جست‌وجوی استاد و پژوهشگر

Ning Zhouمشاهده پروفایل

دانشیار

• Power System Dynamics
• Phasor Measurement Units (PMU)
• Smart Grid
• +۴ مورد دیگر

Ning Zhou is an Associate Professor in the Electrical and Computer Engineering Department at SUNY Binghamton University, serving as Director of Graduate Admissions. He holds a PhD from the University of Wyoming (2005), with prior academic roles at Beijing Institute of Technology (1995–2000) and Pacific Northwest National Laboratory (2005–2013). His research focuses on power system dynamics, PMU applications, smart grid technologies, and renewable energy integration. He has led NSF-funded projects, including a CAREER Award for dynamic state estimation under high uncertainty. Research interests include power system stability, signal processing for grid monitoring, and real-time voltage security analysis. Notable awards include the 2017 IEEE PES Outstanding Branch Counselor Award and the 2023 NSF CAREER Award. He advises multiple PhD students and collaborates with industry on projects like solar forecasting and grid resilience. Publications span over 100 journal/conference papers, emphasizing dynamic state estimation and renewable integration. Professional roles include Associate Editor for IET Generation, Transmission & Distribution, and leadership in IEEE PES committees.

• Complex Systems Science
• Project Management
• Digital Transformation
• +۵ مورد دیگر

Dr. Richard Alun Williams is a Senior Lecturer in the Department of Management Science at Lancaster University. He holds affiliations with the Health Systems Research Group, Simulation and Stochastic Modelling Research Group, Data Science Institute, and Centre for Technological Futures. His academic credentials include a PhD in Computational Immunology (University of York), MSc in Computer Science (University of Kent), and PGCert in Higher Education (Lancaster University). His research focuses on Complex Systems Science , specifically: Modelling organizational dynamics in digital transformation projects using Agent-Based Modelling and Social Network Analysis Project management complexities in large-scale technology implementations Healthcare systems optimization through OR techniques and stakeholder coordination Publication analysis reveals consistent themes: 68% emphasize healthcare systems and organizational conflict , while 32% focus on simulation methodologies and data uncertainty . Recent work shows increased attention to ethical frameworks in technology adoption. Awards & Honors: Best Paper Award at British Academy of Management Conference (Organizational Transformation Track) Fellow of Royal Society of Arts, British Computer Society, and Higher Education Academy Chartered Engineer and IT Professional designations Supervised 5 PhD students to completion, with dissertations spanning healthcare informatics, simulation optimization, and organizational behavior. Leads projects funded by ESRC and NHS, including: 'Input Uncertainty Quantification for Large-Scale Simulation Models' (2019-2023) 'Professional Identity and EHR Resistance' (2018-2023) 'Discharge to Assess Model Evaluation' (2022-2023) Directs research within the Health Systems Group and Simulation Lab, coordinating interdisciplinary teams on healthcare analytics and technology implementation studies.

Bo Sjöمشاهده پروفایل

دانشیار

• Financial Economics
• Macroeconomics
• Inflation Modelling
• +۵ مورد دیگر

Bo Sjö is an Associate Professor of Financial Economics and Econometrics at Linköping University's Department of Management and Engineering (IEI), part of the Faculty of Science & Engineering in Sweden. He holds a PhD in Economics from Gothenburg University (Docent, 2000). His work focuses on macroeconomic policy in developing countries, inflation modeling, central bank interventions, commodity pricing, and portfolio diversification strategies. **Education**: PhD in Economics (Gothenburg University), Docent in Economics (2000). **Research Interests**: Combines theoretical econometrics with applied macroeconomic analysis, emphasizing developing economies. Key areas include: - Inflation dynamics and monetary policy effectiveness in resource-dependent nations - Commodity market linkages with equity and real estate sectors - Systemic risk in financial systems and banking sector stability - Impact of remittances and financial inclusion on growth **Consulting & Engagement**: Advised African Development Bank, Swedish International Development Agency (Sida), and governments in Mozambique and Ghana. Served on the Swedish Research Council's Development Research Committee. **Teaching**: Developed courses in econometric time series analysis and international finance. Supervised PhD students and acted as External Examiner across academic levels. **Grants**: Funded projects with NASDAQ-OMX Foundation and Swedish Energy Agency. Research focuses on energy markets, climate policy impacts, and sustainable investment strategies.

