جست‌وجوی استاد و پژوهشگر

Anqi Liuمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Distributionally Robust Learning
• +۵ مورد دیگر

Anqi Liu (Angie) is an Assistant Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University. She is affiliated with the Data Science and AI Institute, Mathematical Institute for Data Science (MINDS), and Institute for Assured Autonomy (IAA). Her research focuses on developing principled machine learning algorithms for reliable, trustworthy, and human-compatible AI systems in high-stakes applications. PhD in Computer Science from University of Illinois Chicago Postdoctoral Research at Caltech's Department of Computing and Mathematical Sciences Her work emphasizes robustness to changing data environments, uncertainty quantification, and human-AI interaction. Key methodologies include distributionally robust learning, active learning, safe exploration, fair machine learning, and conformal prediction. Applications span healthcare (NIA/NIH-funded), robotics, and computational social science. Amazon Research Award Johns Hopkins + Amazon Initiative for AI Faculty Research Johns Hopkins Discovery Award Institute for Assured Autonomy Challenge Grant She advises PhD candidates in AI safety and fairness, with students co-advised by faculty in Human-Robot Interaction and Computational Linguistics. Collaborations include Center for Language and Speech Processing (CLSP) and Laboratory for Computational Sensing and Robotics (LCSR).

Alejandro Schulerمشاهده پروفایل

استادیار

• Causal Inference
• Biostatistics
• Machine Learning
• +۳ مورد دیگر

Alejandro Schuler is an Assistant Professor at the University of California, Berkeley, specializing in causal inference, biostatistics, and machine learning. He earned his PhD in Biomedical Informatics from Stanford University (2018) and completed a postdoctoral fellowship under Mark van der Laan. His industry experience at Kaiser Permanente’s Division of Research and a health tech startup informs his applied research agenda focused on bridging mathematical formalisms with real-world healthcare challenges. PhD – Biomedical Informatics, Stanford University, 2018 MS – Mechanical Engineering, University of California, Los Angeles, 2013 BS – Mechanical Engineering, University of California, Berkeley, 2012 His research centers on developing statistical methods for causal inference, including the selectively adaptive lasso, prognostic adjustment, and NGBoost. Recent work emphasizes power calculation in randomized trials, robust neural network frameworks, and stochastic intervention models for healthcare and environmental exposure analysis. His publications highlight advancements in targeted maximum likelihood estimation, Riesz regression, and efficient covariate adjustment. These methods aim to improve the accuracy and interpretability of causal effect estimates in complex datasets. Dr. Schuler collaborates with domain experts to translate clinical and public health questions into mathematical models, ensuring his work maintains relevance beyond academia. He is actively involved in methodological development and application to healthcare data, focusing on reducing hospital readmissions and optimizing trial design.

Nenad Šuvakمشاهده پروفایل

دانشیار

• Stochastic Processes
• Diffusion Models
• Statistical Inference for Stochastic Processes
• +۴ مورد دیگر

Nenad Šuvak is an Associate Professor at the School of Applied Mathematics and Informatics, Josip Juraj Strossmayer University of Osijek. His research focuses on diffusion processes and statistical analysis of stochastic systems, with applications spanning finance, epidemiology, neuroscience, and agricultural modeling. Education : PhD in Mathematics (University of Zagreb, 2010), BSc in Mathematics and Computer Science (University of Osijek, 2004) Šuvak's work extends classical stochastic models to heavy-tailed and fractional frameworks. He has developed diffusion models for EEG data in cerebral malaria patients, created non-linear stochastic models for dopamine cycles, and refined epidemic SEIR models for SARS-CoV-2. His methodological contributions include correlated continuous-time random walks and parameter estimation techniques for non-stationary diffusions. His publications demonstrate expertise in stochastic processes with heavy-tailed distributions, fractional calculus applications, and statistical inference for complex systems. Notably, he has worked on financial risk assessment (CROBEX modeling), epidemiological modeling (SARS-CoV-2 spread), and biomedical applications (EEG analysis). Professional Activities : Organizer of mathematical conferences, member of program committees (Young Statisticians Meetings), and reviewer for multiple scientific journals Projects : PI for stochastic models projects, participant in EU COST actions, and international bilateral collaborations

David Ginsbourgerمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Uncertainty Quantification
• +۷ مورد دیگر

