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Stefan Lee is an Associate Professor at Oregon State University in the School of Electrical Engineering and Computer Science. His research focuses on agents that can perceive environments, communicate with humans, and coordinate actions to achieve shared goals. This work spans computer vision, natural language processing, and deep learning, with applications in robotics and embodied AI. Current Role: Associate Professor, Oregon State (2025–) Previous Roles: Assistant Professor (2019–2025), Research Scientist II (2017–2019), Postdoctoral Associate (2016) Research Interests include: Vision-and-Language Navigation (VLN) Multimodal Learning Embodied Artificial Intelligence Robotic Perception and Control Fairness in AI Systems Selected Scientific Awards : ICLR 2023 Best Paper Award EMNLP 2017 Best Short Paper Award CVPR 2019 Oral CVPR 2018 Oral CVPR 2014 Best Paper Award Advising : Mentors 7 PhD students including Zijiao Yang, Xiangxi Shi, and Abhinav Jain. His publications demonstrate consistent leadership in top-tier conferences like ICCV, CVPR, and NeurIPS.

• Computer Vision
• Natural Language Processing
• Deep Learning
• +4 more

Timothy Verstraeten is a postdoctoral researcher at the Vrije Universiteit Brussel , affiliated with the Engineering Technology Acoustics & Vibrations Research Group . His work bridges Wind Energy and Machine Learning , focusing on optimization, condition monitoring, and control systems for wind farms. Role: Researcher in acoustics, vibrations, and intelligent monitoring Key Projects: IOF Gear (2018–2023), Robust Fleet-Dedicated Reinforcement Learning (2017–2020) Research Interests include: Wind turbine operations (power prediction, balancing schemes) Reinforcement learning for hybrid energy systems and fleet control Condition monitoring using Gaussian processes and anomaly detection Deep learning for predictive maintenance and wake loss modeling Scientific Contributions show a focus on Wind turbines (100% Scopus), Farms (58%), and Reinforcement learning (37%), with recent publications on uncertainty quantification and hybrid energy systems. Award: ICTAI Best Student Paper (2018) He has supervised research proposals and presented at conferences like ACM E-Energy and Wind Energy Science, emphasizing control systems , power optimization , and structural health monitoring .

• Wind turbines
• Condition monitoring
• Reinforcement learning
• +4 more

Keivan Navaie is a Professor of Intelligent Networks at Lancaster University’s School of Computing and Communications. He serves as a member of the Independent Scientific Advisory Committee at the Alan Turing Institute, overseeing the £100 million BridgeAI programme, and previously as Principal AI Technology Advisor to the UK Information Commissioner’s Office (ICO). He is recognized with Fellowships from the Institution of Engineering and Technology (IET), Chartered Engineer status in the UK, Senior Fellowship of the Higher Education Academy (HEA), and the IEEE Young Investigator Award. Research Focus: Wireless communications, mathematics, artificial intelligence, 6G networks, blockchain technology, edge computing, cognitive radio networks, and non-orthogonal multiple access (NOMA). Supervision: Actively supervises PhD students in areas like wireless communications and mathematical modeling. Projects: Involved in distributed learning, blockchain integration, 6G research, and spectrum sharing systems. Awards: IEEE Young Investigator Award, Fellow of IET, Chartered Engineer, Senior Fellow of HEA.

• Wireless Communications
• Mathematics
• Artificial Intelligence
• +5 more

Karen Eilbeck is Professor of Biomedical Informatics and Adjunct Associate Professor of Human Genetics at the University of Utah , where she directs a bioinformatics research lab focused on precision medicine and genomic data management. Education B.S., University of Salford, United Kingdom Ph.D., University of Manchester, United Kingdom Research Interests The Eilbeck lab leverages computer science and ontology engineering to address contemporary questions in genomics and molecular biology. Central themes include: Development of ontologies such as the Sequence Ontology (SO) and Non-Coding RNA Ontology (NCRO) to standardize and structure biological data. Design of ontology-enabled software tools that enhance data sharing, variant interpretation, and precision medicine workflows. Metagenomic pathogen detection, clinical genome interpretation, and integration of multi-omics datasets to advance personalized healthcare. Publication Trends Recent publications (2023-2025) emphasize AI-driven clinical decision support, standardized genomic terminology, and large-scale infectious-disease risk modeling. Earlier works (2005-2018) established foundational ontologies and data formats (GVF, VCF) now widely adopted by the genomics community. Contact & Resources Email: keilbeck@genetics.utah.edu Lab website: available via University of Utah Department of Biomedical Informatics Full publication list: PubMed search | Google Scholar

