Find Academic Supervisors

• Computer Vision
• Deep Learning
• Machine Learning
• +11 more

Vittorio Murino is a Full Professor and Department Director at the Department of Computer Science, University of Verona. His academic work spans computer vision, machine learning, and biomedical applications, with significant contributions to both theoretical and applied research across multiple domains. His primary research focuses include: Computer vision and deep learning with emphasis on domain adaptation and generalization Multi-modal deep learning models for human behavior analysis Neuroimaging data analysis using connectomics approaches Industrial applications including visual inspection and automation Underwater vision (acoustical and optical) systems Professor Murino leads the Vision, Image Processing & Sound (VIPS) Laboratory which specializes in analyzing multivariate multidimensional signals through artificial intelligence techniques. His research portfolio spans theoretical machine learning advancements to practical implementations in healthcare, security, and industrial settings. He has secured funding for numerous research projects including: Multimodal Elder Care (MEC) TRANSFER AND ADAPTIVE LEARNING IN IMPERFECT MULTIMODAL DATA SCENARIOS (TALIM) Tecnologie di Intelligenza Artificiale per il Monitoraggio del Comportamento di Pazienti Allettati (TIAMoPA) Studio di tecniche di apprendimento profondo per la segmentazione semantica di immagini As an educator, Professor Murino teaches advanced courses including Computer Vision & Deep Learning and Machine Learning & Deep Learning for the Master's degree in Artificial Intelligence. He has developed curriculum for multiple programs spanning from Digital Image Processing to specialized deep learning applications in medical imaging.

Laine MearsView profile

Professor

• Intelligent Machining Systems
• Manufacturing Process Control
• Electrically-Assisted Manufacturing
• +5 more

Dr. Laine Mears is a Professor in Automotive Engineering at Clemson University's International Center for Automotive Research (CU-ICAR). With over 10 years of industry experience, he leads research in intelligent machining systems, manufacturing process control, and electrically-assisted manufacturing. His research develops model-based control strategies for manufacturing processes, precision positioning systems, and novel techniques for processing difficult materials. Current projects focus on friction welding, tool wear monitoring using computer vision, and explainable AI for manufacturing quality control. Dr. Mears has established the Manufacturing Automation and Intelligence Lab that bridges fundamental research with industrial applications. He has received numerous honors including the NSF CAREER Award, IMechE George Stevenson Gold Medal, and SAE Ralph Teetor Award. Dr. Mears actively contributes to professional societies through leadership roles in ASME and SME.

Dwight DayView profile

Associate Professor

• Digital Image Processing
• Signal Processing
• High Performance Computing
• +3 more

Dwight D. Day is Associate Professor in Electrical and Computer Engineering at Kansas State University's College of Engineering, with research spanning industrial applications of signal processing and embedded systems. His technical portfolio includes image-based quality control systems, road profiling algorithms, and NASA-funded low-power communications. Research domains: Machine vision for industrial inspection Signal processing algorithms for infrastructure assessment High-reliability software for aerospace applications Energy-efficient communication architectures Teaching includes digital design, microprocessor applications, digital signal processing, and numerical methods, informed by industry experience at Texas Instruments and Boeing.

Amir PoursaeeView profile

Professor

• Corrosion
• Metallic Materials
• Cementitious Materials
• +3 more

Dr. Amir Poursaee is a Professor of Civil Engineering and Materials Science and Engineering at Clemson University's College of Engineering, Computing and Applied Sciences, where he directs the Corrosion Research Laboratory. His research focuses on corrosion mechanisms, metallic materials, and cementitious systems, with emphasis on infrastructure durability and electrochemical evaluation techniques. Education includes: B.S. in Materials Engineering (University of Tehran, 1995) M.S. in Mechanical Engineering (University of Waterloo, 2004) Ph.D. in Mechanical Engineering (University of Waterloo, 2007) His research explores electrochemical behavior of materials in construction environments, corrosion protection methods, microstructure influences on degradation, and non-destructive evaluation technologies. Recent work integrates machine learning for corrosion prediction and advanced imaging for infrastructure assessment. Professional contributions include membership in ACI committees (222, 236, 130), NACE, editorial board service for Advances in Civil Engineering Materials , and peer review for leading journals including Corrosion Science and Cement and Concrete Research . He holds professional engineering registration in Ontario, Canada.

