Find Academic Supervisors

• Survey Statistics
• Small Area Estimation
• Non-probability Sampling
• +3 more

Dr. Andrius Čiginas is a Senior Researcher at the Interdisciplinary Statistical Research Group , part of the Institute of Data Science and Digital Technologies (VU DMSTI) at Vilnius University . His work focuses on advancing survey statistics, small area estimation, and the integration of non-probability samples into official statistics. Research Focus Dr. Čiginas specializes in developing statistical methodologies for improving the accuracy and efficiency of population estimates, especially in small domains. His research includes: Composite estimation techniques Non-probability sample integration Real-time prediction using social media and administrative data Bootstrap and Edgeworth approximations for finite populations Doctoral Supervision He currently supervises two doctoral students: Akvilė Vitkauskaitė – Informatics (2024–2028): Parameter estimation and forecasting in population domains using non-probability samples. Ieva Burakauskaitė – Mathematics (2022–2026): Use of additional information in estimating parameters in population domains. Scientific Contributions Dr. Čiginas has published extensively in top-tier journals such as Journal of Official Statistics , Statistics , and Nonlinear Analysis: Modelling and Control . His recent work explores the integration of non-probability data sources into official statistics, consumer confidence estimation, and small area estimation techniques.

Matthias GrossglauserView profile

Professor

• Machine Learning
• Stochastic Networks
• Discrete Choice Models
• +4 more

Matthias Grossglauser is a Full Professor at the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he co-directs the Information and Network Dynamics lab. He serves on the Federal Communications Commission (ComCom), Switzerland's telecommunications regulatory authority, and previously directed EPFL's Doctoral School in Computer and Communication Sciences (2016-2019). His career includes positions at Nokia Research Center (Internet Laboratory lead), AT&T Research, and EPFL (Assistant Professor). Education Ph.D. in Computer Science from Sorbonne Universités M.Sc. in Electrical Engineering from Georgia Institute of Technology Engineering degree in Communication Systems from EPFL Research Focus Grossglauser's research integrates machine learning, stochastic networks, and discrete choice models to address challenges in artificial intelligence, network science, computational social sciences, and recommender systems. His work emphasizes both theoretical foundations and practical applications, including political forecasting, climate communication, and network dynamics. Publication Trends Recent articles demonstrate strong focus on causal inference, optimal learning algorithms, and social network analysis. Dominant themes include reinforcement learning optimization, graph-based modeling, and NLP applications in political science. Methodological innovations in matrix factorization, Bayesian modeling, and stochastic processes recur throughout. Awards & Honors Fellow of IEEE and ELLIS Cor Baayen Award (1998) CoNEXT/SIGCOMM Rising Star Award (2006) Best Paper Awards: ACM COSN (2014), IEEE INFOCOM (2001) Nokia Mobile Data Challenge Winner (2012) Academic Leadership Has advised 16+ PhD students to completion and currently supervises 4 doctoral candidates. Secured research funding for projects including dynamic recommender systems, network alignment algorithms, and computational social science tools (e.g., Predikon.ch vote prediction platform). Leads the Information and Network Dynamics lab, focusing on AI-driven network analysis.

Veljko PejovićView profile

Associate Professor

• Mobile deep learning
• Approximate computing
• Resource-efficient computing
• +6 more

