جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +۳ مورد دیگر

Daolang Huang is a Doctoral Researcher and Student in the Department of Computer Science at the School of Science, affiliated with Professor Samuel Kaski's group. He holds a Bachelor's degree in Engineering and Technology from Jinan University (2020). His research focuses on advanced machine learning techniques, including Bayesian inference, robust statistical modeling, and simulation-based methods. Key areas include experimental design optimization, neural processes, and equivariance in deep learning. Recent work emphasizes decision-aware algorithms and cost-effective simulation frameworks. Huang has collaborated internationally, with publications in top venues like NeurIPS. Despite no listed awards, his work demonstrates significant contributions to probabilistic modeling and optimization. Education : Bachelor's degree in Engineering and Technology, Jinan University (2020) Research Interests : Bayesian methods and amortized inference Robust statistics under model misspecification Continuous control and neural process architectures Optimization algorithms with decision-theoretic foundations Recent Research Trends : His articles (2020–2025) emphasize Bayesian experimental design, preference-based optimization, and equivariant neural networks. Themes include balancing statistical rigor with computational efficiency, particularly in high-dimensional decision-making contexts. Labs/Teams : Active member of Samuel Kaski’s research group, focusing on interdisciplinary applications of machine learning.

Yuting Zhangمشاهده پروفایل

پژوهشگر ارشد

• Probabilistic Geotechnics
• Reliability-Based Design
• Data-Driven Site Characterisation
• +۳ مورد دیگر

Dr Yuting Zhang serves as a Research Fellow within the Department of Civil, Maritime, and Environmental Engineering at the University of Southampton's Faculty of Engineering and Physical Sciences. He is an integral member of the Royal Academy of Engineering Chair Centre of Excellence for Intelligent & Resilient Ocean Engineering (IROE), focusing on machine learning applications for geotechnical site characterization. His educational background includes a Bachelor's degree and MPhil in Geotechnical Engineering from Wuhan University, China, followed by a 2024 PhD from the University of Newcastle, Australia, specializing in probabilistic calibration of resistance factors for piling designs. Zhang's research centers on probabilistic geotechnics and reliability-based design methodologies, with particular emphasis on data-driven site characterization techniques. His work bridges machine learning algorithms with geotechnical and geophysical data analysis to enhance foundation engineering practices, especially in offshore and marine environments. Current projects investigate spatial soil variability effects on pile group reliability, optimization of resistance factors, and innovative data augmentation approaches for rock fracture prediction. His publication record demonstrates consistent output in high-impact journals since 2022, with recent 2025 publications indicating active research momentum. The articles reveal strong thematic focus on probabilistic methods for pile design, integration of diverse geotechnical data sources, and machine learning applications in subsurface characterization. As part of the Infrastructure Group and Southampton Marine and Maritime Institute, Zhang contributes to ocean energy research initiatives while maintaining active collaborations with international researchers including Jinsong Huang, Jiawei Xie, and Anna Giacomini. His work supports the development of more resilient offshore infrastructure through advanced geotechnical reliability frameworks.

Diego Klabjanمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Diego Klabjan is a Professor at Northwestern University within the Department of Industrial Engineering and Management Sciences. He serves as the Founding Director of the Master of Science in Machine Learning and Data Science Program and Director of the Center for Deep Learning. Ph.D. in Algorithms, Combinatorics, and Optimization from Georgia Institute of Technology (1999) B.S. in Applied Mathematics from University of Ljubljana (1994) His research focuses on machine learning, deep learning, and analytics with applications in finance, transportation, sports, and bioinformatics. Key contributions include federated learning algorithms, reinforcement learning for cryptocurrency trading, and neural network applications in impact mechanics. Recent publications highlight advancements in blockchain-based federated learning, second-order policy gradient convergence, and ensemble deep reinforcement learning. His work bridges theoretical foundations with industrial applications across diverse sectors. Preseren’s Award for the Best Undergraduate Thesis (1994) Transportation Science Section Dissertation Prize (2000) Intel's Outstanding Researcher Award (2019) Jack Meredith Best Paper Honorable Mention (2022) Klabjan has advised notable students including Luis Guimarães (2015 APDIO/IO Award winner) and Young Woong Park (2015 INFORMS Computing Society Best Student Paper recipient). His collaborations span Fortune 500 companies and startups in analytics-driven domains.

