جست‌وجوی استاد و پژوهشگر

Simon Adrianمشاهده پروفایل

استاد

• Computational Electromagnetics
• Electromagnetic Integral Equations
• Antenna Design
• +۳ مورد دیگر

Prof. Simon Adrian holds the Chair of Theoretical Electrical Engineering at the Institute of General Electrical Engineering, University of Rostock, Germany. His research focuses on computational electromagnetics with critical applications in antenna design, electromagnetic compatibility, and medical technology. He serves as Associate Editor for the IEEE Transactions on Antennas and Propagation and contributes to the IEEE Antennas and Propagation Society Education Committee, demonstrating significant academic leadership in the global electromagnetics community. His primary research addresses low-frequency instability challenges in electromagnetic integral equations through innovative numerical techniques. Key areas include Calderón preconditioners, quasi-Helmholtz projectors, B-spline discretizations, and adaptive cross approximation methods. These approaches enable robust simulations across diverse applications from radar systems and antenna design to biomedical problems like deep brain stimulation and electroencephalography. Recent work emphasizes broadband stability and efficient solvers for multiply-connected geometries. Analysis of Prof. Adrian's publication trends (2023-2025) reveals a concentrated effort on overcoming fundamental limitations in electromagnetic modeling. His work consistently targets low-frequency regimes where traditional methods fail, developing mathematically rigorous stabilization techniques while expanding into biomedical applications. The integration of isogeometric analysis with specialized discretization strategies represents a cutting-edge direction in computational electromagnetics. Professional engagement includes active membership in the Institute of Electrical and Electronics Engineers (IEEE), IEEE Antennas and Propagation Society, and Union Radio-Scientifique Internationale (URSI), reflecting his commitment to advancing the field through collaborative research and scholarly communication.

Göran Kauermannمشاهده پروفایل

استاد

• Nonparametric Models
• Semi- and nonparametric analysis
• Generalized Linear Models
• +۲ مورد دیگر

Prof. Dr. Göran Kauermann is a Full Professor of Statistics at the Ludwig-Maximilians-University Munich , holding the Chair of Applied Statistics in Social Sciences, Economics and Business . His research spans nonparametric models, generalized linear models, and network data analysis, with applications in economics, epidemiology, and data science. Education: Diplom in Economic Mathematics (1991, TU Berlin), PhD in Statistics (1994), Habilitation (Venia Legendi) in Statistics (2000) Kauermann’s research interests focus on penalized regression , network analysis , and statistical modeling in economics, social sciences, and public health. Recent work explores label uncertainty in machine learning , spatio-temporal conflict diffusion , and dynamic network models for economic and social data. Scientific trends in his publications include penalized splines for nonlinear modeling, network flow estimation in social and economic contexts, and label variation analysis in machine learning. His collaborations span climate zone classification , Covid-19 mortality modeling , and smart city parking analytics . Scientific Awards: Bruce Russett Award (2020) for political network research Leadership Roles: He served as Dean of the Faculty of Mathematics, Informatics and Statistics (2019–2021), Speaker of the Elite Master Program in Data Science (2016–2026), and Chair of the German Statistical Society (2005–2013). He also held editorial roles in journals like AStA Advances in Statistical Analysis and Statistical Modelling .

• Mathematics
• Combinatorics
• Number Theory
• +۳ مورد دیگر

Orli Herscovici is an Assistant Professor in the Department of Mathematics and Computer Science at St. John’s College of Liberal Arts and Sciences, St. John’s University. Her research focuses on advanced mathematical analysis, combinatorics, and special functions, with notable contributions to fractional calculus, polynomial theory, and combinatorial identities. She has published extensively on topics including Gaussian principal frequencies, deformed fractional transforms, and degenerate polynomials. Her work bridges pure mathematics and applications in probability theory and nonlinear systems. Dr. Herscovici’s academic affiliations include the Mathematics and Computer Science department, where she contributes to teaching and research initiatives. Her publications reflect a deep engagement with interdisciplinary areas such as spectral geometry and umbral calculus. While no specific scientific awards or grants are highlighted in the provided text, her scholarly output demonstrates sustained academic engagement in theoretical and applied mathematics.

