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Aditya GuntuboyinaView profile

Associate Professor

• Nonparametric statistics
• High-dimensional statistics
• Shape-constrained statistical estimation
• +1 more

Aditya Guntuboyina is an Associate Professor in the Department of Statistics at the University of California, Berkeley. He has held this position since January 2012, following a postdoctoral stint at the Wharton Statistics Department and a PhD in Statistics from Yale University (2011) under Professor David Pollard. He earned his B.Stat and M.Stat degrees from the Indian Statistical Institute, Kolkata. PhD: Statistics, Yale University (2011) B.Stat/M.Stat: Indian Statistical Institute, Kolkata His research focuses on nonparametric and high-dimensional statistics , particularly shape-constrained estimation and Bayesian/Empirical Bayes methods . Key themes include convex regression, isotonic regression, mixture models, and total variation denoising. Recent publications analyze multivariate scale mixtures, convergence rates, and suboptimality of least squares in constrained settings. Aditya has supervised multiple PhD students in the Berkeley Statistics and EECS programs. He teaches courses such as Time Series Analysis (Stat 153/248), Data, Inference, and Decisions (Data 102), and advanced probability (Stat 201A). His work often intersects with machine learning, optimization, and information theory. Scientific contributions include theoretical advances in shape-restricted regression, adaptation in log-concave density estimation, and risk bounds for convex-constrained models. He has published in top journals like Annals of Statistics , Journal of the Royal Statistical Society: Series B , and IEEE Transactions on Information Theory .

Yuntian DengView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +3 more

Yuntian Deng is an Assistant Professor at the University of Waterloo and a Visiting Professor at NVIDIA. He holds affiliations with Harvard SEAS as an Associate and the Vector Institute as a Faculty Affiliate. He completed his PhD in Computer Science at Harvard under Professors Alexander Rush and Stuart Shieber, followed by a postdoc under Yejin Choi. His research focuses on Natural Language Processing and Machine Learning, with notable contributions in chatbot interaction analysis (WildChat), implicit reasoning models, and markup-to-image generation. He has developed influential tools like OpenNMT and WildVis, and his work has been featured in outlets like the Washington Post and used by OpenAI and Anthropic. Education: PhD in CS (Harvard), Postdoctoral Research (University of Washington). Key achievements include the ACM Gordon Bell Prize for GenSLMs, Best Demo Runner-up at ACL 2017, and Best Paper at DAC 2020. His research emphasizes scalable datasets, efficient reasoning techniques, and real-world applications of AI models. Research interests span NLP, machine learning algorithms, and their applications in areas like dialogue systems, generative models, and ethical AI evaluation. Notable projects include WildChat (1M ChatGPT interactions), implicit chain-of-thought reasoning, and neural steganography for text-based information hiding. His articles explore topics ranging from knowledge distillation to diffusion models, with a focus on bridging theoretical advancements and practical implementations. He actively collaborates with industry partners like NVIDIA and maintains open-source tools to advance AI research accessibility.

Prosper DovononView profile

Professor

• Econometrics
• Time Series Analysis
• Financial Econometrics
• +2 more

Prosper Dovonon serves as a Full Professor in the Department of Economics at Concordia University in Montreal, Canada, where he holds a prestigious Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets. He previously held positions as Associate Professor (2015-2023) and Assistant Professor (2010-2015) at the same institution. Additionally, he maintains an adjunct professorship at the University of Adelaide's School of Economics since 2021 and previously served as a Visiting Professor at HEC Montreal's Department of Finance (2017-2018). His educational background includes a PhD in Economics from Universite de Montreal (2007), an MSc in Statistics and Economics from ENSEA, Abidjan, Cote d'Ivoire (2000), and an MSc in Mathematics from Universite Nationale du Benin, Abomey-Calavi, Benin (1996). Dovonon's research focuses on advanced econometric methodologies, particularly in time series analysis and financial econometrics. His work addresses complex identification issues, develops robust estimation techniques, and creates innovative testing procedures for economic models. He specializes in moment condition models, GMM estimation, volatility modeling, and handling identification failures in econometric frameworks. His publication record shows a consistent focus on theoretical econometrics with practical applications in finance. Recent work emphasizes mixed identification strength scenarios, instrument exogeneity testing, and specification testing under challenging identification conditions. His research demonstrates increasing sophistication in handling complex econometric problems with real-world financial data applications. His notable recognition includes the Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets, highlighting his significant contributions to the field. Dovonon has supervised numerous graduate students and collaborated extensively with leading econometricians worldwide. His research has been supported by institutional funding through his Research Chair position, enabling significant contributions to econometric theory and methodology. He maintains active research collaborations across international institutions and continues to push the boundaries of econometric theory with applications to financial markets and economic modeling.