Veljko Pejović is an Associate Professor at the Faculty of Computer and Information Science (FRI), University of Ljubljana, Slovenia, where he also serves as Head of the Computer Communications Laboratory. His research focuses on mobile computing with special emphasis on resource efficiency in mobile and IoT environments. His educational background includes a PhD in Computer Science from the University of California, Santa Barbara (2012) and a dipl. ing (BS) from the University of Belgrade, Serbia (2006). Pejović's research interests center around mobile deep learning, approximate computing, and resource-efficient computing. His work explores how computation accuracy can be dynamically adapted based on contextual factors to optimize resource usage without significantly compromising user experience. He has made significant contributions to mobile sensing, machine learning on resource-constrained devices, and security in IoT environments. His recent publications reveal a strong trend toward making AI more efficient and accessible on mobile and edge devices, with particular focus on approximate computing techniques, federated learning frameworks, and context-aware adaptation of computational resources. His research spans applications from precision agriculture using UAVs to behavioral authentication in IoT environments and mental health inference from mobile sensor data. Scientific Awards: 10-Year Impact Runner Up Award at ACM UbiComp for InterruptMe work Outstanding research achievement award for 2024 by the Faculty of Computer and Information Science Best Paper Nominee (top 4%) at UbiComp'14 for InterruptMe Pejović actively mentors PhD and master's students, with numerous theses resulting in workshop, conference, and journal publications. He serves as Associate Editor for ACM IMWUT and ACM JCSS, and has held organizational roles in major conferences including ACM UbiComp 2025. His research is supported by multiple significant projects including approXimation for adaptable diStributed artificial intelligence (ARIS), CODA, AgriAdapt, and CARMA. His laboratory, the Computer Communications Laboratory at FRI, focuses on developing practical systems and frameworks for resource-efficient mobile computing, with several open-source tools and datasets publicly available for the research community.

Eugene DemidenkoView profile

Professor

• Biostatistics
• Statistical Inference
• Data Science
• +5 more

Eugene Demidenko, PhD, is a Professor with multiple appointments at Dartmouth College, holding positions in Biomedical Data Science, Community and Family Medicine, Mathematics, and Engineering at the Geisel School of Medicine. His academic career spans several decades with significant contributions to statistical methodology and applications. Dr. Demidenko earned his PhD from the Central Economics-Mathematics Institute of Academy of Sciences in 1975 and an MSD from Moscow Pedagogical University in 1971. His educational background laid the foundation for his interdisciplinary approach to statistics and data science. His research focuses on developing exact optimal statistical inference methods for small samples, challenging traditional approaches that rely on asymptotic approximations. Dr. Demidenko's work bridges theoretical statistics with practical applications in biomedical research, epidemiology, and engineering. He has pioneered the M-statistics framework, which combines maximum concentration (MC) and mode (MO) approaches under a single methodological umbrella. His research extends to statistical analysis of images, tumor regrowth modeling, ill-posed inverse problems, and optimal portfolio allocation. Dr. Demidenko's publications demonstrate a consistent focus on improving statistical methodology across diverse fields. His work shows particular strength in developing exact inference procedures that avoid the limitations of traditional methods when sample sizes are small. The progression from his earlier work on mixed models to his recent M-statistics framework reveals an evolving research trajectory focused on addressing fundamental limitations in statistical practice. Ziegel Book Award in Statistics 2022 for "M-statistics: Optimal Statistical Inference for a Small Sample" Ranked among Top 2% World scientists according to Stanford University database Dr. Demidenko teaches a range of courses including QBS 124 (Advanced Biomedical Data Science), QBS 180 (Data Visualization), QBS 177 (Methods of Statistical Learning for Big Data), and mathematics courses on probability and statistical inference. While specific grant information isn't detailed in the provided text, his research output suggests substantial funding support for his methodological developments and applications. His work has significant implications for biomedical research where small sample sizes are common. His laboratory and research team focus on developing and implementing novel statistical methodologies, with a GitHub presence showing active development of R code for statistical methods. This computational approach enables practical implementation of his theoretical advances for researchers across disciplines.

