جست‌وجوی استاد و پژوهشگر

• Text Analysis
• Information Retrieval
• Machine Learning
• +۳ مورد دیگر

Heri Ramampiaro is a Professor and Head of the Department of Computer Science and Informatics at NTNU. He directs the Norwegian Open AI-Lab and holds roles on multiple national boards, including the Norwegian Data Protection Board and the National Geodata Council. His research focuses on text analysis, information retrieval, and machine learning applications in healthcare, energy markets, and smart cities. Education: PhD in Computer Science (2001), MSc in Information Systems (unspecified). Research interests include algorithm design for pattern mining, anomaly detection in time series, and deep learning for medical imaging and recommendation systems. Recent work emphasizes infant movement analysis for cerebral palsy prediction and fraud detection in electricity markets. Publications span over 50 peer-reviewed articles, with key contributions in IEEE Access , ACM Transactions , and Computerized Medical Imaging and Graphics . His work integrates machine learning with real-world challenges like labor inspection efficiency and urban smart city systems. Leadership roles include directing NTNU's AI-Lab and leading government-appointed expert groups on industrial data sharing (2021-2022). Grant activities include funding from the Research Council of Norway and EU Horizon projects. Labs/Teams: Norwegian Open AI-Lab, Data and Artificial Intelligence research group (former leadership).

Xiaohao Caiمشاهده پروفایل

مدرس

• Computer Science
• Applied Mathematics
• Statistics
• +۴ مورد دیگر

Xiaohao Cai is a Lecturer in Computer Science at the School of Electronics and Computer Science , University of Southampton. Previously, he held postdoctoral and research fellow roles at Technische Universität Kaiserslautern, University of Cambridge (Wellcome Trust and Isaac Newton Trust Fellowships), and University College London (Mullard Space Science Laboratory). Education: PhD in Mathematics from The Chinese University of Hong Kong (2012) Research interests span applied mathematics, statistics, and computer science , focusing on image/signal/data processing, computer vision, machine learning, and optimisation . His work includes segmentation/classification, pattern recognition, wavelets, and applications in digital, medical, and remote sensing imaging , as well as big-data analysis . Recent publications highlight interdisciplinary applications of AI/ML in ecology (mislabelled specimen detection), medical imaging (feature fusion), 3D human motion generation (transformers), and multimodal fake news detection (GAMED framework). Key trends include transformer architectures, graph-based learning, and parameter-efficient optimization . Scientific Awards: Wellcome Trust Research Fellow Isaac Newton Trust Research Fellow Teaching includes modules on computer vision, deep learning, and advanced machine learning . He supervises PhD students in computer science and engineering, with ongoing projects in AI-driven imaging and motion analysis.

Eric Ghyselsمشاهده پروفایل

استاد

• Time Series Econometrics
• Finance
• MIDAS Models
• +۶ مورد دیگر

Eric Ghysels is the Edward Bernstein Distinguished Professor of Economics and Professor of Finance at the Kenan-Flagler Business School, University of North Carolina at Chapel Hill. His research focuses on time series econometrics, mixed data sampling (MIDAS) models, machine learning, and FinTech. He holds a PhD from Northwestern University and a BA from the University of Brussels. Ghysels has published extensively in leading journals and authored influential books such as Applied Economic Forecasting using Time Series Methods . He served as co-editor of the Journal of Business and Economic Statistics and founded the Society for Financial Econometrics (SoFiE). His honors include Fellowships from the American Statistical Association and SoFiE. Education: PhD and MA in Economics from Northwestern University (USA), BA in Economics from the University of Brussels (Belgium). Research Interests: Time series econometrics, MIDAS regression models, machine learning applications in finance, volatility modeling, asset pricing, and financial econometrics. He pioneered the MIDAS methodology for mixed-frequency data analysis and contributed to quantum computing applications in asset pricing. Scientific Awards: Fellow of the American Statistical Association (2020), Fellow of the Society for Financial Econometrics (2009), Duisenberg Fellow at the European Central Bank (2011). Advising & Grants: While specific student names are not listed, Ghysels teaches PhD courses in empirical finance and time series analysis. His research has been supported by initiatives such as a $50M Ripple-funded academic program on blockchain applications in finance. Labs/Teams: Co-founder and president of the Society for Financial Econometrics (SoFiE), fostering collaboration between academia and industry in financial econometrics.