Kane NashimotoView profile

Professor

• Order restricted inference
• Multiple comparisons
• Bayesian inference
• +1 more

Kane Nashimoto, Ph.D., is a Professor in the Department of Mathematics & Statistics at James Madison University (JMU), where he has been affiliated since 2005. His research focuses on order restricted inference, multiple comparisons, and Bayesian inference. He holds a PhD in Statistics from the University of Missouri (2004), an MA in Psychology from the University of North Dakota (1998), and a BA in Psychology from Bemidji State University (1996). No specific articles, grants, or awards are listed in the provided information. His academic role includes departmental scheduling responsibilities alongside his professorship.

Michael RobertsView profile

Professor

• Corporate Finance
• Banking
• Investments
• +6 more

Michael Roberts is the William H. Lawrence Professor and Professor of Finance at the Wharton School of the University of Pennsylvania. He serves as a Research Associate at the National Bureau of Economic Research and is affiliated with the Institute for Law and Economics and the Wharton Financial Institutions Center. His academic career spans teaching roles at UC Berkeley, Duke University, and Wharton, with a focus on corporate finance, banking, investments, and financial education. Education: B.A. in Economics, University of California, San Diego M.S. in Statistics, University of California, Berkeley Ph.D. in Economics, University of California, Berkeley Roberts's research bridges corporate finance, banking, and investments, with recent emphasis on AI and machine learning in financial decision-making. His work on financial contracts, capital structure, and payout policies has significantly advanced empirical finance. He has authored influential studies on debt covenants, credit supply effects, and financial literacy. His publications demonstrate expertise in empirical corporate finance , debt structuring , financial econometrics , and financial education . Key topics include collateralized loan obligations , interest rate impacts on contracts , and capital structure persistence . These works reflect his commitment to integrating rigorous methodology with practical financial challenges. Scientific Recognition: Jensen Prize (2016) Marshall E. Blume Prize (2015) Brattle Prize nominations (2008, 2005) Multiple teaching awards at Wharton and Duke Geewax, Terker, and Co. Prize (2010, 2006) As an educator, Roberts has taught undergraduates, MBAs, PhDs, and executives for over 25 years. He founded the Wharton Financial Analytics initiative and the Financial Proficiency Initiative (FPI), which provides financial education to high school students. His courses include Corporate Finance , Data Science for Finance , and Empirical Methods in Corporate Finance .

• Econometrics
• Time Series Analysis
• Financial Econometrics
• +5 more

Prosper Dovonon is Full Professor of Economics at Concordia University, Montréal, Canada, where he holds the Tier 1 Concordia University Research Chair in Econometrics of Large Datasets . He is concurrently Adjunct Professor at the University of Adelaide, Australia, and has previously served as Associate and Assistant Professor at Concordia, Visiting Professor at HEC Montréal, and Assistant Vice-President at Barclays Wealth in London. Education Ph.D. in Economics, Université de Montréal (2007) M.Sc. in Statistics and Economics, ENSEA, Abidjan, Côte d’Ivoire (2000) M.Sc. in Mathematics, Université Nationale du Bénin, Abomey-Calavi, Benin (1996) Research Interests Professor Dovonon’s research lies at the intersection of theoretical econometrics and financial data applications . He focuses on developing robust inferential procedures for moment-condition models, bootstrap techniques for high-frequency data, identification issues in GMM, and volatility modeling with factor structures that accommodate skewness and leverage effects. His work on large-dimensional datasets emphasizes scalable methods for estimation and testing in big-data environments. Scientific Awards & Recognition Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets (2022–present) Collaborations & Affiliations Beyond Concordia and the University of Adelaide, he is affiliated with the Centre Interuniversitaire de Recherche en Économie Quantitative (CIREQ) in Montréal and has collaborated with leading scholars across North America, Europe, and Australia. His research is frequently cited in top econometrics and statistics journals, attesting to its broad impact.