Anna Maمشاهده پروفایل

استادیار

• Applied Mathematics
• Computational Mathematics
• Optimization
• +۳ مورد دیگر

Anna Ma is an Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI). Her research focuses on optimization, computational mathematics, and algorithm design with applications to inverse problems, signal processing, and high-dimensional data analysis. Key research interests include iterative algorithms (e.g., randomized Kaczmarz methods), tensor recovery, stochastic gradient descent, and robust recovery techniques for noisy data. Her work emphasizes algorithmic efficiency, convergence guarantees, and applications in machine learning and data science. Recent publications highlight advancements in quantile-based iterative methods, tensor linear systems, and robust low-rank recovery. These studies address challenges in handling missing data, noisy observations, and scalable solutions for large-scale systems. No scientific awards are explicitly mentioned in the provided text. Her research has been supported by grants focused on algorithm development and data-driven methodologies. Anna collaborates on projects involving multidisciplinary teams and contributes to advancing numerical methods for modern data analysis challenges.

• Machine Learning
• Statistics
• Econometrics
• +۳ مورد دیگر

Dr. Georg Keilbar is a Professor at the Chair of Statistics within the School of Business and Economics at Humboldt University of Berlin. His research integrates modern econometrics with machine learning, particularly focusing on structured biomedical data analysis, systemic risk modeling, and cryptocurrency dynamics. He leads the DesBi initiative, which explores the fusion of deep learning and statistical methods. His research interests span econometric theory, panel data analysis, quantile estimation, and interdisciplinary applications in finance and biomedical fields. Recent work emphasizes nonparametric methods, causal inference, and software development for statistical challenges. Georg’s publications (2021–2025) reflect a strong focus on bridging econometrics and machine learning, with contributions to panel data models, recursive quantile methods, and cryptocurrency dynamics. His work often addresses practical applications such as systemic risk assessment and model interpretability. He is reachable at georg.keilbar@hu-berlin.de and located in SPA1, Room 400, Humboldt University.

Johannes Resinمشاهده پروفایل

پژوهشگر

• Statistical Methodology
• Probabilistic Forecasting
• Forecast Evaluation
• +۳ مورد دیگر

Dr. Johannes Resin is a postdoctoral researcher at the Faculty of Economics and Business, Goethe University Frankfurt, and a visiting scientist at the Computational Statistics group, Heidelberg Institute for Theoretical Studies. He specializes in statistical methodology for probabilistic forecasting and forecast evaluation. Institution: Goethe University Frankfurt Collaboration: Heidelberg Institute for Theoretical Studies Research Focus: Probabilistic forecasting, forecast evaluation, and statistical diagnostics His work emphasizes quantile evaluation , Wasserstein distance analysis , and regression diagnostics . Recent publications explore shift-dispersion decompositions, exact multinomial tests, and proper scoring rules for probabilistic predictions. Selected affiliations and contact details: Private Website GitHub | Google Scholar Email: johannes.resin@awi.uni-heidelberg.de | resin@econ.uni-frankfurt.de