David Ginsbourger is a Professor and Head of Research Group at the Institute of Mathematical Statistics and Actuarial Science (IMSV) within the University of Bern, Switzerland. He maintains dual affiliations through his role at IMSV and as a member of the Multidisciplinary Center for Infectious Diseases (MCID), reflecting interdisciplinary engagement across statistical methodology and applied domains. His research program centers on advanced statistical methodologies with emphases on Gaussian process modeling, uncertainty quantification, and experimental design for computer experiments. Key contributions include novel kernel constructions for equivariant systems, sequential design strategies for excursion set estimation, and efficient computational frameworks for spatial distributional modeling. His work bridges theoretical statistics with practical applications in agriculture, chemoinformatics, environmental science, and risk assessment, demonstrating consistent innovation in handling complex prediction problems under uncertainty. Analysis of his 15 most recent publications (2024-2025) reveals persistent methodological development in Gaussian process theory alongside expanding application domains. Recurring themes include integration-free kernel design for structured data, rare event probability estimation, and multivariate forecast calibration. His research exhibits strong continuity in addressing computational challenges for large-scale inverse problems while increasingly incorporating domain-specific constraints from fields like molecular chemistry and agricultural science. Ginsbourger leads a dedicated research group at IMSV focused on advancing statistical frameworks for computer experiments and uncertainty quantification. The group maintains active collaborations across disciplines, particularly evident in recent work connecting statistical methodology to infectious disease modeling through MCID affiliations and agricultural optimization projects.

Toni Karvonenمشاهده پروفایل

استادیار

• Statistics and Probability
• Applied Mathematics
• Numerical Analysis
• +۱۰ مورد دیگر

Toni Karvonen is an Assistant Professor of Applied Mathematics at LUT University's Computational Engineering Department, School of Engineering Sciences, since September 2024. His research focuses on statistics, applied mathematics, and computational methods with emphasis on Gaussian processes, uncertainty quantification, and kernel-based approximation. He has held postdoctoral positions at the Alan Turing Institute (2020–21) and the University of Helsinki (2021–24). Research Areas: Statistics and Probability, Applied Mathematics, Numerical Analysis, Gaussian Processes, Positive Definite Kernels Key Trends: His recent work explores probabilistic numerics, Bayesian quadrature, kernel methods for uncertainty quantification, and approximation theory in reproducing kernel Hilbert spaces. Email: Toni.Karvonen@lut.fi

Michael Smithمشاهده پروفایل

پژوهشگر ارشد

• Gaussian Processes
• Air pollution
• Differential Privacy
• +۷ مورد دیگر

Dr Michael Smith is a Research Fellow at the University of Sheffield , affiliated with the Machine Learning research group within the School of Computer Science . His research bridges Gaussian Processes , Differential Privacy , and International Development , with a focus on Air Pollution Modeling in Kampala and Bumblebee Tracking using retroreflective tags. Education: PhD in computational neuroscience from the University of Edinburgh (2010-2014) Previous Affiliation: Lecturer at Makerere University in Kampala (2014) His work includes probabilistic sensor calibration for air pollution monitoring and adversarial attack analysis in Gaussian Process models. Current projects involve low-cost environmental sensor networks and mobile unit calibration systems for policy-driven predictions. Awarded grants include the BEE-TRACK project (Eva Crane Trust, 2025-2027) and the Pollinator Behavior AI grant (BBSRC, 2024-2025). Smith leads outreach initiatives like 20's Plenty Sheffield , advocating for urban speed limit reforms to reduce road collisions and air pollution.

