جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Computational Biology
• Protein Structure Prediction
• +۳ مورد دیگر

Wouter Boomsma is a Professor in the Machine Learning section of the Department of Computer Science at the University of Copenhagen. He leads the BioML research group, which focuses on developing machine learning techniques to address fundamental challenges in biology, particularly in the realm of protein science. His research interests center around understanding the relationship between protein sequence, structure, and function through advanced machine learning approaches. Key research areas include representation learning of protein sequences and structures, 3D structure prediction, and systematic protein optimization using techniques like Bayesian optimization. His work has significant applications in protein engineering, basic biological research on mutation effects in protein interaction networks, and health-related studies on human disease mutations. Boomsma's research program recently received substantial funding from the Novo Nordisk Foundation to establish the Center for Basic Machine Learning Research in Life Science (MLLS), a collaborative effort involving six machine learning research groups in the Copenhagen area. This initiative underscores the significance and potential impact of his work at the intersection of machine learning and life sciences. His publication record demonstrates a consistent focus on applying machine learning to protein science challenges, with recent work emphasizing protein structure prediction, variant effect analysis, and Bayesian optimization for protein engineering. The research output shows strong interdisciplinary connections between computer science, biology, and medicine. Boomsma maintains active collaborations with industry partners and research institutions, with his work gaining recognition through citations and media coverage. His group's research has been featured in high-impact journals including Nature Methods, Nature Communications, and PLOS Computational Biology. The BioML group operates within the Department of Computer Science's Machine Learning section at the University of Copenhagen, with physical location at Universitetsparken 1, Copenhagen Ø. The group maintains an active presence through their website (https://ku-bioml.github.io) and continues to expand its research portfolio in machine learning for biological applications.

Meimei Liuمشاهده پروفایل

استادیار

• Deep learning
• Bayesian data analysis
• Learning theory
• +۳ مورد دیگر

Dr. Meimei Liu is an Assistant Professor in the Department of Statistics at Virginia Tech. She holds a Ph.D. in Statistics from Purdue University (2018), an M.S. from the University of Science and Technology of China (2013), and a B.S. in Mathematics from Anhui University (2010). Prior to Virginia Tech, she conducted postdoctoral research at Duke University under Prof. David Dunson, focusing on Bayesian methods and neural imaging analysis. Her research interests span deep learning (graph embedding, variational autoencoders), Bayesian data analysis, learning theory (stochastic gradient descent, variational inference), big data methodologies (domain adaptation, random projection), and semi/non-parametric inference. She has been recognized with awards including the Virginia Tech ICTAS Diversity Inclusion Seed Fund (2023-2024) and COLT Travel Award (2019). Dr. Liu teaches courses such as CMDA 2005: Integrated Quantitative Science and STAT 3615: Biological Statistics. Her work emphasizes interdisciplinary applications, particularly in neuroscience and medical imaging. She actively contributes to the academic community through memberships in the American Statistical Association, International Chinese Statistical Association, and Women in Machine Learning.

Marion Menzelمشاهده پروفایل

استاد

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Quantitative MRI
• +۴ مورد دیگر

Marion Menzel is a Professor and Vice Dean at the Faculty of Electrical Engineering and Information Technology (Technische Hochschule Ingolstadt). She specializes in Biomechatronics and Sensor Data Analysis, with a focus on Medical Imaging and Magnetic Resonance Imaging (MRI). Her research emphasizes quantitative MRI techniques, including MR fingerprinting, deep learning applications in medical imaging, and hyperpolarized MRI for metabolic analysis. Education highlights include a Ph.D. (1999–2002) and Diplom-Chemiker (1994–1999) from RWTH Aachen. She has held roles at GE Healthcare, Siemens AG, and Forschungszentrum Jülich before joining THI in 2021. Her work bridges engineering and clinical practice, addressing challenges in imaging reconstruction, parameter mapping, and AI-driven diagnostics. Research interests span MRI acceleration, uncertainty quantification, and multimodal data fusion. She has pioneered methods like StoDIP and MRI2Qmap, advancing 3D imaging and compressive sensing. Her contributions to hyperpolarized 13C MRI enable non-invasive metabolic profiling in oncology and diabetes research. Key awards include recognition as an Alumna of the German Academic Scholarship Foundation. Her lab collaborates on projects like CHAIMELEON, a Pan-European initiative for AI-driven cancer management tools. Recent grants focus on improving diagnostic accuracy through AI and spatiotemporal reconstruction algorithms. Menzel’s team develops open-source frameworks for MRI analysis and actively participates in standardizing medical imaging protocols. Her work has direct clinical impact in neuro-oncology, radiology, and precision medicine.