• Structural Engineering
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Professor Ling Li is a faculty member at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), within the Faculty of Science and Engineering. Their research focuses on interdisciplinary applications of machine learning, computer vision, and deep learning in structural engineering and materials science. Notable contributions include advancements in structural health monitoring, blast loading prediction, and 3D displacement measurement using monocular vision. Professor Li has authored numerous peer-reviewed articles and collaborates on projects involving civil infrastructure resilience, smart materials, and AI-driven solutions for engineering challenges. They hold an office in the New Technologies Building at Curtin Perth and can be reached at L.Li@curtin.edu.au.

Zhou Yingمشاهده پروفایل

دانشیار

• Seismology
• Geophysics
• Computational Seismology
• +۳ مورد دیگر

Zhou Ying is an Associate Professor of Geophysics at Virginia Tech's College of Science, Department of Geosciences. Her research focuses on understanding seismic heterogeneities in the Earth's interior through finite-frequency wave propagation theory and diffractional tomography. Ph.D. in Geophysics from Princeton University (2005) Her research interests include: Seismic Wave Propagation in Heterogeneous Media Global and Computational Seismology Finite-Frequency Seismic Tomography 3-D Anelasticity (Q) Structure in the Mantle Seismic Anisotropy in the Upper Mantle and Transition Zone Core-Diffracted Waves and Core-Mantle Boundary Structure Signal Processing and Array Data Analysis Zhou's 15 most recent publications span seismology, geophysics, and unexpected interdisciplinary topics like molecular biology. Most studies focus on mantle discontinuities, finite-frequency tomography, and outer core dynamics, with specific attention to 410-km/660-km discontinuities and surface-wave sensitivity kernels. Current graduate advisee: Zhen Guo (Ph.D. candidate in Geophysics). Former advisees include Youyi Ruan (Ph.D. 2012), Kui Liu (Ph.D. 2014), Kai Deng (MS 2014), and Jing Xue (MS 2014). She teaches undergraduate courses in Physical Geology and Earthquake Seismology, and graduate courses in Theoretical Seismology, Advanced Seismology, and Seismic Data Analysis.

George Papadakisمشاهده پروفایل

استاد

• Aerodynamics
• Fluid Dynamics
• Turbulence
• +۵ مورد دیگر

Professor George Papadakis is a Professor of Aerodynamics at the Department of Aeronautics, Faculty of Engineering at Imperial College London. His research focuses on fundamental analysis and manipulation of transitional or turbulent flows, with applications in aerodynamics, flow control, and mixing enhancement. He leads the Papadakis Lab, which develops computational methods and optimization algorithms for fluid dynamics problems. Education: PhD in Mechanical Engineering (National Technical University of Athens, 1996), BEng in Mechanical Engineering (National Technical University of Athens, 1990). Professional history includes roles as Lecturer at King's College London (1999–2011) and Reader at Imperial College (2011–present). Research interests include turbulence enhancement/suppression, sensitivity analysis of chaotic systems, and DNS/LES simulations. His group is funded by EPSRC, European Union, Leverhulme Trust, and the President's Scholarship Fund. Key affiliations include the Energy Futures Lab and Flow Control networks. Advising and grants: Supervised numerous PhD students (e.g., Dandan Xiao, Felipe Alves Portela) and secured funding from multiple agencies. Current students include Hanxun Yao, Karim Shawki, and others. Research outputs span flow control, vortex dynamics, and industrial mixing applications. Labs/teams: Papadakis Lab focuses on aerodynamics, turbulence, and computational methods. Collaborations include Temasek Labs (Singapore) and Prof. J.C. Vassilicos (Imperial College).