Claudio Schifanellaمشاهده پروفایل

استادیار

• Social Computing
• Distributed Ledger Technologies
• Data Mining
• +۴ مورد دیگر

Claudio Schifanella is an Assistant Professor at the Department of Computer Science, University of Turin, and a member of the Social Computing research group. He holds a PhD in Computer Science from the University of Turin (2008), focusing on automatic reasoning techniques for autonomous entities. His research spans social computing, distributed ledgers, data mining, and knowledge representation. He leads the EU Co-City project as scientific responsible and manages geographical data for the WeGovNow project. He co-founded Sity, an innovative startup transferring research into commercial applications. Schifanella organizes the Sideways workshop series on Social Media World Sensors and serves on program committees for conferences like CIKM. Key collaborations include the European Commission's Joint Research Centre (ConTraffic project) and RAI research center. His work emphasizes applying machine learning and data mining to real-world challenges, such as urban governance, workplace safety, and media analytics. Projects like Librare and SEeS@W showcase his integration of sensor technologies and geospatial data management.

Michael Walterمشاهده پروفایل

استاد

• Quantum Information
• Tensor Networks
• Holography
• +۷ مورد دیگر

Prof. Michael Walter is a Full Professor (W3 Chair) in Quantum Information at Ruhr University Bochum, affiliated with the Faculty of Computer Science and the Cluster of Excellence CASA. Previously, he held a Tenure-Track Assistant Professorship in Mathematics and Theoretical Physics at the University of Amsterdam (2017-2021) and was a Postdoctoral Scholar at Stanford University (2014-2017). He earned his Dr. sc. (PhD) from ETH Zurich in 2014 under Matthias Christandl, with a thesis on quantum state marginals. His research focuses on quantum information theory, tensor networks, and their connections to holography, complexity theory, and invariant theory. Education: Dipl. Math. from University of Karlsruhe (KIT) and Georg-August-University Göttingen (2005-2010), advised by Thomas Schick. Postdoctoral work at Stanford expanded his expertise in quantum many-body systems and computational methods. Research Interests: Quantum information’s intersection with geometry and algebra, including quantum entanglement, tensor network models of holography, and computational complexity. He explores applications of optimization theory and representation theory to quantum problems, such as the quantum marginal problem and non-commutative optimization. Awards: ERC Starting Grant (2022), Early Career Award (KNAW, 2020), NWO Veni Laureate (2017), and ETH Medal (2014). His work bridges quantum computing, mathematics, and theoretical physics, with contributions to quantum gravity via tensor networks and algorithmic approaches to quantum states. Grants & Leadership: Leads the Chair of Quantum Information, collaborating internationally on projects like holographic duality and quantum resource theories. His research group develops theoretical frameworks for quantum technologies and foundational quantum mechanics. Labs/Teams: Active in CASA (Cluster of Excellence) at Ruhr University, focusing on advanced data science and computational methods. Collaborates with institutions like QuSoft (Amsterdam) and IQC (Waterloo).

• Large elastic-plastic deformations
• Nonlinear finite element method
• Metal forming
• +۳ مورد دیگر

Kamyar Ghavam is an Associate Professor (Teaching Stream) at the University of Waterloo. His research focuses on advanced manufacturing processes, computational mechanics, and engineering education. He teaches courses such as ME 321 (Kinematics and Dynamics of Machines), ME 621 (Advanced Finite Element Methods), and MTE 219 (Mechanics of Deformable Solids). His work spans topics like large plastic deformations, nonlinear finite element methods, and molecular dynamics simulations. Dr. Ghavam has contributed to conferences and journals on sintering models, cross-cohort project pedagogy, and material behavior under irradiation. His teaching philosophy emphasizes active learning through anecdotes and interactive activities. He currently does not have graduate students but has advised on capstone projects involving mechanical design and manufacturing systems. His technical expertise includes thermal-mechanical processing of metals, with notable work on aluminum alloy formability and zirconium-based nuclear materials. He actively contributes to curriculum development in mechanical engineering education, focusing on enhancing student collaboration and project-based learning outcomes.

