جست‌وجوی استاد و پژوهشگر

Moi Hoon Yapمشاهده پروفایل

استاد

• Computer Vision
• Medical Image Analysis
• Facial and Gesture Analysis
• +۳ مورد دیگر

Professor Moi Hoon Yap is a Professor of Image and Vision Computing and Lead of Human-Centred Computing at The Manchester Metropolitan University. Her research focuses on computer vision, deep learning, and medical image analysis, particularly in early cancer detection and diabetic foot ulcer tools. She leads projects funded by The Royal Society, EU, EPSRC, Innovate UK, and industry partners. She is an Associate Editor of the Journal of Computers and Programs in Biomedicine and has developed datasets and grand challenges for reproducible research, such as the DFU-2021 challenge. Her work includes facial analysis, gesture interpretation, and human behavior quantification. Key Projects: FAST Healthcare NetworksPlus Award (2020-2021), Royal Society PhD Studentship (2018-2022), FootSnap Cloud Infrastructure (2019-2021). Awards: Royal Society Industry Fellowship, Marie Skłodowska-Curie Innovative Training Network Grant. Research Interests: Medical imaging, facial/gesture analysis, human behavior analysis, dataset creation, and sustainable software development. Professor Yap supervises PhD students and teaches Data Analytics, Deep Learning, and related courses. She is actively involved in international collaborations to advance biomedical data sharing and federated learning mechanisms.

Miguel A. Bandresمشاهده پروفایل

استادیار

• Topological photonics
• Spatiotemporal pulse shaping
• Nonlinear optics
• +۲ مورد دیگر

Miguel A. Bandres is an Assistant Professor at CREOL, The College of Optics and Photonics at the University of Central Florida. His research focuses on topological photonics, spatiotemporal light control, and ultrafast optical phenomena. Research directions include: Topological protection mechanisms for light propagation Synthesis of complex spatiotemporal waveforms Optical analogies to condensed matter phenomena Novel beam solutions for imaging and sensing His group (Bandres Group) integrates theoretical modeling, optical fabrication, and experimental characterization. Current projects explore Lorentz-invariant wavepackets, topological lasers, and multidimensional pulse shaping techniques.

Craig Leth-Steensenمشاهده پروفایل

دانشیار

• Cognitive Psychology
• Quantitative Methods in Psychology
• Reaction Time Analysis
• +۴ مورد دیگر

Craig Leth-Steensen is an Associate Professor in the Department of Psychology at Carleton University, affiliated with the Faculty of Arts and Social Sciences. He holds a Ph.D. from McGill University. His research focuses on cognitive psychology, particularly the application of quantitative methods and computational models (e.g., neural networks, random walks) to analyze human cognitive performance, reaction time distributions, and symbolic comparative judgments. Recent interests include early childhood cognitive development and interventions through programs like PlayWisely. His work spans experimental psychology and psychometrics, with key contributions to reaction time analysis, spatial-numerical associations (SNARC effect), and task-switching dynamics. He has published widely in journals like Psychological Review , Journal of Experimental Psychology , and Frontiers in Psychology . Dr. Leth-Steensen has no listed awards but has collaborated with institutions globally. His research emphasizes methodological rigor, integrating statistical techniques with experimental designs to explore information processing and decision-making mechanisms.

Thomas A. Runklerمشاهده پروفایل

استاد مدعو

• Machine Learning
• Data Analytics
• Pattern Recognition
• +۳ مورد دیگر

Thomas A. Runkler is an Adjunct Professor at the Technical University of Munich (TUM), holding a Chair in the Foundations of Software Reliability and Theoretical Computer Science within the TUM School of Computation, Information and Technology. Since 1999, he has taught computer science at TUM while concurrently serving in various expert and managerial roles at Siemens AG, where he currently holds the position of Senior Principal Research Scientist. His academic journey includes a Master’s and PhD in electrical engineering from TU Darmstadt (1992/1995), followed by postdoctoral research at the University of West Florida (1996–1997). Runkler’s research spans machine learning , data analytics , fuzzy systems , and optimization . He has authored/co-authored over 200 publications, focusing on topics like clustering algorithms, neural networks, and decision-making frameworks. Professional contributions include leadership roles in organizations such as the IEEE CIS committees and the German Association for Computer Science’s Fuzzy Systems group. His teaching includes courses on Data Mining and Knowledge Discovery . Key publications include foundational works on fuzzy preference structures, Bayesian decomposition of dynamical systems, and interpretable reinforcement learning policies. Runkler’s work bridges academia and industry, emphasizing practical applications in automation, recommendation systems, and industrial procurement forecasting. His research often addresses challenges in uncertainty handling, algorithm design, and scalable data analysis.

