جست‌وجوی استاد و پژوهشگر

Hadi Jabbariمشاهده پروفایل

دانشیار

• Unconventional Reservoir Engineering
• Hydraulic Fracturing (HF)
• Enhanced Oil Recovery (EOR)
• +۵ مورد دیگر

Hadi Jabbari is an Associate Professor in the Department of Energy & Petroleum Engineering at the University of North Dakota. His expertise spans reservoir engineering , hydraulic fracturing , and enhanced oil recovery (EOR) , with a focus on unconventional reservoirs and subsurface energy systems. He teaches courses including Numerical Reservoir Simulation and Welltesting. PhD, University of North Dakota (2013) MSc, Reservoir Engineering, Sharif University of Technology (2006) BSc, Production Engineering, Petroleum University of Technology (2004) Dr. Jabbari's research integrates fluid dynamics in porous media and machine learning for energy systems , addressing challenges in underground hydrogen storage (UHS) and carbon capture (CCS) . His work includes experimental and numerical studies on CO2-EOR, pulse decay permeability, and fracture network optimization. Recent publications highlight trends in 3D printing for rock characterization , geomechanical modeling , and subsurface energy storage . Key collaborations with Rice University and HESS Corporation demonstrate his industry-academia bridge. Grants include NDIC funding ($2.98M) for UHS/CCS/EOR research and ACS grants for shale characterization. NDIC grant (Co-PI, $2.98M) for UHS/CCS/EOR ACS grant ($110K) for 3D shale analysis HESS Corporation grant ($67K) for Bakken optimization NDIC renewal ($2.5M) for unconventional reservoirs Dr. Jabbari's laboratory focuses on digital rock physics and reservoir simulation , utilizing CT scan reconstruction and experimental coreflooding. His work on HVFRs in high-TDS environments and fracture network modeling has been presented at leading conferences including SPE and US Rock Mechanics Symposium.

Alexandru-Ionut Petrisorمشاهده پروفایل

استاد

• Land Cover and Land Use Changes
• Transitional Dynamics
• Man-Dominated Ecological Systems
• +۸ مورد دیگر

Prof. Alexandru-Ionut Petrisor is a full professor and Director of the Doctoral School of Urban Planning at Ion Mincu University of Architecture and Urbanism in Bucharest, with an associate professorship at Technical University of Moldova. His career spans over 60 research projects, resulting in 600+ publications and 450+ presentations. Education : BS in Ecology (1997), PhD in Geography (2011) - University of Bucharest; MSPH in Biostatistics (2000), PhD in Ecology (2004) - University of South Carolina; Habilitation in Urban Planning (2014); Highest research rank in Biology (2013) and Geography (2017). His research focuses on land use dynamics , GIS applications , and predatory publishing , with expertise in geostatistics, systems ecology, and urbanization processes. Recent work examines climate adaptation through blue-green infrastructure and waste-to-energy systems. His publications span environmental science, urban planning, and sustainability journals, with methodological contributions to spatial analysis and ecological modeling. Articles demonstrate interdisciplinary approaches combining environmental monitoring with urban development challenges. Awards & Memberships : Sigma Xi Full Membership (recognizing original research contributions) International Invention Awards at Geneva Exhibition Member of Society for Urban Ecology and International Association for Landscape Ecology He serves as Chief Editor of Urbanism Architecture Constructions and editorial board member for journals like Land and Journal of Urban and Regional Analysis , while advising on 30+ urban/spatial plans.

Xun Shiمشاهده پروفایل

استاد

• Geographic Information Systems (GIS)
• Quantitative Spatial Analysis
• Geocomputational Methods
• +۵ مورد دیگر

Xun Shi is a Professor of Geography at Dartmouth College, specializing in Geographic Information Systems (GIS), quantitative spatial analysis, and geocomputational methods. They serve as Chair of the Department of Geography and have applied GIS technologies to interdisciplinary research in health studies, digital soil mapping, urban planning, and renewable energy. Education: B.S. from Peking University, M.S. from the Chinese Academy of Sciences, M.Phil. from the University of Hong Kong, and Ph.D. from the University of Wisconsin. Dr. Shi's research integrates GIS with public health, focusing on spatial epidemiology, disease modeling, and healthcare accessibility. Their work includes developing geospatial tools like ArcGIS-based software for soil mapping and analyzing spatial patterns of diseases such as ALS. Recent publications highlight their contributions to patient registry accuracy, spatiotemporal disease modeling, and environmental risk assessments. These works span interdisciplinary fields including GIS, public health, and environmental science. Scientific Awards: Scholar Innovation and Advancement Awards of Dartmouth (2020) Dr. Shi has secured significant grants from the NIH and NSF for projects on ALS etiology, tick-borne disease prediction, and geospatial modeling. They also teach courses like Geographic Information Systems and Geographies of Health .

