جست‌وجوی استاد و پژوهشگر

Jamie Morgensternمشاهده پروفایل

دانشیار

• Machine Learning
• Ethics & Fairness
• Human-Centered AI
• +۳ مورد دیگر

Jamie Morgenstern is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington . She was previously an assistant professor at Georgia Tech and a Warren Center Fellow at University of Pennsylvania . Expertise: Ethics & Fairness, Human-Centered AI, Machine Learning Education: PhD in Computer Science from Carnegie Mellon University (2015) Her research examines the social impact of machine learning and ensuring ML models do not exacerbate societal inequalities. She investigates robustness to human-generated training data, fairness in clustering and active learning, and algorithmic equity in recommendation systems. Recent publications focus on interactive ML systems , fairness constraints , and privacy-preserving methods across conferences like NeurIPS, ICML, and AIES. Key subfields include multimodal learning , membership inference attacks , and data equity . Scientific Awards: NSF Career award for "Strategic and Equity Considerations in ML" Simons collaboration project Simons Award for Graduate Students in Theoretical Computer Science (2014-2016) NSF GFRP fellowship Microsoft Research Graduate Women's Scholarship Spotlight presentation at NeurIPS 2015 Mentoring: She advises current PhD students Rachel Hong , Jie (Claire) Zhang , and Yuanyuan (Chloe) Yang . Former advisees include Daniel Jiang (MS), Bhuvesh Kumar (PhD), and Angel (Alex) Cabrera (BS). Grants: Funded by NSF Career award and Simons collaboration projects. Previously supported by Simons, NSF, and Microsoft Research fellowships. Labs & Collaborations: Collaborates with researchers like Michael Kearns , Aaron Roth , and Avrim Blum . Affiliated with the Allen School's Artificial Intelligence research group.

• Causal Inference
• Data integration
• Measurement Error
• +۳ مورد دیگر

Qixuan Chen, PhD, is an Associate Professor of Biostatistics at Columbia University Mailman School of Public Health. She obtained her PhD from the University of Michigan in 2009, with dual expertise in biostatistics and survey sampling. Education: BA in Economics (Nankai University), MS in Applied Statistics (Bowling Green State University), PhD in Biostatistics (University of Michigan) Her research focuses on advanced statistical methods for complex surveys, causal inference, and handling missing data. Key contributions include developing Bayesian predictive inference frameworks using machine learning and regularized regression for integrating administrative records with survey samples. Recent publications emphasize environmental health applications, including measurement error correction for immunoassays and variable selection in multiply imputed data. Her work bridges biostatistics with computational methods for data integration. Scientific Awards: NIEHS Career Development Award, Teaching Award, Calderone Research Prize, Bryant Scholarship, Hutzinger Award She actively contributes to public health through dashboards like the New York City Neighborhoods COVID-19 tracker and PRIME radiology diagnostics platform. Grants such as R01ES035784 support her ongoing work in exposure-response analysis.

• Causal Inference
• Bayesian Modeling
• Biostatistics
• +۷ مورد دیگر

Xiaoxiao Zhou is an Assistant Professor in the Department of Biostatistics at the University of Alabama at Birmingham (UAB), affiliated with multiple centers including the Center for Outcomes and Effectiveness Research and Education (COERE), Center for Clinical and Translational Science (CCTS), and the Global Center for Craniofacial, Oral and Dental Disorders (GC-CODED). She holds a PhD in Statistics from The Chinese University of Hong Kong (2022) and completed a postdoctoral fellowship at Duke University's Department of Statistical Science. Her research focuses on causal inference, Bayesian methods, longitudinal data analysis, and survival analysis, with applications in Alzheimer’s disease, cardiovascular conditions, and neurodegenerative disorders. Dr. Zhou’s work integrates advanced statistical techniques with medical and behavioral data, including neuroimaging and latent variable modeling. Key areas include handling intercurrent events in clinical trials, causal mediation analysis, and joint modeling of longitudinal and survival outcomes. She collaborates widely with clinicians and biostatisticians to address real-world challenges in healthcare and disease progression studies. Her scholarly contributions span over a dozen peer-reviewed articles, emphasizing methodological innovations in biostatistics and their practical applications. She advises students such as Zhenying Ding and actively participates in academic committees. Outside academia, she enjoys outdoor activities like mountain hiking and weight lifting.