• Probability Theory
• Financial Mathematics
• Stochastic Processes

Paul Eisenberg is an Assistant Professor at the Institute for Statistics and Mathematics at Vienna University of Economics and Business. He holds a doctorate in Mathematics and has held positions at the University of Liverpool, Vienna University of Technology, and Technical University of Dortmund. His research focuses on probability theory and financial mathematics, particularly stochastic processes, term structure modeling, and applications to financial markets. He teaches courses in Probability, Quantitative Methods, and Business Mathematics. Research outputs concentrate on stochastic processes in finance, including term structure modeling, stochastic optimization, market microstructure, and applications to cryptocurrency markets. Recent work shows emphasis on regime-switching models and high-dimensional stochastic systems.

Szymon Cyganمشاهده پروفایل

استادیار

• Strain Imaging Methods for Echocardiography
• Ultrasonic Imaging
• Biomedical Engineering
• +۵ مورد دیگر

Szymon Cygan, PhD, is an Assistant Professor and Deputy Dean for Students Affairs and Promotion at the Institute of Metrology and Biomedical Engineering , part of the Faculty of Mechatronics at Warsaw University of Technology. His work focuses on strain imaging methods for echocardiography, ultrasonic imaging, and biomedical engineering applications. Education: M.Sc. (2003) in Automatics and Robotics with Biocybernetics and Biomedical Engineering specialization; Ph.D. (2011) on displacements and strain estimation for ultrasonic strain imaging. Research: Specializes in speckle tracking echocardiography, left ventricular phantom modeling, finite element simulations, and validation of strain estimation algorithms using synthetic and physical models. Awards: No specific scientific awards explicitly mentioned. Projects: Involved in 2 projects, including development of low-cost gait analysis systems and synthetic cardiac phantom validation. Labs: Part of Krzysztof Kaluzynski's Lab, focusing on biomedical engineering and cardiac imaging.

• Bayesian Statistics
• Machine Learning
• Approximate Inference Methods
• +۸ مورد دیگر

Dennis Prangle is an Associate Professor in Statistics at the University of Bristol, conducting research at the intersection of Bayesian statistics and machine learning. His academic profile demonstrates expertise in developing novel computational inference methods with applications across multiple scientific domains. Dr. Prangle's primary research interests include: Approximate inference methods such as simulation-based inference and variational techniques Likelihood-free inference through Approximate Bayesian Computation (ABC) Experimental design for high-dimensional problems Applications in population genetics, physics, ecology, and epidemiology Stochastic differential equations and composite likelihood approaches His publication record shows consistent methodological contributions with increasing integration of machine learning techniques. Recent work focuses on normalizing flows with flexible tails for improved density estimation, Bayesian emulation of complex systems, and optimal combination of composite likelihoods. His research bridges theoretical statistics with practical applications in financial modeling, infrastructure engineering, and fair classification algorithms. Dr. Prangle maintains an active academic blog where he discusses technical aspects of Bayesian statistics, experimental design, and computational methods, demonstrating his commitment to scholarly communication. His detailed posts on topics like Fisher information gain versus Shannon information gain in experimental design highlight his theoretical contributions to the field.