• Statistical Inference
• Machine Learning
• Bioinformatics
• +۳ مورد دیگر

Lucy Gao is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC). Her research focuses on statistical inference, machine learning, and bioinformatics, with particular emphasis on clustering methods, high-dimensional data analysis, and optimal design theory. She holds a faculty position within UBC’s Faculty of Science and is affiliated with the Vancouver Campus. Her work spans theoretical developments in statistical methodologies and applications to biological data, such as single-cell RNA sequencing analysis. She has contributed to areas like selective inference for hierarchical clustering and algorithmic optimization for complex systems. Her email is lucy.gao@stat.ubc.ca, and she maintains a website at https://www.lucylgao.com/ . Recent publications highlight her expertise in data thinning techniques, latent variable modeling, and multiview network analysis. She has explored topics ranging from negative binomial count splitting in genomics to bilevel optimization algorithms. Her research bridges statistical theory and practical computational challenges in modern data science. No scientific awards or advising records are explicitly listed in the provided materials. Her academic contributions are primarily through peer-reviewed articles and methodological innovations.

• Dependence Modeling
• Copula Models
• Multivariate Analysis
• +۷ مورد دیگر

Harry Joe is a Professor in the Department of Statistics at the University of British Columbia (Vancouver Campus). His primary research focuses on dependence modeling, copula theory, multivariate analysis, and applications in biostatistics, finance, and psychometrics. He has advised students including Xiaoting Li, Xinyao Fan, and Pavel Krupskiy. Research Interests: - Advanced copula constructions (e.g., vine copulas) - Extreme value theory and tail dependence - Applications in financial risk, biomedical research, and educational measurement - Multivariate time series analysis and non-Gaussian models Publications highlight contributions to copula-based classification methods (2024), factor copula models (2015), and dynamic dependence modeling (2020). His work bridges theoretical developments with practical applications across disciplines. Teaching and advising emphasize methodological innovation. Current research explores high-dimensional dependence structures and computational methods for complex data. No lab/team affiliations explicitly noted in provided materials.

• Environmental Biotechnology
• Wastewater Treatment Processes
• Quantum Computing Applications
• +۶ مورد دیگر

Po-Heng (Henry) Lee is an Associate Professor in the Department of Civil and Environmental Engineering at Imperial College London, part of the Faculty of Engineering. His research focuses on wastewater engineering, environmental biotechnology, and quantum computing applications in microbial systems. He leads the Industrial Biotechnology Hub, Microbiome Network, and Quantum Engineering, Science and Technology initiatives. Dr. Lee holds a PhD from Iowa State University (2010) and earlier degrees from institutions in Taiwan. His academic career includes roles at Hong Kong Polytechnic University (2012-2018) and Inha University (2010-2012) before joining Imperial College in 2019. His research integrates quantum information theory with environmental engineering, exploring electron transfer mechanisms, anaerobic digestion optimization, and water reuse in AI/semiconductor contexts. Techniques include quantum computing platforms (IBM Qiskit, Xanadu), meta-omics, and Gaussian Bosonic Sampling. Key application areas include enhancing antibiotic and nitrogen removal via microbiome-conductive material interactions, smart chemical dosing for industrial cooling systems, and optimizing full-scale wastewater plants. His work bridges quantum mechanics and microbial ecology to address sustainability challenges. Dr. Lee's lab develops innovative solutions for resource recovery, greenhouse gas reduction, and bioplastic production. Major collaborations focus on AI-driven process control and semiconductor manufacturing wastewater treatment.

Hanwen Xingمشاهده پروفایل

عضو هیئت علمی

• Computational Statistics
• Bayesian Statistical Modelling
• Health Informatics
• +۳ مورد دیگر

Hanwen Xing is a Research Associate at St Peter's College and a Postdoctoral Researcher in Artificial Intelligence at the Nuffield Department of Women's & Reproductive Health, University of Oxford. He holds a DPhil in Statistics (2022) and MSc in Statistical Science (2018) from Oxford, following a Bachelor of Mathematics from the University of Waterloo (2017). Affiliations: University of Oxford, St Peter's College Roles: Academic support for MSc/DPhil students, Bayesian methodology development His research focuses on computational statistics and Bayesian modelling, particularly applying approximate Bayesian inference methods to healthcare and medical science challenges. He has developed novel Bayesian approaches for integrating drug response and protein profiling data to identify tumor-specific cancer dependencies, demonstrated through projects like DepInfeR-GP. His work bridges statistical theory with practical applications in precision oncology. Key research outputs include advancements in Gaussian process modelling for single-cell perturbation data, continual learning frameworks using probabilistic methods, and improved bridge estimators via f-GAN techniques. These contributions highlight his expertise in both foundational statistical theory and applied computational methods. No scientific awards explicitly mentioned. He advises students in statistics programs and contributes to collaborative projects involving ex-vivo drug sensitivity analysis. His GitHub repository hosts implementations of his Bayesian models, reflecting a commitment to open-source scientific software development.

