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Elizabeth QianView profile

Assistant Professor

• Scientific machine learning
• Model reduction
• Multifidelity methods
• +5 more

Elizabeth Qian is a tenure-track Assistant Professor at Georgia Tech with joint appointments in the Schools of Aerospace Engineering and Computational Science and Engineering. She holds a visiting Hans Fischer Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2023. Her research develops computational methods for scientific machine learning, model reduction, and multifidelity approaches to accelerate engineering decision-making. Education : PhD (2021), SM (2017), and SB (2014) in Aerospace Engineering from MIT Her work focuses on enabling efficient simulations for complex systems through model reduction , scientific machine learning , and multifidelity methods . Recent publications address Bayesian inverse problems, neural operator learning trade-offs, and ensemble Kalman methods. Scientific awards include the 2025 NSF CAREER Award, 2024 AFOSR YIP Award, 2023 TUM-IAS Fellowship, 2022 SIAM Student Paper Prize, and 2014 Hertz/NSF/Fulbright fellowships. She mentors GT PhD researchers and contributes to diversity initiatives in engineering communities.

• Optimisation Under Uncertainty
• Discrete Choice
• Social Choice
• +4 more

Chung Piaw Teo is the Stephen Riady Professor and Executive Director of the Institute of Operations Research and Analytics (IORA) at the National University of Singapore (NUS) Business School. He has held significant academic roles including Head of Department, Acting Deputy Dean, Vice-Dean of Research and Ph.D. Programs, and Chair of the Ph.D. Committee at NUS. PhD in Operations Research from MIT (1996) Bachelor of Science (Honors) in Mathematics from NUS (1990) Research Interests: Optimisation Under Uncertainty Discrete Choice Modeling Social Choice Theory Inventory Theory Supply Chain Management Combinatorial Optimisation Operations Research Publications focus on stochastic optimization, supply chain resilience, and network design, with recent works spanning 2020–2015 in journals like Management Science , Operations Research , and Mathematical Programming . Key themes include urban logistics, risk mitigation, and decision analytics. Scientific Awards: Stephen Riady Professor (2024) Provost’s Chair (2014) Faculty Outstanding Researcher Awards (2014, 2006, 2003) Nominee for University Outstanding Researcher Award (2006) Grants & Leadership: Served on international committees (INFORMS, LANCHESTER Prize, Fudan Prize) and held visiting/fellow positions at MIT, Northwestern, and Sungkyunkwan University. Currently a department editor for Management Science and associate editor for multiple journals.

Yandi ShenView profile

Assistant Professor

• Nonparametric Statistics
• Semiparametric Statistics
• High-Dimensional Inference
• +6 more

Yandi Shen is an Assistant Professor in the Department of Statistics and Data Science at Carnegie Mellon University (CMU), part of the Dietrich College of Humanities and Social Sciences. He holds a Ph.D. in Statistics from the University of Washington (2021), advised by Fang Han and Daniela Witten. Prior to joining CMU, he was a Kruskal Instructor in Statistics at the University of Chicago (2021–2023) and a postdoctoral researcher at Yale University under Zhou Fan. His research focuses on nonparametric and semiparametric statistics, high-dimensional inference, applied probability, and optimization algorithms. Education: Ph.D. in Statistics (University of Washington, 2021); Postdoctoral work at University of Chicago and Yale University. Research Interests: Nonparametric and semiparametric methods High-dimensional statistical inference Applied probability and stochastic processes Empirical Bayes methods Optimization and algorithm design Statistical machine learning Teaching Experience: University of Washington: Teaching Assistant for advanced inference courses (STAT 581-583), probability (STAT 394-395), and applied statistics (STAT 528). University of Chicago: Instructor for courses like STAT 22400 (Applied Regression Analysis) and STAT 33611 (Gaussian Processes). Recent Research Trends: His publications emphasize theoretical advancements in high-dimensional statistics, including empirical Bayes estimation, gradient flows, and optimization in overparameterized models. Key areas include analyzing phase transitions in spline regression, universality in regularized estimators, and nonparametric mixture models under optimal transport distances. Labs/Teams: While no specific lab is named, his collaborative work spans statistical theory and applications, often involving interdisciplinary teams in data science and machine learning.