Iason Papaioannouمشاهده پروفایل

استاد مدعو

• Uncertainty Quantification
• Structural Reliability
• Reliability-Based Optimization
• +۵ مورد دیگر

Iason Papaioannou is an Adjunct Professor in the area of Uncertainty Quantification at the Technical University of Munich (TUM), affiliated with the Engineering Risk Analysis Group. He holds a habilitation from the TUM School of Engineering and Design and has been tenured since 2021 as an Akademischer Rat. His academic journey includes a Ph.D. in Civil Engineering from TUM (2012), an M.Sc. in Computational Mechanics (2007), and a Diploma in Civil Engineering from the National Technical University of Athens (2005). His research focuses on uncertainty quantification , reliability assessment , and Bayesian updating of engineering systems. Key areas include probabilistic modeling, machine learning applications, spatial variability analysis, and geotechnical reliability. He has pioneered methods for system reliability analysis, adaptive subset simulation, and cross-entropy-based importance sampling. Teaching responsibilities include courses such as Stochastic Finite Element Methods, Structural Reliability Methods, and Elements of Machine Learning. His work integrates advanced computational techniques with practical engineering challenges, emphasizing high-dimensional uncertainty analysis and data-driven model updating.

• Geotechnics
• Numerical Simulations
• Bayesian Inference
• +۴ مورد دیگر

Stéphane Commend is an Associate HES Professor at the Fribourg School of Engineering and Architecture (HEIA-FR) under HES-SO Valais-Wallis. He also holds a lecturer role at the School of Engineering and Management of the Canton of Vaud. His primary research focuses on geotechnics, numerical simulations, and probabilistic modeling applied to infrastructure projects like tunneling and deep excavations. Education and affiliations include roles across multiple HES-SO institutions, with a strong emphasis on integrating advanced computational methods into geotechnical engineering. Notable projects include the Grand Paris Express tunnel project, Bayesian inference for wood constitutive modeling, and probabilistic risk analysis for urban construction. Research interests span soil-structure interaction, finite element modeling, and uncertainty quantification. Recent work emphasizes Bayesian methods for parameter calibration, machine learning in excavation design, and natural hazard vulnerability assessment. Key contributions include frameworks linking ZSOIL and UQLab for reliability analysis, and prototypes like SLIDE-PM for mudflow impact modeling. Current projects (e.g., iBAG and OptiSoil) focus on optimizing construction methods using AI and data-driven approaches. He leads collaborative teams across HES-SO institutes and academic partners like EPFL and CETU. Key Projects: iBAG Project (2022–2025): Bayesian methods in geotechnics OptiSoil (2019–2025): Machine learning for excavation design TULIP Project: TBM-pile interaction probabilistic analysis

Po Wen Chengمشاهده پروفایل

استاد

• Wind Energy
• Aerospace Engineering
• Aerodynamics
• +۶ مورد دیگر

Prof. Dr. Po Wen Cheng is a Professor and Head of the Stuttgart Chair of Wind Energy (SWE) at the Institute of Aircraft Design, University of Stuttgart. His research focuses on wind energy systems, including floating offshore wind turbines, lidar applications in wind farm control, and structural dynamics of renewable energy systems. He leads interdisciplinary projects addressing challenges in mooring systems, aeroelastic analysis, and noise mitigation. Key areas of expertise include aerodynamic load optimization, lidar-assisted control strategies, and numerical modeling of wind farm interactions. His work integrates advanced machine learning techniques for predictive maintenance and performance enhancement. Cheng collaborates with international institutions and participates in high-profile wind energy initiatives like the Alpha Ventus offshore wind farm study. Teaching: Courses on Wind Turbine Design and Wind Energy Utilization Research Labs: Stuttgart Chair of Wind Energy, Institute of Aircraft Design Recent Projects: Scaled Flight Demonstrator e-Genius-Mod, Passively Self-Adjusting Floating Wind Farms, Lidar-Based Virtual Sensors His research emphasizes sustainable energy transitions, with particular attention to offshore wind infrastructure and turbulence mitigation in complex environments.

Bert de Vriesمشاهده پروفایل

استاد

• Bayesian Machine Learning
• Signal Processing
• Biomedical Applications
• +۳ مورد دیگر

