جست‌وجوی استاد و پژوهشگر

Thomas Wiesمشاهده پروفایل

استاد

• Program Analysis
• Program Verification
• Automated Deduction
• +۳ مورد دیگر

Thomas Wies is a Professor of Computer Science at the Courant Institute, New York University, and Chair of the Computer Science Department. His research focuses on program analysis and verification, automated deduction, and concurrency. University : New York University School : Courant Institute Department : Computer Science Academic Rank : Professor His research explores foundational techniques for verifying concurrent systems, including separation logic, shape analysis, and SMT-based methods. Key trends in his recent work include advancements in symbolic verification for concurrent data structures, multiparty protocol analysis, and linearizability proof tools. Scientific awards he has received include: Best Paper Award, ISSRE 2019 Best Paper Award, ACM SIGPLAN OOPSLA 2014 He has advised numerous PhD students and co-advised alumni, including Devora Chait-Roth, Mark Goldstein, Ekanshdeep Gupta, and others. His teaching portfolio includes graduate and undergraduate courses on programming languages, concurrency, and program verification. Professional roles include organizing conferences like VerifyThis 2025 and chairing program committees for VMCAI and ESOP.

Tianbai Xiaoمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Applied Mathematics
• Kinetic Theory
• +۷ مورد دیگر

Tianbai Xiao is a Researcher at the Karlsruhe Institute of Technology (KIT) within the Department of Mathematics and Steinbuch Centre for Computing. His work spans mesoscopic science , uncertainty quantification , and scientific machine learning , focusing on multi-scale, multi-physics problems in flow transport. His research in kinetic theory addresses nonlinear partial differential equations, hyperbolic conservation laws, and the unified modeling of continuum/rarefied flows. He develops high-performance numerical algorithms like the Unified Gas-Kinetic Scheme (UGKS) and Kinetic.jl (a finite volume toolbox for scientific computing). Current projects include mesoscopic science , stochastic data science , and physics-informed neural networks . He contributes to open-source tools including FluxReconstruction.jl for advection-diffusion methods and Langevin.jl for stochastic kinetic modeling. Publications cover Journal of Computational Physics , Engineering Fracture Mechanics , and Entropy , with preprints on arXiv in 2025 addressing force-driven flows and hybrid peridynamics. Teaching activities include the Introduction to Kinetic Theory lecture at KIT, and mentoring in the CAMMP (Computational and Mathematical Modeling Program) to develop problem-solving skills through real-world modeling tasks. He advocates for problem-based learning where students translate non-mathematical problems into mathematical language.

James Raymerمشاهده پروفایل

استاد

• Demography
• Migration
• Spatial Population Analysis
• +۴ مورد دیگر

Prof James Raymer is a Professor at the School of Demography , Australian National University (ANU). He joined ANU in 2013 as a Vice Chancellor’s Strategic Appointment and led the transition of the Australian Demographic and Social Research Institute into the School of Demography, serving as Head until 2016. Education: PhD in Geography from the University of Colorado, Boulder. His research focuses on spatial demographic processes , population projections , and migration modeling . He specializes in understanding how migration influences subnational population change , using Bayesian methods and statistical frameworks. Recent publications (2025-2024) address international migration trends in Australia, demographic forecasting , and school enrollment projections . Key subfields include migration dynamics, population structure analysis, and policy-driven demographic modeling. He has secured major grants from the Australian Research Council , National Health and Medical Research Council , and UK/EU agencies. Consultancy work includes advising the European Commission , UN Statistical Division , and Australian Capital Territory Government. He is affiliated with The Migration Hub and Poplab , collaborating on projects like Long-Term Fertility Projections and Demographic trends in the ACT .

Melanie Schmidtمشاهده پروفایل

استاد

• Geometric Data Analysis
• k-means clustering
• Combinatorial Optimization
• +۳ مورد دیگر