Susan HesterView profile

Associate Professor

• Biosecurity Economics
• Invasive Species Management
• Resource Allocation
• +2 more

Dr. Susan Hester is an Associate Professor at the University of New England Business School and Chief Investigator at the Centre of Excellence for Biosecurity Risk Analysis (CEBRA). With a PhD in Agricultural Economics, she specializes in optimizing resource allocation for invasive species management. Her research integrates bioeconomic modelling, surveillance design, and incentive-based policy frameworks. Key research areas include passive surveillance systems, eradication feasibility, and socio-economic assessments of biocontrol methods. She has developed tools like WaspSim for simulating pest management and contributed to Australia's biosecurity policies. Awards: Best Journal Paper (AJARE 2008), CRC Weed Management Scientific Project Award (2008). Leadership roles: Editorial Board Member for Frontiers in Ecology and Evolution . Her interdisciplinary collaborations span mathematics, ecology, and veterinary science, focusing on maximizing biosecurity budget efficiency through innovative policy design.

Zhaohui GengView profile

Assistant Professor

• Advanced Manufacturing
• Digital Transformation
• Machine Learning
• +4 more

Zhaohui Geng is an Assistant Professor in the Department of Industrial and Systems Engineering at Ohio University's Russ College of Engineering and Technology. He joined Ohio University in 2023, previously serving as an Assistant Professor at the University of Texas Rio Grande Valley and a Health Sciences Research Fellow at the University of Pittsburgh's School of Medicine. His research focuses on the intersection of data science, AI, optimization, and their applications in advanced manufacturing and healthcare systems. Educations: Ph.D. in Industrial Engineering, University of Pittsburgh, 2021 M.A. in Statistics, University of Pittsburgh, 2018 M.S. in Industrial Engineering, University of Pittsburgh, 2016 B.E. in Electronic Science and Technology, Nankai University, 2014 Research Interests: Advanced manufacturing and digital transformation Machine learning and knowledge engineering Optimization techniques for healthcare systems Reverse engineering and additive manufacturing applications His work emphasizes data-driven approaches to improve manufacturing precision and healthcare system efficiency. Recent Research Trends: Recent publications highlight advancements in reverse engineering processes, additive manufacturing optimization, and AI-driven solutions for 3D scanning and quality control. His work bridges theoretical methods like Bayesian networks with practical applications in medical devices and aerospace manufacturing. Labs & Affiliations: Center for Advanced Materials Processing Center for Advanced Systems and Transportation Logistics Engineering Russ College's Industrial and Systems Engineering research groups

• Control Systems
• Vibration Control
• Sensor Technology
• +6 more

Łukasz Ścisło is an Associate Professor at the Department of Automation and Computer Science , part of the Faculty of Electrical and Computer Engineering at Jagiellonian University. He specializes in control systems, vibration analysis, and IoT applications, with a focus on aerospace engineering, structural health monitoring, and environmental monitoring. His work integrates neural networks, sensor technologies, and real-time data analytics to solve complex engineering challenges. Research interests include: Optimization of control systems using neural networks and sensitivity analysis Vibration measurement enhancements via 3D laser Doppler vibrometry IoT-based systems for environmental and building safety monitoring UAV trajectory adaptation for industrial inspection On-board monitoring of helicopter turboshaft engines His recent publications (2021–2025) highlight contributions to: Earthquake impact analysis on high-energy physics facilities Smartphone integration for transport monitoring 3D quality assessment algorithms in manufacturing Comparative analysis of gas/wood furnace systems No scientific awards are listed in the provided texts. Collaborations involve international teams in Ukraine, Germany, and Poland. His research extends to biomedical applications like fetal ECG signal processing, demonstrating interdisciplinary reach.