Xiang Limingمشاهده پروفایل

دانشیار

• Survival Analysis
• Longitudinal/Clustered Data Analysis
• Mixture Modelling
• +۳ مورد دیگر

Assoc Prof Xiang Liming is an Associate Professor in the Division of Mathematical Sciences at Nanyang Technological University (NTU), Singapore, serving as Assistant Chair (Students). She holds editorial roles at *Computational Statistics & Data Analysis* and *Statistics in Medicine*. With a PhD in Statistics (City University of Hong Kong, 2002), her research focuses on survival analysis, longitudinal data analysis, and biostatistical methods. Notable contributions include methodologies for semi-competing risks, interval-censored data, and mixture models. Her work bridges statistical theory with biomedical applications, addressing challenges in clinical trials and public health. Awards include the 2009 IIE Transactions Best Paper Award and the Outstanding Research Thesis Award (2002–2003, CityU). Education: PhD in Statistics, City University of Hong Kong (2002) Postdoctoral Research: Hong Kong University of Science and Technology (2002–2003) and CityU (2003–2006) Research Interests: Survival analysis methodologies, including frailty models, cure models, and quantile regression for censored data. She develops robust statistical approaches for clustered/longitudinal data, addressing missingness and overdispersion. Applications span biomedical research, epidemiology, and quality management. Grants & Collaborations: Her grants include work on robotic-assisted stroke rehabilitation (2021) and LNG cold energy utilization systems (2017–2019). She collaborates with clinical teams on trials involving upper limb neurorehabilitation technologies. Labs & Teams: Leads statistical method development for multi-center clinical trials, particularly in biostatistics and survival analysis frameworks. Active in NTU’s School of Physical & Mathematical Sciences research initiatives.

Marc GENDRON-BELLEMAREمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Deep Learning
• Generative Modeling
• +۳ مورد دیگر

Marc GENDRON-BELLEMARE is an Associate Professor at the Department of Computer Science and Operations Research, Faculty of Arts and Sciences, Université de Montréal. He is also a Chief Scientific Officer at Reliant AI, Adjunct Professor at McGill University, Canada CIFAR AI Chair at Mila, and Associate Fellow at CIFAR LMB Program. His research focuses on reinforcement learning, deep learning, and generative models, with notable contributions to the Atari 2600 benchmark and applications in robotics and stratospheric balloon navigation. He has advised multiple PhD and MSc students, including Pierluca D'Oro and Rishabh Agarwal. Education: PhD from University of Alberta under Michael Bowling and Joel Veness. Notable collaborations include work at Google Brain and DeepMind. Research Interests: Reinforcement Learning, Deep Learning, Probabilistic Models, Online Learning, Generative Models, and Information Theory. Awards: Best Paper Awards at NeurIPS 2021, ICLR 2020, and ICML Exploration Workshop 2019. His work on stratospheric balloon navigation using RL was published in Nature (2020). Grants and Labs: Core member of Mila, involved in projects like the Dopamine research framework and the Arcade Learning Environment (ALE). Active in open-source contributions and industry partnerships through Reliant AI.

Melanie Schienleمشاهده پروفایل

استاد

• Econometrics
• Statistical Methods
• Financial Risk Analysis
• +۴ مورد دیگر

Prof. Melanie Schienle is a Professor and Chair of Statistical Methods and Econometrics at the Department of Economics and Management, Karlsruhe Institute of Technology (KIT). She also holds a professorship in the Department of Mathematics at KIT since 2021. Her expertise spans statistical methods, econometrics, financial risk analysis, and forecasting. She leads the HKMetrics Network and the RespiNow Hub for respiratory disease forecasting. She serves as a Senior Fellow at the Rimini Center for Economic Analysis (RCEA), a steering committee member of the German Economic Association, and a member of the University Research Council at KIT. Education: Ph.D. (Dr. rer. pol.) in Economics from Mannheim University (2008), summa cum laude; Diploma in Mathematics (University of Karlsruhe, 2003) with a minor in theoretical physics. She has held academic positions at Leibniz University Hannover (2012–2015) and Humboldt University of Berlin (2008–2012). Research interests focus on financial networks, systemic risk, time series analysis, and machine learning applications in economics. She co-leads projects on nowcasting and forecasting, including collaborative efforts during the pandemic to predict hospitalizations. Her work integrates advanced statistical techniques with real-world policy implications. Prof. Schienle is an Associate Editor for the International Journal of Forecasting and Journal of Time Series Analysis . She has authored over 50 peer-reviewed publications and contributed to high-impact journals like Nature Communications and Journal of Business & Economic Statistics . She leads the Institute of Statistics at KIT and chairs the MathSEE initiative for interdisciplinary mathematical applications.