• Signal Processing
• Computer Vision
• Ocean Remote Sensing
• +۴ مورد دیگر

Ronan FABLET is a Professor in the Mathematical and Electrical Engineering (MEE) department at IMT Atlantique, a member of the Institut Mines-Télécom. His research focuses on signal processing, computer vision, and machine learning applied to ocean remote sensing and geophysical data analysis. He has held academic and research positions since 2002, including postdoctoral fellowships and industry collaborations. Education and Career: Bachelor's degree from École Nationale Supérieure de l'Aéronautique et de l'Espace (SUPAERO), France (1997) PhD in Signal Processing and Telecommunications from University of Rennes (2001) Postdoctoral fellowship at Brown University, USA (2002) Researcher at Ifremer Brest (2003–2007) Associate Professor at Telecom Bretagne (2008–2012) Professor at IMT Atlantique since 2012 Visiting researcher at IRD/IMARPE (Peru) and IMEDEA (Spain) Research Interests: Dr. FABLET specializes in data assimilation, neural networks for geophysical dynamics, and ocean-atmosphere interactions. His work integrates machine learning with physical models to enhance predictions of climate phenomena like the Atlantic Meridional Overturning Circulation (AMOC) and ocean eddies. Key areas include: Neural data assimilation for regime shift monitoring SWOT satellite data integration for ocean mapping Machine learning for particle trajectory prediction and sediment trap analysis Uncertainty quantification in high-dimensional systems Recent Contributions: His team develops frameworks like 4DVarNet for spatiotemporal interpolation of satellite and in situ data, advancing applications in ocean color reconstruction, wind speed estimation, and submesoscale dynamics. Published work emphasizes hybrid modeling, end-to-end learning, and uncertainty-aware systems. Labs and Collaborations: Active in the Lab-STICC (Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance) and the ODYSSEY team (Océan Dynamique Observations Analyse). Collaborates with institutions like Mercator Ocean International and Naval Group on operational oceanography and maritime applications.

Yan Zhouمشاهده پروفایل

پژوهشگر ارشد

• Data Management
• Knowledge Systems
• Environmental Data Science
• +۳ مورد دیگر

Yan Zhou is a Research Fellow at the University of Oslo's Department of Informatics (Data and Knowledge Management), affiliated with the Faculty of Mathematics and Natural Sciences. His research focuses on applying data science and machine learning to environmental and energy systems, emphasizing reservoir operations, sustainable energy integration, and climate resilience. Key interests include optimizing hydropower systems, flood forecasting, and mitigating carbon emissions through data-driven approaches. His work spans machine learning applications for hydrological modeling, renewable energy forecasting, and multi-objective optimization frameworks. Recent studies address challenges in reservoir management, wind energy clustering, and urban sustainability through interdisciplinary methods. Notable contributions include hybrid deep learning models for algal bloom prediction and frameworks integrating pumped-storage systems with hydropower. Publications highlight innovations in flood risk assessment, carbon budget analysis, and climate-resilient infrastructure. Yan Zhou collaborates on projects combining physical hydrology with advanced analytics, aiming to enhance energy-water-food nexus synergies in dynamic environmental contexts. No specific awards or grants are listed, though his active research portfolio demonstrates significant contributions to environmental data science.

• Cognitive engineering
• Interface design
• Trust in automation
• +۴ مورد دیگر

John D. Lee serves as the Emerson Electric Professor in the Department of Industrial & Systems Engineering at the University of Wisconsin-Madison College of Engineering, with additional affiliation in Civil & Environmental Engineering. His pioneering research examines human-automation interaction and trust dynamics across domains including UAVs, maritime operations, automated vehicles, and deep space exploration. He co-authored the foundational textbook Designing for People: An Introduction to Human Factors Engineering and edited the Oxford Handbook of Cognitive Engineering , with research funded by NSF, ONR, NHTSA, NASA, and major automotive manufacturers. His educational background includes: PhD, University of Illinois, Urbana-Champaign (1992) MS, University of Illinois, Urbana-Champaign (1989) BS, Lehigh University (1988) BA, Lehigh University (1987) Year Abroad Program, University of York (1985) Dr. Lee's research centers on cognitive engineering and trust calibration in human-AI systems. He investigates how interface design, automation transparency, and human adaptation mechanisms affect team performance in safety-critical environments. His work develops frameworks for appropriate reliance on automation through modeling human behavior and designing responsive systems that maintain operator situational awareness. Recent publications (2023-2025) reveal expanding focus on conversational trust metrics, prosocial automation design, and explainable AI interfaces. The research increasingly integrates machine learning for dialogue analysis while maintaining core human factors principles, with growing applications in healthcare automation and environmental AI systems alongside traditional transportation domains. His major recognitions include: Byron Bird Research Publication Award (2022) Paul M. Fitts Education Award (2014) Human Factors and Ergonomics Society Fellow (2011) Emerson Electric Professorship (2009) Dr. Lee mentors graduate researchers through I SY E 699, 790, 890, and 990 courses while leading funded projects from: Federal Agencies: NSF, ONR, NHTSA, NASA Industry Partners: Nissan, Toyota, GM His laboratory develops experimental methodologies for measuring trust dynamics in real-world settings, focusing on driver-automation collaboration and AI explainability challenges in complex operational environments.

