Find Academic Supervisors

• Machine Learning
• Image Processing
• Pattern Recognition
• +1 more

Muntaha Sakeena is a Junior Researcher in the Media Computing Research Group at the Institute of Creative\Media/Technologies, Department of Media and Digital Technologies, St. Pölten University of Applied Sciences (FHSTP) in Austria. She has held this position since 2017 and is currently on leave. Her educational background includes a Master's in Computer Engineering from NUST, Pakistan (2016) and a Bachelor's in Computer System Engineering from NFCIET, Pakistan (2012). Her research focuses on machine learning, image processing, pattern recognition, and computer vision applications across diverse domains including real estate analysis, environmental monitoring, medical imaging, and cybersecurity. She has contributed to projects such as: IMREA (Intelligent Multimodal Real Estate Assessment) Active deep learning for object detection Scribe ID AI InfraBase (Automatic Building Footprint Segmentation) ImmoAge (Visual Age Prediction of Real Estate) Her publications demonstrate consistent focus on computer vision applications, with recent work expanding into digital heritage preservation and environmental disaster response. Research trends show progression from medical imaging (2017-2018) toward geospatial and architectural analysis (2019-2023), utilizing deep learning methods like CNNs and level sets.

Stergios-Aristoteles MitoulisView profile

Associate Professor

• Infrastructure Resilience
• Sustainable Construction
• Digital Transformation of Built Environment
• +5 more

Stergios-Aristoteles Mitoulis serves as Associate Professor in Engineering and Project Management at University College London's Bartlett School of Sustainable Construction. A Chartered Engineer (CEng MICE) with memberships in ASCE, EAEE, IABSE and FHEA, he directs the MetaInfrastructure.org and BridgeUkraine.org initiatives while coordinating major EU-funded projects including ZEBAI (€5M), RISKADAPT (€2.5M), and PORTAL (€1.8M). His research centers on three pillars: (1) threat-agnostic resilience frameworks for infrastructure under cascading risks; (2) AI-driven decision systems integrating remote sensing and digital twins; and (3) sustainable retrofitting of transport assets using recycled materials. With over £8 million in research funding from UKRI and Horizon Europe, his work directly informs Eurocode standards and national infrastructure policies. Analysis of his recent publications reveals strong emphasis on multi-hazard fragility modeling (particularly bridges), AI-enabled damage identification, and climate-resilient transport networks. His work consistently bridges theoretical innovation with practical implementation, evidenced by 20+ peer-reviewed papers in 2024-2025 alone spanning reliability engineering, structural safety, and sustainable development. Thorpe Medal for outstanding engineering research IStructE Research Award for infrastructure innovation Editor-in-Chief of ICE Journal of Bridge Engineering Regular contributor to Eurocode standardisation committees Mitoulis actively supervises 40+ doctoral and postdoctoral researchers across 10+ international projects. His REF2029 Impact Case Study focuses on digital technologies for infrastructure resilience, while his £8M+ grant portfolio includes major collaborations with Ukrainian institutions through bridgeUkraine.org. He leads the MetaInfrastructure research group developing counterfactual engineering approaches for the metaCity concept, with recent media coverage by BBC on conflict-damaged infrastructure recovery. The MetaInfrastructure team operates through dedicated labs for digital twin development, AI/ML applications, and sustainable materials testing. Current initiatives include the PORTAL project for AI-driven infrastructure monitoring and ReCharged for circular economy in transport networks. Mitoulis' educational contributions feature a FutureLearn MOOC on critical infrastructure resilience and CPD training for ICE professionals.

• High-Temperature Corrosion
• Aerospace Materials
• Power Systems & Turbines
• +6 more

Dr. Simon Gray is a Principal Fellow in High Temperature Corrosion and Mechanical Interactions at Cranfield University's Surface Engineering and Precision Centre. His research focuses on materials performance in extreme environments, particularly for the gas turbine industry, with key areas including high-temperature corrosion fatigue, hydrogen testing, accelerated oxidation, and machine learning applications for non-destructive evaluation. Expertise: Aerospace Materials, Power Systems & Turbines, Surface Engineering, Sustainable Manufacturing Collaborators: Rolls-Royce Holdings PLC, Siemens Energy AG, MTU Aero Engines AG, NASA, UK Atomic Energy Authority His recent work includes stress corrosion cracking of single-crystal Ni-base superalloys, AI-driven crack detection in microscopy images, and oxidation studies of intermetallic compounds. Clients include major aerospace companies and energy authorities. Scientific Contributions: Gray's publications address hot corrosion mechanisms, environmental degradation of superalloys, and data-driven battery health classification systems. His team develops lifing models for turbine components and investigates hydrogen embrittlement effects.