Steven KruegerView profile

Professor

• Cloud Systems
• Turbulence
• Boundary Layer Meteorology
• +3 more

Steven Krueger is a Professor at the University of Utah, where he leads the Clouds, Turbulence, and Fire Weather Research Group . His research focuses on atmospheric processes including cloud systems dynamics, turbulence, boundary layer interactions, and fire weather modeling. Krueger employs advanced numerical techniques like Large Eddy Simulation (LES) and cloud-resolving models to study convection, cloud physics, and climate feedback mechanisms. Research Interests: Krueger's work spans cloud modeling (from stratocumulus to cumulonimbus), convective cold pools, stratocumulus entrainment, and wildfire-atmosphere interactions. Key methodologies include LES benchmarks (e.g., GigaLES), super-parameterized climate models (SP-CAM), and observational data analysis from field campaigns like KWAJEX. Publication Trends: His recent articles (2007–2012) emphasize cloud microphysics, turbulence parameterization, fire weather simulations, and Arctic climate feedbacks. Work demonstrates consistent application of high-resolution modeling to quantify convection dynamics, aerosol impacts, and precipitation processes across diverse climates. Group Leadership: Krueger directs a team studying cloud physics, fire weather, and convective analysis. Current projects include developing tools to quantify cloud responses to climate states and improving wildfire spread models through turbulence analysis.

Marius HofertView profile

Associate Professor

• Dependence Modeling
• Computational Statistics
• Quantitative Risk Management
• +3 more

Marius Hofert serves as Associate Professor of Statistics in the Department of Statistics and Actuarial Science at The University of Hong Kong's School of Computing and Data Science, with office in Room 228 of the Run Run Shaw Building. His research fundamentally addresses statistical dependence structures and computational methodologies for risk assessment. His core research domains include: Copula theory and dependence modeling for complex stochastic systems Advanced Monte Carlo and quasi-random sampling techniques Quantitative risk management frameworks for financial and insurance applications Recent publications demonstrate increasing integration of machine learning with traditional statistical methods, particularly through generative neural networks for dependence modeling. His critical examinations of AI tools like ChatGPT in quantitative contexts reveal methodological pitfalls while exploring new computational frontiers. As Academic Adviser in Risk Management, he mentors PhD candidates with strong mathematical backgrounds. He actively develops open-source statistical software through R packages including copula , nvmix , and qrmtools , maintaining the Quantitative Risk Management tutorial website (qrmtutorial.org).

• Statistics
• Meteorology
• Econometrics
• +4 more

Dr. Annette Christine Möller is a Professor at Bielefeld University's Faculty of Economics, where she leads the Chair of Data Science and is affiliated with the Department of Empirical Methods. She serves as a key researcher at the Bielefeld Center for Data Science (BiCDaS). Her contact information includes email (annette.moeller@uni-bielefeld.de) and phone (+49 521 106-4877). Her research focuses on: Developing statistical methods for weather forecast post-processing Copula-based modeling of multivariate dependencies Ensemble forecasting for climate risk mitigation Applications in renewable energy prediction Statistical computing and open-source tool development Her publication record (2019-2025) demonstrates strong emphasis on: Probabilistic weather forecasting techniques Vine copula applications in meteorology Statistical calibration of ensemble models Interdisciplinary work spanning climatology, epidemiology and education She leads significant research grants including: DFG project: 'Statistical post-processing of ensemble forecasts' (2018-2024) DFG project: 'Mitigating climate risks via copula-based forecast improvement' (2024-2026) She collaborates with the Technical University of Munich and maintains active research teams focused on statistical meteorology and climate informatics.

Yongyi GuoView profile

Assistant Professor

• Statistical Learning
• Decision-Making Methods
• Digital Health Interventions
• +3 more

Yongyi Guo serves as an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison, following a postdoctoral fellowship at Harvard University under Susan A. Murphy. Her academic foundation includes a Ph.D. in Statistics from Princeton University (2022) advised by Jianqing Fan and a B.S. in Mathematical Sciences from Peking University (2016). Dr. Guo's research integrates statistical learning with digital health applications, specializing in reinforcement learning for personalized mobile interventions and causal inference methodologies. Her work demonstrates particular expertise in micro-randomized trials for cannabis use reduction, where she developed the MiWaves and reBandit algorithms to optimize just-in-time adaptive interventions through online decision-making frameworks. Analysis of her publication trends reveals consistent focus on bridging theoretical statistics with health implementation challenges, featuring innovations in anytime-valid inference for N-of-1 trials, communication-efficient distributed estimation, and robust regression techniques for dependent data structures.