Professor Melanie Schmidt is a faculty member in the Department of Computer Science at Heinrich-Heine-Universität Düsseldorf, where she leads the Algorithms and Data Structures research group. Previously, she was affiliated with the University of Bonn's Institute of Computer Science, where she completed her PhD under Prof. Dr. Heiko Röglin and headed a subgroup on "clustering for big data" within his research group. Current Position: Professor at Heinrich-Heine-Universität Düsseldorf Previous Position: Researcher and lecturer at University of Bonn PhD Advisor: Prof. Dr. Heiko Röglin Her research focuses on geometric data analysis, particularly k-means clustering in data streams, combinatorial optimization, and approximation algorithms. Her work bridges theoretical computer science with practical applications in big data processing. She has made significant contributions to understanding the theoretical foundations of clustering algorithms while developing efficient implementations for real-world applications. Professor Schmidt's publication record shows a consistent evolution from theoretical analysis of k-means to practical implementations for big data environments. Her recent work explores fairness in clustering, privacy-preserving techniques, and efficient algorithms for high-dimensional data. She has published in top-tier conferences including SODA, ICALP, ESA, and ITCS, demonstrating both theoretical rigor and practical relevance. Best Student Paper Award at ESA 2012 (joint work with Martin Groß, Jan-Philipp W. Kappmeier, and Daniel Schmidt) She actively supervises numerous Master's and Bachelor's students, with current advisees including Lena Carta, Lukas Drexler, and Anna Arutyunova. Her research group includes members such as Anja Rey, Julian Wargalla, and Annika Hennes. She teaches advanced courses in algorithms and data structures, with a focus on randomized algorithms and efficient algorithm design for big data problems. Professor Schmidt leads the Algorithms and Data Structures research group at Heinrich-Heine-Universität Düsseldorf, which focuses on developing and analyzing efficient algorithms for fundamental computational problems, with particular emphasis on clustering, geometric data analysis, and big data applications.

• Seismic Hazard Assessment
• Earthquake Risk Modeling
• Natural Disaster Risk Analysis
• +۵ مورد دیگر

L. Oostwegel is a Researcher at GFZ German Research Centre for Geosciences, working within the Seismic Hazard and Risk Dynamics department. The researcher specializes in earthquake exposure modeling, building stock characterization, and multi-hazard risk assessment using geospatial data and open-source information. Oostwegel's research focuses on seismic hazard and risk assessment, with particular emphasis on building exposure modeling using OpenStreetMap and remote sensing data. Key research areas include earthquake risk modeling, flood risk assessment, landslide risk management, and the development of tools for multi-hazard risk assessment. The researcher has made significant contributions to understanding building stock characteristics at various scales, from individual buildings to global assessments. Analysis of recent publications reveals a strong focus on utilizing volunteered geographic information and Earth observation datasets for risk assessment. Oostwegel has developed methods for assessing the completeness of OpenStreetMap building data globally and has created tools like 'risk-calculator' for multi-hazard risk assessments. The research shows an interdisciplinary approach combining geophysics, geospatial analysis, and disaster risk reduction. Top-down or bottom-up in earthquake exposure modeling (2025) Safe Haven – Landslides: A Serious Game for Enhancing Risk Awareness (2025) A model of European buildings (2024) From Shelters to Skyscrapers: Worldwide Building Exploration (2024) Seismic loss assessment sensitivity study (2023) Oostwegel has collaborated extensively with researchers including Evaz Zadeh, T., Schorlemmer, D., and others across multiple projects focused on natural disaster risk assessment. The research has practical applications in urban planning, disaster risk reduction, and climate adaptation strategies, particularly for coastal megacities and areas prone to seismic activity.

Luis Antonio Belanche Muñozمشاهده پروفایل

استاد

• Machine Learning
• Kernel Methods
• Neural Networks

Luis Antonio Belanche Muñoz is a Professor at the Department of Computer Science , Faculty of Informatics of Barcelona (FIB) , Universitat Politècnica de Catalunya (UPC) . He is affiliated with research groups SOCO - Soft Computing and IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Group . His career spans over 25 years, with 216 documented activities. His research focuses on Machine Learning , Kernel Methods , and Neural Networks . He has pioneered techniques in feature selection, similarity measures, and hybrid models connecting deep learning with kernel methods. His work applies to diverse domains including finance, microbiology, cancer diagnostics, and environmental engineering. Recent publications highlight trends in kernel matrix analysis using entropy, microbiome data integration , and drug resistance prediction in HIV. Earlier work includes knowledge-based systems for wastewater treatment diagnostics and educational technologies for MOOC environments. He has collaborated with 75+ researchers across UPC's research network, contributing to projects funded under Spain's State Research Plans and Catalonia's RIS3CAT strategy. His 2011 thesis on Feature selection in brain tumor MRS data demonstrates interdisciplinary applications.