Zimo Wangمشاهده پروفایل

استادیار

• Smart Manufacturing
• Advanced Manufacturing Processes
• Material Characterization
• +۵ مورد دیگر

Zimo Wang is an Assistant Professor at the School of Systems Science and Industrial Engineering, Binghamton University. He holds a PhD in Industrial Engineering from Texas A&M University (2020), an MS in Mechatronics Engineering, and a BS in Industrial Engineering, both from Harbin Institute of Technology, China. His research focuses on smart manufacturing, integrating sensors, AI, and data science to enhance manufacturing processes, including additive manufacturing, precision machining, and material characterization. He explores applications in autonomous systems, smart logistics via UAVs, and sustainable manufacturing. Education: PhD in Industrial Engineering, Texas A&M University (2020) MS in Mechatronics Engineering, Harbin Institute of Technology (China) BS in Industrial Engineering, Harbin Institute of Technology (China) Research Interests: Smart manufacturing and advanced manufacturing processes Machine learning and statistical modeling for process monitoring Acoustic emission (AE) sensing and material characterization Unmanned aerial vehicle (UAV) applications in smart logistics Sustainable manufacturing and quality assurance Recent work emphasizes real-time process diagnostics, nanomachining optimization, and resilient manufacturing systems. His articles highlight innovations in sensor-based approaches, such as Bayesian filtering for dynamic intermittency and vision-based UAV control for material handling. Awards and Recognition: NSF Travel Awards (2015, 2018) Best Student Paper Award, ISERC QCRE Division (2014) INFORMS Student Chapter Cum Laude Award (2017) Advising and Grants: While no students are listed here, his research has been supported by grants from NSF and industry collaborations. Notable contributions include developing autonomous laser kirigami systems and smart sensing frameworks for precision manufacturing. Labs and Teams: Active in Binghamton’s Systems Science and Industrial Engineering research groups, focusing on cyber-physical systems integration and sustainable manufacturing innovations.

• Econometrics
• Operations Research
• Data Analytics
• +۵ مورد دیگر

R.J. de Almeida e Santos Nogueira is an Assistant Professor in the Department of Quantitative Economics at the School of Business and Economics. His research focuses on econometrics, operations research, and machine learning applications in finance, healthcare, and policy analysis. He is affiliated with the Data Analytics and Digitalisation unit, emphasizing modern computational methods for complex decision-making. His work spans causal inference, optimization algorithms, and text analytics, with notable contributions to portfolio rebalancing, order batching in logistics, and healthcare analytics for inflammatory bowel disease prediction. Recent studies explore risk perception of AI technologies and causal modeling with discretized data. No scientific awards or grants are explicitly listed in the provided texts. He has advised no students, and his lab/team affiliations are unspecified.

Yangkang Chenمشاهده پروفایل

عضو هیئت علمی

• Seismic Signal Analysis
• Seismic Modeling and Inversion
• Deep Learning
• +۴ مورد دیگر

Yangkang Chen is a Research Assistant Professor at the Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin. He leads AI/ML research at the Texas Seismological Network (TexNet). His expertise includes seismic signal analysis, deep learning, and high-performance computing. Chen holds a Ph.D. in geophysics from UT Austin (2015) and was a postdoc at Oak Ridge National Laboratory (2016-2018). Research Focus: AI-driven earthquake monitoring, seismic modeling, and reproducible research. Key Contributions: Developed EQCCT, TXED datasets, and open-source tools like pyseistr and DRR. Leadership: Editor of JGR: Machine Learning and Computation, and AGU fellowships. Education: Ph.D. in Geophysics, UT Austin, 2015 Postdoctoral Research at Oak Ridge National Lab (2016-2018) Research Interests span AI applications in seismology, seismic inversion, and reproducible methodologies. His work bridges computational methods with real-world seismic challenges, such as induced seismicity detection and earthquake cataloging. Awards include First Prize in GeoHackathon 2024, Clarivate Highly Cited Researcher (2020), and multiple IEEE recognitions. He advises over 20 graduate students/postdocs, focusing on ML-driven geoscience problems. Chen's lab develops tools for seismic data analysis, DAS processing, and machine learning frameworks.