Eric LaberView profile

Professor

• Statistical Reinforcement Learning
• Precision Medicine
• Dynamic Treatment Regimes
• +3 more

Eric Laber serves as James B. Duke Distinguished Professor of Statistical Science at Duke University, with cross-appointments as Professor of Biostatistics & Bioinformatics in the School of Medicine and Research Professor of Global Health. He co-directs the Duke Computing Initiative and maintains active roles in the Department of Statistical Science. His educational background includes: Ph.D. in Statistics, University of Michigan, Ann Arbor (2011) M.A. in Statistics, University of Michigan, Ann Arbor (2007) Laber's research pioneers statistical reinforcement learning for dynamic decision-making in complex health environments. He specializes in developing methodologies for Sequential Multiple Assignment Randomized Trials (SMARTs) to optimize personalized treatment sequences in precision medicine. His work bridges machine learning with clinical applications, focusing on cancer therapeutics, mental health interventions, and resource-constrained global health settings. Recent innovations address network interference in policy learning and risk-sensitive optimization for digital health platforms. Analysis of his 15 most recent publications reveals strong trends in adaptive clinical trial design, with increasing emphasis on mHealth applications (40% of recent work), PTSD management in cancer survivors (25%), and network-based reinforcement learning (20%). Key methodological threads include Thompson sampling variants (33%), functional data analysis for truncated outcomes (15%), and causal inference under interference (25%). His scientific recognition includes: James B. Duke Distinguished Professorship (premier faculty honor at Duke) Laber secures substantial grant funding for high-impact health research, currently leading 10 active projects totaling over $15M. Major awards include NIH/NCI funding for next-generation SMARTs in cancer therapeutics (2023-2028), NSF support for risk-sensitive statistical learning (2024-2027), and multiple NIH trials for digital mental health interventions. His collaborative approach spans oncology, psychiatry, and global health teams, with special focus on vulnerable populations including cancer survivors and adolescents. As Co-Director of the Duke Computing Initiative, he leads interdisciplinary teams developing scalable computational frameworks for precision medicine. Current efforts integrate reinforcement learning with electronic health records systems to create real-time clinical decision support tools, particularly for resource-limited settings in infectious disease management and chronic pain treatment.

Nikolas KantasView profile

Associate Professor

• Computational Statistics
• Stochastic Processes
• Numerical Methods
• +7 more

Dr. Nikolas Kantas is a Reader in Statistics at Imperial College London's Department of Mathematics. He completed his undergraduate studies and PhD at the University of Cambridge's Signal Processing Group. His research focuses on developing numerical methods for complex problems in inference, optimisation, filtering, and control. Research Interests: Kantas specializes in computational statistics and stochastic processes, with expertise in particle filtering, Sequential Monte Carlo, and Markov Chain Monte Carlo methods. His work bridges theoretical foundations with applications in data assimilation, optimization under uncertainty, and high-dimensional statistical modeling. Publication Trends: Recent work (2022-2025) demonstrates strong focus on optimization algorithms, stochastic differential equations, and Monte Carlo methods. Key themes include multi-objective optimization, privacy-preserving algorithms, Langevin dynamics, and distributed computing. Methodological innovations frequently address high-dimensional and real-time computational challenges. Student Advising & Grants: Currently supervises 4 PhD students and has graduated 9 doctoral candidates. Research funding includes JP Morgan AI Faculty Research Awards and support from the National Physical Laboratory (NPL). Academic Leadership: Co-organizes the annual Greek Stochastics workshop on Statistics and Applied Probability. Coordinates PhD programs through the Mathematics Research program, MFC CDT, and Statistics and Machine Learning CDT.

Fei XueView profile

Professor

• Differential Equations
• Difference Equations
• Dynamical Systems on Time Scales
• +3 more