Roderick M. Rejesusمشاهده پروفایل

استاد

• Agricultural Economics
• Risk Management in Agriculture
• Crop Insurance Policies
• +۴ مورد دیگر

Roderick M. Rejesus is a Professor and Extension Specialist in the Department of Agricultural and Resource Economics at North Carolina State University. He holds a Ph.D. from the University of Illinois at Urbana-Champaign. His work focuses on applied production economics, agricultural risk management (including crop insurance and safety-net policies), and economic impacts of agricultural technologies. He teaches ECG 741: Agricultural Production and Supply in Fall semesters. Education: Ph.D. in Agricultural Economics, University of Illinois at Urbana-Champaign (2001) Research Interests: Dr. Rejesus examines policy-driven agricultural practices such as soil health management and cover crops, their resilience to climate extremes, and their economic implications. His work integrates empirical analysis of crop insurance mechanisms, yield risk, and environmental sustainability. He evaluates conservation programs (e.g., no-till practices) and their effects on land values and erosion. His studies often address global challenges like climate change and food security, with projects in the U.S., Philippines, and other regions. Recent Research Trends: His articles emphasize climate-smart agriculture, crop insurance dynamics under extreme weather, and policy incentives for sustainable practices. Key themes include quantifying yield sensitivity to environmental factors (e.g., warming temperatures), analyzing soil health benefits, and modeling production risk in agricultural systems. Advising & Grants: While specific grants or advisees are not listed, his role as an Extension Specialist suggests involvement in applied research partnerships with farmers and policymakers. His work often involves collaborative projects with international institutions. Labs/Teams: Affiliated with North Carolina State’s Extension programs and interdisciplinary teams focused on agricultural economics and policy.

Chen Zhiمشاهده پروفایل

استادیار

• Innovation Sourcing
• AI Development
• Wisdom of Crowds
• +۲ مورد دیگر

Chen Zhi is an Assistant Professor in the Department of Analytics and Operations at the NUS Business School, National University of Singapore, where he has been employed since July 2020. His academic work focuses on operational challenges in innovation-driven value chains, with particular expertise in new product development, innovation sourcing, AI economics, and wisdom of crowds methodologies. Dr. Chen received his Ph.D. in Management (Decision Sciences) from INSEAD (2014-2020) and his B.E. with First Class Honors in Industrial and Systems Engineering from the National University of Singapore (2009-2013). During his academic journey, he completed an overseas exchange at the Georgia Institute of Technology and a research internship at École Polytechnique in France. Chen's research spans two primary streams. The first addresses R&D phase challenges in new products, examining how firms can optimally design supplier bases and informational policies to procure innovation from suppliers, with recent focus on policy impacts on AI-based product development such as autonomous vehicles. The second stream investigates demand forecasting for new products, exploring how to aggregate diverse expert insights into consensus forecasts and quantify underlying uncertainty through obtained forecasts. His publication record demonstrates a strong trajectory in top-tier operations and management journals, with recent work appearing in Management Science, Manufacturing & Service Operations Management, and International Journal of Forecasting. Chen's research shows increasing focus on AI development challenges, uncertainty quantification in forecasting, and strategic approaches to innovation sourcing in complex technological environments. At NUS Business School, Chen teaches Managerial Operations and Analytics for the MBA program and Decision Analytics Using Spreadsheets for undergraduate students. Previously at INSEAD, he taught tutorials for Uncertainty, Data and Judgment, and Production and Operations Management courses.