• Scientific Computing
• Inverse Problems
• Numerical Analysis
• +۴ مورد دیگر

Marta M. Betcke is a Professor of Scientific Computing at the Department of Computer Science, University College London (UCL). She is affiliated with the UCL Centre for Inverse Problems and the Centre for Medical Image Computing. Her research focuses on inverse problems, numerical analysis, scientific computing, and machine learning, with applications in tomographic image reconstruction, compressed sensing, and PDE-based forward problem solving. She holds an EPSRC Postdoctoral Research Fellowship (2010–2013) and has contributed to projects like the Rapiscan system recognized with the Engineer Technology & Innovation Defence & Security Award (2010). She supervises PhD students through the FAI and i4Health CDT programs and leads initiatives such as the EPSRC-funded Inverse Problems Network (2016–2017). Her work integrates numerical analysis with machine learning to address real-world challenges in imaging and data science. Her academic journey includes postdoctoral research at the University of Manchester (2009) on X-ray CT scanners for security screening and a PDRA role in Manchester’s School of Mathematics. She teaches courses like Numerical Optimization and co-founded the MSc Scientific Computing program at UCL. Her research spans interdisciplinary collaborations in inverse problems, medical imaging, and computational methods, with notable contributions to photoacoustic tomography, compressed sensing, and Bayesian uncertainty quantification. Key awards include the 2010 Defence & Security Award for her work on novel X-ray CT scanners. She organizes conferences such as the INI Rich and Nonlinear Inverse Problems workshop (2023) and has been a coordinator for multiple academic initiatives. Her lab focuses on advancing imaging technologies through mathematical rigor and computational innovation.

Kathrin Gruberمشاهده پروفایل

استادیار

• Econometrics
• High-Dimensional Data Analysis
• Bayesian Statistics
• +۵ مورد دیگر

Kathrin Gruber is an Assistant Professor at the Department of Econometrics, Erasmus School of Economics, Erasmus University Rotterdam. Her research bridges high-dimensional econometric modeling with probabilistic machine learning, focusing on computational efficiency and uncertainty quantification. She holds a PhD in Economics & Social Sciences from Vienna University of Business and Economics, with earlier degrees in Quantitative Psychology (MSc) and Mathematics & Statistics (BSc). Education: PhD, Economics and Social Sciences, WU Vienna University of Business and Economics MSc, Quantitative Psychology, University of Vienna BSc, Mathematics and Statistics, University of Vienna Her research spans high-dimensional econometric models , Bayesian formulations , and generative AI , with applications in marketing, psychometrics, and healthcare. She actively develops uncertainty quantification methods using quantile models and deep ensembles, and contributes to synthetic data generation frameworks via GANs and diffusion models. Recent publications highlight her expertise in probabilistic machine learning, psychometric modeling, and multivariate quantile regression. She serves as maintainer of the CRAN Task View on Psychometric Models and Methods, and participates in organizing international workshops on psychometric computing.

• Algorithmic development
• Stochastic modelling
• Risk/reliability analysis
• +۹ مورد دیگر

Dr. Tanmoy Chatterjee is a Lecturer in Resilient Design at the University of Surrey's School of Mechanical Engineering Sciences, affiliated with the Surrey Institute for People-Centred AI and the Institute for Sustainability. Previously, he was a postdoctoral researcher at Swansea University (2018–2022), funded by the EPSRC 'DigiTwin' grant. His research focuses on algorithmic development, stochastic modeling, and machine learning for structural systems, with applications in nonlinear dynamics, metamaterials, and energy harvesting. Education: PhD in Civil Engineering (2014–2018), Indian Institute of Technology Roorkee Masters in Structural Engineering (2011–2013) Key Research Areas: Resilient design of structures under uncertainty Data-driven discovery of nonlinear dynamics Metamaterials for wave propagation control Machine learning for uncertainty quantification Publications: Recent work includes stochastic model updating, Bayesian inference for aeroelastic systems, and PINN-based reliability analysis. His 2025 paper on nonlinear structural joints introduced a novel framework combining experimental data with deep learning. Awards: Recipient of CSIR and Ministry of Education scholarships for his master's and PhD. Supervision: Co-supervised PhD students James Smith (University of Surrey) and Pushpa Pandey (Swansea University). Open to exceptional candidates for funded PhD projects in AI-driven engineering and resilient design. Labs/Teams: Active in Surrey's Centre for Engineering Materials and collaborations with industry partners like Autodesk and UK Atomic Energy Authority.