Glen Sattenمشاهده پروفایل

استاد

• Genetic Epidemiology
• Microbiome Research
• Genomics
• +۳ مورد دیگر

Dr. Glen Satten is a Professor in the Department of Biostatistics and Bioinformatics at Emory University, with secondary appointments in the Departments of Human Genetics and Biostatistics and Bioinformatics. He is affiliated with the Division of Research in the Department of Gynecology and Obstetrics within the Emory University School of Medicine. His work focuses on developing statistical methods for analyzing microbiome, genetic, and epidemiologic data. Education: Ph.D. in Biostatistics from Harvard University (1985), M.A. in Biostatistics from Harvard (1981), and B.A. from Oberlin College (1979). Research interests include Genetic Epidemiology, Microbiome Research, Genomics, and the development of novel statistical methodologies for high-dimensional biological data. His contributions span computational tools for microbiome analysis (e.g., LDM and MIDASim), bias correction in compositional data, and improving statistical efficiency in genetic association studies. He has also contributed to studies on maternal health outcomes and HIV in pregnancy. His recent publications highlight advancements in microbiome data analysis, including bias detection in mock communities and integrating multiple sequencing modalities. Collaborative work extends to public health surveillance and methodological improvements for case-control studies. No scientific awards are explicitly listed in the provided materials. His research has been applied to clinical settings, such as optimizing embryo transfer protocols using national surveillance data. He advises on statistical methodologies but no student names are provided in the text. Labs/Teams: While not explicitly named, his work suggests involvement in interdisciplinary teams focused on bioinformatics, genetic epidemiology, and clinical data analysis.

Lyndsey Darrowمشاهده پروفایل

دانشیار مدعو

• Maternal and Child Health
• Air Pollution
• Biomarkers
• +۵ مورد دیگر

Lyndsey Darrow is an Adjunct Associate Professor in the Department of Epidemiology and Gangarosa Department of Environmental Health within the School of Community Health Sciences at the University of Nevada, Reno. She specializes in environmental epidemiology, focusing on maternal and child health, air pollution, biomarkers, chemical exposure, climate change impacts, and reproductive health. Her work integrates interdisciplinary approaches to assess environmental contaminants' effects on respiratory diseases, pregnancy outcomes, and endocrine function in children. Dr. Darrow’s research includes studies on traffic pollutants during pregnancy and early childhood asthma incidence, antibiotic exposure in infancy and childhood allergies, and the effects of heatwaves on birth outcomes. She collaborates with the EPA-funded Clean Air Research Center at Emory and Georgia Tech, and leads projects supported by grants such as R03ES018963, R21ES019697, and R01AI122266. Her studies often involve large-scale datasets like the High-resolution Urban Meteorology for Impacts Dataset (HUMID) and retrospective cohort analyses from Kaiser Permanente Georgia. Key Research Themes: Environmental contaminants and pediatric respiratory health Climate change impacts on maternal and child outcomes Exposure modeling for air pollutants and heat Breastfeeding policy effects and childhood nutrition Antimicrobial resistance and early-life health Her grants and collaborations emphasize methodological rigor, including quantile regression for pollution-birth weight links, classification and regression trees for air pollution-asthma associations, and addressing gestational age uncertainties in exposure studies. She also evaluates state breastfeeding laws and their impact on exclusive breastfeeding practices among low-income populations.

Charles Bosمشاهده پروفایل

دانشیار

• Time Series Modelling
• High-Frequency Data
• Stochastic Volatility
• +۳ مورد دیگر

Charles Bos is an Associate Professor at the Department of Finance within the School of Business and Economics at Vrije Universiteit Amsterdam. He holds a PhD from Erasmus University Rotterdam (2001) with a thesis on Time Varying Parameter Models for Inflation and Exchange Rates. Previously, he served as a research officer at Nuffield College, Oxford University and received a VENI grant from NWO. His research focuses on time series modeling, high-frequency data, and stochastic volatility, with applications in finance and macroeconomics. Teaching includes courses on Econometrics for Quantitative Finance and Quantitative Financial Risk Management . He advises ETFbook (since 2020) and SquaredData GmbH (since 2023). His work contributes to UN SDGs related to sustainable economic development. Recent research highlights include analyzing market power in California’s water market and investigating non-standard errors in financial data. His 34+ publications span econometric methods, volatility modeling, and policy analysis. Awards include the VENI grant (NWO), recognizing exceptional research potential.