Steven T. GarrenView profile

Professor

• Nonparametric statistics
• Sample surveys
• Order restricted inference
• +2 more

Steven T. Garren serves as PAC Chair and Professor in the Department of Mathematics and Statistics at James Madison University (JMU), where he has been employed since 2000. Currently holding the rank of Professor, he previously advanced from Associate Professor (2000-2006) to full Professor in 2006 after receiving tenure in 2003. Education: Ph.D. in Statistics (1994), University of North Carolina at Chapel Hill M.S. in Statistics (1992), University of North Carolina at Chapel Hill B.S. in Computer Science & Statistics, Mathematics, Physics (1989), Roanoke College Research Interests: Dr. Garren specializes in nonparametric statistics , sample surveys , order restricted inference , Markov chain Monte Carlo , and statistical computing . His methodological work addresses complex estimation challenges in constrained parameter spaces and survey data analysis, with emphasis on computational solutions for variance estimation and bias correction. Publications Trend: His 2009-2014 publications reveal consistent focus on delete-a-group jackknife methodology for survey statistics, demonstrating expertise in small-sample bias correction and effective degrees of freedom calculation. Complementary work on order-restricted exponential parameter estimation highlights his dual strength in theoretical development and practical statistical computing applications. Awards: No scientific awards documented in available sources. Advising and Grants: Public records contain no information regarding graduate students, research grants, or funded projects. Labs and Teams: No dedicated research laboratories or collaborative teams are referenced in institutional materials.

• Nonparametric function and density estimation
• Shape-restricted inference
• Constrained regression models
• +2 more

Mary C. Meyer is a Professor in the Department of Statistics at Colorado State University. Her research focuses on nonparametric function and density estimation, particularly under shape restrictions, with applications across ecological modeling, survey methodology, and statistical computing. Education: Ph.D. in Statistics from the University of Michigan (1996) Research Interests: Nonparametric function and density estimation Shape-restricted inference Constrained regression models Bayesian nonparametric methods Penalized isotonic regression Statistical software development Recent Publications show a focus on constrained regression splines, domain mean estimation, and applications in forest dynamics and survey methodology. She has developed several R packages including cgam and tools for cone projections. Grants: Funded by NSF grant DMS-0905656 for research on constrained optimization algorithms. Email Contacts: meyer@stat.colostate.edu and mmeyer@stat.uga.edu

Qiyang HanView profile

Associate Professor

• Mathematical Statistics
• High Dimensional Probability
• Empirical Process Theory
• +7 more

Qiyang Han is an Associate Professor in the Department of Statistics at Rutgers, The State University of New Jersey, within the School of Arts and Sciences. His research lies at the theoretical interface of statistics, probability, and algorithms, with a strong focus on foundational and high-dimensional problems. His research interests include mathematical statistics , high-dimensional probability , empirical process theory , nonparametric and shape-restricted inference , and Bayesian nonparametrics . He also investigates high-dimensional statistics , convex optimization , and large-scale iterative algorithms , with increasing attention to gradient descent dynamics and message passing methods. His recent publications reveal a consistent focus on precise asymptotic analysis, universality phenomena, and inference under geometric constraints. Themes across his work include robustness, adaptivity, and the theoretical underpinnings of modern statistical learning in overparameterized regimes. Ph.D. in Statistics, University of Washington, 2018 Supervised by Professor Jon A. Wellner He has collaborated with prominent researchers such as C.-H. Zhang, Bodhisattva Sen, Kengo Kato, and Richard J. Samworth. His work is published in top journals including The Annals of Statistics , Journal of the Royal Statistical Society Series B , and IEEE Transactions on Information Theory . Although no formal advising list is provided, his collaborative output suggests active mentorship and research leadership. He is involved in theoretical research with implications for machine learning, signal processing, and statistical inference. His lab or research group focuses on developing rigorous mathematical frameworks for understanding complex statistical algorithms and models, particularly in high-dimensional settings.