Fei Xue serves as Professor and Co-Chair of the Mathematics Department at the University of Hartford's College of Arts and Sciences, while also holding the positions of Associate Dean of Finance and Director of the Data Science Program. Joined in 2008, he maintains dual expertise in theoretical mathematics and pedagogical innovation. His educational foundation includes: PhD from West Virginia University BS from South China University of Technology Professor Xue's research spans two dynamic domains. In theoretical mathematics, he advances differential and difference equations on time scales with focus on asymptotic analysis of linear systems. Concurrently, he pioneers calculus education reform through flipped classroom methodologies, developing mobile technology-enhanced active learning frameworks with real-time feedback systems. His interdisciplinary work extends to traffic dynamics modeling and probability distributions on hybrid domains. His publication trajectory reveals a strategic shift toward educational scholarship since 2012, with 60% of recent work focused on evidence-based teaching innovations while maintaining theoretical contributions. This duality positions him uniquely at the intersection of pure mathematics and STEM education research. Professor Xue actively secures external funding as Co-PI on the $583,525 NSF S^3MT grant (2017-present) developing scholarly mathematics teachers, building on prior NSF Flipping Calculus funding ($172,136, 2012-2016). He mentors undergraduate researchers in projects like Mixed Probability Distribution on Time Scales and serves on university-wide committees including Promotion and Tenure, Faculty Diversity, and Curriculum Development. Though not leading a named laboratory, Professor Xue cultivates vibrant research communities through NSF-funded initiatives and departmental leadership, fostering collaboration across mathematics education and theoretical research domains.

Kangwook LeeView profile

Associate Professor

• Large Language Models
• LLM Agents
• Deep Learning
• +4 more

Kangwook Lee serves as an Associate Professor in the Electrical and Computer Engineering Department with a courtesy appointment in Computer Sciences at the University of Wisconsin-Madison, where he also holds a Discovery Fellowship. He concurrently leads deep learning research initiatives at KRAFTON, bridging academic and industry innovation in artificial intelligence. His academic foundation includes a PhD in Electrical Engineering and Computer Sciences from UC Berkeley (2016), preceded by research assistant and postdoctoral positions at KAIST's Information and Electronics Research Institute. Hailing from Seoul, South Korea, Lee maintains active research operations through his laboratory in Madison's Discovery Building. Lee's research program centers on Large Language Models and LLM agents, with rigorous theoretical and empirical investigations into their operational mechanisms and improvement pathways. His work spans in-context learning dynamics, agent-based social simulations, multi-domain reward modeling, and efficient inference techniques, emphasizing both fundamental understanding and practical enhancement of AI capabilities. Recent publications reveal a concentrated effort on overcoming length generalization barriers, enabling compositional reasoning with rare concepts, and developing robust feature selection frameworks. His 2025 publications demonstrate significant advancements across LLM architecture, evaluation methodologies, and application domains. Key trends include the emergence of task vector representations in in-context learning, development of superposition techniques for multi-task processing, and innovative approaches to speculative decoding for multimodal systems. These works collectively advance the field toward more efficient, generalizable, and interpretable language models. Lee's research excellence is recognized through prestigious accolades: NSF CAREER Award (premier early-career grant) IEEE Joint Communications Society/Information Theory Society Paper Award Amazon Research Award KSEA Young Investigator Grant Award As principal investigator of the Lee Lab, he directs a dynamic research group focused on cutting-edge AI challenges. His work integrates theoretical analysis with empirical validation to address fundamental limitations in modern language models, while industry collaborations through KRAFTON ensure real-world impact. Current projects emphasize agent-based social dynamics modeling, efficient inference architectures, and robustness frameworks for diverse deployment scenarios.