Gerda Claeskensمشاهده پروفایل

استاد

• Model Selection
• Model Averaging
• Post-Selection Inference
• +۴ مورد دیگر

Gerda Claeskens is a full professor of Statistics at the Faculty of Economics and Business (FEB) at KU Leuven, Belgium. She holds positions in the Operations Research and Statistics Research Group (ORSTAT) and is affiliated with the Leuven Statistics Research Center. Her academic journey includes a Licentiate in Mathematics (Summa Cum Laude) from the University of Antwerp and a Ph.D. in Statistics from Limburgs Universitair Centrum (now Hasselt University). Research Interests: Her work focuses on model selection, post-selection inference, nonparametric methods, and high-dimensional statistics. She has contributed extensively to methodologies like focused information criteria, penalized splines, and quantile regression. Awards and Honors: Notable accolades include Fellow of the American Statistical Association (2019), Goodeve Medal (2017), and Medallion Lecturer (2016). She has also held editorial roles in top journals such as Biometrika and Journal of the American Statistical Association . Teaching and Mentoring: She has advised over 20 PhD students and mentored postdoctoral researchers. Her teaching spans advanced statistical methods, probability theory, and business statistics. She has delivered invited lectures globally, including at the European Meeting of Statisticians and the International Society for NonParametric Statistics. Key Contributions: Her book Model Selection and Model Averaging (2008) is a seminal work in statistical methodology. Her research addresses challenges in high-dimensional data, survival analysis, and model averaging, with applications in finance, insurance, and biostatistics.

Zhenyu Cuiمشاهده پروفایل

دانشیار

• Financial Engineering
• Insurance Analytics
• Operations Research
• +۵ مورد دیگر

Zhenyu Cui is an Associate Professor of Financial Engineering at the School of Business, Stevens Institute of Technology. He holds a PhD in Statistics from the University of Waterloo and a BS in Actuarial Science from the University of Hong Kong. His research focuses on financial engineering, insurance analytics, and operations research, with notable contributions to stochastic volatility models, Monte Carlo simulation, and financial systemic risk. He has published in top journals such as Mathematical Finance, SIAM Journal on Financial Mathematics, and European Journal of Operational Research. Education: PhD (2013, Statistics, University of Waterloo); MS (2010, Quantitative Finance, University of Waterloo); BS (2008, Actuarial Science, University of Hong Kong). Research Interests: Financial Systemic Risk, Monte Carlo Methods, Stochastic Volatility, Option Pricing, and Risk Management. His work bridges theoretical finance and practical applications, emphasizing robust pricing techniques and computational methods. Grants include leadership roles in NSF-funded projects on quantum algorithms for financial risk management and collaborations with institutions like Accenture and the Society of Actuaries. Awards include recognition for top-cited articles and editorial excellence.

Mats G Larsonمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +۳ مورد دیگر

Mats G Larson is a Professor at the Department of Mathematics and Mathematical Statistics at Umeå University. He holds the research qualification of Docent and specializes in computational mathematics, numerical analysis, and finite element methods. His work focuses on advancing numerical techniques for partial differential equations, including CutFEM, Isogeometric Analysis (IGA), and hybridized methods for complex geometries and multiphysics problems. Research interests include error estimation, stabilized finite element methods, computational mechanics, and applications in engineering and fluid-structure interaction. Larson leads projects such as the 2022–2025 'Multi-shell CutFEM for problems with mixed dimensions' and the 2017–2021 project on computational methods for elliptic problems. His publications appear in top journals like Computer Methods in Applied Mechanics and Engineering . Key contributions include developments in CutFEM for embedded surfaces, augmented Lagrangian methods for contact problems, and geometric modeling with CAD integration. His research bridges theoretical analysis and practical applications, addressing challenges in mesh generation, stability, and high-performance computing.

• Bayesian Statistics
• Statistical Ecology
• Environmental Statistics
• +۳ مورد دیگر

Professor Paul Blackwell is a faculty member at the University of Sheffield within the School of Mathematical and Physical Sciences . His academic work focuses on Bayesian statistics , statistical ecology , and inference for stochastic processes , applied to disciplines including environmental science, archaeology, and marine biology. His recent research involves animal movement modeling using continuous-time Bayesian frameworks , radiocarbon calibration for archaeological dating, and ecological uncertainty quantification . Publications span Bayesian computation , habitat selection , and ecosystem dynamics , with applications in reindeer behavior , coral reef resilience , and fisheries management . He contributes to Bayesian methodology for ecological and environmental modeling , integrating stochastic processes with real-world data . His projects often involve interdisciplinary collaboration, particularly with marine and climate scientists .