Mihaela Anca CiupalaView profile

Associate Professor

• Structural Engineering
• Seismic Damage Assessment
• Sustainable Retrofitting
• +6 more

Dr Mihaela Anca Ciupala is a Reader in Structural Engineering at the University of East London's Department of Engineering & Construction within the School of Architecture, Computing and Engineering. With over 30 years of research and consultancy experience, she specializes in structural seismic damage assessment, sustainable retrofitting solutions, and AI applications in structural engineering. Her interdisciplinary work bridges civil engineering, environmental science, and digital technologies. Her educational background includes a PhD in Earthquake Engineering and Safety of Structures from Technical University 'Gh Asachi' Iasi, Romania (1999), an MEng Honours in Civil Engineering from the same institution (1991), and an MBA in General Management from the University of East London (2009). Dr Ciupala's research focuses on structural seismic damage assessment , sustainable seismic retrofitting , AI and Cyber Physical Systems for engineering applications , and sustainable built environments . Her work integrates advanced computational methods with practical engineering solutions to address climate resilience and structural safety challenges. She has pioneered research in FRP composites for structural strengthening and sustainable green infrastructure solutions. Her publication portfolio spans seismic engineering, sustainable materials, and AI applications, demonstrating a clear trajectory toward data-driven structural solutions and sustainable building practices. Recent work emphasizes deep learning for post-earthquake damage classification and cyber-physical systems for structural monitoring. EU Marie Curie Individual Research Fellowship recipient Active reviewer for international journals and research councils Member of International Federation for Structural Concrete Task Group 5.1 Expert evaluator for EU Horizon Europe and Horizon 2020 programs As an educator, Dr Ciupala has supervised numerous PhD students across interdisciplinary topics including structural health monitoring, sustainable housing finance, and green infrastructure. She has led significant research projects including the Global Challenge Research Fund initiative on seismic retrofitting in Turkey and UEL-funded cyber-physical systems research. Her leadership extends to roles as Research Degrees Leader (2007-2023) and Sustainability Champion within her school. Dr Ciupala maintains active collaborations with institutions including Istanbul Technical University, Tongji University, and the University of Michigan, focusing on sustainable engineering solutions and advanced material applications.

Yang ZhangView profile

Academic

• Human-centered Artificial Intelligence
• Crowd Intelligence
• AI Fairness
• +13 more

Yang Zhang is a Teaching Assistant Professor at the School of Information Sciences (iSchool) at the University of Illinois Urbana-Champaign (UIUC). He holds a Ph.D. in Computer Science and Engineering from the University of Notre Dame and is affiliated with the Social Sensing & Intelligence Lab at UIUC as a Senior Researcher. His research focuses on Human-Centered Artificial Intelligence , emphasizing collaboration between humans and AI to enhance model performance, fairness, and accountability. Education: Ph.D. in Computer Science and Engineering, University of Notre Dame; First Class Scholarship, Wuhan University Yang’s work integrates Crowd Intelligence to optimize AI models for applications in disaster response, smart urban sensing, and social impact-aware systems. His recent publications explore hybrid learning frameworks, neural architecture search, and hyperparameter optimization. Yang has received multiple accolades, including a Best Paper Award at ACM/IEEE ASONAM 2022 and a Data Science Scholar Fellowship from Indiana University. Scientific Awards: Best Paper Award (ASONAM 2022), Best Paper Honorable Mention (SMARTCOMP 2022), Outstanding Graduate Research Assistant (Notre Dame), Video Presentation Award (IWQoS 2020), IEEE Student Travel Awards (BigData 2018, 2019), Data Science Scholar Fellowship (Indiana University), First Class Scholarship (Wuhan University) Yang is actively involved in graduate education as a Graduate Faculty member at UIUC. He has cohosted international scholars through the Bolashak International Scholarship Program and been recognized for excellence in teaching. His collaborations span institutions like Argonne National Laboratory, where he served as a W. J. Cody Research Associate.