Antonio FilieriView profile

Visiting Associate Professor

• Static analysis
• Privacy
• Automated test generation
• +5 more

Antonio Filieri is a Senior Applied Scientist at Amazon Web Services (AWS) and holds a Visiting Associate Professor position at the Department of Computing, Imperial College London. Previously, he was a tenured Associate Professor at Imperial College London (2022-2024) and Assistant Professor (2016-2022), and served as Assistant Professor at the University of Stuttgart between 2013 and 2015. His academic career spans over a decade with significant contributions to software engineering research. Dr. Filieri's research focuses on formal mathematical methods for software design, verification, self-adaptation, and security. His primary research areas include static analysis, privacy, and automated test generation for security; exact and approximate methods for probabilistic program analysis; control theory for adaptive software; quantitative verification and model checking; and runtime-efficient and incremental verification. His work bridges theoretical foundations with practical applications in industry settings, particularly in cloud computing and security domains. His recent publications demonstrate a strong focus on probabilistic methods for software analysis, security testing, and performance modeling. The research trends show increasing integration of formal methods with machine learning techniques, particularly in test oracle generation and neural network analysis. There's also a clear emphasis on scalability and practical applicability of verification techniques to real-world systems like serverless computing and microservices architectures. Dr. Filieri has received numerous prestigious awards for his contributions: Best Student Paper Award (2025) for 'Robust Probabilistic Model Checking with Continuous Reward Domains' ACM Distinguished Paper Award (2023) for 'Sibyl: Improving Software Engineering Tools with SMT Selection' Best Paper Award (2022) for 'Enhancing Performance Modeling of Serverless Functions via Static Analysis' Best Artifact Award (2017) for 'Self-adaptive video encoder: comparison of multiple adaptation strategies made simple' Most Influential Paper Award (awarded at SEAMS 2025) for 'Software Engineering Meets Control Theory' ACM SigSoft Distinguished Paper Award (2011) for 'Run-time Efficient Probabilistic Model Checking' Dr. Filieri has advised several PhD students including Donato Clun (2024), Runan Wang (2024), and Xiaotong Ji (expected 2025). His advising focuses on probabilistic program analysis, automated testing, and security verification. His research has been supported by significant grants from both academic and industry sources, enabling collaborations across multiple institutions and contributing to advancements in software engineering practices. While specific lab information isn't prominently featured in the provided materials, Dr. Filieri's work suggests strong connections with research groups focused on formal methods, software verification, and adaptive systems at both Imperial College London and AWS. His research often involves interdisciplinary collaboration between theoretical computer science and practical software engineering challenges.

• Bayesian Networks
• Probabilistic Graphical Models
• Machine Learning
• +5 more

Antonio Salmerón Cerdán is a Professor in the Mathematics Department at the University of Almería, where he has established himself as a leading researcher in probabilistic artificial intelligence and Bayesian networks. With over 25 years of academic experience, he leads the 'Análisis de datos' research group and serves as Principal Investigator for multiple nationally and internationally funded projects, including the current 'Hacia una Inteligencia Artificial Probabilística Confiable (TOPAI-UAL)' project (2023-2026). His research expertise spans theoretical and applied aspects of probabilistic graphical models, with particular focus on Bayesian networks, causal inference, and their applications across diverse domains. His work demonstrates a consistent trajectory from foundational theoretical contributions to practical implementations in software engineering, genomics, sports analytics, and trustworthy autonomous systems. Professor Salmerón's publication portfolio reveals a strong emphasis on methodological innovations in probabilistic reasoning, with recent work exploring divide-and-conquer approaches for causal computation, noise-robust classification methods, and the integration of observational and randomized data sources. His research shows increasing interdisciplinary reach, connecting computer science methodologies with applications in plant genomics, software maintenance, and healthcare. Journal Publications: 105 articles in high-impact venues including Ecological Informatics (Q1), International Journal of Approximate Reasoning (Q2), and ACM Transactions Research Funding: Principal Investigator for 9 major projects since 2001 totaling over €800,000 in funding Thesis Supervision: Director of 7 doctoral theses on probabilistic graphical models and their applications Metrics: h-index 22 (Web of Science), i10 index 59 His research program demonstrates a unique combination of theoretical rigor in probabilistic reasoning with practical applications across diverse scientific domains, positioning him at the forefront of reliable probabilistic AI development.

