جست‌وجوی استاد و پژوهشگر

• Communications Systems
• Defence Information Systems
• Cybersecurity
• +۷ مورد دیگر

Dr Philip Nobles is a Lecturer at Cranfield University, affiliated with the Centre for Defence Engineering and Physical Science within the College of Engineering and Physical Sciences. He holds a PhD and MEng from Swansea University and has over 25 years of experience in academic and applied research in communications and cybersecurity. PhD, Swansea University MEng, Swansea University His research expertise lies in communications systems, defence information systems, and cybersecurity, with a focus on wireless networks, software-defined networking, military communications, and intrusion detection. He leads the development and delivery of an MSc module on Networked Systems and Cybersecurity for Connected and Autonomous Vehicles, and supervises research students. His work is supported by collaborations with DSTL/MOD, Airbus, EPSRC, BBC, and Philips Research Laboratories. Dr Nobles has published extensively on topics including intrusion detection sensor characterization, false positive responses, insider threat detection, and wireless communication systems. His publications reflect a strong trend in network security, sensor deployment optimization, and secure communications for defence and autonomous systems. Notable scientific contributions include work on: Characterising intrusion detection sensors False positive response mechanisms Scalable intrusion detection architectures Bayesian approaches to insider threat detection He actively supervises research students and contributes to high-impact projects funded by EPSRC and other strategic partners. His research integrates theoretical modeling with practical system deployment, particularly in military and critical infrastructure contexts. Dr Nobles is associated with the Department of Informatics and Sensors at Cranfield University, where he contributes to advanced research in network-centric warfare concepts and secure communication systems.

Roberto Furfaroمشاهده پروفایل

استاد

• Guidance, Navigation and Control
• Machine Learning in Aerospace
• Space Situational Awareness
• +۷ مورد دیگر

Roberto Furfaro is a Professor in the Department of Systems and Industrial Engineering and the Department of Aerospace and Mechanical Engineering at the University of Arizona, College of Engineering. He serves as Deputy Director of the Space, Security, Safety & Sustainability Center (S4C) and is a key figure in space situational awareness and planetary exploration systems. His educational background includes a Laurea Degree (MS equivalent) in Aeronautical Engineering from the University of Rome 'La Sapienza' (1998) and a PhD in Aerospace Engineering from the University of Arizona (2004). His research spans guidance and control of planetary landers, machine learning applications in space systems, hypersonic vehicle G&C, and systems engineering for small body proximity operations. He leverages advanced AI, particularly reinforcement learning and deep learning, to solve complex aerospace challenges. The recent publications reflect a strong trend toward intelligent, autonomous systems in spaceflight, especially using deep reinforcement learning for planetary landing, guidance under uncertainty, and space-based object tracking. His work bridges control theory, astrodynamics, and artificial intelligence. 2021 Da Vinci Fellow, College of Engineering AIAA Associate Fellow (2022) AAS Fellow (2021) Asteroid 133474 Roberto Furfaro named in honor of OSIRIS-REx contributions Prof. Furfaro has served as PI or Co-PI on over $80M in research grants from NASA, AFRL, and DoD. He has advised numerous students and leads research teams in autonomous space systems and machine learning applications. He is the Target Follow-up Working Group lead for the NASA NEO Surveyor Mission and was Systems Engineering Lead for SPOC during OSIRIS-REx Phase B-D. He is actively involved in professional service as Associate Editor for IEEE Transactions on Aerospace and Electronic Systems and technical member of the AIAA Astrodynamics Committee and AAS Space Surveillance Committee.

Larry Headمشاهده پروفایل

استاد

• Systems Engineering
• Industrial Engineering
• Intelligent Transportation Systems
• +۷ مورد دیگر

