جست‌وجوی استاد و پژوهشگر

Costas Smaragdakisمشاهده پروفایل

استادیار

• Numerical Analysis
• Scientific Computing
• Mathematical Modelling
• +۹ مورد دیگر

Costas Smaragdakis is an Assistant Professor in Numerical Analysis and Scientific Computing at the Department of Statistics and Actuarial - Financial Mathematics, University of the Aegean. He is also a Member of the Institute of Applied and Computational Mathematics (IACM) at FORTH. His work bridges numerical methods, machine learning, and applied mathematics, with a focus on solving complex problems in finance and oceanography. Research Interests : Numerical Analysis Scientific Computing Mathematical Modelling Deep Learning Machine Learning Applications to PDEs/PIDEs Recent Research Trends : His articles highlight a strong focus on integrating deep learning with traditional numerical methods for functional minimization, PDE/PIDE solutions, and financial applications. Earlier work emphasizes acoustic signal processing, inverse problems in oceanography, and wavelet-based analysis. Events : Organized a mini-symposium on Machine Learning Methods in Finance (ICCF24, Amsterdam) and attended international workshops in Canada and Greece. Contact : kesmarag@aegean.gr , kesmarag@iacm.forth.gr , Office A5, Vourlioti Building, Karlovassi, Samos, Greece.

Alwell Julius Oyetمشاهده پروفایل

استاد

• Longitudinal Data Analysis
• Spatial Data Analysis
• Time Series Analysis
• +۳ مورد دیگر

Alwell Julius Oyet is a Professor of Statistics in the Department of Mathematics and Statistics at Memorial University of Newfoundland. He earned his B.Sc. (1986) and M.Sc. (1992) from the Federal University of Technology, Owerri, Nigeria, and his Ph.D. (1997) from the University of Alberta, Canada. His research focuses on longitudinal data analysis, spatial statistics, time series, statistical computing, multinomial data, and robust methods. He maintains active research collaborations in these areas. Oyet teaches undergraduate and graduate courses including Design of Experiments, Time Series Analysis, Multivariate Analysis, and Computational Statistics. He has extensive experience developing course materials and statistical computing resources.

Nicolas Rodrigueمشاهده پروفایل

دانشیار

• Molecular Evolution
• Bayesian Statistics
• Computational Biology
• +۳ مورد دیگر

Nicolas Rodrigue is an Associate Professor in the Department of Biology at Carleton University, with a cross appointment in Biology. He holds degrees from Bishop's University (BA), McGill University (BSc), École de technologie supérieure (MSc), and Université de Montréal (PhD). His research focuses on Bayesian modeling of molecular evolution, particularly protein-coding genes and evolutionary forces like mutation, selection, and drift. He also explores Markov chain Monte Carlo methods, protein structure modeling, and next-generation sequencing for viral/microbial evolution studies. Education: BA, Bishop's University BSc, McGill University MSc, École de technologie supérieure PhD, Université de Montréal Research Interests: Bayesian phylogenetic models Mutation-selection dynamics Protein sequence and structural evolution High-performance statistical computing Microbial genomics and clinical evolution Recent publications highlight advancements in phylogenetic modeling, adaptive evolution detection, and computational methods for analyzing molecular data. His work integrates statistical frameworks with evolutionary biology to address complex questions about genomic adaptation and sequence evolution. Awards: None explicitly listed. Advising/Grants: No student advising or grant details provided in the text. Lab/Teams: No specific lab or team affiliations mentioned.

Ricardo Melo Czeksterمشاهده پروفایل

مدرس

• Cyber Threat Intelligence
• Cyber-Physical Systems
• Risk Assessment
• +۳ مورد دیگر

Ricardo Melo Czekster is a Lecturer in Software Engineering & Cybersecurity at Aston University's School of Computer Science and Digital Technologies. His research develops methods for securing critical infrastructure through formal modeling and threat intelligence. Education: PhD in Performance Evaluation, Pontifical Catholic University of Rio Grande do Sul (2010) MSc in Analytical and Environmental Chemistry, University of Benin (2009) BSc in Industrial Chemistry, Ambrose Alli University (2004) Research Focus: Czekster specializes in cybersecurity for cyber-physical systems, particularly smart grids and active buildings. His work develops STIX-based threat intelligence frameworks, attack path modeling with BDMPathfinder, and dynamic risk assessment methods. Recent projects address security in medical IoT and transportation systems. Teaching: Leads modules on Secure Programming, Security Governance, and Secure Software Delivery.