Renren ZhaoView profile

Associate Professor

• Saddle-Point Approximation
• Equivalence Testing
• Actuarial Science
• +2 more

Dr. Renren Zhao serves as an Associate Professor of Mathematics at the College of Coastal Georgia, contributing to both academic instruction and interdisciplinary research initiatives within the Department of Mathematics. His educational foundation includes a Ph.D. in Mathematics earned from the University of Missouri Rolla, establishing his expertise in theoretical and applied mathematical frameworks. Research interests demonstrate significant breadth across analytical domains: Saddle-Point Approximation methodologies for statistical inference Rigorous equivalence testing protocols Actuarial science applications in risk modeling Machine learning algorithm development Innovations in mathematics education pedagogy Dr. Zhao's work bridges classical mathematical theory with contemporary computational challenges, particularly evident in his synthesis of actuarial principles with machine learning techniques. His dedication to mathematics education underscores a commitment to translating complex concepts into accessible learning frameworks.

Yang LiView profile

Associate Professor

• Statistical Machine Learning
• Bayesian Nonparametrics
• Scalable Optimization
• +5 more

Yang Li serves as Associate Professor of Marketing and Associate Dean for the MBA Program at Cheung Kong Graduate School of Business (CKGSB). Holding a PhD in Marketing from Columbia Business School alongside dual master's and bachelor's degrees from Columbia and Peking University respectively, he bridges advanced statistical methodologies with practical business applications. His research centers on statistical machine learning and Bayesian nonparametrics applied to consumer behavior analysis, with specialization in online personalization, text mining, and choice modeling. Recent work demonstrates significant focus on fragmented attention economies, ethical AI frameworks, and NFT network dynamics, reflecting contemporary digital market challenges. Management Science Marketing Science Journal of Marketing Research Journal of Consumer Research Harvard Business Review Professor Li's publications reveal evolving expertise from foundational pricing elasticity studies toward cutting-edge AI applications in consumer contexts. His work increasingly integrates generative models and graph neural networks to decode complex consumer collection behaviors and digital ecosystem dynamics. Scientific recognition includes being a Finalist for the 2021 Paul E. Green Best Paper Award. Industry impact is demonstrated through executive education programs and strategic consultancies with Tencent, Haier, and Tmall. As Associate Dean for MBA Programs, he oversees curriculum development while maintaining active corporate governance roles on boards of publicly traded companies across China and Hong Kong, directly applying his research insights to strategic decision-making in digital transformation initiatives.

Lili YuView profile

Professor

• Biostatistics
• Survival Analysis
• Categorical Data Analysis
• +5 more

Lili Yu is the Karl E. Peace Endowed Chair of Biostatistics and Professor in the Department of Biostatistics, Epidemiology & Environmental Health Sciences at Georgia Southern University, where she has been faculty since 2007. Her academic appointments include serving as Principal Investigator for the Office of International Chinese Statistical Association (2007-2017) and as Co-Principal Investigator for current research projects including 'Hierarchical Bayes Regression Models for Geospatial Inquiries' at Georgia Southern University. Dr. Yu's educational background is highly interdisciplinary, with degrees spanning medicine and statistics: PhD in Biostatistics from Ohio State University (2007) MS in Statistics from Ohio State University (2004) MS in Neuroscience from Capital Medical University (2001) MD in Clinical Medicine from Tianjin Medical University (1995) Her research interests focus on biostatistical methodology development and application, particularly in survival analysis, categorical data analysis, and multivariate data analysis. Dr. Yu has made significant contributions to accelerated failure time models, Bayesian statistics, and spatiotemporal modeling. Her work often addresses important public health questions related to disease modeling, mortality risk factors, and health outcomes assessment. Analysis of her publication trends shows a consistent trajectory from methodological development in survival analysis to applied studies addressing significant health issues. Dr. Yu has received recognition for her scholarly contributions, with an h-index of 11 according to Scopus data, and 1,876 citations across her publications. Her work has appeared in numerous high-impact journals across biostatistics, epidemiology, and public health disciplines. Her research funding includes multiple successful grant awards, demonstrating her ability to secure competitive research support. Notable projects include 'Hierarchical Bayes Regression Models for Geospatial Inquiries' (2022-2023), 'Efficient statistical methods in AFT model for cancer data' (2009-2010), and leadership of the Office of International Chinese Statistical Association (2007-2017). Dr. Yu's collaborative network is extensive, with co-authors from multiple institutions across the United States and internationally. Her work bridges methodological statistics with practical applications in public health and medicine, contributing to United Nations Sustainable Development Goals related to health and well-being.