• Bioinformatics
• Genomics
• Precision Medicine
• +5 more

Maria De Iorio is a Professor at the Yong Loo Lin School of Medicine and holds a joint appointment at the Faculty of Science , both at the National University of Singapore. Her work bridges statistics, epidemiology, and clinical sciences through advanced Bayesian modeling and computational methods. Key research areas: Statistics, Epidemiology, Genetics, Clinical sciences, Medical biochemistry Primary affiliations: Paediatrics department (YLLSoM) and interdisciplinary projects in Science faculty Recent publications focus on extreme value theory, digital numismatics, metabolomic simulations, and maternal-child health Her statistical innovations include: Repulsive mixture models for interpretable clustering Bayesian semi-parametric approaches for longitudinal studies Novel prior distributions for graphical models Application domains span: Pediatric oncology (ALL treatment response modeling) Metabolic syndrome analysis (obesity, diabetes, bone health) Digital humanities (coin distribution patterns, ceramic artifact analysis) Financial forecasting (discrete survival methods for invoice prediction)

• Statistics
• Epidemiology
• Genetics
• +7 more

Stefano Castruccio is a Notre Dame Collegiate Associate Professor at the University of Notre Dame, specializing in environmental statistics and spatio-temporal modeling since joining in 2017. His research bridges statistical methodology with critical environmental applications including renewable energy assessment and air pollution impact analysis. His educational background includes: Ph.D. in Statistics from the University of Chicago (2013) M.S. in Mathematical Engineering from Politecnico di Milano, Italy (2007) B.S. in Mathematical Engineering from Politecnico di Milano, Italy (2005) Prof. Castruccio's research centers on developing computationally efficient spatio-temporal models for environmental systems, with major contributions in wind energy resource assessment and climate modeling. His methodological innovations integrate physics-informed machine learning with traditional statistical frameworks to address challenges in high-resolution data analysis and uncertainty quantification. His recent publications (2018-2025) reveal a strategic evolution toward deep learning integration for wind modeling and physics-constrained statistical frameworks, significantly advancing computational approaches for renewable energy forecasting and environmental risk assessment. His scientific recognition includes: Elected fellow of the American Statistical Association Elected member of the International Statistical Institute 2024 Gordon Bell Prize for Climate Modelling 2023 TIES President's invited lecture 2021 TIES Abdel El-Shaarawi Early Investigator Award 2020 Early Investigator Award from ASA Section on Statistics and the Environment Research funding includes major grants from NSF, NASA, and KAUST, most recently a $240,000 NSF CDS&E-MSS award for physics-informed forecasting of high-resolution spatio-temporal data. He maintains active international collaborations with Politecnico di Milano and KAUST through his research group's GitHub repository. His team focuses on open-source development of statistical tools for environmental applications, with ongoing projects in turbulent boundary layer modeling and high-resolution wind energy forecasting.

• Environmental Statistics
• Machine Learning
• Spatio-temporal Statistics
• +7 more

Kambiz Rakhshanbabanari is a Lecturer in Construction Engineering and Management and Deputy Programme Leader for the Construction Engineering Management (CEM) BSc program. His research focuses on integrating computer vision, machine learning, and deep learning into sustainable building design and management, with an emphasis on energy efficiency, thermal performance, and circular economy frameworks. He holds a PhD and has over 15 years of industry experience in the Oil and Gas sector, specializing in mega-projects and applied data science. Key research interests include building damage detection via computer vision, predictive thermal performance modeling using IR thermography, and optimizing building operations through Bayesian Networks and Gaussian Processes. He advocates for the circular economy in construction through machine learning-driven resource optimization and explores renewable energy integration, HVAC systems, and whole-building design for sustainability. Ongoing projects include the Upgrading Britain’s Homes coalition (UKGBC Home Retrofit Investment Calculator) and the Demonstration of Energy Efficiency Potential (DEEP) initiative. His work also addresses barriers to circular economy adoption in construction, economic analyses of sustainable practices, and life cycle assessments (LCA/LCCA) to enhance environmental and economic outcomes. He contributes to conferences like the Sustainable Ecological Engineering Design for Society (SEEDS) and collaborates on technology evaluations such as the SMETER project.