Eric V. SludView profile

Professor

• Mathematical Statistics
• Probability
• Census Statistics
• +11 more

Eric V. Slud is a Professor in the Statistics Program within the Department of Mathematics at the University of Maryland, College Park. He has taught numerous graduate-level statistics courses including Mathematical Statistics, Categorical Data Analysis, and High-Dimensional Statistics since at least 2017. Dr. Slud's research spans several key areas of mathematical statistics and probability. His work focuses on: Census statistics, particularly demographic modeling of nonresponse to national surveys Small Area Estimation (SAE) with applications to the SAIPE program Survival data analysis including semiparametric inference and clinical trial design Meta-analysis in biostatistics Pharmaceutical statistical methods Large-scale data problems with emphasis on cross-classified data His publications demonstrate continued contributions across these domains, with particular emphasis on small area estimation methodologies, survival analysis techniques, and applications in survey statistics. Dr. Slud has developed innovative approaches to weighting adjustment, nonresponse correction, and estimation with complex survey data. Dr. Slud has advised several PhD students including Yang Cheng (2004), Sophie Tsou (2005), and others working on topics ranging from Factor Analysis to Survival Data. He has collaborated extensively with researchers at the Census Bureau and FDA. Dr. Slud maintains active research programs through multiple Research Interaction Teams (RITs) at the University of Maryland, focusing on advanced statistical methodologies and their applications to real-world problems in census methodology, healthcare, and other domains requiring sophisticated statistical analysis.

• Statistics
• Econometrics
• Linear Regression Models
• +5 more

Shalabh is a Professor in the Department of Mathematics & Statistics at the Indian Institute of Technology Kanpur (IITK), specializing in Statistics and Econometrics. He earned his PhD in Mathematical Statistics from the University of Lucknow and undertook post-doctoral research at Ludwig Maximillians University, Munich, and the University of Pittsburgh. Education: Post-doc in Statistics, Ludwig Maximillians University, Munich, Germany (2007) Post-doc in Statistics, University of Pittsburgh, USA (2002) PhD in Mathematical Statistics, University of Lucknow, India (1996) M.Sc. in Mathematical Statistics, University of Lucknow, India (1992) Undergraduate degree, University of Lucknow, India (1990) Research Interests: His research spans Linear Regression Models , Econometric Modelling , Measurement Error Models , Missing Data Models , Panel Data Models , Restricted Regression , Spatial Models , Forecasting Techniques , and Sampling Theory . He has contributed extensively to the development of improved estimation techniques under measurement errors and missing data scenarios. Publications Trend: Shalabh has published prolifically in top-tier journals, focusing on theoretical and methodological advances in regression analysis, measurement error models, and sampling theory. His work often introduces novel estimation procedures that enhance efficiency and robustness under non-standard conditions. Scientific Awards & Honors: IISA Young Researcher Award 2014 (Theory Category) National Award for Young Statisticians – Professor C.R. Rao Award 2011 Mahalanobis Memorial Medal (MMM) 2008 ISCA Young Scientists' Award 1999 Jan Tinbergan Award 1995 Humboldt Fellowship 2006 BOYSCAST Fellowship 2001 CSIR Junior Research Fellowship 1993 Professional Affiliations: He is a life member of several national and international statistical societies and a Fellow of the Royal Statistical Society, London, since 2000. Contact & Resources: Office: Department of Mathematics & Statistics, IIT Kanpur, Kanpur 208016, India. Email: shalab@iitk.ac.in Phone: +91-512-259-7905

Guido ImbensView profile

Professor

• Econometrics
• Causal Inference
• Program Evaluation
• +5 more

Guido Imbens is a Professor of Economics at Stanford University's Graduate School of Business and a Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR). He is also a Faculty Affiliate at the Institute for Human-Centered Artificial Intelligence (HAI) and Faculty Director of the Stanford Causal Science Center. His research focuses on causal inference in observational studies, program evaluation, and econometric methods, with a particular emphasis on matching, instrumental variables, and regression discontinuity designs. Education: Ph.D. in Economics, Brown University; Academic appointments at Harvard, UCLA, UC Berkeley prior to Stanford (2012). Key Research Areas: Econometrics, Causal Inference, Bayesian Methods, Semi-Parametric Analysis, Instrumental Variables, Machine Learning applications. Scientific Awards: Honorary Doctorate, Brown University (2022) Fellow, National Academy of Sciences (2022) Nobel Memorial Prize in Economic Sciences (2021) Amman Mineral Faculty Fellow (2021–2022) Recent Publications: His work spans causal inference in panel data, synthetic control methods, experimental design, and statistical significance interpretation. Articles in journals like Journal of Econometrics , Econometrica , and Management Science highlight his contributions to statistical methodology and policy analysis. Advising: Guido actively mentors doctoral students and postdoctoral researchers, serving as advisor for Lea Bottmer, Janelle Nelson, and others. He also advises undergraduate majors and contributes to curriculum development in econometrics education.