• Numerical Analysis
• Multivariate Approximation Theory
• Polynomial Interpolation
• +۵ مورد دیگر

Marco Vianello is a Full Professor of Numerical Analysis at the Department of Mathematics of the University of Padova, Italy. He has held this position since May 2017, following his tenure as Associate Professor (2001-2016) and Lecturer (1993-2000) at the same institution. Professor Vianello is Co-Editor-in-Chief of the Journal of Approximation Software (2024-) and was previously Managing Editor of Dolomites Research Notes on Approximation (2008-2022). He is the co-founder of the CAA: Padova-Verona Research Group on Constructive Approximation and Applications and an active member of the national research networks RITA (Rete ITaliana di Approssimazione) and SIMAI ANA&A Activity Group. Professor Vianello's research focuses on Multivariate Approximation Theory, with particular emphasis on Polynomial Interpolation, Numerical Quadrature and Cubature, Meshless Approximation, and Polynomial Optimization. His work includes the development of special point sets such as Padua Points, Approximate Fekete Points, Subperiodic Trigonometric Points, and CATCH (Caratheodory-Tchakaloff) Points. His research has significant applications in numerical methods for PDEs, computational mechanics, and scientific computing. His research has been recognized with the Optimization Letters Best Paper Award (2020), and his work has accumulated approximately 3,700 citations on Google Scholar. His algorithms have been implemented in several major software packages including ZEMAX OpticStudio, CP2K for molecular dynamics, and the Chebfun package. Optimization Letters Best Paper Award (2020) for 'Quadrature Based Polynomial Optimization' Approximately 3,700 citations on Google Scholar Approximately 1,200 citations by about 700 authors on MathSciNet Implementation of algorithms in ZEMAX OpticStudio, CP2K, Chebfun, and other scientific software Professor Vianello has advised 3 Ph.D. students to completion and approximately 60 B.Sc. and Master's dissertations. As a research coordinator from 2003-2025, he has secured approximately 150 kEuros in funding, supporting an average of 5 researchers per year. He has served on numerous hiring and evaluation committees, including the ASN (Italian National Scientific Habilitation) Committee in Numerical Analysis (2024-26) and hiring committees for professorships at Padova and Calabria Universities. His research group, the CAA: Padova-Verona Research Group on Constructive Approximation and Applications, maintains an active international collaboration network with researchers from institutions including Lawrence Livermore National Laboratory, Jagellonian University, University of Calgary/Verona, University of Toulouse, Colorado School of Mines, ANU Canberra, Indiana University, and several others across Europe, North America, and Asia.

Anatole von Lilienfeldمشاهده پروفایل

استاد

• Chemical Physics
• Machine Learning
• Computational Materials Science
• +۱۰ مورد دیگر

Professor Anatole von Lilienfeld is affiliated with the University of Toronto's Department of Chemistry and Department of Materials Science & Engineering, with cross-appointments to the Department of Physics and the Vector Institute. His research focuses on quantum machine learning for chemical compound space exploration, combining physics-based models with high-performance computing. Chemical Physics Quantum Machine Learning Computational Materials Design Density Functional Theory Molecular Dynamics Excited States Nuclear Quantum Effects His recent work demonstrates how machine learning accelerates solving the Schrödinger equation for novel materials, achieving speedups of multiple orders of magnitude compared to traditional quantum chemistry methods. Key contributions include Coulomb matrix representations and alchemical perturbation techniques. Scientific achievements include the Ed Clark Chair for Advanced Materials and an ERC Consolidator Grant . He leads the Chemspacelab, an interdisciplinary team developing open-source tools like LERULI.com for real-time molecular property predictions.