• Bayesian statistics
• Hierarchical modeling
• Image analysis for super-resolution microscopy
• +۴ مورد دیگر

Dr. Miguel Juarez is a Lecturer in Statistics at the University of Sheffield's School of Mathematical and Physical Sciences. He holds a PhD in Mathematical Sciences from Universidad de Valencia (2004), an MSc in Economics from CIDE (Mexico), and a BSc in Actuarial Sciences from ITAM (Mexico). His research focuses on Bayesian hierarchical modeling for panel/longitudinal data with applications in biology, medicine, and econometrics. Key projects include the STriTuVaD initiative for integrating computer simulations with clinical trials and developing models for super-resolution microscopy image analysis. He has contributed to advancing in silico trial methodologies through the UISS-TB simulator and works on objective Bayesian methods for non-Gaussian data. Professional activities include teaching MAS2010 Statistical Inference and Modelling. His recent work emphasizes accelerating tuberculosis vaccine development via augmented clinical trials and establishing credibility frameworks for in silico trials. He collaborates with interdisciplinary teams in systems biology and biomedical informatics. Research outputs span Bayesian statistical theory, computational epidemiology, and medical technology innovation. Notable collaborations include the Warwick Systems Biology Centre and EU-funded H2020 projects.

Ruggero Carliمشاهده پروفایل

دانشیار

• Control Systems
• Robotics
• Machine Learning
• +۶ مورد دیگر

Ruggero Carli is an Associate Professor at the Department of Information Engineering, University of Padova. His research focuses on control systems, robotics, and optimization, with emphasis on model-based reinforcement learning, distributed optimization algorithms, and energy systems. His work bridges theoretical advancements with real-world applications, including autonomous robotics, smart grids, and nonlinear control. Key contributions include physics-informed machine learning frameworks, ADMM-based distributed optimization methods, and MPC-driven control solutions for underactuated systems. Research interests include: Model-Based Reinforcement Learning for Robotics Nonlinear Model Predictive Control (NMPC) Distributed Optimization and ADMM Variants Energy Networks and Smart Grids Robot Dynamics and System Identification Recent publications emphasize: Continual learning for driver behavior analysis Physics-informed control for underactuated systems Robust optimization in unreliable networks Autonomous robotic manipulation with large language models His research integrates control theory with modern machine learning techniques, addressing challenges in edge computing, distributed systems, and real-time implementation.

• Image and Video Processing
• Multimedia Analysis
• Computer Vision
• +۵ مورد دیگر

Prof. Tan Yap Peng is a Professor and Chair of the School of Electrical & Electronic Engineering at Nanyang Technological University (NTU), Singapore. He holds the President's Chair in Electrical and Electronic Engineering and serves as Associate Vice President (Lifelong Learning – Postgraduate Programmes by Coursework). His research focuses on multimedia analysis, computer vision, machine learning, and data analytics. He earned his B.S. from National Taiwan University and M.A./Ph.D. from Princeton University. He has led major initiatives including the INFINITUS Infocomm Research Centre and contributed to IEEE technical committees. His over 200 publications span image/video processing, neural network robustness, and cross-modal systems. Awards include IEEE Fellow status. Education: B.S. Electrical Engineering (NTU), M.A./Ph.D. (Princeton) Research interests emphasize interactive digital media, content-based analysis, and AI-driven solutions for visual and signal processing. His work addresses challenges in adversarial attacks, video generation, and low-light image enhancement. He has held editorial roles at IEEE Transactions and EURASIP journals. Conference leadership includes chairs for ICME and ICIP. His contributions bridge academia and industry through collaborative research networks.

