María Ángeles Saavedra González is a researcher at the Department of Statistics and Operational Research, University of Vigo, Spain. She earned her doctorate from the University of Oviedo in 1998 with a thesis on non-parametric density estimation for moving average processes under the supervision of Dr. Ricardo Cao Abad. University: University of Vigo Department: Statistics and Operational Research Education: PhD (1998) from University of Oviedo Her research spans Statistics, Operational Research, and Probability Theory, focusing on nonparametric estimation, functional data analysis, and stochastic processes. Recent work includes environmental applications like air quality outlier detection and oil spill modeling. Her publications (10 since 1999) include collaborations in computational statistics, energy optimization for atomic clusters, and environmental simulations. Notable co-authors include Ricardo Cao, Susana Gómez, and David Romero. She has contributed to zbMATH with 10 publications, covering topics in time series analysis, global optimization algorithms, and environmental remediation strategies.
Dr. Eunsook Kim is a Professor in the College of Education at the University of South Florida (USF Tampa campus). Her research specializes in advanced quantitative methodologies for educational and psychological measurement. Research Focus: Dr. Kim's work centers on psychometric innovations, including multilevel modeling, structural equation modeling, factor mixture approaches, and rigorous testing of measurement invariance in complex datasets. She frequently employs Monte Carlo simulations to evaluate methodological robustness in cross-cultural, longitudinal, and hierarchical research designs. Publication Trends: Her recent articles (2011-2017) demonstrate consistent focus on improving statistical methods for detecting measurement bias, validating instruments across populations, and developing reporting standards for multilevel analyses. Collaborative work features prominently, often addressing methodological challenges in large-scale educational and psychological assessment.
Dr. John Maheu is a Professor and Business Research Chair at the DeGroote School of Business, McMaster University. His research focuses on financial econometrics, time series forecasting, and stochastic volatility modeling, with a particular emphasis on structural breaks and Bayesian methodologies. University: McMaster University School: DeGroote School of Business Department: Finance and Business Economics Maheu’s work explores how macroeconomic and financial factors influence market volatility, bull/bear cycles, and portfolio allocation. His models incorporate flexibility to adapt to unforeseen changes, such as those observed during the COVID-19 pandemic. He advocates for the use of disaggregated data to sharpen risk estimates and improve investment decisions. His recent publications analyze topics like infinite hidden Markov models, Bayesian forecasting frameworks, and jump processes in financial markets. These works highlight advancements in modeling market regimes and volatility dynamics under uncertainty. Scientific awards include the Social Sciences and Humanities Research Council (SSHRC) grant, which supports his long-term research initiatives. This funding enables him to address complex financial and economic challenges through innovative econometric modeling. Maheu’s research bridges theory and practice, offering insights into market risk, structural instability, and the efficacy of volatility models. His contributions extend to advising on financial decision-making tools that account for evolving economic conditions.
Dr. Amit Moscovich Eiger is a Senior Lecturer (tenure-track Assistant Professor) in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University, where he has been employed since 2021. Prior to this position, he was a Postdoctoral Research Associate in the Program in Applied and Computational Mathematics (PACM) at Princeton University (2018-2021) and a Postdoctoral Fellow at the School of Mathematical Sciences, Tel Aviv University (2017-2018). His educational background includes: Ph.D. (direct track) in the Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 2016 Physics and mathematics studies, the Hebrew University of Jerusalem, 2008 B.A. in computer science, the Open University of Israel, 2003 Dr. Moscovich Eiger's research focuses on developing methodology for statistics and machine learning, with particular emphasis on tools for mapping and analyzing large volumetric datasets. His work is motivated by key challenges in structural biology, specifically the 3D reconstruction and analysis of flexible proteins and other macromolecules from cryo-electron microscopy data. His research spans geometric estimation problems, manifold learning, computational statistics, and machine learning applications in biological imaging. Analysis of his recent publications reveals a strong focus on applying manifold learning techniques to structural biology problems, particularly cryo-EM data analysis. His work bridges theoretical statistics and practical computational methods, with significant contributions to handling heterogeneity in biological structures, developing fast algorithms for statistical computations, and creating novel approaches for analyzing protein conformations. A notable trend is the integration of optimal transport theory with manifold learning for biological applications. His scientific achievements include: Joseph Putter Ph.D. dissertation award, awarded by the Israeli Statistical Association, 2017 Dr. Moscovich Eiger is actively involved in mentoring graduate students, currently supervising multiple Ph.D. and M.Sc. students in statistics, data science, and operations research. His research is supported by prestigious funding sources including the Israel Science Foundation (ISF), the United States-Israel Binational Science Foundation (BSF), and the United States National Science Foundation (NSF). He leads a research group focused on computational methods for structural biology, particularly developing algorithms for cryo-electron microscopy data analysis. His team collaborates with researchers from institutions including Princeton University, Duke University, Hebrew University of Jerusalem, and KTH Royal Institute of Technology, creating a strong interdisciplinary network for advancing computational approaches in structural biology.