Ciprian Crainiceanuمشاهده پروفایل

استاد

• Biostatistics
• Functional Data Analysis
• Wearable and Implantable Technology
• +۱۵ مورد دیگر

Ciprian Crainiceanu is a Professor in the Department of Biostatistics at the Bloomberg School of Public Health, Johns Hopkins University. His research spans biostatistical methodology and applications in public health, with a focus on high-dimensional data from wearable devices and medical imaging. Education: PhD, Cornell University, 2003 MS, University of Bucharest, 1998 His research interests include functional data analysis, measurement error, longitudinal modeling, Bayesian inference, and nonparametric statistics, with applications in sleep, aging, multiple sclerosis, Alzheimer’s disease, and cancer. He develops statistical tools tailored to complex data from accelerometers, neuroimaging (MRI, CT, SPECT), and surgical monitoring. His recent work involves dynamic prediction models, step-counting algorithms for NHANES and ARIC data, and methods for high-dimensional functional and imaging data. The most recent publications highlight advancements in wearable data analysis, medical imaging platforms like Neuroconductor, and novel resampling methods such as the upstrap. His work integrates statistical theory, software development, and interdisciplinary collaboration. Scientific Awards: Fellow of the American Statistical Association (ASA) Chair, Statistics in Imaging Section of ASA (two terms) Chair, Biostatistics Methods and Research Design (BMRD) NIH review section Crainiceanu is actively involved in mentoring, teaching, and collaborative research. He co-founded the SMART (Statistical Methods and Applications for Research in Technology) research group and Neuroconductor, fostering interdisciplinary innovation. His work emphasizes scalable, software-backed methods and close collaboration with domain scientists. He has led methodological developments in variance components testing, functional regression, population value decomposition, and dynamic prediction, applied to real-world health challenges. Labs and Research Groups: Co-founder, SMART (Statistical Methods and Applications for Research in Technology) Co-founder, Neuroconductor (open-source platform for medical imaging in R) Wearable and Implantable Technology (WIT) group MAGIC (Methods and Applications Group for Imaging in the Clinic)

Thomas Huckleمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Scientific Computing
• Parallel Computing
• +۷ مورد دیگر

Prof. Thomas Huckle is a Professor of Scientific Computing at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. His research focuses on numerical linear algebra, parallel computing, and their applications in physics and computer science. Key interests include solving linear problems on parallel architectures, image processing, multigrid methods, preconditioning, and tensor-based high-dimensional problem approximation. Education: Studied mathematics and physics at the University of Würzburg (diploma in mathematics, 1985 PhD, 1991 habilitation). Professional History: DFG-funded research at Stanford University (1993–1994), appointed to TUM in 1995, and member of the Mathematics Department since 1997. Research Interests: Prof. Huckle’s work spans numerical methods for large-scale systems, including structured matrices, regularization techniques, and quantum computing applications. He develops algorithms for parallel computing environments and contributes to software tools like ELPA for eigenvalue problems. Grants and Labs: Engaged in projects such as the ELPA-AEO eigensolver and ESSEX-II initiatives. Active in the SCCS (Scientific Computing and Computational Science) group at TUM, focusing on high-performance computing and numerical methods.

Mahalakshmi Sabanayagamمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning Theory
• Graph-based Learning
• +۳ مورد دیگر

Mahalakshmi Sabanayagam is a Researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Theoretical Foundations of Artificial Intelligence. Her research focuses on the theory of deep learning and graph-based learning, with notable contributions to adversarial robustness, neural network certification, and graph representation learning. She currently serves as a Teaching Assistant for the course 'Gems of Informatics 3 (IN2176): Modelling and analysis of real-world graphs.' Her work bridges theoretical foundations and practical applications, including analysis of neural tangent kernels in graph networks, robustness certification against adversarial attacks, and cross-domain machine learning techniques. Key research directions include understanding decision boundaries via Hessian analysis, tensorized autoencoder architectures, and graphon-based network clustering. Publications span topics from adversarial machine learning to interdisciplinary applications in physics and finance. While currently no awards or grants are listed, her active publication record reflects ongoing contributions to artificial intelligence theory and graph-based methodologies. She is part of Prof. Debarghya Ghoshdastidar's research group at TUM's Faculty of Computer Science. Lab affiliations include the Theoretical Foundations of Artificial Intelligence (I7) group, focusing on rigorous mathematical analysis of AI systems and their real-world graph applications. No advising relationships or student collaborations are explicitly documented in available materials.