• Econometrics
• Applied Microeconomics
• Quantitative Methods
• +3 more

Daniel Gutknecht, Ph.D., is a Professor in the Department of Economic Policy & Quantitative Methods (EQ) at the Faculty of Economics, Goethe University Frankfurt am Main. His academic work spans econometrics, applied microeconomics, and quantitative methods, with a focus on causal inference and time series analysis. Econometrics Methodology Quantile Regression Panel Data Analysis Nonlinear Models Recent research contributions include advanced econometric techniques such as staggered adoption DiD designs, sparsity tests for high-dimensional regressions, and intercept estimation in nonlinear selection models. His publications address critical challenges in quantile forecast optimality, nowcasting monotonicity, and heaped duration data modeling, reflecting interdisciplinary applications in public health and macroeconomic policy. Current teaching includes Advanced Econometrics 1 and Fundamentals of Econometrics for the Winter semester 2025/26. Contact details: Office RuW 3.211, Theodor-W.-Adorno-Platz 4, Frankfurt am Main; email: gutknecht@wiwi.uni-frankfurt.de .

• Econometrics
• Applied Economics
• Quantitative Finance
• +3 more

Professor Dennis Kristensen is a faculty member at the Department of Economics, University College London (UCL). He holds affiliations with prominent institutions including CeMMAP, the Institute for Fiscal Studies, Aarhus Center for Econometrics (ACE), and the Centre for Macro and Financial Econometrics at Essex University. His research focuses on econometric theory, applied microeconomics, and quantitative finance. Key areas include structural dynamic models, nonlinear econometrics, and financial econometrics. His work integrates advanced computational methods and nonparametric techniques to address complex economic problems. Research interests span stochastic volatility models, demand inversion in consumer behavior, and indirect estimation methods. He has contributed to methodologies for handling unobserved heterogeneity and time-varying parameters in economic models. Prof. Kristensen's publications emphasize methodological innovation, with recent work addressing continuous-time Markov models, diffusion copulas, and dynamic discrete choice frameworks. His articles often bridge theoretical econometrics with applied contexts, such as corporate defaults and financial market analysis. He is actively engaged in the academic community, contributing to journal editorials and interdisciplinary collaborations. His affiliations reflect a commitment to advancing econometric theory and its applications in policy and finance.

• Time Series Analysis
• Statistics
• Regression Modeling
• +3 more

Rongning Wu is an Associate Professor at the Paul H. Chook Department of Information Systems and Statistics within the Zicklin School of Business at Baruch College, City University of New York . Holding a Ph.D. and M.S. in Statistics from Colorado State University and a B.S. in Applied Mathematics from Southeast University, Dr. Wu specializes in advanced statistical modeling with particular focus on time series analysis and count data models. Education : Ph.D./M.S. in Statistics (Colorado State University), B.S. in Applied Mathematics (Southeast University) Current Roles : Associate Professor, Committee Chair (Statistics Faculty Recruiting), Co-organizer (Department Research Seminar Series) Dr. Wu's research explores robust statistical methodologies for complex time series data, including Least Absolute Deviation estimation , single-index models with time series errors , negative binomial models for count series , and tail-trimmed absolute deviation techniques . His work addresses challenges in parameter estimation, variance modeling, and structural change detection for both finite and infinite variance processes. Recent publications highlight advancements in change-point estimation for count time series , conditional maximum likelihood for INGARCH models , and semiparametric approaches for discrete-valued processes . Dr. Wu's methodological contributions find applications in financial data analysis and public health research, particularly in modeling HIV transmission dynamics related to incarceration. Scientific Honors : Honored Faculty, Baruch College (2009-2011) IMS Travel Award (2009) James L. Madison Memorial Award (2004) Franklin A. Graybill Award (2003) As an active researcher, Dr. Wu has secured multiple PSC-CUNY grants (2008-2022) totaling over $52,000 for projects spanning time series estimation, regression modeling, and empirical likelihood methods. He has served on various academic committees including the ZSB Graduate Curriculum Committee and the Statistics Faculty Recruiting Committee, while maintaining editorial reviewing roles for 15+ international journals.