Larry Head is a Professor in the Department of Systems and Industrial Engineering at the University of Arizona, College of Engineering. He serves as Director of the Craig M. Berge Engineering Design Program and is a Member of the Graduate Faculty. His extensive leadership roles include former Interim Dean of Engineering and Director of the Transportation Research Institute. He is actively engaged in transportation policy as a member of the Arizona Governor’s Task Force for Self-Driving Vehicles. Education: PhD in Systems and Industrial Engineering, University of Arizona MS in Systems Engineering, University of Arizona BS in Systems Engineering, University of Arizona Larry Head's research centers on intelligent and connected transportation systems, with a strong focus on cyber-physical systems, adaptive traffic signal control, and urban traffic operations. His work integrates real-time data from connected and automated vehicles to optimize traffic flow, improve intersection safety, and support multi-modal transportation. He also contributes significantly to engineering education, teaching courses in systems engineering, simulation, and financial modeling. His recent publications (2014–2022) demonstrate a consistent trajectory in leveraging connected vehicle technology for adaptive signal control, pedestrian safety, and traffic efficiency. Key themes include priority control for emergency and freight vehicles, smooth progression modeling, and data-driven safety assessment. The research spans disciplines such as transportation engineering, machine learning, and cyber-physical systems. Scientific Awards: Trevor O. Jones Outstanding Paper Award (2021) Exceptional Paper Award, TRB Traffic Signal Systems Committee (2017) 2016 Best ITS Implementation Project 2016 Member of the Year, ITS Arizona da Vinci Fellow, College of Engineering D. Grant Mickle Award for Outstanding Paper Best Dissertation Advisor Award, COTA Larry Head has advised numerous graduate students, many of whom are co-authors on his publications. He has secured significant research funding in intelligent transportation systems, though specific grants are not listed. His professional service includes editorial work for Transportation Research – Part C and leadership in TRB, SAE, IEEE, and other societies. He leads research initiatives at the ATLAS Research Center and collaborates on interdisciplinary transportation projects. He is an active member of several research teams focused on connected vehicle systems, traffic safety, and urban mobility. His lab work emphasizes real-world implementation of intelligent signal control and data analytics for transportation infrastructure.

• Control Engineering
• Hydrodynamics
• Marine Control Systems
• +۳ مورد دیگر

Asgeir Johan Sørensen is a Professor at the Department of Marine Engineering, Faculty of Engineering at NTNU. He holds a Dr. Eng. in Technical Cybernetics (1993) and an M.Sc. in Marine Engineering (1988), both from NTH (now NTNU). His research focuses on control engineering, hydrodynamics, autonomous systems, and marine robotics. He leads projects like Oppdrag Mjøsa , advancing autonomous marine operations and environmental monitoring. Key contributions include risk-based path planning, hydrogen fuel cell systems for marine propulsion, and hyperspectral imaging for underwater mapping. Research interests emphasize autonomous systems integration, underwater vehicle control, and sustainable marine technologies. He co-founded the Marine Cybernetics Laboratory (MC-lab) and Applied Underwater Laboratory (AUR-Lab) . His work bridges academia and industry, addressing challenges in marine robotics, energy systems, and satellite-based Earth observation. Recent publications highlight advancements in marine autonomy, including collision avoidance algorithms, hybrid power systems, and Arctic satellite missions. He collaborates widely, contributing to national and international initiatives in marine technology and environmental data governance.

Fadi Fathallahمشاهده پروفایل

استاد

• Ergonomics
• Biomechanics
• Musculoskeletal Disorders Prevention
• +۴ مورد دیگر

Fadi Fathallah, Ph.D., is a Professor and Department Chair in the Department of Biological and Agricultural Engineering at the University of California, Davis. He leads the Western Center for Agricultural Health and Safety and CalAgrAbility. His research focuses on ergonomics, biomechanics, and musculoskeletal disorder prevention in agricultural work, particularly addressing stooped postures, ladder design, and youth safety in agricultural machinery operation. Dr. Fathallah’s educational background includes advanced degrees in Industrial Engineering with a specialization in biomechanics. His work integrates engineering principles with public health objectives to improve worker safety in labor-intensive sectors. Key areas of expertise include spinal biomechanics during stooped labor, ergonomic tool development, and sensor-based monitoring in robotic agricultural systems. His research has produced interventions such as ergonomic ladder designs, weight transfer devices for stooped work, and autonomous agricultural vehicles. Recent studies emphasize reducing injury risks for youth operators of all-terrain vehicles and optimizing manual materials handling techniques. Dr. Fathallah has been awarded the ASABE SMV Technologies Ergonomics, Safety and Health Award (2021) for his contributions to agricultural safety. His work has been supported by grants such as NIOSH funding for the Western Center for Agricultural Health and Safety (2022). As a mentor, he oversees interdisciplinary projects at the Occupational Biomechanics Lab and collaborates with industry partners to translate research into practical solutions. His lab also develops technologies like GPS-based weed control systems and co-robotic systems to enhance agricultural efficiency while prioritizing worker health.