Bert de Vries is a Professor at the Signal Processing Systems Group at Eindhoven University of Technology (TU/e), where he has been employed since January 2012. He maintains a dual career, also working at GN Hearing in the hearing aids industry since April 1999, where he holds both research and managerial roles. His academic journey began at TU/e, where he earned his MSc in Electrical Engineering in 1986, followed by a PhD from the University of Florida in 1991. Between 1992 and 1999, he worked at Sarnoff Research Center in Princeton, NJ, contributing to diverse signal and image processing projects. Professor de Vries's research centers on Bayesian Machine Learning, with particular focus on the Free Energy Principle and its applications to engineering problems. His work bridges theoretical neuroscience with practical signal processing systems, especially in biomedical applications. He directs the BIASlab research team at TU/e, which develops probabilistic programming tools including RxInfer.jl, ForneyLab.jl, GraphPPL.jl, ReactiveMP.jl, and Rocket.jl. His research spans active inference, variational message passing, probabilistic programming, and Bayesian neural networks, with applications ranging from hearing aids to multi-agent systems. Analysis of his recent publications reveals a strong trend toward practical implementations of Bayesian inference frameworks, particularly through Julia-based probabilistic programming tools. His work shows increasing focus on active inference applications, message passing algorithms, and the intersection of Riemannian geometry with probabilistic modeling. The research demonstrates consistent progression from theoretical foundations toward real-world engineering applications, particularly in biomedical signal processing and autonomous systems. Professor de Vries teaches a graduate-level course on Bayesian Machine Learning at TU/e and actively contributes to open-source software development through his GitHub profile (bertdv), with recent activity as recent as August 2025. His research team has developed several influential probabilistic programming libraries that have gained significant attention in the machine learning community. The BIASlab research group continues to advance the state of the art in Bayesian inference methods with applications in hearing technology, robotics, and signal processing.

Andreas Bjerre-Nielsenمشاهده پروفایل

دانشیار

• Educational Public Policy
• AI for Public Policy
• Social Networks
• +۶ مورد دیگر

Andreas Bjerre-Nielsen is an Associate Professor at the Department of Economics and Copenhagen Center for Social Data Science (SODAS) within the Faculty of Social Sciences at the University of Copenhagen. His work bridges economics and data science to analyze education-related behavior and policies. Research Focus: School choice, digital technology in education, predictive analytics for interventions, and social network effects. Methodology: Combines econometrics with machine learning techniques to evaluate policy impacts. Research Trends: Recent publications emphasize algorithmic fairness in college admissions, socioeconomic impacts of school boundary policies, and behavioral insights from large-scale datasets. His 2025 Scientific Reports study reveals nation-scale social network dynamics. Awards and Grants: Tietgen Prize (2021) for young social science researchers 2024: Independent Research Fund Denmark grant for 'Coded Clues' project 2023: Major grant for school choice research Collaborations: Works with Danish Ministry of Children and Education through UDDanKvant unit, and collaborates with multidisciplinary researchers including Sune Lehmann and David Dreyer Lassen.

• Environmental Systems
• Urban Water Sustainability
• Energy-Water Nexus
• +۳ مورد دیگر

Jeanne VanBriesen is the Duquesne Light Company Professor at Carnegie Mellon University, affiliated with the Department of Civil and Environmental Engineering and the Department of Engineering and Public Policy. She serves as director of the Center for Water Quality in Urban Environmental Systems (Water QUEST) and focuses on environmental systems, urban water sustainability, and the energy-water nexus. Ph.D., Civil Engineering, Northwestern University (1996) M.S., Civil Engineering, Northwestern University (1993) B.S., Education, Northwestern University (1990) Her research explores interactions between water infrastructure, energy systems, and urban environments, with particular attention to disinfection byproducts, bromide dynamics, and climate change resilience. She has secured funding from the National Science Foundation, Department of Defense, and other foundations. VanBriesen has supervised 21 Ph.D. students and 6 M.S. theses, while serving on influential boards including the U.S. EPA Science Advisory Board and the Consortium of Universities for the Advancement of Hydrologic Sciences (CUAHSI). Fellow of ASCE and EWRI 2015 Margaret S. Peterson Woman of the Year (ASCE) 2013 Philip L. Dowd Fellowship 2015 Barbara Lazarus Award for Mentoring 2009 ASCE Pittsburgh Chapter Professor of the Year Her recent work examines climate change hazard indexes, bromide transport from power plants, and innovative water treatment technologies. VanBriesen also contributes to national discussions about hydrologic science education and research policy.