Shihao Yangمشاهده پروفایل

استادیار

• Statistics
• Machine Learning
• Data Science
• +۵ مورد دیگر

Shihao Yang serves as a Harold E. Smalley Early Career Professor and tenure-track Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. He maintains critical affiliations with the Institute for Data Engineering and Science (IDEaS), Center for Machine Learning at Georgia Tech (ML@GT), and Institute of People and Technology (IPaT). His academic credentials include: Ph.D. in Statistics (2019), Harvard University A.M. in Statistics (2016), Harvard University B.Sc. in Actuarial Science (2014), University of Hong Kong Professor Yang's research program centers on harnessing big data through methodological development, computational tools, and probabilistic modeling to solve real-world problems. His expertise spans statistics, machine learning, and data science with concentrated applications in infectious disease modeling, dynamic system inference (parameter estimation in differential equations), and electronic medical records mining. His work consistently bridges theoretical innovation with practical healthcare implementations. Analysis of his recent publications reveals dominant themes in time series forecasting, causal inference, and physics-informed machine learning. His research integrates deep learning with statistical methods to enhance dynamic system analysis and healthcare applications, particularly in electronic health records interpretation and infectious disease prediction using internet search data. This synthesis drives methodological advances in handling noisy, sparse real-world datasets. His notable recognition includes: Harold E. Smalley Early Career Professorship Professor Yang actively mentors undergraduate, master's, and PhD students, encouraging prospective candidates to contact him with CVs and transcripts. His research receives substantial institutional support through Georgia Tech's interdisciplinary centers. He leads methodological development for real-world data challenges while maintaining strong industry and healthcare partnerships. As a core member of IDEaS, ML@GT, and IPaT, he drives cross-disciplinary collaboration between engineering, computing, and social sciences to advance data-driven solutions for complex societal problems.

Sarat Mokaمشاهده پروفایل

مدرس

• Probability Theory
• Data Science
• Statistics
• +۲ مورد دیگر

Sarat Moka is a Lecturer in the School of Mathematics & Statistics at The University of New South Wales and holds an Honorary Lecturer position at Macquarie University. His research spans Probability Theory, Data Science, Statistics, Deep Learning, and Monte Carlo Simulation. Education: PhD (Tata Institute of Fundamental Research, 2017), Master of Engineering (Indian Institute of Science, 2008), Bachelor of Engineering (Andhra University, 2006) Previous Roles: Research Fellow at Macquarie University (2021–2023), ACEMS Postdoctoral Research Fellow at The University of Queensland (2017–2021), Scientist at Indian Space Research Organization (2008–2010) His research integrates theoretical and applied approaches, focusing on rare event simulation, optimization algorithms, and statistical modeling. Recent publications emphasize continuous optimization for machine learning tasks (e.g., best subset selection, change point detection) and Monte Carlo methods. Scientific awards include the prestigious ACEMS Postdoctoral Research Fellowship. His collaborative work involves institutions like UNSW, Macquarie University, and The University of Queensland.

Thierry Kleinمشاهده پروفایل

استاد

• Statistics
• Sensitivity analysis
• Machine learning
• +۷ مورد دیگر

Thierry Klein is a Professor of Statistics at École Nationale de l'Aviation Civile (ENAC) and a member of the Toulouse Institute of Mathematics. His research spans statistical methodology with applications in diverse fields including aviation safety, environmental science, and computational mathematics. Dr. Klein's research focuses on advanced statistical methods, particularly in sensitivity analysis, probability theory, and machine learning. His work on Sobol indices has contributed significantly to variance-based sensitivity analysis, while his research on Wasserstein spaces has advanced the understanding of probability distributions in metric spaces. He has developed innovative methods for Gaussian process regression and has applied statistical techniques to problems in aviation safety, coastal flooding prediction, and paleoenvironmental reconstruction. His recent publications demonstrate a strong focus on sensitivity analysis methods, particularly Sobol indices, with applications across multiple domains. He has also made significant contributions to the theory of Wasserstein spaces and their applications in statistical learning. His work bridges theoretical statistics with practical applications in engineering, environmental science, and aviation. Dr. Klein is currently involved in three major funded projects: the PEPR PDE-AI project (2023-2028) on Partial Differential Equations for Artificial Intelligence, the ANR GATSBII project (2025-2029) as project leader, and the ANR MBAP-P project (2025-2029) as a member. These projects reflect his interdisciplinary approach, combining statistical theory with applications in artificial intelligence, engineering, and environmental science.

Krzysztof Podgórskiمشاهده پروفایل

استاد

• Stochastic Processes
• Random Spatio-temporal Fields
• Non-Gaussian Stochastic Models
• +۷ مورد دیگر

Krzysztof Podgórski is a Professor and Head of Department at the Department of Statistics, Lund University. He maintains an active research profile with ongoing projects and recent publications extending into 2025. His research focuses on three main areas: theory and applications of multivariate non-Gaussian stochastic models, statistical analysis of spatial random fields evolving in time with non-trivial dynamics, and statistical distributions at random crossing events. He has made significant contributions to ergodic theory of stochastic processes, bootstrap methods for regression analysis, theory of heavy-tailed distributions, and extreme value theory. Analysis of his recent publications reveals a strong emphasis on spline theory applications to functional data analysis alongside continued development of non-Gaussian distributions for engineering applications. His work consistently bridges theoretical statistical developments with practical implementations across diverse fields. Professor Podgórski supervises multiple PhD students and research projects, including a Swedish Research Council-funded project running through 2025. His research has practical applications in mechanical engineering, ocean engineering, spatial econometrics, actuarial sciences, and mathematical finance. He maintains an active presence in the academic community through conference organization, invited talks, and journal peer review activities, with recent engagements documented through late 2024.

