robotics vision and sensor-based control Offshore mechatronics and crane dynamics Grants & Roles: Strategic University Program in Marine Cybernetics (1997–2004) Work Package Lead in SFI Offshore Mechatronics Research collaborations in European Journal of Control and IEEE Transactions Labs & Teams: Active in NTNU’s robotics and control research groups, contributing to industrial partnerships like Marine Cybernetics AS and SFI initiatives.

Hasnain Zohaibمشاهده پروفایل

استادیار

• Autonomous Systems
• Fluidic Dynamics
• Robotics
• +۱ مورد دیگر

Hasnain Zohaib is an Assistant Professor in the Department of Mechanical Engineering at Texas A&M University. His research develops computational and experimental frameworks for autonomous systems, aerospace applications, and robotic perception. Education: Ph.D. Aerospace Engineering, University of Maryland (2014) M.S. Aerospace Engineering, University of Maryland (2012) B.S. Aerospace Engineering, University of Maryland (2008) Research integrates machine learning with fluid dynamics for aerospace challenges, including lunar landing systems and high-speed autonomy. Robotic perception work advances damage assessment algorithms using 3D point clouds. Recent projects focus on sim-to-real transferability and extraterrestrial infrastructure development. Publications demonstrate expertise in computational modeling of particulate flows, robotic localization in low-feature environments, and adaptive aerospace structures. Applied research supports NASA lunar missions through erosion mitigation and landing technologies. Leads projects funded by aerospace and robotics consortia, collaborating with industry partners on space exploration and terrestrial automation.

ChaBum Leeمشاهده پروفایل

دانشیار

• Semiconductor Metrology
• Interferometry
• 3D Imaging
• +۳ مورد دیگر

ChaBum Lee is an Associate Professor and Morris E. Foster Faculty Fellow in Mechanical Engineering at Texas A&M University. His research advances metrology and inspection for semiconductor manufacturing and precision systems. Research focuses on interferometry, 3D imaging, machine tool metrology, robotic machining, and optical spectroscopy. Key innovations include diffraction-based via inspection, autonomous wafer defect detection, and non-contact surface profiling. Recent publications emphasize semiconductor metrology advancements, including through-silicon via characterization, wafer edge inspection, and machine learning-driven quality control. Articles demonstrate integration of optics, sensors, and AI for manufacturing applications. Awards include: ASME Blackall Machine Tool Award (2020) Institute of Physics Emerging Leader (2021) ASPE Best Researcher (2017) Leads the Precision Metrology and Instrumentation Group (PMIG), collaborating with semiconductor industry partners including Samsung and Honeywell. Teaches courses in precision engineering and instrumentation.

Jyhwen Wangمشاهده پروفایل

استاد

• Material Deformation Processes
• Additive Manufacturing
• Smart Manufacturing
• +۳ مورد دیگر

Jyhwen Wang is a Professor in the Department of Engineering Technology and Industrial Distribution at Texas A&M University, with affiliations in Materials Science & Engineering, Mechanical Engineering, and Multidisciplinary Engineering. He holds the Allen-Bradley Endowed Professorship. His expertise spans material deformation, additive manufacturing, smart manufacturing, and engineering education. Education: Ph.D., Mechanical Engineering, Northwestern University (1991) M.Eng., Manufacturing Engineering, Northwestern University (1986) M.S., Industrial Engineering and Operations Research, Syracuse University (1985) B.S., Industrial Engineering, Tunghai University (1983) Research Interests: Material deformation processes and their applications in advanced manufacturing Optimization of product and process designs for smart manufacturing systems Additive manufacturing techniques and their material science implications Coating and surface modification to enhance material performance Integration of engineering education with Industry 4.0 technologies Publications Trends: Recent work focuses on additive manufacturing (e.g., 3D-printed polymers, bi-material structures), smart manufacturing (energy efficiency, process control), and advanced forming processes (springback prediction, tribology). Awards & Memberships: Fellow, Society of Manufacturing Engineers (2015) Fellow, The American Society of Mechanical Engineers (2008) College of Engineering Faculty Fellow, Texas A&M University (2006) Grants & Advising: Leads NSF-funded REU sites in metrology and inspection, emphasizing student development in advanced manufacturing. Collaborates on projects involving smart control systems and material characterization. Labs & Teams: Engaged in multidisciplinary teams at Texas A&M, focusing on cyber-enabled manufacturing and Industry 4.0 applications.

• Photovoltaic systems
• Power electronics
• Renewable energy integration
• +۱ مورد دیگر

Dezso Sera is Associate Professor in Power Engineering at Queensland University of Technology. His research focuses on photovoltaic systems, power electronics, and renewable energy integration. He previously led the Photovoltaic Systems Research Programme at Aalborg University (2009-2019). Research Focus: Dr. Sera develops advanced technologies for solar energy systems including: PV array modeling and diagnostics Maximum power point tracking algorithms Grid integration of renewable energy Hybrid energy storage solutions His recent publications show strong emphasis on power converter topologies, machine learning applications for PV maintenance, and optimization of off-grid renewable systems.

• Structural Engineering
• Civil Engineering
• Railway Infrastructure
• +۳ مورد دیگر

Gunnstein Thomas Frøseth is an Associate Professor in the Department of Structural Engineering at NTNU. His research focuses on structural health monitoring, fatigue analysis of railway bridges, and improving prediction methods for remaining service life. He specializes in combining numerical simulations with field measurements to enhance accuracy in structural assessments. His work addresses challenges in railway infrastructure, including load modeling, corrosion detection, and vibration analysis of steel bridges. Key contributions include advancements in machine learning applications for structural diagnostics and photogrammetry techniques for railway catenary systems. As part of his research group, he collaborates on projects like the 'Hell Bridge Test Arena,' exploring sensor-based inspections and damage detection. His interdisciplinary approach integrates civil engineering, materials science, and data-driven methods to tackle critical infrastructure challenges.