Yongli Sangمشاهده پروفایل

دانشیار

• Statistics
• High-dimensional Inference
• Correlated Data Analysis
• +۳ مورد دیگر

Yongli Sang is an Associate Professor of Statistics in the Department of Mathematics at the University of Louisiana at Lafayette. She earned her Ph.D. in Statistics (2017) and M.S. in Statistics (2014) from the University of Mississippi, and holds a M.S. (2012) and B.S. (2009) in Mathematics from institutions in China. Ph.D. in Statistics, University of Mississippi (2017) M.S. in Statistics, University of Mississippi (2014) M.S. in Mathematics, Central China Normal University (2012) B.S. in Mathematics, Shandong Normal University (2009) Her research focuses on advanced statistical methodologies for high-dimensional data, time series analysis, nonparametric statistics, and robust statistical techniques for correlated data. She has developed innovative jackknife empirical likelihood methods for testing diagonal symmetry, homogeneity of variances, and K-sample problems with applications in diverse fields. Recent publications highlight her work on Gini correlations in high-dimensional settings, computational efficiency, and confidence interval estimation. She also explores transformations of linear processes and their memory properties, bridging theoretical statistics with applied data analysis challenges. At the University of Louisiana at Lafayette, she supervises Ph.D. student Sameera Hewage and teaches graduate courses including Mathematical Statistics, Regression Analysis, and Biometry.

Keith Freelandمشاهده پروفایل

دانشیار

• Mathematical Finance
• Financial Risk Management
• Equity-Linked Insurance Products
• +۳ مورد دیگر

Keith Freeland is an Associate Professor (Teaching Stream) at the University of Waterloo's Department of Statistics and Actuarial Science, and Director of the Business Administration and Mathematics Double Degree Program. He holds a PhD in Business Administration from the University of British Columbia and a BSc in Actuarial Science from the University of Calgary. His career includes roles as an assistant professor at Waterloo from 2000–2010. Research interests historically focused on equity-linked insurance products and discrete-valued time series analysis, though he is no longer actively researching. He teaches courses like MATBUS 470 (Derivatives) and MATBUS 472 (Risk Management), emphasizing mathematical finance and financial risk management. Notable publications include work on categorical ARMA models, quasi-locally powerful tests for conditional variance, and integer-valued time series analysis. He holds the ASA designation from the Society of Actuaries (1991). Freeland's contributions bridge actuarial science, statistics, and financial mathematics, with teaching and past research centered on practical applications in risk management and insurance product modeling.

Goran Peskirمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Optimal Stopping
• +۴ مورد دیگر

Professor Goran Peskir holds the Chair in Probability at the School of Mathematics, The University of Manchester. His research focuses on stochastic calculus, optimal stopping, and free boundary problems with applications in financial mathematics and economics. He completed his DrScient, PhD, MSc, and BSc qualifications, and his work bridges theoretical probability with practical applications in finance and stochastic processes. Research interests include Brownian motion, Markov processes, stochastic control, and the mathematical foundations of financial models. His recent publications emphasize real-time detection of drifts in stochastic processes and optimal stopping theory. Over 18 supervised works reflect his mentorship in advanced probability topics. His research has been published in leading journals such as Annals of Applied Probability and SIAM Journal on Control and Optimization . No scientific awards are explicitly listed, but his contributions to probability theory are recognized through his extensive publication record and editorial roles. He is affiliated with research groups in Industrial and Applied Mathematics, Mathematical Finance, and Probability and Stochastic Analysis.

• Complex Systems
• Applied Mathematics
• Cognitive Sciences
• +۴ مورد دیگر

Hardik Rajpal is a Researcher in the Mathematics Department at Imperial College London, affiliated with the Faculty of Natural Sciences. His work bridges applied mathematics, cognitive sciences, and complex systems. He completed his PhD at Imperial's Center for Complexity Science, focusing on modelling social, ecological, and brain dynamics using statistical mechanics and information theory. His research explores emergent phenomena in systems ranging from neuroscience to music performance. Education: Bachelor's in Physics at Indian Institute of Technology Kharagpur, India PhD in Complexity Science at Imperial College London Research Interests: His work centers on information-theoretic tools for analyzing brain complexity, social systems, and creative processes. Notable areas include: Neuroscience: Consciousness disorders and psychedelic effects Social Dynamics: Opinion formation and network nodality Music Science: Improvisation's impact on performer-audience synchrony Mathematical Modelling: High-order information in complex systems His recent publications (2020-2025) focus on: Interdisciplinary studies of music performance and biophysics Network analysis of political discourse on social media Machine learning applications in medical diagnostics Affiliations include the Center for Complexity Science and Mathematics Research Group at Imperial College.