Robert Brandon Gramacyمشاهده پروفایل

استاد

• Bayesian modeling
• Statistical computing
• Monte Carlo inference
• +۷ مورد دیگر

Robert Brandon Gramacy is a Professor and Department Head in the Department of Statistics within the College of Science at Virginia Polytechnic and State University (Virginia Tech). He is also affiliate faculty in VT's Computational Modeling and Data Analytics (CMDA) program. Previously, he was an Associate Professor of Econometrics and Statistics at the University of Chicago's Booth School of Business and a fellow of the Computation Institute. Dr. Gramacy specializes in computational statistics, focusing on areas where ideal modeling approaches are impractical or current solutions lack fidelity. His research spans Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty. He develops new models, methods, and algorithms while remaining grounded in real-world applications across diverse fields including aerospace engineering, climate science, epidemiology, finance, and environmental management. His work on NASA rocket booster design led to the development of the tgp R package, which has found wide applicability across numerous scientific domains. His recent research focuses on large-scale surrogate modeling, particularly with local approximate Gaussian processes (laGP) and heteroskedastic Gaussian processes (hetGP). He has made significant contributions to predicting atmospheric drag of satellites in orbit and is currently leading an NSF-funded project to forecast phytoplankton blooms for drinking water quality management. His publications demonstrate a consistent focus on scalable methods for complex computer experiments and real-world applications. Savage Award for best Ph.D. thesis in Bayesian applied methodology (2006) Facebook faculty award for research on large scale surrogate modeling (2017) Outstanding Service Award from the ASA Section on Physical and Engineering Sciences (2022) Editor-in-Chief of Technometrics (since Jan 1, 2023) Lloyd S. Nelson Award and Youden Award for significant contributions to applied statistics Dr. Gramacy mentors numerous graduate students and postdocs, emphasizing reproducible research, high-quality software development, and clear scientific communication. His lab members work on cutting-edge problems in statistical methodology and applications across multiple domains. As Editor-in-Chief of Technometrics, he plays a key role in shaping publication standards for applied statistics research and has published guidance for authors on writing effective statistical papers.

Godmar Backمشاهده پروفایل

دانشیار

• Computer Systems
• Operating Systems
• Programming Languages
• +۵ مورد دیگر

Godmar Back is an Associate Professor in the Department of Computer Science at Virginia Tech's College of Engineering. He holds a Ph.D. in Computer Science from the University of Utah (2002) and specializes in systems research, programming languages, and performance analysis. His work focuses on foundational aspects of operating systems, distributed systems, and cloud computing, with notable contributions to memory management, concurrency, and cybersecurity education. His research explores how hardware and software interact to optimize system performance, including studies on SPEC benchmarking, garbage collection, and undefined behavior in programming languages. He has developed frameworks like CloudBrowser 2.0 for cloud-based applications and the VT-ASOS system for many-core platforms. Back is also active in pedagogy, integrating DevOps practices into undergraduate research and incorporating cybersecurity modules into core curricula through spiral theory-based methods. Key projects include the Pintos instructional operating system kernel, the VirtuOS virtualized OS, and ReplayCache for predictive computations. His work frequently bridges theoretical systems research with practical applications, emphasizing reproducibility and real-world impact. His educational initiatives aim to broaden participation in systems research through high-impact practices and collaborative development environments.

• Machine Learning
• Predictive Analytics
• Time Series Analysis
• +۵ مورد دیگر

Nurilla Avazov is an Associate Professor of Data Science at the Inland School of Business and Social Sciences, Inland Norway University of Applied Sciences. He holds dual PhDs: a PhD in Computer Science (2021) from the University of Auckland and a PhD in Information and Communication Technology (2015) from the University of Agder. His research focuses on machine learning, predictive analytics, time series analysis, wireless communication modeling, IoT systems, e-healthcare solutions, and human activity recognition through advanced signal processing techniques. Dr. Avazov’s academic career includes significant contributions to high-impact refereed journals and conferences. His work spans theoretical developments in algorithm design, practical implementations in sensor networks, and interdisciplinary applications in healthcare and smart environments. He has developed novel trajectory-driven channel models for mm-wave systems and pioneered backscattering-based human activity recognition methods. His expertise combines data science with telecommunications engineering, addressing challenges in non-stationary channel analysis, radar systems for indoor localization, and cybersecurity through wireless signal inference. Current research trends emphasize multimodal sensor fusion, real-time activity tracking, and privacy-aware IoT architectures. Dr. Avazov has advised no formally recorded students in the provided data. His research grants and funded projects are not explicitly detailed here, though his publications indicate sustained external collaboration and funding support. He is affiliated with the Business Analytics research group at his institution.