• Sustainable Building Design
• Energy Efficiency
• Machine Learning in Construction
• +4 more

Charles L. Isbell Jr. is a Professor in the College of Computing at Georgia Institute of Technology. His work spans foundational research in artificial intelligence, machine learning, and robotics, with a focus on human-robot interaction, reinforcement learning, and educational technology. He leads efforts in scalable online education programs and contributes to policy discussions on AI's societal impact. Research Interests: Isbell's research emphasizes practical and theoretical advancements in AI systems, including Bayesian methods, multi-agent systems, and the ethical implications of AI. He explores how machines can learn from human interaction and adapt to complex environments. His recent work addresses challenges in robotics collaboration, scalable educational platforms, and the development of robust reinforcement learning algorithms. Publications Trends: His articles reflect a commitment to advancing AI through interdisciplinary approaches, with contributions to robotics collaboration (e.g., Nash equilibrium frameworks), scalable education systems, and foundational work in reinforcement learning and imitation learning. Recent collaborations include studies on machine learning systems engineering and long-term AI policy. Awards: No awards explicitly listed in the provided text. Advising & Grants: While specific grants are not detailed here, his extensive publication record indicates sustained research funding and mentorship of students in AI and robotics. His work on online education at scale highlights contributions to pedagogical innovation.

• Machine Learning
• Artificial Intelligence
• Robotics
• +3 more

Rahul Mourya is a Lecturer in Computer Science at the Faculty of Science and Engineering , University of Wolverhampton, UK. He joined in October 2023 and is affiliated with the Digital Innovations and Solution Centre (DISC) , which focuses on fundamental and applied research with societal and economic impact. PhD & MSc in Computer Science (Signal/Image/Vision) from Université Jean Monnet Saint-Etienne, France BEng in Electronics & Telecommunications from University of Pune, India His research develops foundational tools for machine learning, computer vision, inverse problems , and signal/image processing , with applications in autonomous systems, robotics, and sensor networks . Teaching includes computational mathematics, robotics engineering, and deep learning modules. Collaborations span institutions like Heriot-Watt University, Telecom ParisTech, and interdisciplinary projects in underwater acoustics and astronomical imaging. Current research explores measurement-consistent neural networks and optimization algorithms for inverse problems.

• Machine Learning
• Computer Vision
• Inverse Problems
• +6 more

Xumin Liu is a Professor in the Department of Computer Science at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). His research focuses on Artificial Intelligence, Data Science, Data Mining, and Service-Oriented Computing. He holds a PhD in Computer Science from Virginia Tech, an ME from Jinan University (China), and a BE from Dalian University of Technology (China). Education: BE in Computer Science, Dalian University of Technology (China) ME in Computer Science, Jinan University (China) PhD in Computer Science, Virginia Tech Research Interests: Artificial Intelligence Data Science Education and Applications Service-Oriented Computing (SOC) Machine Learning for Web Services Workflow Mining and Clustering Data Management and Analytics Publications highlight contributions to service mashup popularity prediction, workflow mining, and service recommendation systems. His recent work emphasizes bridging data science education for non-technical students and integrating SOC principles into curricula. Teaching Contributions: CSCI-621: Foundations of Database Systems CSCI-724: Web Services and Service-Oriented Computing ISCH-370: Principles of Data Science

• Artificial Intelligence
• Data Science
• Data Management
• +3 more

Dimah Dera is an Endowed Assistant Professor at the Chester F. Carlson Center for Imaging Science, College of Science, Rochester Institute of Technology (RIT). She holds a Ph.D. and M.S. in Electrical and Computer Engineering, and an M.A. in Mathematics from Rowan University. Her research focuses on robust and trustworthy machine learning, integrating Bayesian theory and statistical signal processing into modern ML frameworks for healthcare, remote sensing, and surveillance systems. She is an NVIDIA Deep Learning Institute University Ambassador and active in IEEE Signal Processing and ACM SIGHPC. Dr. Dera has received prestigious awards including the NSF CRII Award (2023), NSF REU Supplement (2024), and IEEE Benjamin Franklin Key Award (2021). Her work emphasizes Bayesian uncertainty propagation for robust AI systems, with applications in sequential time-series analysis and medical imaging. Her scholarly contributions span robust image classification, uncertainty-aware neural networks, and Bayesian vision transformers. Teaching includes courses like Mathematical Methods for Imaging and Image Processing & Computer Vision II . She actively mentors students and leads research initiatives funded by NSF and industry collaborations.