• Financial Time Series Analysis
• Insurance Data Analysis
• Bayesian Computational Methods
• +۳ مورد دیگر

Associate Professor Boris Choy is affiliated with the University of Sydney Business School and holds memberships in the China Studies Centre and Sydney Southeast Asia Centre. He holds a PhD in Statistics (Imperial College London), MPhil in Statistics (Chinese University of Hong Kong), and BSc (First Class Honours) in Mathematics (University of Leeds). His research focuses on robust statistical analysis of financial time series and insurance data using Bayesian methods. Key topics include GARCH models, stochastic volatility, loss reserving, option pricing, and survival analysis across finance, medicine, and actuarial science. Recent work explores machine learning applications in asset management and agricultural economics. Award-winning educator, Choy has received multiple accolades including the Vice-Chancellor’s Award for Outstanding Teaching (2016) and the Wayne Lonergan Outstanding Teaching Award (2015). He advises PhD students on projects like interpretable financial forecasting and stochastic loss reserving. Notable publications span journals like Journal of Productivity Analysis and Computational Statistics , with contributions to econometrics, insurance modeling, and agricultural policy analysis. His research bridges statistical theory and applied domains, emphasizing Bayesian techniques and heavy-tailed distributions.

Alex Gorodetskyمشاهده پروفایل

دانشیار

• Autonomous decision making under uncertainty
• Uncertainty quantification
• Statistical inference
• +۹ مورد دیگر

Alex Gorodetsky is an Associate Professor of Aerospace Engineering at the University of Michigan , part of the College of Engineering . His research focuses on autonomous decision-making under uncertainty , leveraging applied mathematics and computational science. Key areas include uncertainty quantification, machine learning, control systems, and tensor decompositions. He leads the Gorodetsky Group , which develops algorithms for high-fidelity simulations and scalable digital twins. Education: PhD (2016) and SM (2012) from MIT in Aeronautics/Astronautics; BSE (2010) in Aerospace Engineering from the University of Michigan. Research Interests: Innovating methods for managing uncertainty in complex systems like autonomous aircraft and electric propulsion, with applications in aerospace, bioengineering, and climate modeling. Techniques include Bayesian inference, multi-fidelity surrogate modeling, and compressed data analytics. Notable Contributions: Developed the MFNETS framework for efficient multi-fidelity surrogate networks. Recipient of the NSF CAREER Award (2023) and Air Force Young Investigator Award (2018) . Active in NASA-funded initiatives like the Joint Advanced Propulsion Institute (JANUS) . Recent Work: Focus on GPU-accelerated plasma simulations, automated statistical estimation, and low-rank tensor methods for high-dimensional problems. Over 50 peer-reviewed articles, with recent highlights in Computer Methods in Applied Mechanics and Engineering , SIAM Journal on Scientific Computing , and Journal of Computational Physics . Teaching & Mentorship: Advises over 20 graduate and undergraduate students, with emphasis on interdisciplinary training in computational methods and AI-driven science.

Richard Verasمشاهده پروفایل

استادیار

• High Performance Computing
• Parallel Algorithms
• Computational Linear Algebra
• +۴ مورد دیگر

Dr. Richard Veras is an Assistant Professor in the School of Computer Science at the University of Oklahoma . His research focuses on High Performance Computing (HPC), with emphasis on code synthesis, parallel algorithms, and optimizing computational workflows for modern hardware architectures. Education: Ph.D. and M.S. in Electrical and Computer Engineering from Carnegie Mellon University B.S. in Mathematics and Computer Science from The University of Texas at Austin Research Interests: High Performance Computing (HPC) Parallel algorithm design and implementation Computational linear algebra and signal processing Graph analytics and network modeling Compiler optimizations and automated code generation Performance portability across hardware architectures Professional Experience: Research Scientist at Louisiana State University Postdoctoral Researcher at Carnegie Mellon University Labs/Teams: Leads HPC research initiatives at OU, focusing on code synthesis tools and performance optimization frameworks.