Xin Xingمشاهده پروفایل

استادیار

• Bioinformatics
• Metagenomics
• Single-cell analysis
• +۷ مورد دیگر

Overview Xin Xing is an Assistant Professor in the Department of Statistics at Virginia Tech. He holds a PhD from the University of Georgia (2018), an M.S. and B.S. from the University of Science and Technology of China (2013, 2010). His research focuses on statistical methodology development in bioinformatics, machine learning, and computational biology. He has received grants including the NSF ATD (2022-2024) and NVIDIA GPU Grant (2019). Education Ph.D. – Statistics – University of Georgia (2018) M.S. – Statistics – University of Science and Technology of China (2013) B.S. – Statistics – University of Science and Technology of China (2010) Research Interests Dr. Xing's work spans three core areas: Bioinformatics: Metagenomics, single-cell analysis, epigenomics, neuroimaging. Statistics: Minimax nonparametric testing, smoothing splines, dimension reduction, controlled variable selection, causal inference. Machine Learning: Deep learning, generative models, transfer learning, neural network compression. Key Awards New Faculty Mentoring Grant, Virginia Tech (2020-2022) Best Senior PhD Student Award, University of Georgia (2018) Outstanding Dissertation Award, USTC (2010) Teaching Recent courses include CMDA 3654 (Intro to Data Analytics & Visualization) and CMDA 4654 (Deep Learning). Office: D&DS 308.

• Robotics
• Manufacturing Engineering
• Mechanical Engineering Design
• +۵ مورد دیگر

Fabio Pini is an Associate Professor at the University of Modena and Reggio Emilia's Department of Engineering 'Enzo Ferrari'. His research focuses on robotics, manufacturing engineering, and advanced design methodologies. He leads the IDEALab (Intelligent Design, Engineering, and Automation Laboratory) and teaches courses in Smart Robotics, Automotive Design, and Additive Manufacturing. Education: Not explicitly stated in provided texts. Research Interests: Collaborative robotics and human-robot interaction Computer-aided design (CAD) and topology optimization Additive manufacturing processes and sustainability Automotive component design and lightweighting Robotics in medical applications (e.g., surgical navigation) Manufacturing process integration and automation Teaching: Smart Robotics (Master's in Electronics/Computer Engineering) Formula SAE Vehicle Design (Mechanical Engineering) Integrated Computer-Based Design (Mechanical Engineering) Automotive Computer-Aided Design (Advanced Automotive Engineering) Labs/Teams: IDEALab focuses on intelligent design systems, robotic automation, and advanced manufacturing solutions. Collaborations include industry partners for automotive and medical robotics projects.

• Industrial Automation
• Mechatronics
• 3D Printing Technologies
• +۴ مورد دیگر

Marco Silvestri is an Associate Professor at the University of Parma's Department of Industrial Systems and Technologies, where he has worked since 2002. He also serves as a Docent Researcher at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI) since 2011. Co-founder and CTO of M3datek (2016–2020) Head of Automated Production Systems Lab at University of Parma Principal Investigator for competitive projects with Innosuisse, EU FP7, and regional funding Research Focus : Industrial automation, mechatronics, and predictive maintenance with applications in food industry machinery and medical device manufacturing. His work spans robotics, B-spline trajectory optimization, and vibration control of flexible structures. Award Highlights : Best Paper at 32nd DAAM Symposium (2021) Horizon 2020 Seals of Excellence (2017, 2018) Marzotto Prize & Start Cup Emilia-Romagna (2016) Academic Leadership : Member of the PhD School in Industrial Engineering board, scientific committee for Area 09 (Industrial and Information Engineering), and technical committees for industry-focused postgraduate programs.