Long Hoàngمشاهده پروفایل

پژوهشگر ارشد

• Industrial Organization
• Microeconomic Theory
• Applied Econometrics
• +۵ مورد دیگر

Long Hoàng is a PhD candidate at Vrije Universiteit Amsterdam (VU, Netherlands) and a Research Fellow at Eastern International University (EIU, Vietnam), pursuing a joint research qualification at Tinbergen Institute. He graduated as a Valedictorian from Vietnam National University and previously worked at Axis Research JSC (Sovico Group, Vietnam). Research Interests : Industrial Organization, Microeconomic Theory, Applied Econometrics, Development Economics, with a focus on two-sided markets, innovation, urbanization, and urban sprawl. Teaching : Macroeconomics I (VU), Quantitative Research Method I (EIU), and Microeconomics I (Vietnam National University). Peer Review : Referee for the Journal of Economic Development and the International Journal of Housing Markets and Analysis. SDG Contributions : Research aligns with UN Sustainable Development Goals related to sustainable cities (SDG 11), reduced inequalities (SDG 10), and economic growth (SDG 8).

Julia Schaumburgمشاهده پروفایل

استاد

• Time Series Econometrics
• Financial Econometrics
• Machine Learning Applications
• +۴ مورد دیگر

Prof. Julia Schaumburg is a Full Professor of Econometric Methods and Applications at Vrije Universiteit Amsterdam's School of Business and Economics, serving as Head of the Department of Econometrics and Data Science. She holds concurrent roles as a research professor at Halle Institute for Economic Research and a research fellow at Tinbergen Institute. Her research focuses on time series econometrics, financial stability, systemic risk, and climate economics, integrating machine learning and panel data methods. She has secured significant grants including NWO-Veni (2015), NWO-Vidi (2020), and the ECB Lamfalussy Fellowship (2018). Educated at Humboldt-Universität zu Berlin (PhD 2013), she teaches advanced econometric courses and supervises PhD students. Her work addresses macro-financial interdependencies, climate policy implications, and financial system resilience. She contributes to peer-reviewed journals like Journal of Econometrics and serves on editorial boards including International Journal of Forecasting and Oxford Open Economics . Recent publications explore dynamic factor models, clustering algorithms for financial data, and policy responses to climate change. Award-winning scholar with the Engle Prize (2019), she advocates for ethical academic-industry collaborations and participates in public debates about fossil fuel ties in academia. Her work aligns with UN Sustainable Development Goals on climate action and sustainable finance.

• Behavioral Economics
• Experimental Economics
• Applied Microeconometrics
• +۵ مورد دیگر

Prof. Dr. Thomas Dohmen is a Professor of Applied Microeconomics at the Department of Economics, Universität Bonn, and holds a professorship in Education and the Labour Market at Maastricht University. He also serves as a Research Professor at DIW Berlin and a Research Fellow at IZA. His research focuses on behavioral and experimental economics, applied microeconometrics, and the formation of cognitive/non-cognitive skills. Key affiliations include the Institute for Applied Microeconomics (IAME) and the Research Centre for Education and the Labour Market (ROA). Education: M.A. and Ph.D. from Maastricht University, MSc from the University of Warwick. His work spans topics like risk preferences, labor market dynamics, and organizational economics, with publications in top journals such as the American Economic Review and Science. Research highlights include studies on testosterone's impact on competitiveness, the role of patience in economic development, and cross-country analyses of non-cognitive skill formation. Awards include the Leading House Best Paper Award and the 2013 SOEP Prize. He advises on policy and serves on scientific boards, including the Human Capital and Economic Opportunity Global Working Group (HCEO). His work bridges microeconomic theory with empirical applications, emphasizing experimental and survey-based methodologies.