• Statistical Learning
• Game Theory
• Decision-Making Algorithms
• +۶ مورد دیگر

David Leslie is a Professor of Statistics and Director of Engagement in the Department of Mathematics and Statistics at Lancaster University. He specializes in statistical learning, decision-making algorithms, and game theory, with applications in real-time website optimization through bandit algorithms. Previously, he served as a Senior Lecturer at the University of Bristol and co-directed a cross-disciplinary decision-making research group. He has led significant projects such as the EPSRC/NERC-funded DSNE initiative and contributed to strategic partnerships like ALADDIN (with BAE Systems and EPSRC) and NG-CDI (with BT). His work emphasizes bridging theoretical research with practical industry solutions. Education details are not explicitly provided, but his career trajectory includes prior roles at prestigious institutions. Research interests focus on statistical methodologies, AI-driven decision systems, and environmental data science. He advises multiple PhD students in areas like Statistical AI and Extreme Value Theory. Notable projects include AI Hub, NABS+, and ProbAI, reflecting his engagement with cutting-edge technologies and interdisciplinary collaboration. David actively participates in research groups such as STOR-i Centre for Doctoral Training and the Statistical Artificial Intelligence group. His contact information includes an office at B73 in the PSC building and a direct email address. No specific scientific awards are listed, but his extensive project leadership and contributions to foundational AI research highlight his academic impact.

• Non-parametric statistics
• High-dimensional statistics
• Bioinformatics
• +۴ مورد دیگر

Professor Jian Zhang is a Professor of Statistics at the University of Kent's School of Mathematics, Statistics and Actuarial Science. His research focuses on non-parametric and high-dimensional statistics, bioinformatics, computational biology, statistical genetics, neuroimaging methods, and Bayesian modeling. He has advised students including Jie Li and Tong Wang. His work spans theoretical advancements and applied methodologies across diverse fields such as genomics, neuroimaging, and biomedical data analysis. Publications highlight contributions to Bayesian inference, neuroimaging techniques, and statistical genetics. Notable collaborations include studies on mixture models for genetic association analysis and beamforming methods for functional connectivity. He holds an ORCID iD and is based at Canterbury Campus, University of Kent.

Raktim Bhattacharyaمشاهده پروفایل

استاد

• Uncertainty Quantification
• Robust Control
• Nonlinear Systems
• +۳ مورد دیگر

Raktim Bhattacharya is a Professor in the Department of Aerospace Engineering at Texas A&M University, serving as Director of the Graduate Studies Program. He holds a Ph.D. in Aerospace Engineering from the University of Minnesota (2003) and a B.Tech from IIT Kharagpur (1996). His research focuses on uncertainty quantification, robust control, and nonlinear systems, with applications in aerospace systems design and control. Dr. Bhattacharya leads the Intelligent Systems Research Laboratory (ISRL), advancing algorithms for next-generation aerospace systems operating in uncertain environments. His work integrates optimal control, stochastic modeling, and data-driven methods to enhance system reliability and performance. Notable contributions include probabilistic robustness analysis, model validation frameworks, and sparse sensing architectures. Recent research trends emphasize optimal transport theory for state estimation, privacy-aware machine learning, and sensor-actuator co-design for resource-constrained systems. His publications span topics like UAV configuration optimization, LPV control frameworks, and invariant set estimation using physics-informed neural networks. Dr. Bhattacharya’s lab collaborates on projects involving tensegrity structures, cyber-physical systems, and space situational awareness. His work addresses challenges in hypersonic flight dynamics, celestial navigation, and resilient control under actuator degradation.

Douglas Allaireمشاهده پروفایل

دانشیار

• Multidisciplinary design optimization
• Bayesian optimization
• Multifidelity methods
• +۴ مورد دیگر

Douglas Allaire is an Associate Professor and Sallie and Don Davis '61 Faculty Fellow in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University, part of the College of Engineering. He leads the Computational Design Laboratory, focusing on computational methods for complex engineered systems. His research spans multidisciplinary design optimization, Bayesian optimization, machine learning, and materials design. Education: Ph.D., Aerospace Engineering, Massachusetts Institute of Technology (2009) M.S., Aerospace Engineering, Massachusetts Institute of Technology (2006) B.S., Aerospace Engineering, Massachusetts Institute of Technology (2004) Research Interests: Bayesian optimization and uncertainty quantification Materials design using machine learning Autonomous experimentation and data fusion Predictive analytics for engineering systems Recent Trends in Publications: His work emphasizes integrating Bayesian methods with materials discovery, autonomous systems, and high-fidelity modeling. Key themes include optimizing multifidelity systems, inverse microstructure design, and real-time decision frameworks. Awards: ASEM Fellow (2024) AIAA Associate Fellow (2023) ASME Young Engineer Award (2018) Advising & Grants: Advised students like Jaylen James (Ph.D. 2022) and Danial Khatamsaz. Active in securing grants for computational design and materials research. Collaborates with institutions like the American Institute of Aeronautics and Astronautics. Labs & Teams: Directs the Computational Design Laboratory, part of the Engineering Systems Design Group. Engages in interdisciplinary projects with Texas A&M’s Multidisciplinary Engineering program.