Gundolf Haaseمشاهده پروفایل

استاد

• Scientific Computing
• High Performance Computing
• Parallel Algorithms
• +۷ مورد دیگر

Gundolf Haase is a Professor at the Department of Mathematics and Scientific Computing at the University of Graz, Austria. His academic career spans several decades with significant contributions to parallel computing and numerical methods for partial differential equations. He maintains an active research profile with numerous publications in high-impact journals and conferences. His research interests primarily focus on Scientific Computing, High Performance Computing, Parallel Algorithms, Domain Decomposition Methods, Finite Element Methods, Multigrid Methods, and GPU Programming. His work has increasingly incorporated biomedical applications, particularly in cardiac electrophysiology modeling and computational fluid dynamics for cardiovascular applications. Haase's recent publications demonstrate a strong focus on many-core parallelization techniques, particularly for GPUs, with applications spanning from computational cardiology to engineering simulations. His research shows a consistent pattern of developing efficient numerical methods for solving complex partial differential equations across various scientific domains. He teaches courses including Scientific Computing and FEM, Computer Mathematics, Programming in C++, and High Performance Computing at both undergraduate and graduate levels, demonstrating his commitment to education alongside research.

Kypros Milidonisمشاهده پروفایل

استادیار

• Renewable Energy Technologies
• Solar Energy Systems
• Energy Conversion
• +۶ مورد دیگر

Kypros Milidonis is an Assistant Professor in the Energy Department of the Energy, Environment, and Water Research Center (EEWRC) at the Cyprus Institute (CYI). His expertise spans renewable energy technologies, with a focus on solar energy systems, advanced energy conversion, and computational engineering. He holds an integrated Master’s Degree (2008) and a doctorate (2014) in Mechanical Engineering and Aeronautics from the University of Patras. Prior to joining CYI, he worked as a Research Associate at the University of Cambridge (2015-2018), focusing on energy conversion systems. Dr. Milidonis leads research projects spanning theoretical development (TRL 1-4) to pre-industrial applications (TRL 5-7), emphasizing solar energy innovation. His work includes optimizing heliostat fields, anti-soiling strategies, UAV-assisted monitoring, and AI-driven design. He actively collaborates with industry and manages international projects, such as SolarPACES Task III. His research integrates interdisciplinary approaches, combining mechanical engineering, computational modeling, and material science. Key contributions include advancements in heliostat field optimization, solar receiver design, and the application of unmanned aerial vehicles (UAVs) in solar plant maintenance. His publications span solar thermal systems, computational tools like SunPATH and fluxtracer, and historical engineering reconstructions (e.g., ancient Greek artifacts). He is a Principal Investigator and coordinator, emphasizing translational research bridging academia and industry.

Sourav Sahaمشاهده پروفایل

استادیار

• Mechanistic Computational Intelligence
• Computational Mechanics
• Computational Nanomaterials
• +۵ مورد دیگر

Sourav Saha is an Assistant Professor in the Department of Aerospace and Ocean Engineering at Virginia Tech. His research focuses on mechanistic computational intelligence, computational mechanics, and materials science, with a particular emphasis on integrating artificial intelligence with engineering challenges. He holds a Ph.D. from Northwestern University and M.S./B.S. degrees from Bangladesh University of Engineering and Technology (BUET). Key professional roles include a visiting position at Sandia National Laboratories and prior teaching at BUET. His honors include the Richter Memorial Fellowship (2023–24), NSF INTERN Award (2022–23), and NIST Additive Manufacturing Benchmark Challenge win (2022). He leads the IMMPACT Lab, which develops AI-driven tools for advanced manufacturing and materials innovation. Research interests span multiscale modeling, nanomaterials, and additive manufacturing. His recent articles explore topics like physics-informed machine learning, kernel-based neural architectures, and fatigue life estimation. He actively participates in professional societies such as the Minerals, Metals & Materials Society and the American Society of Thermal and Fluid Engineers.

• Ocean Acoustics
• Signal Processing
• Underwater Communications
• +۲ مورد دیگر

Zoi-Heleni Michalopoulou is Professor in Mathematical Sciences at New Jersey Institute of Technology, specializing in ocean acoustics and signal processing. Her research develops advanced algorithms for underwater signal detection, geoacoustic inversion, and ocean parameter estimation. Current projects include Navy-sponsored investigations into shallow water inversion using machine learning and optimization methods. Research integrates Gaussian processes, genetic algorithms, and Bayesian methods for solving inverse problems in uncertain ocean environments. Recent publications focus on multichannel signal detection in distorted underwater channels, direction-of-arrival estimation, and particle filter optimization. Maintains active collaborations with Navy research divisions. Publications demonstrate consistent methodological innovation in statistical signal processing, with applications to underwater sensor networks and acoustic characterization. Currently leads NSF-funded nanotechnology education initiatives.