Art GleasonView profile

Research Professor

• Remote Sensing
• Marine Conservation
• Coral Reef Science
• +3 more

Art Gleason is a Research Professor at the University of Miami, jointly affiliated with the Department of Physics and Department of Marine Geosciences. His research integrates geophysics, biogeography, and engineering to develop remote sensing tools for environmental conservation. He holds a Master’s degree from the University of Maryland and leads interdisciplinary teams focused on marine technology innovation. His research spans three themes: 1) Satellite product validation (e.g., AVHRR orbital drift correction for net primary production calculations), 2) Marine habitat mapping using hyperspectral/acoustic classification, and 3) Underwater photomosaic technology for coral reef monitoring. His 2020 Pew Marine Fellowship supports advanced applications of photomosaics in coral restoration. Gleason’s publications emphasize coral reef dynamics, optical oceanography, and automated image analysis. Trends include high-resolution mapping, resilience assessment, and technology development for conservation. His collaborative network includes NOAA, Scripps Institute of Oceanography, The Nature Conservancy, and international research groups. Awards & Grants: Pew Marine Fellow (2020) – For advancing underwater mosaic technology in coral reef conservation He directs the Large Area Imaging (LAI) initiative, conducting biweekly office hours and courses on underwater data acquisition. His lab partners with 15+ organizations globally to deploy photomosaic technology for reef science.

Jay ThiagarajahView profile

Assistant Professor

• Paediatric Gastroenterology
• Congenital Enteropathies
• Inflammatory Bowel Disease
• +2 more

Jay Thiagarajah, MD, PhD, is Assistant Professor of Pediatrics at Harvard Medical School and Co-Director of the Congenital Enteropathy Program at Boston Children's Hospital. He serves as an Attending Physician in the Division of Gastroenterology, Hepatology and Nutrition. His research focuses on understanding how intestinal epithelium senses and interacts with its environment, particularly in congenital enteropathies and inflammatory bowel disease. He develops therapeutic approaches for conditions like microvillus inclusion disease using patient-derived enteroid models. His clinical expertise includes pediatric gastrointestinal disorders such as congenital diarrheas, allergic colitis, eosinophilic esophagitis, and inflammatory bowel disease. Dr. Thiagarajah leads the Thiagarajah Lab which investigates epithelial membrane channels, host-microbial symbiosis, and cellular metabolism.

• Construction Materials
• Remote Sensing
• Structural Health Monitoring
• +7 more

Mohamad Alipour is a Research Assistant Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. He holds a Ph.D. from the University of Virginia and M.S./B.S. degrees from Tehran Polytechnic. His academic roles include: Faculty Fellow, National Center for Supercomputing Applications (NCSA) Co-Guest Editor for Journal of Sensors and Associate Editor for Journal of Nondestructive Evaluation Education: Ph.D. in Civil Engineering (Structures), University of Virginia, 2019 M.S. in Structural Engineering, Tehran Polytechnic, 2010 B.S. in Civil Engineering, Tehran Polytechnic, 2008 Research Interests: Focuses on scalable digitization, automation, and AI-driven solutions for infrastructure challenges. Key areas include: Remote sensing and machine learning for infrastructure monitoring Wildfire fuel mapping using multimodal data fusion Structural health monitoring via computer vision and digital twins Automated UAV-based inspection systems Recent Research Trends: Articles emphasize Bayesian inversion for subsurface sensing, wildfire fuel estimation via AI, and UAV optimization. Themes include environmental sustainability, data fusion, and infrastructure resilience. Awards and Honors: NCSA Outstanding Mentor (2024) Aftab Mufti Medal for Best Paper (2022) Young Professionals Award, ASCE (2020) Advising & Grants: Supervises Ph.D. students (e.g., Ishfaq Aziz) and leads projects funded by NASA, Microsoft, and Amazon. Current openings exist for motivated students in machine learning and infrastructure analytics. Labs & Teams: Directs the Alipour Research Group, specializing in digital twins for smart infrastructure. Collaborates with NCSA and industry partners on AI-driven solutions for natural hazard mitigation.

• Agricultural Economics
• Resource Management
• Climate Change Policy
• +3 more

David A. Fleming-Muñoz is a Professor of Agricultural and Resource Economics at the La Trobe Business School and co-leads the 'Food Business, Food Security and Digital Agriculture' domain at the La Trobe Institute for Sustainable Agriculture and Food (LISAF). He is also an Honorary Senior Fellow at the UQ School of Economics. With over 20 years of policy and research experience in Australia, Chile, the United States, and New Zealand, his work focuses on agricultural and resource economics, climate change mitigation, and policy design. He has published over 60 research papers and won awards such as the 2020 Best Paper Award in the Australian Journal of Agricultural and Resource Economics and the 2023 CSIRO Collaboration Medal Award. His research spans energy transitions, drought resilience, and the economic impacts of resource booms. He led projects at CSIRO, Motu Economic and Public Policy Research, and contributed to international organizations like the OECD and the Inter-American Development Bank. He served as Editor-in-Chief of Resources Policy from 2017 to 2024. His work integrates econometric analysis, policy evaluation, and interdisciplinary collaboration to address sustainability challenges in agriculture, energy, and climate resilience. Key research interests include: Economic resilience to climate change and natural disasters Policy evaluation for sustainable resource management Regional development in mining and agricultural sectors Global agricultural trade and development Adoption of climate-smart agricultural practices His recent work explores the intersection of artificial intelligence in agriculture, critical mineral governance, and long-term energy transition strategies. His research has been cited across policy reports, Wikipedia pages, and international media, reflecting its practical and theoretical impact.