Turaj Ashuriمشاهده پروفایل

استاد

• Multiphysics Modeling
• Design Optimization
• Renewable Energy Systems
• +۵ مورد دیگر

Dr. Turaj Ashuri is an Assistant Dean of Research Initiatives and Graduate Programs (interim) at the College of Engineering and Engineering Technology, Kennesaw State University. He previously held faculty positions at the University of Texas at Dallas and Arkansas Tech University, where he served in leadership roles such as Director of the Interdisciplinary Research Center and Graduate Program Director. Before academia, he accumulated over a decade of industry experience as a Structural Design Engineer, Research Engineer, and R&D Project Manager. PhD, Aerospace Engineering, Delft University of Technology MSc, Aerospace Engineering, Sharif University of Technology BSc, Mechanical Engineering, Tehran Azad University Postdoctoral Research Fellowship, University of Michigan - Ann Arbor Dr. Ashuri's research focuses on Multiphysics Modeling and Design Optimization , spanning theoretical and applied domains. His work develops numerical methods for unsteady flow fields, nonlinear composite structures, and machine learning algorithms, while applying these techniques to soft robots, unmanned aerial vehicles, and renewable energy systems. He actively explores Uncertainty Quantification and Optimal Control in energy systems, with particular emphasis on wind turbine technology and biomedical soft robotics. His recent publications demonstrate expertise in Wind Farm Optimization (2016-2018), Soft Robotics (2020), and Hydrogen Fuel Cell Vehicles (2019). These works intersect Computational Design , Control Theory , and Renewable Energy Systems , reflecting his multidisciplinary approach. Scientific recognitions include: Associate Fellow, American Institute of Aeronautics and Astronautics (AIAA) Chair, 2023 AIAA Aviation Forum Discipline Committee Dr. Ashuri's lab at KSU conducts research in Multidisciplinary Design Optimization and Machine Learning applications for intelligent mechanical systems. The lab receives funding from NASA and NIH , supporting work on soft robots, wind turbine engineering, and energy systems optimization.

• Sustainable Architecture
• Life Cycle Assessment (LCA)
• Life Cycle Costing (LCC)
• +۱۲ مورد دیگر

Elena Fregonara is a professor in the Department of Architecture and Design at Politecnico di Torino, where she conducts research at the intersection of sustainable architecture, real estate economics, and environmental policy. Her work emphasizes life cycle thinking, circular economy principles, and the integration of environmental costs into financial decision-making in the construction sector. Institution: Politecnico di Torino Department: Department of Architecture and Design Research Focus: Sustainable Architecture, Life Cycle Assessment, Real Estate Appraisal, Circular Economy Her research interests revolve around sustainable urban development, with a strong emphasis on the economic evaluation of building sustainability. She investigates how embodied energy, carbon emissions, daylight, and green attributes influence real estate values and investment decisions. Her work bridges theoretical economics with practical applications in public procurement, urban regeneration, and building retrofitting. The trends in her recent publications show a consistent focus on integrating environmental externalities into traditional economic models. She employs life cycle costing (LCC), life cycle assessment (LCA), structural equation modeling, and net present value (NPV) frameworks to evaluate sustainability across scales—from materials to urban systems. Her research increasingly addresses policy implications, especially in public procurement and urban governance. Elena Fregonara has contributed to significant research initiatives such as the “Circular Design for Natural Fibers” (CD4NF) project under the PNRR, reflecting her engagement with national sustainability strategies. She has no listed scientific awards in the provided data. She has collaborated extensively with researchers such as Diego G. Ferrando, Alice Barreca, and Irene Rubino on topics including building upcycling, discount rate modeling, and sustainable public procurement. While her mentorship of students is not explicitly detailed, her collaborative work suggests active engagement in research teams and academic networks. She is involved in developing methodological tools like the “Smart SPP LCC-CO2 Tool” for sustainable decision-making. Her research lab or team is not explicitly named, but her involvement in EU and PNRR-funded projects indicates leadership or key roles in interdisciplinary research groups focused on sustainable construction and urban development.

Helen Wongمشاهده پروفایل

استاد

• Quantum Topology
• Hyperbolic Geometry
• Applications of Topology in Molecular Biology
• +۳ مورد دیگر

Helen Wong is a Professor of Mathematics in the Department of Mathematical Sciences. Her research focuses on quantum topology, hyperbolic geometry, and applications of topology to molecular biology, data analysis, and quantum computation. Education: BA from Pomona College; PhD from Yale University. She has held notable fellowships including the Simons Fellowship (2021-22), Joan and Joseph Birman Fellowship (2021-22), and von Neumann Fellowship (2017-18). She also received the Prize Teaching Award at Yale University and a Fulbright Research Award in Budapest. Her research explores quantum invariants' connections with hyperbolic geometry, applications in molecular biology (e.g., protein folding), and quantum computing. Recent work includes studies on skein algebras, knot theory in 3D manifolds, and topological models for biopolymers. Helen has secured multiple NSF grants: RUI: Pure and Applied Knot Theory (2023-2026) RUI: Knots in Three-Dimensional Manifolds (2019-2023) RUI: Skeins on Surfaces (2015-2019) Her work bridges pure mathematics with interdisciplinary applications, particularly in biology and quantum technologies.