• Machine Learning
• Signal and Image Processing
• Data Fusion
• +۷ مورد دیگر

Anthony (Tony) Pinar is an Associate Teaching Professor in the Department of Electrical and Computer Engineering at Michigan Technological University, where he also serves as the Senior Design Coordinator. He earned his PhD, MS, and BS in Electrical Engineering from the same institution in 2017, 2014, and 2011, respectively. Education: PhD, Electrical Engineering, Michigan Technological University MS, Electrical Engineering, Michigan Technological University BS, Electrical Engineering, Michigan Technological University Dr. Pinar's research lies at the intersection of machine learning, signal and image processing, data fusion, and electronic design . His work emphasizes explainable AI , particularly through the use of fuzzy integrals and Choquet integral-based models , with applications in explosive hazard detection using ground-penetrating radar and autonomous underwater vehicles. He has contributed significantly to decision-level fusion, multiple kernel learning, and robust classification under limited data . His recent publications reflect a strong trend toward interpretable machine learning models , especially in safety-critical domains. He integrates fuzzy measures, feature fusion, and nonlinear aggregation operators to improve both performance and transparency in AI systems. Applications span defense (hazard detection), marine robotics (underwater gliders), and sensor networks. Dr. Pinar has no listed scientific awards in the provided text. He advises senior design projects and mentors students in hands-on engineering, particularly through the development of the ROUGHIE (Research Oriented Underwater Glider for Hands-on Investigative Engineering). Prior to academia, he worked as an electrical design engineer at GE Aviation, designing test equipment for avionic systems, and continues to consult independently on engineering problems. He has secured research contributions through collaborations but no specific grants are mentioned. His work is closely aligned with research groups focusing on intelligent systems, fusion algorithms, and autonomous platforms, particularly in collaboration with researchers such as T.C. Havens, D.T. Anderson, and N. Mahmoudian.

• Applied Probability
• Operations Research
• Resource Allocation
• +۴ مورد دیگر

J. G. Jim Dai is the Leon C. Welch Professor of Engineering at Cornell University's School of Operations Research and Information Engineering. He specializes in applied probability, resource allocation in processing networks, and operations optimization for healthcare systems and service industries. His research includes fluid and diffusion models, stochastic processes, and reinforcement learning. He co-authored Processing Networks: Fluid Models and Stability (2020) and has advised over 20 PhD students. Dai chairs the ACM SIGMETRICS Achievement Award committee and serves as a trustee for the Applied Probability Trust, overseeing journals like Journal of Applied Probability . His work addresses challenges in semiconductor fabrication, data centers, and hospital inpatient operations. Recent research focuses on reinforcement learning applications in healthcare and ridesharing systems. Education: Ph.D. in Industrial Engineering (Georgia Tech, 1994) Research interests span applied probability with an emphasis on fluid models, diffusion approximations, and stochastic networks. He applies these methods to optimize systems like customer contact centers, hospital operations, and ridesharing networks. Recent publications include advancements in diffusion approximations and steady-state analysis of queueing networks. His former students hold academic and industry positions globally, including at Northwestern University, Duke University, and Meta. Dai organized the 2022 Stochastic Network Conference at Cornell and will co-chair the 2024 edition in Sweden.