Zheng FangView profile

Associate Professor

• Econometrics
• Statistical Inference
• Quantile Regression
• +3 more

Zheng Fang is an Associate Professor in the Department of Economics at Emory University. His research focuses on econometrics, with expertise in statistical inference, nonparametric methods, and hypothesis testing. He holds a Ph.D. in Economics from the University of California, San Diego (2015), an MS in Statistics from the same institution (2013), and a BA and MA in Economics from Tsinghua University (2008–2010). His work emphasizes methodological advancements in econometrics, including quantile regression, shape-restricted inference, and large-scale linear system analysis. Recent contributions address topics like matrix rank testing and convex cone frameworks for hypothesis testing. Fang's research bridges theoretical developments with practical applications, often implemented in statistical software (e.g., Stata). His articles cover diverse subfields, including nonparametric estimation, bootstrapping, and concavity testing, reflecting his commitment to advancing statistical tools for economic analysis. No specific awards or grants are listed, but his CV highlights ongoing contributions to econometric theory and methodology.

Hammou ElbarmiView profile

Professor

• Order Restricted Inference
• Survival Analysis
• Categorical Data Analysis
• +3 more

Hammou Elbarmi is a Professor at the Paul H. Chook Department of Information Systems and Statistics within the Zicklin School of Business at Baruch College, CUNY. His academic journey includes a PhD in Statistics from the University of Iowa (1993), an MS in Statistics (1989), a DEA in Applied Mathematics from University Mohamed V, Morocco, and a BS in Applied Mathematics from the same institution. PhD, Statistics, University of Iowa (1993) MS, Statistics, University of Iowa (1989) DEA, Applied Mathematics, University Mohamed V, Morocco BS, Applied Mathematics, University Mohamed V, Morocco Elbarmi specializes in Order Restricted Inference , Survival Analysis , and Categorical Data Analysis . His research focuses on statistical methods for comparing survival and cumulative incidence functions under stochastic ordering constraints, with applications to competing risks and biased sampling. He has developed novel nonparametric estimation techniques for distributions with type I/II bias and pioneered empirical likelihood approaches for hypothesis testing under inequality constraints. His work spans over two decades of funded research projects from PSC-CUNY , including grants for "Nonparametric estimation under stochastic precedence" (2020-2022) and "Consistent estimation of survival functions under uniform stochastic ordering" (2023-2024). He has delivered over 30 presentations at institutions like Rice University, University of Iowa, and Joint Statistical Meetings. Elbarmi has received the Faculty Scholarship and Creative Achievement Award at Baruch College annually from 2003-2011 and served as a Co-Chair for the Seminar Series in Statistics & Operations Research. His editorial and peer-review roles include associate editorships for journals like Statistica Sinica and Journal of Nonparametric Statistics .

• Entropy Estimation
• Theory of Stochastic Orders
• Estimation in Restricted Parametric Space
• +1 more

Neeraj Misra is a Professor in the Department of Mathematics & Statistics at the Indian Institute of Technology Kanpur (IIT Kanpur), where he has held faculty positions since 1996. He earned his PhD (1991) and M.Sc. (1985) in Statistics from IIT Kanpur, and a B.Sc. from Kanpur University (1982). His research focuses on theoretical statistics and applied probability. Research Interests: His work spans entropy estimation, stochastic ordering theory, estimation under parameter restrictions, and ranking/selection methodologies, with applications in reliability engineering and molecular sciences. Key themes include: Development of nonparametric entropy estimators Stochastic comparisons for system reliability Optimization in statistical decision theory Inference for restricted parameter spaces Publication Trends: His recent articles (2003–2015) demonstrate a consistent focus on reliability modeling, stochastic ordering, and entropy estimation. Works frequently involve gamma distributions, redundancy allocation in engineering systems, mixture models, and multivariate circular distributions, blending theoretical rigor with practical applications. Awards & Honors: Jacob Wolfowitz Award (2003) for theoretical advances C. L. Chandna Mathematics Award (2003) for research/teaching excellence All India Rank #2 in Mathematics (National Entrance Exam, 1985) Outstanding Teaching Recognition at IIT Kanpur He has held visiting positions at West Virginia University, Marquette University (USA), and KU Leuven (Belgium).