• Machine Learning
• Signal Processing
• Time Series Analysis
• +2 more

Richard Povinelli is Professor and Director of Computer Engineering Laboratories at Marquette University's Opus College of Engineering. His research develops machine learning methods for time series analysis with applications in energy forecasting, biomedical signal processing, and industrial systems. Award-winning contributions include PhysioNet Challenge solutions for cardiac arrhythmia detection. Research pillars: Deep neural networks for natural gas demand forecasting Anomaly detection in smart meter data Nonlinear signal processing for ECG classification Computer vision for industrial defect detection Recent publications demonstrate innovation in hierarchical forecast reconciliation for energy systems, dynamical modeling of medical prognostics, and image-based quality control. Collaborative projects with Apache Corporation have developed pipeline alarm prediction systems using extremal quantile regression.

Ned TobinView profile

Lecturer

• Erosion of materials by liquid impact
• Rain erosion mitigation
• UAV-based inspection and defect detection
• +5 more

Ned Tobin is a Lecturer in the Department of Aerospace and Mechanical Engineering at South East Technological University, within the Faculty of Engineering. He teaches at undergraduate and postgraduate levels and supervises research students at Masters and PhD levels. His work contributes to UN Sustainable Development Goals related to affordable and clean energy and industry innovation. Education: BEng in Aeronautical Engineering, University of Limerick (2005) PhD in Engineering (Evaluation and Correlation of Rain-eroded Aircraft Coatings Using Surface Topography), University of Limerick (2015) Research focuses on erosion mechanics, materials durability, and UAV-based inspection technologies. Key projects include mitigating erosion damage on wind turbine blades and developing sol-gel coatings for fouling prevention. Recent work emphasizes droplet impact analysis and deep learning for aircraft defect detection. Awards : SETU Research Excellence Awards Nominee: Supervisor Award (2022, 2023) Advising and grants: Active in supervising student research and leading the SOLOblade project (2024–2027) focused on nanomaterials for wind energy systems. Collaborates on interdisciplinary projects combining fluid dynamics, materials science, and aerospace engineering. Labs/Teams: Involved in the Department’s wind energy and advanced materials research groups, contributing to test facilities for erosion testing and UAV-based inspection systems.

Yuchu QinView profile

Senior Lecturer

• Design for Additive Manufacturing
• Intelligent Manufacturing
• Knowledge Representation and Reasoning
• +7 more

Dr. Yuchu Qin serves as a Senior Lecturer (Equivalent) in the School of Computing and Engineering and Senior Research Fellow with the EPSRC Future Advanced Metrology Hub for Sustainable Manufacturing at the University of Huddersfield. With dual PhD qualifications in Instrument Science and Technology and Mechanical Engineering, Dr. Qin bridges mechanical engineering and computer science disciplines to advance research in additive manufacturing and intelligent systems. Dr. Qin's research spans design for additive manufacturing, intelligent manufacturing systems, knowledge representation, and decision analysis, with particular expertise in fuzzy sets, optimization techniques, and category theory applications. Recent work demonstrates strong integration of AI methodologies with traditional manufacturing processes, creating innovative solutions for quality control and process optimization in additive manufacturing. Publications over the past three years reveal a clear trajectory toward AI-enhanced manufacturing solutions, with emphasis on surface characterization, process parameter optimization, and knowledge representation. The research combines deep theoretical foundations with practical manufacturing applications, addressing critical challenges in additive manufacturing quality and efficiency. Research Degree Scholarship in Engineering (2017) National Scholarship for Doctoral Students (2015) National Scholarship for Master's Students (2012) National Scholarship for Undergraduate Students (2008) As Principal Investigator for a Royal Society International Exchanges Grant and core researcher for major EPSRC funding totaling over £23 million, Dr. Qin actively supervises three PhD students while serving as reviewer for over 70 journals and holding editorial positions with multiple publications. Dr. Qin's teaching includes professional development courses and data mining instruction for both undergraduate and postgraduate students. Research activities center around the EPSRC Future Advanced Metrology Hub, where Dr. Qin contributes to developing next-generation metrology solutions for sustainable manufacturing through both theoretical advancements and practical implementations that address real-world manufacturing challenges.