• Robust Machine Learning
• Trustworthy AI
• Bayesian Uncertainty Propagation
• +5 more

Dr. Konstantinos Kyriakopoulos is a Senior Lecturer in Cyber Intelligence, specializing in cybersecurity challenges and AI-driven solutions for converged Information and Operational Technology infrastructures. His work integrates machine learning with cyber-physical systems to enhance decision-making under uncertainty, with a focus on resilient AI algorithms and autonomous defense mechanisms. He has contributed to projects funded by Dstl, Innovate UK, and industry partners, addressing Industry 4.0 security, smart vehicles, electromagnetic attacks, and smart buildings. His research interests include adversarial machine learning, evidence theory for uncertainty modeling in ML, and autonomous cyber defense systems. He has developed novel approaches using reinforcement learning, hidden Markov models, and Bayesian optimization. His work emphasizes creating ML algorithms resistant to adversarial attacks while improving cybersecurity resilience in dynamic environments. Dr. Kyriakopoulos has also explored applications of AI in educational technology, such as automated assessment of spoken English proficiency. His recent publications focus on federated learning security, causal reinforcement learning, and anomaly detection techniques. He has experience in translating research into practical solutions, including business model pitches to investors in Canary Wharf for threat modeling tools. His contributions span over 40 peer-reviewed articles, with notable work in network intrusion detection, attack prediction via HMMs, and wavelet-based compression for network measurements. His expertise bridges academia and industry, addressing both theoretical advancements and real-world implementation challenges.

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +4 more

Dr. Brian Sheil is the Laing O'Rourke Associate Professor of Construction Engineering at the University of Cambridge and holds a Visiting Fellowship at the University of Oxford. He holds a BE from the National University of Ireland, Galway (NUIG) and a PhD from a collaborative program between NUIG and the University of California, Berkeley. His research focuses on soil-structure interaction, underground construction monitoring, and machine learning applications in geotechnical engineering. He leads major projects such as the EPSRC-funded FOCUS initiative for sensor development and the iDRIVE project for offshore monopile forecasting. His research interests include geotechnical BIM, digital twins, and the integration of fiber optic sensors and Bayesian algorithms for real-time construction monitoring. He is an editor for journals like Data-Centric Engineering and sits on technical committees such as ISSMGE TC222. He has received awards including the 2022 Young Researcher Award and the Bright Spark Lecture Award. As a Principal Investigator, he oversees projects commercializing fiber optic force sensors and collaborates with industry partners. His work aims to bridge geotechnical science with advanced data analytics, addressing challenges in underground construction and infrastructure resilience.

• Geotechnical Engineering
• Civil Engineering
• Construction Engineering
• +6 more

Dr. Xinyu Zhang is a Research Fellow at the Australian Institute for Machine Learning (AIML), University of Adelaide's Faculty of Sciences, Engineering and Technology. Her research bridges computer vision and machine learning, focusing on image/video generation, self-supervised learning, and multimodal retrieval for human-centric AI applications. Zhang's current investigations include: Causal representation learning and multimodal integration Bayesian deep learning frameworks Video generation with temporal consistency Lightweight detection transformers Unsupervised person re-identification Analysis of recent publications reveals strong emphases on generative modeling innovations (especially video synthesis), efficient transformer architectures for real-time applications, and self-supervised representation learning. Her work frequently addresses the alignment between latent representations and human perception across vision-language tasks. Dr. Zhang co-supervises graduate students on projects involving multi-agent 3D scene generation and knowledge transfer in low-supervision learning. She serves as conference reviewer for premier venues including CVPR, ICCV, and NeurIPS, contributing to the advancement of computer vision research.

• Computer Vision
• Metric Learning
• Image Generation
• +3 more

Rui Chen, PhD, is a Researcher in the Department of Molecular Physiology and Biophysics at Vanderbilt University School of Medicine. Their work focuses on integrating multi-omics data with advanced computational methods to uncover genetic mechanisms in complex diseases, including schizophrenia, cancer, and neurodevelopmental disorders. Dr. Chen’s research emphasizes leveraging machine learning for precision medicine applications, such as predicting cancer outcomes and identifying druggable genes. Key areas of expertise include functional genomics, Bayesian statistical frameworks, and computational tool development for genomic data quality control (e.g., the DRAMS tool). Their studies often bridge basic science and clinical translation, using zebrafish models and biobank resources for disease discovery. Dr. Chen has contributed to advancements in understanding gene regulatory networks in plants (e.g., rice stamen development) and has pioneered methods for analyzing non-coding variants using deep learning. Their work consistently addresses translational challenges in genetics, such as improving rare variant association studies and refining GWAS interpretations through multi-omics integration. Contact: rui.chen.1@vanderbilt.edu

• Genomics
• Machine Learning
• Cancer Biology
• +3 more