• Marine Geology
• Geophysics
• Seafloor Mapping
• +۵ مورد دیگر

Adam Skarke is an Associate Professor of Geology in the Department of Geosciences at Mississippi State University, a position he has held since 2020 after serving as Assistant Professor from 2014-2020. His research integrates marine geology and geophysics to investigate sediment transport, seafloor morphology, and fluid dynamics across continental margins from coastal zones to deep-sea environments. Education: Ph.D. in Geological Sciences, University of Delaware (2013) M.S. in Geological Sciences, University of Delaware (2008) B.A. in Geology, Colgate University (2003) Research Focus: Dr. Skarke's work centers on seafloor gas seeps , coastal morphodynamics , and submarine groundwater discharge , employing field-based quantitative analysis of geological, geophysical, and oceanographic datasets. His innovative use of environmental observing sensors and platforms enables investigation of physical relationships across diverse spatial and temporal scales, with particular emphasis on methane dynamics and sedimentary processes. Publication Trends: Recent publications (2021-2025) demonstrate a significant methodological shift toward machine learning applications for seafloor feature detection, particularly in gas seep identification. His work bridges marine geology with computational science, while maintaining strong field components through NOAA expeditions. Key thematic clusters include methane seep inventory development, coastal wetland restoration efficacy, and interdisciplinary studies of continental margin processes. Scientific Recognition: Faculty Research Award from Mississippi State University College of Arts and Sciences Early Career Research Fellowship from National Academies Gulf Research Program Mentorship & Funding: Dr. Skarke advises graduate students Jeneil Patel, Ramon Kalski, and Jonathan Leard, with research supported by federal grants including his National Academies fellowship. His NOAA collaborations fund extensive field campaigns using advanced platforms like Wave Gliders and AUVs for seafloor mapping and environmental monitoring. Research Infrastructure: He actively participates in NOAA Okeanos Explorer missions, contributing to deep-sea exploration across Atlantic and Gulf of Mexico margins. His work integrates data from multibeam echosounders, ROVs, and autonomous systems within collaborative frameworks involving geological, biological, and computational specialists.

Konstantinos D. Blekasمشاهده پروفایل

استاد

• Machine Learning
• Autonomous Agents
• Computer Vision
• +۱ مورد دیگر

Konstantinos D. Blekas is a Professor at the Department of Computer Science & Engineering , Polytechnic School, University of Ioannina. His career spans over 25 years with research focusing on Machine Learning , Autonomous Agents , Computer Vision , and Bioengineering . Education: Diploma in Electrical Engineering (1993) and PhD in Electrical & Computer Engineering (1997) from NTUA. Teaching: Offers courses like Probabilities & Statistics (MY304), Machine Learning (MYE002), Data Mining, and Bioinformatics. Research Interests include advanced machine learning techniques, reinforcement learning applications in traffic and robotics, image segmentation, and bioinformatics for medical data analysis. His work bridges theoretical innovation with practical implementations in autonomous systems and biomedical research. Recent Publications highlight trends in multiagent reinforcement learning , neural network optimization , and fMRI data modeling , with applications in robotics, air traffic management, and biomedical imaging. Collaborative projects often integrate spatial constraints and generative models. Scientific Awards Best Paper Award, DASC 2018 Best Student Paper Award, SETN 2018 2nd Winner, AIBIRDS 2014 Competition Professional Impact includes mentoring students, developing educational curricula, and cross-disciplinary research in medical AI. His work is widely cited (>1000 citations) with contributions to journals like Neural Networks , Medical Imaging , and AI Magazine .

Qianwen Liمشاهده پروفایل

استادیار

• Intelligent Transportation Systems
• Vehicle Automation
• Traffic Safety and Efficiency
• +۴ مورد دیگر

Dr. Qianwen (Cami) Li is an Assistant Professor in the School of Environmental, Civil, Agricultural and Mechanical Engineering (ECAM) at the University of Georgia. She leads the Cooperative Automated Mobility Innovations (CAMIs) Lab, focusing on enhancing transportation safety and efficiency through automation, vehicle communication, electrification, and smart infrastructure integration. Her work spans theoretical modeling, simulation tools, and real-world experimentation in connected and automated vehicle systems. Education details are not explicitly stated in the provided text, but her title indicates a doctoral degree. Her research emphasizes cutting-edge topics such as trajectory optimization for modular buses, safety-aware neural networks for AVs, and energy consumption analysis in transit systems. She has contributed to foundational studies on mixed traffic dynamics, pedestrian safety through roadway lighting, and policy frameworks for ACES (Automated, Connected, Electric, Shared) transportation systems. Dr. Li’s publications (15 most recent listed) highlight her expertise in CAV technologies, traffic modeling, and infrastructure integration. She has developed datasets for AV-traffic light interactions and pioneered methods to identify automated vehicles in mixed traffic using machine learning. Her work also addresses challenges like nighttime pedestrian crash modeling and optimal evacuation planning for electric vehicles. While no specific awards or grants are listed, her lab’s focus on cooperative automated mobility suggests active engagement in funded research projects. The CAMIs Lab collaborates on real-world applications, such as Florida’s ACES roadmap and trajectory control experiments for platooned vehicles.