• Optimization
• Scheduling Theory
• Game Theory
• +5 more

Eddie Anderson is a Professor of Decision Sciences at the University of Sydney Business School. He previously held the position of Associate Dean (Research) and Associate Dean (Research Education) at the same institution. Before joining the University of Sydney in 2008, he was at the Australian Graduate School of Management, University of New South Wales. He is also a Member of the Sydney Institute of Agriculture. His educational background includes an honours degree in Mathematics from Cambridge and a PhD in Operations Management, also from the University of Cambridge. Professor Anderson's research spans several interconnected areas in decision sciences and operations research. His work focuses on optimization , scheduling theory , and game theory , with applications in supply chains (particularly contracts and risk), energy markets , and more recently, agriculture supply chains . He has made significant contributions to understanding competitive interactions in markets, especially in wholesale electricity markets where he has analyzed supply function equilibria. His research often incorporates stochastic elements to model uncertainty and risk, recognizing that firms make decisions considering both expected profits and risk exposure. Analysis of Professor Anderson's recent publications reveals a strong focus on supply chain management, risk analysis, and game-theoretic approaches to market competition. His work spans theoretical developments in optimization and game theory while maintaining strong connections to practical applications in energy markets, procurement, and agricultural supply chains. A notable trend is the increasing integration of risk considerations into traditional optimization frameworks, reflecting the growing importance of risk management in business decision-making. Professor Anderson has published 69 papers in international academic journals, with Google Scholar showing 4300 career citations and an h-index of 33. He served as the Area Editor for Energy at the journal Operations Research from 2012-2017. His 2014 book "Business Risk Management: Models and Analysis" has become a significant reference in the field. Professor Anderson has successfully supervised 14 PhD students to completion. His academic leadership roles have been extensive, including Associate Dean Research at AGSM, UNSW (2002-2004), Acting Dean AGSM (2006), Postgraduate Associate Dean Faculty of Business, UNSW (2006-2008), and Chair of the Discipline of Business Analytics at Sydney University (2008-2013). He has secured multiple research grants, including projects on "Digitally Decommoditised Agriculture" (2017), "Robust optimization under uncertainty with limited data" (2016), and "Competitive supplier bidding in supply chains" (2011).

• Mathematical Statistics
• Generalized Distributions
• Statistical Distribution Theory
• +3 more

Ahmad Alzaghal serves as an Associate Professor of Mathematics at Farmingdale State College with a 10-month faculty appointment. He teaches core undergraduate mathematics courses including Calculus I-III, Statistics, Probability, and Statistical Data Mining across both in-person and online formats, demonstrating significant curriculum development expertise through recent Fall 2025 course offerings. His academic credentials include: Ph.D. in Mathematics (Statistics) from Central Michigan University (2014) M.Sc. in Mathematics from the University of Jordan (2006) B.Sc. in Mathematics and Statistics from Al-Hussein Bin Talal University (2002) Professional development encompasses a Graduate Certificate in Data Mining (Central Michigan University, 2011) and SUNY certifications in online course design (2020-2022). Dr. Alzaghal's research centers on Mathematical Statistics with pioneering work in generalized distribution theory. He develops novel distribution families—including Exponentiated Exponential, Log-Logistic, Lindley, and Lomax variants—while investigating their theoretical properties, estimation methodologies, and practical implementations. His scholarship bridges abstract statistical concepts with tangible applications in medical modeling (e.g., bladder cancer susceptibility) and artificial intelligence systems, consistently emphasizing bimodality characteristics and regression frameworks. His publication record (2013-2024) reveals progressive specialization in distribution extensions, evolving from foundational T-X family research toward increasingly sophisticated bimodal models with direct medical and AI applications. Recent work demonstrates methodological maturation through simultaneous estimation techniques and real-world validation. No scientific awards or major honors were documented in available materials. While active scholarship is evident through consistent publications and conference presentations (including Joint Mathematics Meetings), no graduate student advising relationships or external research grants were specified in the source documentation. His research appears primarily theoretical with collaborative applications rather than laboratory-based work.