Xiongjie Chenمشاهده پروفایل

پژوهشگر ارشد

• Statistical Machine Learning
• Bayesian Inference
• Uncertainty Quantification
• +۵ مورد دیگر

Dr. Xiongjie Chen is a Research Fellow at the University of Surrey's School of Computer Science and Electronic Engineering, affiliated with the Computer Science Research Centre. He holds a B.Sc from Fujian University of Technology, an M.Eng from Tongji University, and a PhD in Computer Science from the University of Surrey under Dr. Yunpeng Li's supervision. Chen's research develops uncertainty quantification methods to enhance trustworthiness in machine learning systems. His work spans statistical machine learning, Bayesian inference, Monte Carlo methods, and AI applications in healthcare—particularly dental radiography analysis. Current projects focus on AI-assisted dental disease detection, differentiable particle filtering for complex systems, and efficient optimal transport techniques. His publications predominantly explore differentiable particle filters, uncertainty quantification, and optimal transport, with recent emphasis on integrating these techniques with neural networks for healthcare and dynamical systems. Research demonstrates consistent focus on Bayesian methods, sequential Monte Carlo, and practical applications in computer vision and intelligent systems. Chen has contributed as a demonstrator for courses including Computational Intelligence and Data Science at Surrey. His academic background and research collaborations reflect strong foundations in both theoretical machine learning and applied AI solutions.

• Machine Learning
• Cybersecurity
• Bayesian Networks
• +۵ مورد دیگر

Zhou Zhou is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. His research focuses on machine learning, adversarial robustness, Bayesian networks, and cybersecurity. He explores topics such as test-time adaptation, neural network robustness, and graph-based methods. Zhou has contributed to advancements in stochastic neural networks, feature distribution optimization, and multi-relation extraction for cybersecurity applications. His work bridges theoretical foundations and practical implementations in domains like combat network analysis and structural deformation monitoring. Education details are not explicitly provided in the text, but his research spans interdisciplinary areas including artificial intelligence, data science, and network analysis. His publications address challenges in model generalization, adversarial defense, and optimization algorithms. Zhou's research trends emphasize robustness in machine learning models, with a focus on adapting to real-world data variations. His work on Bayesian networks integrates ensemble methods and edge constraints to improve structure learning. In cybersecurity, he applies ontology-enhanced learning and graph theory to extract multi-relation insights. Recent articles highlight innovations in DAG learning, confidence calibration, and hybrid remote sensing techniques for infrastructure monitoring. He has no listed awards or grants mentioned in the text. His affiliations include Queen Mary's School of Electronic Engineering and Computer Science, where he contributes to research groups focused on AI, cybersecurity, and computational methods.

• Aerospace Engineering
• Control Systems
• Deep Learning
• +۵ مورد دیگر

Dr Will Jacobs is a Research Fellow at the University of Sheffield's School of Electrical and Electronic Engineering. His work focuses on advanced control systems, nonlinear system identification, and deep learning applications in aerospace and energy systems. He is affiliated with the Amy Johnson Building in Sheffield, S1 3JD, and can be contacted at w.jacobs@sheffield.ac.uk or +44 114 222 5671. Research interests include distributed digital twins for fleet management, fault detection in civil nuclear plants, and interpretable machine learning for complex systems. His recent work emphasizes resource-constrained system monitoring, economic control strategies for aerospace fleets, and frequency-domain analysis for nonlinear systems. Jacobs' interdisciplinary approach bridges electrical engineering, control theory, and applied mathematics. Publications span 2014–2024, with a focus on gas turbine health monitoring, Bayesian system identification, and convolutional neural networks for in-flight anomaly detection. His research contributes to both theoretical advancements and practical industrial applications in energy systems and aerospace engineering. No scientific awards are explicitly listed in the provided information. His advising record is not publicly detailed here, though his involvement in doctoral training and research collaborations is implied through project participation.