• Statistical Literacy
• Financial Literacy
• Statistics Education
• +۵ مورد دیگر

Dr. Péter Kovács is an Associate Professor and Chair of the Department of Statistics and Demography at the University of Szeged, serving as Dean of the Faculty of Economics and Business Administration since 2017. Previously, he was Vice Dean (2014–2017). He holds a PhD and habilitation in Economics and an MA in Mathematics. His research focuses on statistical and financial literacy, multivariate statistical methods, and education technology. He leads the Financial Literacy Research Group and participates in initiatives like ProCivicStat. He has extensive editorial roles, including membership in the editorial boards of Pakistan Journal of Statistics and Operation Research , Journal of Applied Economics and Business , and others. He chairs the Hungarian Academy of Science's Statistics Scientific Subcommittee and holds leadership roles in international organizations like the International Association for Statistical Education (IASE) and the International Statistical Literacy Project (ISLP). His research interests bridge statistical methodology with societal issues, including the acceptance of autonomous vehicles, maternal mortality in sub-Saharan Africa, and the integration of visualization tools in education. He has been awarded the Péter György Award (2006) for teaching and recognized as Ambassador of Financial Literacy (2021). Teaching responsibilities include courses on statistics, econometrics, and business software. He emphasizes modern pedagogy, including technology-driven methods and collaborative learning approaches. His work addresses both theoretical advancements and practical applications in education and policy.

Lance Sherryمشاهده پروفایل

استاد

• Air Transportation System Modeling and Simulation
• Data Analytics
• Stochastic Simulation
• +۱۲ مورد دیگر

Lance Sherry is a Professor in the Department of Systems Engineering and Operations Research at George Mason University’s College of Engineering and Computing. He serves as Director of the Center for Air Transportation Systems Research (CATSR), where he leads cutting-edge research in air transportation systems, data analytics, and autonomous systems. Education: PhD, Industrial and Systems Engineering, Arizona State University MS, Industrial and Systems Engineering, Arizona State University BS, Electrical Engineering, Brown University His research focuses on the modeling and simulation of complex adaptive systems in aviation, including air traffic control, airport operations, and unmanned aerial vehicles. He has pioneered work in human-computer interaction, operator training, and big data applications in aviation safety. His recent publications reflect strong trends in predictive analytics, environmental sustainability in aviation, and decision support systems for complex operational challenges. Scientific Awards and Recognitions: U.S. Patent and Provisional Patent for probabilistic alerting of aircraft unstable approaches using big data Multiple 1st and 2nd place awards in IEEE SIEDS, PITCH-IT, and Capstone competitions Best paper award at ICRAT Conference (2016) Senior design teams consistently recognized for innovation in systems engineering Lance Sherry has advised numerous PhD students, many of whom have gone on to prominent roles in aviation, government, and tech. He has secured research funding from organizations such as the Decision Science Research Institute, Crown Consulting Inc., and LMI. His leadership extends to professional service, including chairing the IEEE ICNS Conference and serving on NSF merit panels and AIAA technical committees. He is also actively involved in promoting drone technology education and urban air mobility research. Laboratories and Teams: He leads the Center for Air Transportation Systems Research (CATSR), which conducts interdisciplinary research in aviation systems, big data analytics, and human factors. The center supports undergraduate and graduate student teams in national design competitions and collaborates with FAA, NASA, and industry partners.