Ray Baiمشاهده پروفایل

استادیار

• Bayesian Statistics
• High-Dimensional Modeling
• Scalable Machine Learning
• +۷ مورد دیگر

Ray Bai is an Assistant Professor in the Department of Statistics at the University of South Carolina (USC), part of the McCausland College of Arts and Sciences. Effective August 2025, he will join the George Mason University (GMU) Department of Statistics as a faculty member. His research focuses on Bayesian statistics, deep learning, and causal inference, with applications to biomedical and public health challenges such as genomic studies, drug repositioning, and electronic health records analysis. Bai holds a PhD in Statistics from the University of Florida (2018), an MS in Applied Mathematics from the University of Massachusetts Amherst, and a BA from Cornell University. His work has been supported by the National Science Foundation (NSF). Education: PhD in Statistics, University of Florida (2018) MS in Applied Mathematics, University of Massachusetts Amherst BA, Cornell University Research interests include scalable algorithms for high-dimensional data, nonconvex optimization, and distributed inference methodologies. His work bridges statistical theory with practical applications in healthcare, emphasizing robustness and computational efficiency. Recent contributions address challenges in single-index models for skewed data, generative quantile regression, and Bayesian varying-coefficient models. Advising includes supervising PhD students Zile Zhao and Shijie Wang, who have contributed to survival analysis and deep learning frameworks. Future openings for students at GMU focus on Bayesian methodology and machine learning. Labs/Teams: Collaborates on projects involving interdisciplinary teams in biostatistics and computational biology.

Liang Zhangمشاهده پروفایل

استاد

• Higher Education Economics
• Public Policy Analysis
• Educational Finance
• +۳ مورد دیگر

Liang Zhang is a Professor of Higher Education at New York University's Steinhardt School, specializing in higher education economics, finance, and public policy. He previously taught at the University of Minnesota, Vanderbilt University, and Penn State University. His research examines the role of governments and institutions in shaping institutional performance and student outcomes, with a focus on college access, labor markets, and policy efficacy. Dr. Zhang holds dual PhDs from Cornell University (Economics) and the University of Arizona (Higher Education). His work has been published in leading journals such as Review of Higher Education , Economics of Education Review , and Harvard Education Review . Key research areas include the impact of state policies on college enrollment, peer effects in academic decisions, and the global dynamics of scientific productivity. Recent studies highlight his analysis of the Post-9/11 GI Bill’s effects on veteran education access and outcomes, as well as the stratification of faculty employment in U.S. higher education institutions. His work consistently bridges economic theory and policy practice, offering actionable insights for institutional leaders and policymakers.

Charles Dossمشاهده پروفایل

دانشیار

• Nonparametric Estimation
• Empirical Process Theory
• Shape-Constrained Inference
• +۴ مورد دیگر

Charles Doss is an Associate Professor in the School of Statistics at the University of Minnesota. He earned his PhD from the University of Washington in 2013 under Jon Wellner and holds a B.S. in Mathematics from the University of Chicago. His research focuses on empirical process theory, nonparametric estimation/inference for functions with shape constraints (e.g., concavity, log-concavity), and applications to causal inference, birth-death processes, and unlinked regression. His recent publications address problems such as doubly robust estimation for continuous treatments, heteroscedasticity detection, and convex stochastic optimization. He has received significant funding, including NSF grants DMS-2210312 and DMS-1712664, as well as institutional awards. Warwick Mid-Career Faculty Research Award (2022–2023) NSF DMS-2210312 Grant NSF DMS-1712664 Grant He has served as an Associate Editor for The Electronic Journal of Statistics (2022–present) and The American Statistician (2020–2024). He mentors students such as Guangwei Weng, Daeyoung Ham, and Oliver VandenBerg and contributes to outreach programs like Run the World, a Machine Learning summer camp for high school students.

Efthymios Pavlidisمشاهده پروفایل

استاد

• Macroeconomics
• International Finance
• Time Series Econometrics
• +۴ مورد دیگر