Frank Woodمشاهده پروفایل

دانشیار

• Probabilistic Programming
• Deep Generative Models
• Reinforcement Learning
• +۴ مورد دیگر

Frank Wood is an Associate Professor of Computer Science at the University of British Columbia and a Canada CIFAR AI Chair at AMII. He directs the Pacific Laboratory for Artificial Intelligence (PLAI) research group and co-founded Inverted AI, a spin-out focused on advanced simulation for autonomous vehicles. His research focuses on probabilistic programming, deep generative models, and reinforcement learning, with applications in autonomous driving, robotics, and vision. Wood teaches courses such as Machine Learning and Data Mining (CPSC 340) and Topics in Artificial Intelligence (CPSC 532W). He has been on academic leave from January to December 2025. His work integrates theoretical advancements with practical applications, including projects like PLAICraft, a large-scale embodied AI dataset. Key awards include the ICML Best Paper Honourable Mention and contributions to probabilistic programming systems like Anglican. His research group collaborates on tools such as TorchDriveEnv for autonomous driving benchmarking and explores cutting-edge methods in diffusion models and continual learning.

• Statistical Inference
• Machine Learning
• Bioinformatics
• +۳ مورد دیگر

Lucy Gao is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC). Her research focuses on statistical inference, machine learning, and bioinformatics, with particular emphasis on clustering methods, high-dimensional data analysis, and optimal design theory. She holds a faculty position within UBC’s Faculty of Science and is affiliated with the Vancouver Campus. Her work spans theoretical developments in statistical methodologies and applications to biological data, such as single-cell RNA sequencing analysis. She has contributed to areas like selective inference for hierarchical clustering and algorithmic optimization for complex systems. Her email is lucy.gao@stat.ubc.ca, and she maintains a website at https://www.lucylgao.com/ . Recent publications highlight her expertise in data thinning techniques, latent variable modeling, and multiview network analysis. She has explored topics ranging from negative binomial count splitting in genomics to bilevel optimization algorithms. Her research bridges statistical theory and practical computational challenges in modern data science. No scientific awards or advising records are explicitly listed in the provided materials. Her academic contributions are primarily through peer-reviewed articles and methodological innovations.

• Dependence Modeling
• Copula Models
• Multivariate Analysis
• +۷ مورد دیگر

Harry Joe is a Professor in the Department of Statistics at the University of British Columbia (Vancouver Campus). His primary research focuses on dependence modeling, copula theory, multivariate analysis, and applications in biostatistics, finance, and psychometrics. He has advised students including Xiaoting Li, Xinyao Fan, and Pavel Krupskiy. Research Interests: - Advanced copula constructions (e.g., vine copulas) - Extreme value theory and tail dependence - Applications in financial risk, biomedical research, and educational measurement - Multivariate time series analysis and non-Gaussian models Publications highlight contributions to copula-based classification methods (2024), factor copula models (2015), and dynamic dependence modeling (2020). His work bridges theoretical developments with practical applications across disciplines. Teaching and advising emphasize methodological innovation. Current research explores high-dimensional dependence structures and computational methods for complex data. No lab/team affiliations explicitly noted in provided materials.

Philip B. Starkمشاهده پروفایل

استاد

• Uncertainty Quantification
• Election Auditing
• Earthquake Prediction
• +۷ مورد دیگر

Philip B. Stark is a Distinguished Professor of Statistics at the University of California, Berkeley, where he holds a faculty position in the Department of Statistics. His research focuses on uncertainty quantification, election integrity, earthquake prediction, and applications of statistics in physical and social sciences. He has developed auditing methods used in over 15 U.S. states, and his work on the U.S. Census adjustment and election security has influenced policy and legal proceedings. Stark’s research spans computational statistics, risk-limiting audits, and environmental science, with notable contributions to soil carbon sequestration and food equity. He co-created SticiGui, an online introductory statistics course that pioneered MOOCs at UC Berkeley, reaching over 50,000 students. His consulting expertise spans litigation, clinical trials, and election integrity, with roles on advisory boards like the Election Assistance Commission and OSET Institute. Key research themes include statistical methodology for complex systems, election auditing innovations, and interdisciplinary applications in law, geophysics, and nutrition. His work emphasizes rigorous statistical methods to address societal challenges, from ensuring voting integrity to advancing sustainable food systems.