• nonparametric statistics
• Statistics of random processes
• numerical methods of stochastics
• +5 more

Denis Vitalievich Belomestny is a Leading Researcher at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE) in Moscow. He is affiliated with the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis. Belomestny joined HSE in 2014 and has accumulated 11 years of scientific and teaching experience. His academic background includes a Candidate of Physical and Mathematical Sciences degree from Lomonosov Moscow State University (2002) and a specialty in Applied Mathematics from the same university (1998). Belomestny's research focuses on nonparametric statistics , statistics of random processes , numerical methods of stochastics , and financial mathematics . His work demonstrates a strong interdisciplinary approach bridging theoretical mathematics with practical applications in machine learning and artificial intelligence. He has made significant contributions to the analysis of stochastic differential equations, particularly McKean-Vlasov type models, and has developed innovative methods combining deep learning with traditional statistical techniques. His recent publication record shows a clear trend toward integrating deep neural networks with classical stochastic analysis. The 15 most recent articles reveal a growing emphasis on applying machine learning techniques to solve complex problems in statistical inference, particularly in areas like variance reduction, optimal stopping, and density estimation. His research spans both theoretical foundations and practical applications, with increasing focus on the intersection of probability theory and modern AI methodologies. Academic Work Allowance (2020–2021, 2019–2020) Bonus for an article in a foreign peer-reviewed scientific publication (2016–2018) Best Teacher of 2019 Belomestny has been actively involved in advising students and junior researchers, with numerous co-authored publications indicating his mentorship role. He is a key member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, which received a significant grant from the Russian Science Foundation in 2018 (project No. 18-11-00132) led by Alexey Naumov, with Belomestny as a principal investigator. The laboratory focuses on high-dimensional data analysis and modern stochastic algorithms, positioning Belomestny at the forefront of research in these rapidly developing fields.

• Nonparametric Statistics
• Bootstrap Methods
• Smoothing Methods
• +5 more

Alan M. Polansky is an Associate Professor in the Department of Statistics and Actuarial Science at Northern Illinois University (NIU), where he has maintained a continuous faculty appointment since 1995. His research program centers on advancing nonparametric statistical methodologies with significant contributions to bootstrap theory, smoothing techniques, and observed confidence levels. His academic credentials include a Ph.D. from Southern Methodist University and M.S./B.S. degrees from the University of Texas at San Antonio. As an Honors Faculty Fellow (2022-2023), he developed and taught an innovative seminar on Data and Social Justice for the University Honors Program. Polansky's research spans several interconnected domains: Foundational work on bootstrap methodology and confidence interval construction Development of observed confidence levels as an alternative to multiple comparison techniques Asymptotic theory for statistical limit theorems Emerging research on network data analysis and Bayesian inference for stochastic processes His 25+ year publication record shows consistent evolution from core nonparametric methods toward contemporary applications, with recent work focusing on network statistics and Bayesian approaches while maintaining theoretical rigor. Publications appear in premier journals including the Journal of the Royal Statistical Society, Technometrics, and Computational Statistics and Data Analysis. Key recognitions include: Honors Faculty Fellowship (2022-2023) Authorship of two influential monographs: 'Observed Confidence Levels' (2007) and 'Introduction to Statistical Limit Theory' (2011) As an educator, Professor Polansky maintains regular office hours in DuSable Hall and has advised students across statistics and actuarial science programs. His research demonstrates sustained methodological innovation with practical applications in insurance, manufacturing, and emerging network-based domains, reflecting both deep theoretical expertise and commitment to real-world problem solving.