Hussam MuhamedsalihView profile

Research Fellow

• Optical Metrology
• Surface Measurement
• Interferometry
• +4 more

Dr Hussam Muhamedsalih is a Principal Enterprise Fellow and senior research fellow at the Centre for Precision Technologies (CPT) and EPSRC Future Metrology Hub at the University of Huddersfield. He is affiliated with the Department of Engineering in the School of Computing and Engineering, where he serves as a module leader for Mathematics for first-year undergraduate engineering students with a class capacity of 150 students. Dr Muhamedsalih earned his MSc in Control Systems and Instrumentation from the University of Huddersfield in February 2008, followed by a PhD from the same institution in May 2013. His doctoral research focused on 'Investigation of wavelength scanning interferometry (WSI) for embedded metrology' under the supervision of Prof. Dame Xiangqian (Jane) Jiang. He was appointed as a full-time research fellow at the CPT on May 9, 2013. Dr Muhamedsalih's research spans optical metrology, surface measurement, and precision engineering, with particular expertise in interferometry, wavelength scanning techniques, and focus variation microscopy. His work has significant applications in roll-to-roll manufacturing, flexible electronics, and in-process surface metrology. He has developed innovative approaches for vibration immunity in measurement systems and advanced algorithms for surface characterization. His recent publications (2023-2025) show a continued focus on chromatic focus variation techniques, in-situ metrology applications, and real-time surface inspection systems for industrial manufacturing processes. Winner in Manufacturing Technology category of IET Innovation Awards (2014) Highest Fellowship Award from Worshipful Company of Scientific Instrument Makers (2016) Postgraduate Award from Worshipful Company of Scientific Instrument Makers (2013) Enterprise Fellowship by the Royal Academy of Engineering Dr Muhamedsalih currently co-supervises 2 PhD students and supervises 4 BSc students. His research has been supported by major projects including EU FP7 NanoMend, where he delivered a fully operational optical inspection system, and an ERC Proof of Concept project, where he developed advanced WSI technology in collaboration with IBS Precision Engineering in the Netherlands. He was awarded the Enterprise Fellowship by the Royal Academy of Engineering to develop and deliver a clear strategy from fundamental research through to business innovation. He is an active member of the Centre for Precision Technologies and the EPSRC Future Metrology Hub, where his work contributes to advancing precision measurement techniques for industrial applications. His research has practical implications for quality control in manufacturing processes, particularly in the production of flexible photovoltaics and electronic devices. His work also contributes to United Nations Sustainable Development Goals related to industry, innovation, and infrastructure.

Yinan WangView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Uncertainty Quantification
• +3 more

Yinan Wang serves as an Assistant Professor in the Department of Industrial and Systems Engineering at Renssela Polytechnic Institute (RPI), focusing on engineering-driven machine learning applications for advanced manufacturing and robotics systems. Education: Ph.D., Industrial and Systems Engineering, Virginia Tech, 2022 M.S., Electrical Engineering, Columbia University, 2019 B.S., Electrical Engineering and Automation, Xi'an Jiaotong University, 2017 Research Interests: Dr. Wang's work bridges machine learning with industrial engineering through deep learning, uncertainty quantification, and system intelligence. His research targets advanced manufacturing challenges including robotic path planning, 3D anomaly detection, and environmental monitoring systems. Key application areas span aerospace assembly, toxic plume prediction, and quality control in industrial settings. Publication Trends: His 2024-2025 publications reveal three dominant themes: (1) transformer model compression for time-series forecasting (Smartformer), (2) geometric contrast learning for industrial point cloud segmentation (GeoContrast), and (3) multimodal physiological signal analysis for aviation safety prediction. These works consistently integrate physics-based constraints with deep learning architectures. Scientific Awards: Mary G. and Joseph Natrella Scholarship, American Statistical Association (ASA), 2022 Featured Article in ISE Magazine, Institute of Industrial and Systems Engineers (IISE), 2022 SPES + Q&P Best Student Paper Award, American Statistical Association (ASA), 2022 Educational Foundation Scholarship and Analysis Division Scholarship, International Society of Automation (ISA), 2021 Gilbreth Memorial Fellowship, Institute of Industrial and Systems Engineers (IISE), 2021 Data Mining & Decision Analytics (DMDA) Best Theoretical Paper Award, INFORMS, 2021 Finalist of Best Student Paper Award, Quality, Statistics & Reliability (QSR) Section, INFORMS, 2021 Best Poster Award, Manufacturing Science & Engineering Conference (MSEC), ASME, 2021 Advising and Grants: No publicly listed advisees or specific grant awards appear in the source material, though his extensive publication record and research center affiliations suggest active grant-supported projects. Labs and Teams: Dr. Wang contributes to RPI's Institute for Data Exploration and Applications (IDEA) and Center for Materials, Devices, and Integrated Systems (CMDIS), participating in interdisciplinary teams focused on data-driven engineering solutions.