Shanghong Xieمشاهده پروفایل

استادیار

• Machine Learning
• Network Analysis
• Precision Medicine
• +۱۲ مورد دیگر

Shanghong Xie is an Assistant Professor in the Department of Statistics at the University of South Carolina, College of Arts and Sciences. She is also a member of the Carolina Autism and Neurodevelopment (CAN) Research Center and the Institute for Mind and Brain (IMB), reflecting her interdisciplinary focus on statistical methods for neuroscience and public health. Dr. Xie earned her PhD in Biostatistics from Columbia University in 2019 and completed her postdoctoral training there. Her research centers on developing machine learning and generative models to tackle complex challenges in functional data analysis, network modeling, causal inference, and precision medicine. She applies these methods to neuroimaging (fMRI, DTI, EEG), genetic biomarkers, and public health data, particularly in mental health and neurological disorders. Her recent publications demonstrate a strong trajectory in methodological innovation with high-impact applications, including COVID-19 forecasting models adopted by the CDC, mediation analysis with neuroimaging, and personalized treatment regime estimation. The research trends highlight expertise in high-dimensional data, graphical models, and integrative learning across multi-modal biomarkers. American Statistical Association (ASA) Statistics in Imaging Section First Prize Student Paper Award American Statistical Association (ASA) Mental Health Statistics Section Best Student Paper Award International Conference on Health Policy Statistics (ICHPS) Student Travel Award Dr. Xie advises several graduate students and co-founded the Functional and Complex Data Analysis (FUNCODA) Working Group, fostering collaboration on advanced data modeling. She has been involved in significant grant-supported work, notably contributing to the CDC’s ensemble forecasting effort during the pandemic through her survival-convolution model. Her lab emphasizes computational statistics and open science, with code available on GitHub.

• Muscle thermodynamics
• Bioengineered muscle systems
• Molecular mechanisms of muscle contraction
• +۳ مورد دیگر

Josh Baker is a Professor and Associate Vice President for Research at the University of Nevada, Reno, affiliated with the University of Nevada School of Medicine's Department of Pharmacology. His research focuses on the thermodynamics of muscle contraction, quantum heat engines, and entropic stability of biological systems. Ph.D. in Biochemistry, Biophysics, and Molecular Biology (University of Minnesota, 1999) B.S. in Physics (Hamline University, 1987) Research interests span muscle thermodynamics, mechanochemical coupling, and multiscale modeling of biological systems. His work explores the intersection of physics and biology to understand fundamental cellular processes. Current research trends examine quantum thermodynamics in muscle systems, entropic forces in cellular stability, and multiscale modeling approaches. Publications analyze phenomena from single molecule dynamics to whole-cell behaviors. Notable roles include Director of NIH NV INBRE since 2016 and leadership in research innovation at the University of Nevada. His work bridges experimental and theoretical approaches in biophysics.

Martha Ivon Cárdenas Domínguezمشاهده پروفایل

مدرس ارشد

• Intelligent Data Analysis
• Machine Learning
• Personalized Medicine
• +۳ مورد دیگر

Dr. Martha Ivon Cárdenas Domínguez is a Senior Lecturer at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Science. She is an active member of the IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, as well as the SOCO (Soft Computing) research group. Education: BSc in Mathematics PhD in Artificial Intelligence (UPC) Master's in Artificial Intelligence Her research focuses on applying computational intelligence and machine learning techniques to biomedical data analysis, particularly for pharmacoproteomic applications involving G protein-coupled receptors. She specializes in kernel-based methods and data visualization techniques for sequence analysis and classification tasks. Recent publications demonstrate her expertise in manifold learning, subtype discrimination of GPCRs, and phylogenetic tree integration with visualization techniques. Her work addresses challenges in classification errors, mislabeling, and sequence transformation for complex biomedical datasets. Dr. Cárdenas employs advanced visualization strategies to explore metabotropic glutamate receptors and analyze protein sequence overlaps through manifold-based approaches. Her research bridges computational methods with biomedical applications, particularly in personalized medicine contexts. She actively contributes to competitive R&D+i projects and participates in international conferences. Her professional activities include 5 conference presentations, 2 book chapters, and 2 journal articles focusing on data science applications in bioinformatics. Dr. Cárdenas utilizes computational intelligence for analyzing receptor sequences and has developed innovative approaches for visualizing complex biological data structures. Her work has implications for drug discovery and personalized treatment development through enhanced understanding of receptor classification.