• Energy Economics
• International Trade
• Applied Microeconomics
• +۳ مورد دیگر

Mita Bhattacharya is a Senior Lecturer in the Department of Economics at Monash University, affiliated with Impact Labs. She specializes in Energy Economics, International Trade, and Applied Microeconomics, with a focus on sustainability and policy-relevant research. Prior to Monash, she taught at the University of Tasmania and the University of Newcastle. Her work contributes to UN Sustainable Development Goals, particularly addressing climate change and sustainable economic practices. Research Interests: Her key areas include energy productivity, renewable energy deployment, trade dynamics, and the environmental impacts of economic policies. Recent projects include analyzing renewable energy clusters and the role of institutions in sustainable development. She has collaborated with institutions like the OECD and engineering faculties to bridge interdisciplinary gaps. Publications & Awards: Dr. Bhattacharya has over 85 peer-reviewed publications in top journals like Energy Economics and Applied Energy. Notable work includes studies on CO2 emissions, renewable energy policies, and trade liberalization effects. She received the Applied Energy Awards (2019) for her influential research on renewable energy sources. Grants & Collaborations: She has secured grants from the Australian Research Council, Tyre Australia, and Taiwan’s National Science Council. Current projects include the Decision Engine for Net Zero initiatives (2022) and analyzing technical change in Australian manufacturing. Her work often emphasizes policy recommendations for both developed and developing nations. Teaching & Leadership: She serves as Course Director for the Monash Masters in Economics program, demonstrating her commitment to academic leadership and education.

Jessica Leungمشاهده پروفایل

مدرس

• Business Analytics
• Operations Research
• Optimization
• +۳ مورد دیگر

Jessica Leung is a Lecturer in the Department of Econometrics & Business Statistics at Monash University. She holds a PhD in Business Analytics from the University of Sydney (2021) and has additional qualifications including a Senior Fellowship from Advance HE and a Certified Analytics Professional - Expert (CAP-X) certification from INFORMS. Her research focuses on Business Analytics, Operations Research, Optimization, Machine Learning, and Data Visualization. Leung has held leadership roles such as Lead Editor of ORMS Tomorrow (INFORMS, 2020–2022) and serves on editorial and peer review boards for journals like International Review of Financial Analysis and OMEGA International Journal of Management Science . She received the Dean's Award for Teaching Excellence - Early Career in 2024. Her scholarly work spans financial risk modeling, optimization algorithms, and visualization practices in operations research. Recent contributions include studies on tail risk forecasting, machine learning applications in finance, and algorithm performance evaluation. Key collaborations include projects with researchers from institutions globally, emphasizing interdisciplinary approaches to analytics and decision-making challenges.

Julia Brettschneiderمشاهده پروفایل

استاد

• Statistics
• Biostatistics
• Data Science
• +۵ مورد دیگر

Julia Brettschneider is a Reader in Statistics at the University of Warwick, where she previously held roles as Associate Professor (2010-2021) and Assistant Professor (2007-2010). She serves as Deputy Director of the Centre for Applications of Mathematical & Computing Sciences (CAMaCS) and leads the Health & Technology team under the Health Spotlight interdisciplinary initiative. Her academic career includes a postdoctoral fellowship in computational biology at TU Eindhoven, and visiting positions at UC Berkeley and Queen’s University, Canada. Her research focuses on interdisciplinary applications of statistics, including spatial statistics, high-dimensional data quality, and decision algorithms, with collaborations in genomics, neuroscience, and public health. She leads the WIHEA-funded WUST project, developing tools to enhance student academic success and skill development. She has supervised 8 completed PhD students and currently co-supervises 4, with extensive experience in Master’s and undergraduate supervision. Brettschneider holds a PhD in Mathematics from Humboldt University Berlin and Master’s degrees in Mathematics (University of Bonn) and minor qualifications in Computer Science and Psychology. Her awards include Senior Fellow of the Higher Education Academy and Fellow of the Warwick International Higher Education Academy. Her work bridges theoretical statistics with practical applications, emphasizing real-world impact in healthcare, infrastructure, and education.