• Resilient Infrastructure Systems
• Machine Learning
• Deep Learning
• +5 more

Kaijian Liu is an Assistant Professor in the Department of Civil, Environmental, and Ocean Engineering at Stevens Institute of Technology. He joined Stevens in October 2019 and holds a Ph.D. in Civil Engineering from the University of Illinois at Urbana-Champaign. His research focuses on resilient infrastructure systems, machine learning, and cyber-physical systems for sustainability. He has been funded by NSF, USGS, NOAA, and the U.S. Army. Education: Ph.D. (2019), University of Illinois at Urbana-Champaign (Civil Engineering) M.S. (2014), University of Illinois at Urbana-Champaign (Civil Engineering) B.A. (2012), Tongji University (Construction Engineering and Management) Research interests include: Machine learning applications in civil infrastructure Ontology-driven data fusion for bridge analytics Human-behavior modeling for energy efficiency Cyber-physical systems for smart cities His work emphasizes integrating AI with physical systems to enhance infrastructure resilience. Professional contributions include roles as: Associate Editor, ASCE Journal of Computing in Civil Engineering Member of ASCE Computing Division committees (Data Sensing & Analysis, Visualization, Education) Awards include the 2023 ASCE Outstanding Reviewer Award and the 2017 CSE Fellowship. His teaching spans numerical modeling, machine learning in civil engineering, and construction informatics.

David ElizondoView profile

Professor

• Intelligent Transport Systems
• Computational Intelligence
• Network Security
• +1 more

David Elizondo is Professor in Intelligent Transport at De Montfort University's School of Computer Science and Informatics, leading the Interdisciplinary Group in Intelligent Transport Systems (DIGITS). His research applies computational intelligence to transportation challenges, including traffic management, anomaly detection, and AI systems. Recent publications focus on few-shot learning for object segmentation, network security through payload entropy analysis, and explainable AI frameworks. Work integrates machine learning, computer vision, and natural language processing techniques for applications in intelligent transport and cybersecurity.

• Data Science
• Machine Learning
• Medical Informatics
• +5 more

Professor Mo Saraee holds a chair in Data Science at the University of Salford , where he serves as the head of Computer Science and Software Engineering and program leader for the MSc Data Science and MSc IoT programs. With a PhD in Computer Science from the University of Manchester, he has published over 180 research outputs and led projects funded by Innovate UK, ERDF, and HEIF. His work bridges data science, machine learning, and real-world applications in healthcare, transportation, and cybersecurity. Head of Computer Science and Software Engineering Program Leader for MSc Data Science and MSc IoT Editor-in-Chief of International Journal of Web Research £1.4M+ in project funding Research Areas include: Data Science Machine Learning NLP Medical Informatics Big Data Connected Autonomous Vehicles (CAVs) Recent Publications demonstrate expertise in anomaly detection, deepfake classification, and healthcare applications of machine learning, with a focus on industrial and societal impact. His scientific contributions include: Shortlisted for Data Strategy Awards Editorial leadership in computational fields Collaborations span NHS, Salford City Council, Irwin Mitchell, and Bupa, with a commitment to translating research into practical solutions.