Sergio Campobassoمشاهده پروفایل

مدرس ارشد

• Computational Fluid Dynamics (CFD)
• Renewable Energy
• Turbomachinery Design
• +۶ مورد دیگر

Dr. Sergio Campobasso is a Senior Lecturer in Renewable Energy Engineering at the School of Engineering, Lancaster University , where he has worked since January 2013. He holds a Postgraduate Diploma in Turbomachinery from the Von Karman Institute (Belgium, 1996) and a PhD in Numerical Analysis from Oxford University (2004). Education: Postgraduate Diploma in Turbomachinery (Von Karman Institute), PhD in Numerical Analysis (Oxford) Professional Experience: BMW-Rolls Royce (aerodynamicist/stress engineer), Rolls-Royce CFD UTC (Oxford), Cranfield University (uncertainty propagation), Glasgow University (wind turbine research) His research focuses on advancing Computational Fluid Dynamics (CFD) technologies for wind and gas turbine design, incorporating stochastic uncertainty from manufacturing and environmental variability. Key projects include: Development of harmonic balance Navier-Stokes solvers for unsteady flows Low-speed preconditioning methods for mixed-speed flow analysis Novel boundary conditions for complex turbomachinery geometries Hybrid parallelization techniques for high-performance computing His recent publications (2020-2024) emphasize blade erosion modeling, offshore wind turbine dynamics, and machine learning applications for energy loss prediction. Current research projects (2021-2024) address predictive maintenance, emissions reduction, and multi-rotor system optimization.

Ramin Moghaddassمشاهده پروفایل

دانشیار

• Predictive Analytics
• Machine Learning
• Industrial Engineering
• +۴ مورد دیگر

Ramin Moghaddass is an Associate Professor in the Department of Industrial Engineering at the University of Miami's College of Engineering. He serves as Director of both the Industrial Research and Assessment Center and the Building Training, Research, and Assessment Center. His work bridges machine learning with industrial engineering to solve complex system monitoring and maintenance challenges. University of Miami, College of Engineering Director, Industrial Research and Assessment Center Director, Building Training, Research, and Assessment Center His research focuses on: Deep state-space modeling for dynamic systems Graph neural networks for smart grid and network anomaly detection Thermal-RGB sensor fusion for manufacturing plant efficiency Image processing for vegetation risk analysis in power networks Bayesian filtering techniques with stochastic neural networks Recent publications highlight trends in sensor-driven system modeling (2025), thermal-RGB fusion for HVAC optimization (2025), anomaly detection in smart grids (2025), and adaptive inspection protocols for large-scale networks (2024). His work combines recurrent neural networks with dynamic Bayesian layers for predictive analytics while exploring graph topology integration. Contact: rxm991@miami.edu | (305) 284-9505

David S. Biedenharnمشاهده پروفایل

استاد مدعو

• Hydraulic Engineering
• River Mechanics
• Fluvial Geomorphology
• +۳ مورد دیگر

Dr. David S. Biedenharn is an Adjunct Professor at Tulane University's School of Science & Engineering and a Research Hydraulic Engineer with the U.S. Army Engineer Research and Development Center (ERDC). With over 40 years of experience in hydraulics, river engineering, and fluvial geomorphology, he has led numerous projects on the Mississippi River system, including the development of the Sediment Impact Analysis Method (SIAM) model. His work spans flood control design, channel restoration, and sediment management frameworks. BS in Wood Science, Mississippi State University (1978) BS in Civil Engineering, Louisiana Tech University (1979) MS in Civil Engineering, Colorado State University (1983) PhD in Civil Engineering, Colorado State University (1995) Dr. Biedenharn's research focuses on hydraulic engineering , fluvial geomorphology , and sediment transport dynamics . His expertise includes the analysis of large river systems, regional sediment management strategies, and the development of engineering tools for channel stabilization and restoration. Recent work emphasizes probabilistic geomorphic modeling for long-term river evolution. The selected publications reflect his specialization in Mississippi River hydrology , sediment load quantification , and river infrastructure impacts . Key themes include channel geometry trends, bed material analysis, and floodplain management techniques. Dr. Biedenharn has developed and taught training programs on streambank stabilization and channel restoration for the U.S. Army Corps of Engineers. His current role involves mentoring young engineers and scientists at ERDC while advancing innovative geomorphic modeling approaches. He leads a multidisciplinary team at ERDC, focusing on river engineering research and applied geomorphology for national waterway projects. His career includes service as Channel Improvement Coordinator for the Mississippi River Channel Improvement Project, managing an annual $100M+ budget.