Yiming DengView profile

Professor

• Nondestructive Evaluation (NDE)
• Sensors and Sensing Systems
• Artificial Intelligence (AI)-enabled NDE/SHM
• +6 more

Yiming Deng is a Professor of Electrical and Computer Engineering (ECE) at Michigan State University (MSU), serving as the Associate Chair for Graduate Studies. He leads the Nondestructive Evaluation Laboratory (NDEL), focusing on advanced sensor technologies, AI-driven NDE/SHM systems, and uncertainty quantification. His research addresses critical infrastructure safety, energy systems, and aerospace applications. Education: Ph.D., Electrical Engineering, Michigan State University, 2009 B.E., Electrical Engineering, Tsinghua University, Beijing, China, 2003 Research Interests: Yiming Deng specializes in NDE 4.0/5.0 systems, AI-enhanced sensor fusion, and multi-physics simulations. His work bridges theoretical models with experimental validations, emphasizing practical applications like pipeline inspection, composite materials analysis, and food quality assessment. Recent projects include robotic NDE systems for cylindrical structures and eddy current-based temperature monitoring in additive manufacturing. Publications & Trends: His 190+ peer-reviewed articles span AI-driven defect detection, multi-modal sensing, and sensor automation. Notable themes include fusion of heterogeneous data, ML-based material characterization, and real-time monitoring systems for industrial and agricultural applications. Awards: ASNT Fellowship Award (2021) Best Paper/Poster Awards (ISEM, ENDE, ANTEC) FAA Better Way Award (2005) Advising & Grants: Deng has advised over 20 graduate students and secured funding from DOE, NSF, DOT/PHMSA, Office of Naval Research, and industry partners like Con Edison. His lab collaborates on projects such as AI-enabled robotic inspection robots and multi-modal pipeline assessment systems. Labs & Teams: The NDEL group develops cutting-edge tools like the Nonlinear Eddy Current Inspection System and Wireless Sensor Networks for real-time structural health monitoring. Ongoing efforts include miniaturized sensing systems and hybrid NDE frameworks for complex material analysis.

Kwanghee WonView profile

Associate Professor

• Computer Vision
• Deep Convolutional Neural Networks
• Deep Reinforcement Learning
• +4 more

Kwanghee Won is an Associate Professor at the McComish Department of Electrical Engineering and Computer Science, South Dakota State University. His research focuses on computer vision, deep learning, and their applications in autonomous systems, precision agriculture, and structural health monitoring. He holds a Ph.D. from Kyungpook National University, Korea. Education: B.S., M.S., and Ph.D. in Computer Engineering from Kyungpook National University, Daegu, Korea Research Interests: Deep convolutional neural networks for anomaly detection and classification Autonomous vehicle systems and augmented reality applications Computer vision in smart agriculture and infrastructure assessment Model compression techniques for efficient neural networks Professional Contributions: Upsilon Pi Epsilon honor society faculty advisor (2021–present) ACM member and SIG Applied Computing participant Labs and Teams: Director of the Intelligent Vision Systems Laboratory , focusing on advanced vision-based solutions for real-world challenges.

Ali Abdul-AzizView profile

Associate Professor

• Nondestructive Evaluation
• Materials Characterization
• Structural Analysis
• +3 more

Dr. Ali Abdul-Aziz is an Associate Professor in the College of Aeronautics & Engineering at Kent State University, specializing in applied engineering with expertise in nondestructive evaluation (NDE), materials characterization, and structural analysis. He holds leadership roles in curriculum development for the Aerospace Program and manages the Materials Research Lab, supported by NASA Glenn Research Center contracts. Education: PhD Mechanical Engineering, Cleveland State University (1985) MS Mechanical Engineering, Cleveland State University (1981) BS Mechanical Engineering, Cleveland State University (1980) Research Focus: His work spans composites, propulsion health monitoring, experimental mechanics, and finite element analysis. Recent projects include turbine engine diagnostics and ceramic matrix composites durability, often integrating NDE techniques like digital image correlation for aerospace applications. Publications: His recent articles (2017-2022) show a strong emphasis on structural health monitoring, turbine engine failure simulations, and sensor technologies, reflecting interdisciplinary approaches combining mechanics, materials science, and computational modeling. Awards and Recognition: No major scientific awards listed in provided materials. Academic Leadership: Actively involved in ABET accreditation processes, laboratory development, and advising for aerospace engineering initiatives. Administers collaborative research through the Materials Research Lab.