Ivan Markovskyمشاهده پروفایل

دانشیار

• Structured Low-Rank Approximation
• System Identification
• Data-Driven Control
• +۷ مورد دیگر

Ivan Markovsky is an Associate Professor at the Department of Fundamental Electricity and Instrumentation (ELEC) of the Vrije Universiteit Brussel. He previously held positions at the School of Electronics and Computer Science (ECS) at the University of Southampton. His research focuses on structured low-rank approximation, system identification, and data-driven control, with applications in signal processing and control systems. Education: PhD in Electrical Engineering from Katholieke Universiteit Leuven (2005). Research Interests: Specializes in low-rank approximation methods, system identification, and their applications in control theory and signal processing. Develops numerical optimization algorithms for structured matrices and tensors, with emphasis on practical engineering problems. His work bridges theoretical mathematics and applied control systems engineering. Awards: Received an ERC Starting Grant (2011) for research on structured low-rank approximation. Grants & Projects: ERC Starting Grant (2011-2016) - Structured Low-Rank Approximation FWOAL827 (2017-2020) - Block-oriented nonlinear identification using Volterra series FWOAL758 (2015-2018) - Decoupling multivariate polynomials in nonlinear system identification Labs/Teams: Leads research groups focused on system identification, matrix approximation, and data-driven control methodologies. Collaborates internationally on projects involving tensor approximation and nonlinear system modeling.

Lev Kantorovichمشاهده پروفایل

استاد

• Theoretical Surface Science
• Atomic Force Microscopy (AFM)
• Scanning Tunnelling Microscopy (STM)
• +۷ مورد دیگر

Professor Lev Kantorovich is a distinguished academic at King’s College London’s Department of Physics, affiliated with the Thomas Young Centre and the Centre for Non-Equilibrium Science (CNES). He holds a Professorship since 2009, progressing from a Lecturer role (2002) and Reader (2005). His academic journey includes a PhD from Latvian University (1975), followed by postdoctoral work at Keele University and University College London. His research focuses on theoretical simulations of solid surfaces, atomic-scale imaging via AFM/STM, and self-assembly of molecular systems. Key areas include non-equilibrium statistical mechanics, quantum transport in nanojunctions, graphene growth kinetics, and embedded-cluster methods for electronic structure calculations. He develops advanced computational techniques like non-equilibrium Green’s functions (NEGF) and kinetic Monte Carlo simulations. Recent work explores photon-assisted phenomena in nanojunctions, photoinduced surface reconstructions, and catalytic applications of molybdenum subnanoclusters. His mathematical contributions span transforms (Laplace, Fourier), linear algebra, and partial differential equations, underpinning interdisciplinary modeling. Affiliated with CNES, he collaborates globally on non-equilibrium science. His research addresses energy, materials, and nanotechnology challenges, with applications in molecular electronics and surface engineering.

Thomas Blesgenمشاهده پروفایل

استاد

• Nonlinear Mechanics
• Electronic Structure Calculations
• Multiscale Modeling
• +۳ مورد دیگر

Prof. Thomas Blesgen is a Professor of Mathematics and Digital Technology at Technische Hochschule Bingen, Department 2. He holds a habilitation and PhD in Mathematics from the University of Bonn and University of Leipzig respectively. His career includes research stays at the University of Cambridge, California Institute of Technology, and Max Planck Institute for Mathematics in the Sciences. Research focuses on nonlinear mechanics, electronic structure calculations, multiscale modeling, and materials science. He has pioneered work on Cosserat plasticity, two-phase flow models, and continuum limits of nanomaterials. His methods integrate partial differential equations, calculus of variations, and geometric measure theory. Notable projects include DFG-funded dynamic recrystallization studies and NSF/AFOSR-sponsored electronic structure computations. Recent work addresses microstructure formation in nonlinear elasticity. He teaches applied mathematics across bachelor's and master's programs.