Professor Efthymios Pavlidis is a faculty member in the Department of Economics at Lancaster University Management School (LUMS). He holds the rank of Professor and specializes in macroeconomics, international finance, and time series econometrics. His research focuses on housing market dynamics through collaborations like the International Housing Observatory (with the Federal Reserve Bank of Dallas) and the UK Housing Observatory. He is a Fellow of the Higher Education Academy, reflecting his commitment to academic excellence in teaching and research. His research interests include speculative bubble detection, real estate price forecasting, and testing parity conditions in financial markets. Pavlidis actively supervises PhD students in applied time series econometrics, emphasizing practical applications in financial markets and housing economics. He is involved in numerous academic activities, including organizing conferences and workshops such as the Dynare Conference and the Lancaster Economics Seminar. Key contributions include developing econometric methods for detecting market exuberance and analyzing real exchange rates. His work bridges theoretical econometrics with practical policy implications, particularly in housing and energy markets. Pavlidis collaborates internationally, evidenced by his participation in global academic networks and institutions like the European Economic Association and the Royal Economic Society. His teaching includes the course ECON222 Intermediate Macroeconomics I, and he maintains an office in the Management School (B015), with weekly office hours on Tuesdays. A comprehensive overview of his research and projects is available at his personal webpage: https://sites.google.com/view/etpavlidis/ .

• Extreme Value Statistics
• Financial Risk Management
• Mathematical Statistics
• +۷ مورد دیگر

Chen Zhou is a Full Professor of Mathematical Statistics and Risk Management at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. He is a member of the Research Advisory Committee of Erasmus School of Economics and actively contributes to academic leadership and research governance. His research focuses on extreme value statistics and financial risk management , with significant contributions to the theoretical and applied understanding of extreme events in financial and statistical contexts. His work bridges mathematical rigor with practical applications in finance and econometrics. The recent publications highlight a strong trend in advancing methodologies for extreme value estimation, including bootstrapping techniques, tail copula modeling, dimension reduction for extremes, and semi-supervised frameworks. These works are published in high-impact journals such as the Journal of the American Statistical Association , Bernoulli , and the Journal of Finance , indicating broad disciplinary relevance across statistics, econometrics, and finance. Editorial work: Editor, Extremes (since 2015) He teaches in the Bachelor program of Econometrics and Management Science and the MSc program in Quantitative Finance, and is affiliated with the Tinbergen Institute. He has supervised multiple doctoral students, reflecting his active role in academic mentorship and research training. Chen Zhou leads a research network focused on extreme value theory, systemic risk, and statistical inference, collaborating with leading scholars in the field. His work continues to shape methodological developments in the analysis of rare and high-impact events.

Sabyasachi Chatterjeeمشاهده پروفایل

دانشیار

• Nonparametric Signal Estimation
• Shape Constrained Estimation
• Statistical Information Theory
• +۵ مورد دیگر

Sabyasachi Chatterjee is an Associate Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. He joined UIUC in 2017 after serving as a Kruskal Instructor at the University of Chicago. He earned his PhD in Statistics from Yale University (2014), advised by Andrew Barron. His research focuses on nonparametric signal estimation, shape-constrained estimation (monotonicity, convexity, unimodality), statistical information theory, and resampling methods like cross-validation. He also explores statistical learning theory, online learning, and applied probability. His recent work includes advancements in quantile regression via dyadic CART, adaptive estimation of piecewise polynomials, and spatially adaptive prediction algorithms. Key contributions involve risk bounds for trend filtering and cross-validation frameworks for signal denoising. His research is supported by NSF Grant DMS-1916375 on nonparametric estimation under shape/norm constraints. Chatterjee collaborates on grants and has advised multiple students (though specific names are not listed in available texts). His lab’s work bridges theoretical statistics with practical applications in data science and signal processing.

Mawuli Kouami Segnonمشاهده پروفایل

پژوهشگر

• Econometrics
• Time Series Analysis
• Financial Forecasting
• +۵ مورد دیگر

Dr. Mawuli Kouami Segnon is a researcher at the Chair of Empirical Economics, School of Business and Economics, University of Münster. His work focuses on econometric modeling, financial time series analysis, and volatility forecasting across various domains including cryptocurrencies, energy markets, and macroeconomic indicators. Research interests include: Development of advanced volatility models (GARCH, multifractal, regime-switching) Applications to financial markets, energy economics, and macroeconomic policy High-frequency data analysis and mixed-frequency forecasting Count data modeling with conditional heteroscedasticity Portfolio risk management using copula and multifractal approaches Recent publications demonstrate expertise in: Geopolitical risk impacts on stock volatility Comparative analysis of realized variance measures Inflation uncertainty modeling in G7 countries Electricity price volatility in Australian markets Bitcoin market forecasting Historical economic data analysis Current projects (since 2020) involve: Innovative economic/financial time series forecasting Financial market volatility modeling Applications of multifractal structures in econometrics