Dr. Mariusz Bieniek is a Professor at the Department of Applied Mathematics, Faculty of Mathematics, Physics and Computer Science, Maria Curie-Skłodowska University. His research focuses on distribution theory, generalized order statistics, and probabilistic modeling. Research interests include: Generalized order statistics and their applications Sharp bounds in statistical expectations Regression-based distribution characterizations Reliability theory and coherent systems Spine analysis in Fleming-Viot processes Recent publications emphasize inequalities in generalized order statistics, quantile estimation techniques, and probabilistic properties of record values. Key themes span mathematical statistics , stochastic processes , and statistical modeling .
Benjamin Ruttenberg is an Associate Professor in the Biological Sciences Department at California Polytechnic State University (Cal Poly) and serves as the Director of the Center for Coastal Marine Sciences (CCMS), which includes the Cal Poly Pier in Avila Beach and several research vessels. His work bridges marine ecology, conservation biology, and applied marine policy. Education: Ph.D. in Ecology, Evolution, and Marine Biology, University of California, Santa Barbara M.S. in Environmental Studies, Yale University B.A. from Tufts University Research Focus: Ruttenberg’s research is highly applied and centers on understanding and managing human impacts on marine systems. His current projects include assessing the potential for offshore renewable energy along California’s central coast, studying the decline and recovery of Pismo clams, evaluating the impacts of marine protected areas and climate change on nearshore fisheries, and using artificial intelligence to enhance marine monitoring systems. Research Themes: His work spans several key areas: - Offshore wind energy development and its environmental implications - Coral reef resilience, particularly the role of parrotfish in reef recovery - Climate change impacts on marine ecosystems and fisheries - Integration of AI and machine learning for ecological monitoring - Marine conservation policy and stakeholder engagement Recent Publications: Ruttenberg’s recent work reflects a strong emphasis on marine spatial planning, fisheries science, and coral reef ecology. His 2024 publications focus on offshore wind energy planning, parrotfish behavior, and collaborative fisheries research. Earlier works delve into coral reef health, marine protected area effectiveness, and the ecological impacts of climate change. Scientific Contributions: While no specific awards are listed, his extensive publication record (28+ peer-reviewed papers) and leadership roles at CCMS underscore his impact on marine science and policy. Advising & Lab: Ruttenberg leads the Marine Conservation Lab at Cal Poly. He typically accepts graduate students for specific funded projects and emphasizes hands-on research experience. Prospective students are encouraged to contact him directly with their qualifications and research interests.
Anjin Liu is a Researcher at the Centre for Artificial Intelligence , Faculty of Engineering and Information Technology , University of Technology Sydney . His research focuses on Concept Drift , Adaptive Data Stream Learning , Multi-stream Learning , Machine Learning , and Big Data Analytics . His work addresses challenges in nonstationary environments through innovative methods like evolving gradient boosting , ensemble diversity optimization , and dynamic drift detection . He develops real-time systems for applications such as train carriage load prediction in collaboration with Sydney Trains. Scientific Contributions: Proposed EI-kMeans for drift detection via cluster-based histograms Developed Fuzzy Decision Trees to handle uncertainty in stream learning Advanced Multi-stream aggregation techniques for improved generalization He is available for Masters Research or PhD student supervision and has received funding for real-time transportation analytics projects.
Professor Hans Kristian Eriksen is a leading cosmologist at the Institute of Theoretical Astrophysics, University of Oslo, specializing in primordial gravitational wave detection through cosmic microwave background (CMB) analysis. His work bridges advanced computational methods with high-precision observational cosmology, focusing on next-generation satellite missions like LiteBIRD and ground-based experiments including COMAP and SPIDER. His research centers on developing exascale computational frameworks for Bayesian end-to-end CMB analysis, with particular emphasis on extracting faint primordial B-mode signals from overwhelming foreground contamination. Key methodologies include Monte Carlo Markov Chain samplers, component separation techniques, and systematic error propagation models that enable unprecedented precision in cosmological parameter estimation. Recent publications demonstrate dominant focus on the LiteBIRD satellite mission across simulation frameworks, scanning optimization, and science forecasts, alongside significant contributions to CO intensity mapping through the COMAP project. These works reveal evolving trends toward multi-messenger cosmology and increasingly sophisticated foreground mitigation strategies. ERC Starting Grant ERC Consolidator Grant H2020 COMPET-4 project (BeyondPlanck) Eriksen serves as principal investigator for major computational cosmology projects, including the Cosmoglobe initiative for end-to-end CMB analysis. His supervisory role encompasses computational astrophysics projects focused on massive parallelization of CMB analysis codes, with funding secured through competitive European grants. His team maintains active collaborations with NASA/JPL, Caltech, Oxford, and international consortia. He leads the Cosmoglobe group implementing one of the world's most comprehensive cosmological MCMC samplers, while actively contributing to the COMAP, PASIPHAE, Planck, QUIET, and SPIDER collaborations. Current efforts concentrate on preparing computational pipelines for LiteBIRD data analysis and advancing Bayesian methods for next-generation CMB polarization experiments.