Norma Bargaryمشاهده پروفایل

استاد

• Functional Data Analysis
• Statistical Modeling of High-Dimensional Data
• Biostatistics
• +۶ مورد دیگر

Norma Bargary is a Professor in the Department of Mathematics and Statistics at the University of Limerick. She holds multiple leadership roles in national research centers, including Principal Investigator at the Confirm Centre for Smart Manufacturing, Insight Centre for Data Analytics, SSPC (Research Ireland Centre for Pharmaceuticals), and MACSI (Mathematics Applications Consortium for Science and Industry). She serves as Vice-Director of the SFI Centre for Research Training in Foundations of Data Science and collaborates with institutions like the Royal Statistical Society, where she is a statistical Ambassador based in Ireland. Chair of Data Science and Statistical Learning, University of Limerick (2021→) Senior Lecturer in Statistics, University of Limerick (2020→) Lecturer roles at University of Limerick and University College Dublin (2013–2020) Her research focuses on statistical models for large, high-dimensional, time-varying data (e.g., sensor measurements, human movement, -omics datasets). Applications span biostatistics, biomechanics, pharmaceuticals, and smart manufacturing. She specializes in functional data analysis, mixed-effects smoothing, finite mixture clustering, and Gaussian process modeling. Recent publications highlight trends in industrial analytics (Gaussian process models for manufacturing), health data science (Raman spectroscopy for drug identification), and epidemiological modeling (SEIR models for pandemics). Her methodological work includes functional principal component analysis and nonlinear filtering techniques. Scientific Awards : Advanced Scholar Award, Hamilton Prize, Silver Medal, Best Postgraduate Presentation (Conference for Applied Statistics in Ireland), and functional data analysis recognition. Her €16M+ funded work integrates academia and industry through grants from SFI, Enterprise Ireland, EU programs (FP7, H2020), and corporate partnerships. She leads the SFI Frontiers of the Future grant and contributes to UN Sustainable Development Goals via data science education.

Tibor Kmeťمشاهده پروفایل

استاد

• Applied Informatics
• Neural Networks
• Mathematical Modeling
• +۵ مورد دیگر

Professor Tibor Kmeť is a distinguished academic at J. Selye University's Faculty of Economics and Informatics, where he serves as a Professor in the Department of Informatics. He has been affiliated with the university since 2006 and holds significant responsibilities as the person responsible for multiple study programs including Teaching of Informatics, Applied Informatics, and Theory of Teaching Mathematics and Informatics. Dr. Kmeť completed his higher education at Comenius University in Bratislava, where he earned his degree in Mathematics from the Faculty of Science (1975-1980), followed by rigorous examinations (1981), doctoral studies (1984-1987), and habilitation (1996), all in Mathematics. In 2006, he completed his professorial inauguration at Constantine the Philosopher University in Nitra in the Theory of Mathematics Education. His research spans multiple domains at the intersection of mathematics, computer science, and education. Professor Kmeť has made significant contributions to neural network applications in solving complex mathematical problems, particularly in optimal control theory and epidemiological modeling. His work on applying computational methods to educational contexts, especially in mathematics education, demonstrates his commitment to bridging theoretical research with practical applications. He has also conducted important research in geometry education, developing methods for visual explanation in dynamic geometry environments. An analysis of his recent publications reveals a consistent focus on applying neural networks and computational methods to solve complex mathematical problems. His research trajectory shows progression from foundational work on neural network solutions for optimal control problems to more applied work in epidemiological modeling and educational technology. The interdisciplinary nature of his work is evident in publications spanning computer science, mathematics, epidemiology, and education journals. Professor Kmeť has successfully led multiple research projects including 'Digital Smart Household' (2019-2020), 'Creation of 3D Model Based on Drone Photographs Using Photometric Method' (2019-2021), and 'Modeling Stock Market Rates Using Neural Networks' (2020-2021), demonstrating his ability to secure research funding and lead projects with practical applications. His academic career includes previous positions at Constantine the Philosopher University in Nitra (1992-2017) and the Institute of Experimental Biology and Ecology SAS Bratislava (1980-1992), where he served as an independent scientific worker and researcher. This diverse background has informed his interdisciplinary research approach that combines mathematical rigor with practical applications in various domains.