Andrea Simonettoمشاهده پروفایل

استاد پژوهشی

• Optimization
• Control Theory
• Quantum Computing
• +۵ مورد دیگر

Andrea Simonetto is a Research Professor at the Applied Mathematics Unit (UMA) , ENSTA Paris, Institut Polytechnique de Paris. His work spans optimization, control theory, and learning algorithms for large-scale and streaming data , with applications in smart grids, intelligent transportation, personalized health, and quantum computing. Current research focuses on online algorithms for time-varying optimization , personalized optimization for cyber-physical systems , and variational quantum algorithms . Past contributions include theoretical and algorithmic advances in convex/non-convex optimization, distributed optimization (robotic networks, smart grids), and signal processing for sparse reconstructions and parallel computing in particle filtering. Key application domains include renewable energy integration , quantum state preparation , and human-in-the-loop control systems . His research is published in journals like ACM Transactions on Quantum Computing , IEEE Control Systems Letters , and Automatica .

George Hazelriggمشاهده پروفایل

استاد مدعو

• Systems Engineering
• Engineering Design Decision-Making
• Aerospace Engineering
• +۲ مورد دیگر

George Hazelrigg is an Adjunct Professor and Affiliate Faculty in the Department of Mechanical Engineering at George Mason University, with a career spanning academia, industry, and the National Science Foundation (NSF). He earned BS and MS degrees in mechanical engineering from NJIT, and MA, MSE, and PhD in aerospace engineering from Princeton University. Education: PhD, MSE, MA in Aerospace Engineering (Princeton University); MS, BS in Mechanical Engineering (NJIT) Hazelrigg’s research focuses on systems engineering and engineering design decision-making, with current work supported by the NSF titled “Toward a Theory of Systems Engineering.” This project explores mathematical foundations for optimizing design requirements and validating systems engineering processes. His publications from 2019–2025 emphasize systems engineering theory, model validation, tolerancing, decision-making under uncertainty, and pedagogical approaches. Recent work addresses incentive structures, differentiable variables in design, and validation frameworks. Throughout his career, Hazelrigg has taught at prestigious institutions including Princeton University, UC San Diego, and Ajou University in Korea. At the NSF, he managed research programs across four divisions, oversaw $210 million in annual budgets, and served as deputy and acting division director for the Division of Civil, Mechanical, and Manufacturing Innovation (CMMI).

Mohan Kelkarمشاهده پروفایل

استاد

• Reservoir Engineering
• Production Optimization
• Petroleum Economics
• +۲ مورد دیگر

Mohan Kelkar serves as an Applied Professor of Petroleum Engineering at The University of Tulsa, holding a prominent position within the McDougall School of Petroleum Engineering under the College of Engineering & Computer Science. With over 60 refereed publications, 250+ technical presentations, and three authoritative books—including the SPE best-seller 'Applied Geostatistics for Reservoir Description'—he has established himself as a leading figure in petroleum engineering research and education. His academic credentials include: Ph.D. in Chemical Engineering, University of Pittsburgh (1982) M.S. in Petroleum Engineering, University of Pittsburgh (1981) B.S. in Chemical Engineering, University of Mumbai (1979) Dr. Kelkar's research centers on reservoir optimization, production modeling, and development planning for oil and gas reservoirs, with significant contributions to petroleum economics and expert consultation in oilfield legal disputes. His work bridges advanced engineering techniques with economic evaluation frameworks, particularly evident in his books 'Natural Gas Production Engineering' and 'Petroleum Economics and Property Evaluation.' Analysis of his 15 most recent publications reveals a dominant focus on reservoir simulation methodologies, unconventional resource development, and innovative optimization techniques. Key trends include the application of fast marching methods for well placement, liquid loading prediction in gas wells, and shale reservoir modeling, demonstrating consistent evolution from foundational reservoir engineering toward cutting-edge computational approaches. His exceptional contributions have been recognized through numerous prestigious awards: SPE International Honorary Member Award (2024) SPE Distinguished Service Award (2018) The University of Tulsa Outstanding Teacher Award (2014) SPE International Distinguished Member Award (2010) Tau Beta Pi Teaching Excellence Award (2001) Dr. Kelkar maintains active professional engagement through SPE leadership roles—including Board of Directors service (2010-2013) and General Chair of the SPE IOR meeting (2006)—while balancing academic responsibilities with community leadership as President of the South Asian Performing Arts Foundation. His career exemplifies the integration of scholarly rigor, industry impact, and cultural contribution within the petroleum engineering discipline.