• Artificial Intelligence
• Robotics
• Machine Learning
• +6 more

Dr Fateme Dinmohammadi is an Associate Professor and Senior Lecturer in AI & Robotics at the University of West London (UWL) , serving as Course Director for the MSc Software Engineering program within the School of Computing and Engineering . Her research focuses on AI, Machine Learning, Robotics, Predictive Analytics, and IoT-driven systems, with applications in energy efficiency, healthcare, and infrastructure monitoring. She has contributed to over 50 peer-reviewed articles and industry-funded projects through EPSRC, InnovateUK, H2020, and industry collaborations. Previously, she held senior research roles at University College London (UCL) , Cranfield University , and the Edinburgh Robotics Centre . Her work spans AI-driven solutions for smart buildings, battery management in EVs, structural health monitoring of wind turbines, and medical imaging applications like echocardiography. Projects emphasize real-world deployment through digital twins, Bayesian networks, and edge computing. Dr Dinmohammadi teaches across undergraduate and postgraduate programs, including BSc Computer Science, Cyber Security, and AI-focused degrees. Her expertise bridges academic research and industry needs, addressing challenges in Industry 4.0, sustainable energy systems, and autonomous robotics.

• Vision-Language
• Multimodal Deep Learning
• Semantic Segmentation
• +3 more

Philipp Jonas Rösch is a Researcher and PhD Candidate at Bundeswehr University Munich, holding the position of Research Head for Artificial Intelligence at the Institute for Distributed Intelligent Systems (VIS). He is affiliated with the Chair of Data Science under Prof. Michaela Geierhos at the Research Institute CODE. He holds a Master's degree in Statistics from Ludwig-Maximilians-Universität München and has industry experience prior to academia. His research focuses on Vision-Language systems, Multimodal Deep Learning, and Damage Recognition, with notable contributions to datasets like dacl10k and InpaintCOCO . He co-organizes the Machine Learning Interest Group (MLIG), a platform for researchers at UniBw M and HSU focusing on ML/AI topics. Rösch manages the GPU cluster 'Monacum One' and advises students on Bachelor's/Master's theses in Deep Learning or Vision-Language domains. His work emphasizes real-world applications in infrastructure inspection and military vehicle detection, leveraging both academic and industrial perspectives.

• Soft Robotics
• Haptic Modeling
• Tactile Sensing
• +4 more

Arsen Abdulali is a Research Fellow in the Department of Engineering at the University of Cambridge, specializing in soft robotics, haptic modeling, and data-driven control systems. He is a key member of the Bio-Inspired Robotics Laboratory led by Professor Fumiya Iida, where he has made significant contributions to robotic manipulation, tactile sensing, and culinary robotics applications. Dr. Abdulali's research focuses on advancing the capabilities of soft robotic systems through innovative approaches to haptic perception and control. His work bridges material science, machine learning, and practical robotic applications, with a strong emphasis on robotic cooking systems that can perceive and respond to food properties during preparation. His methodology combines experimental work with physical platforms, data-driven modeling, and machine learning techniques for real-world validation. His publication record demonstrates a consistent trajectory of impactful research, with 38 publications spanning from 2015 to 2025. A notable trend in his recent work (2022-2025) shows a strong focus on culinary robotics, where he has developed systems capable of tactile discrimination of food materials, closed-loop control for cooking processes, and multi-modal feedback for optimizing culinary outcomes. His research has practical implications for domestic service robotics, food industry automation, and assistive technologies. Dr. Abdulali maintains productive collaborations across multiple institutions including Fumiya Iida, Josie Hughes, and Grzegorz Sochacki at Cambridge, along with international collaborators from earlier phases of his career. His contributions to the robotics community include serving as co-editor of the proceedings for the 24th Annual Conference on Towards Autonomous Robotic Systems (TAROS 2023) held at the University of Cambridge, indicating recognition of his standing within the field.