Peter H. GruberView profile

Senior Lecturer

• Asset Pricing
• Risk Premia
• Numerical Methods
• +4 more

Peter H. Gruber is a Senior Lecturer and Senior Scientist at the Università della Svizzera Italiana (USI), Faculty of Economics, in Lugano, Switzerland. He has been affiliated with USI since 2008. PhD in Physics from TU Wien PhD in Finance from Università della Svizzera Italiana His research spans multiple disciplines, focusing on: Asset Pricing, particularly risk premia and stochastic volatility models Numerical Methods in finance and econometrics Economics of Cryptocurrencies and Entrepreneurship High-Performance Computing applications in economics His publication trends highlight expertise in option pricing models , matrix affine jump diffusion (MAJD) frameworks, and stochastic skewness analysis. Earlier work in particle physics demonstrates interdisciplinary technical proficiency. At USI, he teaches numerical methods using MATLAB and R, and develops computational resources for financial econometrics and macroeconomic analysis.

Andrew LimView profile

Professor

• Stochastic Control
• Operations Research
• Financial Engineering
• +3 more

Andrew Lim is a Professor in the Department of Analytics and Operations and the Department of Finance at the National University of Singapore (NUS). He serves as Deputy Director of the Institute for Operations Research and Analytics. Previously, he was on the faculty of the Department of Industrial Engineering and Operations Research at the University of California, Berkeley (2002–2014). His research focuses on stochastic control, robust decision-making, decentralized optimization, and applications in financial engineering and operations research. His work has been published in top journals like Operations Research , Management Science , and IEEE Transactions on Automatic Control . Lim has received the National Science Foundation CAREER Award and has held editorial roles at prominent journals. His research explores topics such as dynamic portfolio selection, robust empirical optimization, and stochastic systems analysis. He has contributed to advancing methodologies in risk-sensitive control, multi-agent systems, and machine learning applications in finance. His recent work emphasizes data-driven approaches and the integration of learning algorithms into decision-making frameworks. Lim’s academic career spans over two decades, with a strong focus on bridging theoretical advancements with practical applications in finance and operations. His interdisciplinary research addresses challenges in inventory management, demand forecasting, and optimal resource allocation. He collaborates with institutions globally to develop innovative solutions for complex stochastic systems.

Zdravko BotevView profile

Lecturer

• Monte Carlo Methods
• Network Reliability
• Kernel Density Estimation
• +3 more

Dr. Zdravko Botev is a Lecturer in Computational Statistics and Applied Probability at the School of Mathematics and Statistics, UNSW Sydney. He holds a PhD from the University of Queensland (2010) and has held postdoctoral fellowships at the Universities of Montreal and Cambridge. His research focuses on adaptive Monte Carlo methods, rare-event probability estimation, network reliability modeling, kernel density estimation, and model selection. He is the author of widely used MATLAB scripts for nonparametric density estimation and has contributed to influential texts like Data Science and Machine Learning: Mathematical and Statistical Methods . Education: PhD in Statistics, University of Queensland, Australia, 2010 Research Interests: Dr. Botev's work emphasizes computational statistics and rare-event simulation, with applications in network reliability and kernel density estimation. His methods address challenges in high-dimensional data analysis, Monte Carlo algorithms, and efficient statistical computation. Key areas include: - Adaptive Monte Carlo methods for rare-event simulation - Kernel density estimation techniques and boundary conditions - Variance reduction strategies in statistical modeling Professional Engagement: Member of the Australian Academy of Sciences Christopher Heyde Medal Awards Committee (2021–2023) Steering Committee member for the Monte Carlo Methods Conference Associate Editor for INFORMS Journal of Computing and Australian & New Zealand Journal of Statistics Chair of the organizing committee for the 12th International Conference on Monte Carlo Methods and Applications (2019) Teaching: Dr. Botev has taught courses such as Data Mining (MATH5836), Geotechnical Data Collection and Analysis (MINE8680), and Probability and Statistics (MATH2901/MATH2931). His teaching has received positive student evaluations for clarity and engagement.