Lameck Onsarigo serves as an Associate Professor in the College of Architecture & Environmental Design at Kent State University, specializing in construction management and sustainable building technologies with expertise spanning economic analysis, trenchless infrastructure methods, and emerging construction materials. His academic foundation includes: Bachelor of Science in Construction Management from Jomo Kenyatta University of Agriculture and Technology Master of Technology Management and MBA from Bowling Green State University PhD in Technology Management from Indiana State University Research focuses on construction economics (prevailing wage impacts, bid competitiveness), trenchless technologies (horizontal auger boring, pipe bursting), and sustainable construction (mass timber systems, environmental value engineering). His work integrates engineering principles with economic and environmental considerations to address industry challenges in cost management, risk mitigation, and sustainable practices. Publication trends reveal an evolution from traditional construction economics toward sustainable material systems, with mass timber research emerging as a dominant theme since 2022 while maintaining contributions to trenchless technology and construction safety. This trajectory aligns with industry shifts toward carbon-neutral building practices and advanced risk management methodologies.
Ismail Șenöz is a University Researcher in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), specializing in Signal Processing Systems and affiliated with BIASlab (Bayesian Inference and Signal Analysis Lab). His research focuses on developing advanced message passing algorithms for signal processing applications, particularly in hearing care technology. His primary research interests include: Bayesian Inference and Message Passing techniques Probabilistic modeling for signal processing Hearing impairment solutions and hearing aid technology System identification and parameter estimation Dr. Șenöz's recent work demonstrates a strong trend toward applying message passing algorithms to practical hearing care applications, with significant contributions to trajectory planning, amplitude demodulation, and system identification. His research combines theoretical advances in Bayesian computation with real-world engineering problems. He is actively involved in two major research projects: Auto-AR: Automated Situated Design of Augmented Hearing Reality Algorithms (2021-2026) CoHear: Hearing Care Collaborative Hearing Device Design (2017-2025) His collaborative work with A. de Vries and other researchers at TU/e has resulted in numerous conference publications with practical applications in hearing technology and signal processing systems.
Olaf Posch is a Professor of Economics, specializing in Methods of Economics, at the Department of Economics within the Faculty of Economics and Social Sciences at the University of Hamburg. His research integrates macroeconomic theory, econometrics, and computational methods to analyze dynamic equilibrium models, asset pricing, and policy design. Research Interests: His primary research areas include Macroeconomics, Dynamic Stochastic General Equilibrium (DSGE) models, Asset Pricing, Monetary and Fiscal Policy, Econometrics, Structural Estimation, and Continuous-Time Modeling. He investigates the implications of uncertainty, rare disasters, heterogeneous agents, and public policy on macroeconomic dynamics and financial markets. Recent Research Trends: His recent publications demonstrate a consistent focus on advancing the estimation and solution methods of macroeconomic models. He explores the role of uncertainty (e.g., Peso problems, Poisson shocks), the impact of fiscal policy and debt maturity (e.g., FTPL), and the importance of risk in model approximations. His work often bridges theoretical innovation with empirical application, using mixed-frequency data and structural estimation techniques. Scientific Grants: DFG Project No. 268475812: 'Dynamic equilibrium models' – Developing a continuous-time New Keynesian model. DFG Project No. 446166239: 'Fiscal Sustainability'. DFG Project No. 510995673: 'Fiscal policy in models with heterogeneous agents'. Advising and Grants: While specific advisees are not listed, he frequently collaborates with co-authors on research projects and working papers, indicating an active role in mentoring junior researchers. He is the principal investigator on multiple DFG-funded projects, demonstrating significant grant acquisition and research leadership. Laboratories and Research Teams: He leads a research team at the University of Hamburg, including Corinna Kienle, Réka Rátfai, and Josie Oetjen, focused on dynamic equilibrium models and macroeconomic policy analysis.
Jeremy HENG is an Associate Professor at ESSEC Business School , specializing in Information Systems, Data Analytics and Operations . He is affiliated with both the France and Singapore campuses, with his current position at ESSEC France since 2024 and prior experience at Harvard University as a Postdoctoral Fellow (2017–2019). His research focuses on Bayesian computation , sequential Monte Carlo methods , diffusion processes , and Monte Carlo variance reduction techniques , particularly for high-dimensional and discretized models. PhD in Statistics (University of Oxford, United Kingdom, 2017) BSc in Statistics (University College London, United Kingdom, 2012) The majority of his publications address Bayesian inference , Monte Carlo methods , and stochastic differential equations , with applications in financial econometrics , generative modeling , and optimal control . He has received the 2022 Blackwell-Rosenbluth Award for his contributions to Bayesian analysis and has served as Co-Editor-in-Chief of Statistics and Computing (2022–2023).
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.
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 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.