• Mathematical Methods in Signal Processing
• Data Science
• Dynamical Systems
• +۵ مورد دیگر

Nada Sissouno is a Professor of Mathematics and Didactics of Mathematics at the Faculty of Electrical Engineering, Media and Informatics at Amberg-Weiden University of Applied Sciences since November 2023. She also serves as Vice Dean and Co-head of the Competence Center Grundlagen (CCG). Additionally, she maintains a position as a guest researcher at the Research Group: Applied and Numerical Analysis and Optimization and Data Analysis at the Technical University of Munich (TUM). Her educational background includes a Doctorate in Mathematics (Dr. rer. nat.) from TU Darmstadt (2007-2011) and a Diplom in Mathematics with a minor in psychology from TU Darmstadt (2000-2007). She has completed further education as a Diversity Manager in 2021 and holds certificates in teaching in higher education from the Bavarian Universities (2014-2016). Professor Sissouno's research focuses on mathematical methods in signal and image processing, data science, dynamical systems, numerical simulation, and approximation theory. Her work particularly emphasizes spline functions on domains, wavelets and frames, and evidence-based development of teaching methodologies. Her recent publications demonstrate strong expertise in mathematical imaging, phase retrieval problems, and approximation theory, with applications spanning ptychographic imaging, variational inpainting methods, and structural sparsity in multiple measurements. Her collaborative research bridges theoretical mathematics with practical applications in signal processing and image analysis, with a particular focus on developing robust numerical algorithms for complex data analysis problems. She has published in prestigious journals including Advances in Computational Mathematics, Journal of Fourier Analysis and Applications, IEEE Transactions on Signal Processing, and Inverse Problems. Referentin für Talentmanagement & Diversity at TUM (2022-2023) Deputy spokesperson of Research Associates' Council of the TUM (2019-2023) Gender equality officer of Department of Mathematics (2019-2022) Professor Sissouno teaches mathematics courses for engineering and computer science students, with a focus on making mathematical concepts accessible and relevant to practical applications. She has been involved in teacher training and the evidence-based development of teaching methodologies, demonstrating her commitment to both research excellence and educational innovation.

• Numerics of Partial Differential Equations (PDEs)
• Finite Element Methods (FEM)
• Boundary Element Methods (BEM)
• +۳ مورد دیگر

Dirk Praetorius is a Professor of Numerics of Partial Differential Equations at the Vienna University of Technology (TU Wien), within the Institute for Analysis and Scientific Computing, part of the Faculty of Mathematics and Geoinformation. He leads research in numerical methods for PDEs, with a focus on Finite Element Methods (FEM), Boundary Element Methods (BEM), and computational micromagnetics. His work emphasizes adaptive algorithms, a-posteriori error estimation, and efficient numerical solvers. Praetorius has received awards such as the TU Best Teacher Award (2021) and TU Best Lecture Award (2019). He is also a Senior Editor of Computational Methods in Applied Mathematics and serves on the editorial board of Applied Numerical Mathematics . His research interests span numerical analysis, including adaptive methods for PDEs, matrix compression techniques (e.g., hierarchical matrices), and software development for computational science. Notable contributions include the development of open-source software like MooAFEM (adaptive FEM in MATLAB), Commics (micromagnetic simulations), and HILBERT (BEM library). These tools address challenges in adaptive mesh refinement, iterative solvers, and large-scale scientific computing. Praetorius has supervised numerous PhD students and postdocs, contributing to impactful publications in journals such as Numerische Mathematik , SIAM Journal on Numerical Analysis , and Computer Physics Communications . His work bridges theoretical analysis and practical applications, with a focus on achieving optimal computational efficiency and rigorous error control. In addition to research, Praetorius has led initiatives like TUForMath to foster academic engagement and has been a key contributor to collaborative projects in computational electromagnetics and materials science.