• Internet Security
• Operating Systems
• Programming
• +۱۲ مورد دیگر

Dr. Xiaochun Cheng is a Senior Lecturer in the Department of Computer Science at Middlesex University, School of Science and Technology. Their research spans Internet Security, Operating Systems, and Programming, with a strong focus on cybersecurity, artificial intelligence, and IoT applications. They are actively contributing to both academic and applied research in secure systems and intelligent computing. Research Interests: Dr. Cheng's work is centered on cybersecurity, deep learning, and intelligent systems. Key areas include intrusion detection in IoT, secure communication in autonomous vehicles, medical image analysis using neural networks, and privacy-preserving data sharing in healthcare systems. Their interdisciplinary approach bridges computer science with biomedical and social applications. The recent publications reflect a strong trend toward applying deep learning and hybrid AI models to critical domains such as healthcare (e.g., diabetic retinopathy, melanoma detection), cybersecurity (e.g., malware classification, threat detection), and smart infrastructure (e.g., smart buildings, UAVs). These works often combine advanced neural architectures with real-world constraints like resource limitations and data privacy. Scientific Contributions: Dr. Cheng has contributed significantly to journals such as IEEE Transactions on Intelligent Transportation Systems, Computers & Security, PLoS ONE, and Applied Sciences. Their collaborative research spans multiple continents and disciplines, including computer science, public health, and bioinformatics. Grants and Advising: While specific grants and students are not mentioned in the provided text, Dr. Cheng's extensive publication record suggests active research supervision and project leadership. They likely mentor students in AI, cybersecurity, and software engineering, and may be involved in funded projects related to secure IoT and intelligent transportation systems. Laboratories and Teams: Dr. Cheng appears to be part of a multidisciplinary research group at Middlesex University focused on intelligent systems and cybersecurity. Their collaborations with researchers in blockchain, cloud computing, and medical AI suggest involvement in a dynamic, cross-functional research environment.

Abbas Mehrabidavoodabadiمشاهده پروفایل

استادیار

• Mobile Edge Computing
• Internet of Things (IoT)
• Smart Grids and Microgrids
• +۷ مورد دیگر

Abbas Mehrabidavoodabadi is an Assistant Professor in the Department of Computer and Information Sciences at Northumbria University, UK, and a member of the CyberNets research group. He previously served as a Lecturer at Nottingham Trent University (2019–2020) and a Postdoctoral Researcher at Aalto University, Finland (2017–2019). He holds a PhD in Computer Science from Gwangju Institute of Science and Technology (GIST), South Korea, awarded in 2017. His educational background includes: PhD in Computer Science, Gwangju Institute of Science and Technology (GIST), South Korea (2013–2017) His research focuses on optimization and resource allocation in mobile edge computing (MEC), Internet of Things (IoT), smart grids/microgrids, and vehicular edge/fog networking. He explores energy-efficient mobile computing, Quality of Experience (QoE) optimization, and adaptive mobile video streaming. His work integrates algorithmic design with real-world applications in sustainable energy and intelligent transportation systems. His recent publications reveal a strong trend in intelligent energy management for electric vehicles, edge-assisted autonomous driving, federated learning for privacy-preserving AI, and green computing in mobile networks. His research bridges computer science with energy systems and transportation, emphasizing sustainability, efficiency, and user-centric design. His scientific awards and honors include: Fellow of the UK Higher Education Academy (FHEA) Member of IEEE Associate Member of the British Computer Society (BCS) He has been involved in research projects funded by Innovate UK, Horizon Europe, and the Marie Skodowska-Curie programme. He serves as a reviewer for top journals such as IEEE Transactions on Mobile Computing, IEEE Transactions on Vehicular Technology, and ACM Transactions on Multimedia Computing. He is currently supervising PhD students, including Amin Mansour Saatloo, whose work focuses on sustainable energy systems with electric vehicles. He is affiliated with the CyberNets research group at Northumbria University, which focuses on cybersecurity, edge computing, and networked systems. His teaching includes courses on Computer Networks, Security, and Operating Systems.

Fred Fengمشاهده پروفایل

دانشیار

• Human Factors and Ergonomics
• Human-Machine Interaction
• Data Analytics and Visualization
• +۶ مورد دیگر

Fred Feng is an Associate Professor in the Industrial and Manufacturing Systems Engineering department at the University of Michigan-Dearborn's College of Engineering and Computer Science. His work bridges human factors, data science, and transportation safety. Ph.D. in Industrial and Operations Engineering from University of Michigan, Ann Arbor (2015) M.S. in Mechanical Engineering from Tsinghua University (2009) B.E. in Automotive Engineering from Tsinghua University (2006) Research Focus: Human factors in automated vehicles, road safety analysis, human-machine interaction design, and data-driven modeling of mobility systems. His work combines naturalistic driving data with statistical and computational methods to improve transportation safety for vulnerable road users. Publication Trends: Recent work (2022-2023) explores micro-mobility sustainability frameworks and safety-critical scenarios for autonomous vehicles. Earlier studies (2017-2019) focused on driver distraction detection, overtaking behavior analysis, and vehicle dynamics modeling. Grants: Recipient of multiple competitive grants including NSF CAREER funding (2022-2027) and multiple industry-sponsored research projects with Toyota and Ford.