Qiang Sunمشاهده پروفایل

دانشیار

• Generative AI
• Trustworthy AI
• Next-generation Statistics
• +۸ مورد دیگر

Qiang Sun is an Associate Professor of Statistics and Computer Science at the University of Toronto (UofT), holding cross-appointments in the Department of Computer Science and the Department of Computer and Mathematical Sciences. He leads the StatsLE (Statistics, Learning, and Engineering) Lab, focusing on generative AI, trustworthy AI, and next-generation statistics. His research bridges statistical theory with applied challenges in industry, emphasizing ensemble learning, transfer learning, and reinforcement learning, with applications in tech, finance, and science. Education: PhD from the University of North Carolina at Chapel Hill (UNC-CH) BS in SCGY from the University of Science and Technology of China (USTC) Research Interests: Sun’s work spans foundational AI and statistical methods, including GenAI, robust algorithms, and interdisciplinary applications. His lab explores topics like resistant clustering, physical commonsense violation detection in gameplay, and microbiome analysis. Recent trends in his publications emphasize robustness (e.g., heavy-tailed errors), multimodal learning, and lifelong learning frameworks. Scientific Awards: Connaught Award James E. Grizzle Distinguished Alumni Noah’s Ark Distinguished Lecture Advising & Grants: While student names are not listed, his grants and collaborations reflect industrial and academic partnerships. He serves as an associate editor for the Electronic Journal of Statistics and as an area chair for conferences like AISTATS and COLT. Labs & Teams: The StatsLE Lab at UofT drives his research, integrating statistical theory with cutting-edge AI applications.

Josip Matejašمشاهده پروفایل

استاد

• Mathematical Programming
• Operations Research
• Optimization
• +۳ مورد دیگر

Prof. Josip Matejaš is a Professor in the Department of Mathematics at the Faculty of Economics, University of Zagreb. His expertise lies in mathematical programming, numerical analysis, and optimization, with a focus on multi-objective programming and its applications in economics and business strategy. He has contributed to advancements in iterative methods for solving complex optimization problems and numerical accuracy in algorithms. His research spans interdisciplinary areas, including the application of Benford's Law in auditing, demographic business strategy modeling through numerical interpolation, and sustainable principles in multi-objective decision-making. He has authored numerous articles on topics such as high-accuracy matrix computations, algorithm efficiency comparisons, and special number theory problems. Prof. Matejaš is actively involved in teaching and research, contributing to the Faculty's academic mission through his work in mathematics and its practical applications in economics and business disciplines.

Nic Fiorentinoمشاهده پروفایل

دانشیار

• Biomechanics
• Orthopaedics
• Medical Imaging
• +۳ مورد دیگر

Nic Fiorentino is an Associate Professor in the Department of Mechanical Engineering at the University of Vermont, affiliated with the College of Engineering and Mathematical Sciences. He also holds secondary appointments in the Departments of Electrical & Biomedical Engineering and Orthopaedics & Rehabilitation. His research focuses on musculoskeletal biomechanics, orthopedics, medical imaging, and motion capture techniques. Fiorentino leads the Musculoskeletal Imaging and Orthopaedic Biomechanics (MIOB) Lab, which explores advanced imaging modalities and biomechanical models to improve understanding of musculoskeletal disorders. Fiorentino earned a B.S. in Mechanical Engineering from the University of Florida and a Ph.D. from the University of Virginia. Postdoctoral training at the University of Utah Orthopedic Research Laboratory further developed his expertise in clinical biomechanics. His research integrates imaging technologies like MRI and fluoroscopy with computational modeling to study joint mechanics, cartilage degeneration, and injury mechanisms. Recent work examines sex differences in post-ACL-reconstruction outcomes, hip joint kinematics in impingement syndromes, and athlete muscle phenotyping through high-dimensional clustering. Notable contributions include advancements in arthrokinematic measurement accuracy, novel EMG-driven wearable analysis methods, and validation of MRI phantoms for cartilage assessment. His projects aim to reduce musculoskeletal disability by improving diagnostic precision and treatment strategies. Fiorentino teaches courses including Biomechanics of Human Motion, Continuum Mechanics, and Biomedical Imaging Systems. He actively mentors students through UVM’s Mechanical Engineering and Biomedical Engineering programs.