Lucia Bandieraمشاهده پروفایل

پژوهشگر ارشد

• Systems Biology
• Synthetic Biology
• Bioengineering
• +۳ مورد دیگر

Dr. Lucia Bandiera is a Chancellor’s Fellow in Engineering Biology at the University of Edinburgh’s School of Engineering. Her research focuses on cybergenetic platforms to model and control biological systems, with applications in precision medicine and synthetic biology. She holds a PhD in Bioengineering from the University of Bologna (2013–2016) and has held postdoctoral positions at the University of Edinburgh’s Centre for Synthetic and Systems Biology. Key roles include Deputy Head of Graduate School (Postgraduate Experience) and Deputy Biological Safety Officer. Her academic qualifications include a BSc and MSc in Biomedical Engineering from the University of Bologna (2007–2012). She is an Associate Fellow of the Higher Education Academy (AFHEA, 2020) and has received prestigious funding such as the EPSRC Postdoctoral Fellowship (2017) and a tenure-track fellowship (2021). Her teaching includes courses on Lab-on-Chip Technologies and Engineering Principles. Research interests include mathematical modeling, optimal experimental design, and microfluidics. She leads projects like CONDSYC (EPSRC-funded), focusing on synthetic biology systems design. Her work spans biomolecular systems, cyber-physical platforms, and applications in liver disease and pandemic mitigation. Notable achievements include organizing the Institute for Bioengineering’s seminar series and contributing to datasets like Face coverings and respiratory tract droplet dispersion . Her lab develops tools for quantitative analysis of biological networks and precision medicine.

Karun Adusumilliمشاهده پروفایل

استادیار

• Econometrics
• Causal Inference
• Missing Data
• +۶ مورد دیگر

Karun Adusumilli is an Assistant Professor of Economics at the University of Pennsylvania, affiliated with the Department of Economics within the School of Arts and Sciences. He holds a PhD from the London School of Economics (2018), an MSc in Econometrics and Mathematical Economics (2013), and a B.Tech in Engineering Physics from the Indian Institute of Technology, Delhi (2011). His research focuses on advanced econometric methodologies, including causal inference, missing data analysis, dynamic treatment allocation, and reinforcement learning applications in economics. Education: PhD in Economics, LSE (2018) MSc Econometrics and Mathematical Economics, LSE (2013) B.Tech in Engineering Physics, IIT Delhi (2011) Adusumilli’s research interests span Econometrics , Causal Inference , Dynamic Treatment Regimes , and Statistical Learning . He has pioneered work on optimal policy design in bandit experiments, minimax allocation strategies, and addressing unobserved heterogeneity in econometric models. His contributions bridge theoretical econometrics with practical applications in healthcare, education policy, and sequential decision-making frameworks. He has received notable awards, including the Kravis Award for Teaching Excellence (2021) and the John Hicks Prize for his doctoral research (2019). His publications appear in top-tier journals like Econometrica and Review of Economic Studies . Adusumilli teaches graduate and undergraduate courses in econometrics and statistical learning, including Econometrics I and Econometric Machine Learning . He has served on scientific committees for major conferences such as the NBER-NSF Time Series Conference and the AEA-ASSA Annual Meetings .

Andrew B. Nobelمشاهده پروفایل

استاد

• Statistical Genomics
• Machine Learning
• Network Analysis
• +۳ مورد دیگر

Andrew B. Nobel is the Robert Paul Ziff Distinguished Professor of Statistics and Operations Research and Professor of Biostatistics at the University of North Carolina at Chapel Hill (UNC). He holds affiliations with the Lineberger Comprehensive Cancer Center and the Computational Genomics Program. His research focuses on statistical genomics, machine learning, network analysis, and inference from dynamical systems. Nobel has collaborated extensively with researchers in Biology, Computer Science, Genetics, and Mathematics. Dr. Nobel earned his PhD in Electrical Engineering from Stanford University (1992), an MS from Stanford (1988), a Certificate of Study in Mathematics from Cambridge University (1986), and a BS in Electrical Engineering from Cornell University (1985). He has taught courses ranging from undergraduate discrete mathematics to advanced graduate-level theoretical statistics and machine learning. His research interests include developing methodologies for analyzing complex biological and network data, with applications to cancer genomics, systems biology, and medical informatics. Recent work emphasizes optimal transport methods for network analysis and statistical approaches for genomic data integration. Awards: Elected Fellow of the Institute of Mathematical Statistics (2008) National Science Foundation CAREER Grant (1995) Beckman Institute Fellow (1992–1995) Churchill Scholar (1985–1986) Dr. Nobel serves on the editorial board of the Journal of the Royal Statistical Society, Series B and has previously contributed to the Annals of Statistics and IEEE Transactions on Information Theory . His work bridges theoretical statistical foundations with practical applications in public health and biomedical research.