Claus Thorn Ekstrømمشاهده پروفایل

استاد

• biostatistics
• statistics
• bioinformatics
• +۵ مورد دیگر

Claus Thorn Ekstrøm is a Professor at the University of Copenhagen , affiliated with the Faculty of Health and Medical Sciences and the Section of Biostatistics . His work integrates advanced statistical methods with biomedical and public health research. Contact details include ekstrom@sund.ku.dk and phone numbers +4535327597, +4535327629. The Section of Biostatistics is located at Øster Farimagsgade 5 opg. B, 1014 København K. Research Focus : Bioinformatics, statistical genetics, genetic epidemiology Teaching : Biostatistics, statistics, and bioinformatic methods Ekstrøm’s recent research trends emphasize causal inference , longitudinal data modeling , and integrated analysis of genetic and metabolic data . His publications span topics like biomarker development in psychiatric disorders , statistical frameworks for complex family data , and computational efficiency in Gaussian process regression , reflecting interdisciplinary applications in public health, genetics, and epidemiology.

• Control Systems
• Signal Processing
• Sensor Fusion
• +۷ مورد دیگر

Fredrik Gunnarsson is an Adjunct Professor at Linköping University's Department of Electrical Engineering (ISY), specializing in Automatic Control (RT). His research focuses on control systems, signal processing, and sensor fusion, with applications in positioning, navigation, and optimization for autonomous systems. University: Linköping University Department: Department of Electrical Engineering (ISY) Academic Rank: Adjunct Professor Research Interests Fredrik's work spans advanced signal processing, sensor fusion, and control algorithms. He contributes to 5G positioning, Gaussian process state-space models, and indoor localization. His research intersects with optimization-based control for autonomous systems and machine learning techniques in dynamic environments. Article Trends His recent publications emphasize wireless positioning, statistical filtering, and sensor fusion methodologies. Topics include Kalman filtering, Gaussian process modeling, particle methods for indoor navigation, and optimization algorithms for control systems. Keywords span Control Systems , Signal Processing , Machine Learning , and Wireless Communication . Labs and Collaborations LINK-SIC - Linköping Center for Sensor Informatics and Control Automotive Systems research SEDDIT competence center

Xinwei Dengمشاهده پروفایل

استاد

• Statistical Learning
• Uncertainty Quantification
• High-Dimensional Data Analysis
• +۷ مورد دیگر

Xinwei Deng is a Professor of Statistics and Data Science Faculty Fellow at Virginia Tech, affiliated with the Department of Statistics in the College of Science. He also co-directs the VT Statistics and Artificial Intelligence Laboratory (VT-SAIL). He earned his Ph.D. from Georgia Institute of Technology in 2009 under Professors C.F. Jeff Wu and Ming Yuan, and joined Virginia Tech in 2011. Education: PhD in Statistics (Georgia Tech, 2009), B.S. in Mathematics (Nanjing University, China, 2003). Research Interests: Focuses on the interface between experimental design and machine learning, uncertainty quantification, covariance matrix estimation, high-dimensional data analysis, and applications in nanotechnology, manufacturing, and healthcare. His work bridges statistical theory with practical challenges in emerging technologies like tissue engineering and environmental science. Publications: Over 80 peer-reviewed articles in top journals such as Journal of the American Statistical Association , Technometrics , and Proceedings of the National Academy of Sciences . Recent work emphasizes scalable algorithms, AI resilience in manufacturing, and uncertainty quantification in complex systems. Awards/Honors: Includes Data Science Faculty Fellowships, ISI election (2017), and multiple best paper awards. Recognized for contributions to statistical methodologies in quality engineering and AI robustness. Teaching & Advising: Teaches advanced courses like Data Analytics, Experimental Design, and Multivariate Analysis. Mentors students in interdisciplinary projects spanning statistics, computer science, and engineering. Labs/Teams: Leads VT-SAIL, fostering AI-driven statistical solutions for real-world problems. Collaborates with industry partners (e.g., P&G, Deloitte) and government agencies (NSF, AFRL) on applied research.