Composite Likelihood Methods Professors and Supervisors

Pamela ShawView profile

Adjunct Associate Professor

University of WashingtonUnited States

Pamela Shaw is an Adjunct Associate Professor at the University of Pennsylvania's Department of Biostatistics, Epidemiology, and Informatics, and a Senior Investigator at the Kaiser Permanente Washington Health Research Institute. She holds a PhD in Biostatistics from the University of Washington and has extensive experience in clinical trials, measurement error correction, and survival analysis. Her research focuses on improving statistical methods for observational studies and electronic health record (EHR) data, addressing covariate and outcome errors in HIV/AIDS and chronic disease studies. She co-authored the textbook Essentials of Probability Theory for Statisticians (2016). Education: PhD in Biostatistics, University of Washington, 2006 MS in Mathematics, University of Washington, 1994 BA in Mathematics and French, Grinnell College, 1990 Research Interests: Dr. Shaw specializes in methodological advancements for error-prone data, including measurement error correction in nutritional epidemiology, survival analysis with correlated errors, and optimal study designs for two-phase sampling. Her work bridges biostatistics and clinical application, with a focus on infectious diseases, cancer immunotherapy, and public health outcomes. Grants & Collaborations: NIH R01 Grant: Statistical Methods for Correlated Outcome and Covariate Errors in HIV/AIDS Studies PCORI Methods Grant: Addressing Errors in EHR-Based Studies Labs/Teams: Leads the Shaw-Shepherd Research Collaborative, advancing statistical methods for observational data quality and audit-driven inference.

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Sebastian SoteloView profile

Associate Professor

University of Michigan-Ann ArborUnited States

Sebastian Sotelo is an Associate Professor in the Department of Economics at the University of Michigan and a Research Associate at the National Bureau of Economic Research (NBER). His research focuses on international trade, labor economics, political economy, and environmental economics, with an emphasis on structural change, trade policies, and their socio-economic implications. He has conducted influential work analyzing the impact of trade liberalization, occupational trends, and corporate political speech. Research Highlights: Explores how domestic and global trade dynamics affect labor markets and economic inequality Examines environmental consequences of deforestation linked to global supply chains Investigates historical patterns of migration and economic specialization in Brazil and Latin America Develops econometric methods for analyzing trade and technology impacts Teaching & Professional Roles: Currently teaches advanced courses in international economics, trade theory, and applied econometrics at the University of Michigan Previously instructed undergraduate and graduate courses on international finance and trade policy Recipient of the AEJ: Applied Economics Best Paper Award (2023) for groundbreaking work on occupational evolution His research frequently employs quantitative methods to bridge micro and macroeconomic perspectives, with notable contributions to understanding how technological and trade-driven structural changes reshape labor markets and regional economies. Co-authored studies often address pressing policy questions related to development, sustainability, and global economic integration.

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Dietmar BauerView profile

Professor

Bielefeld UniversityGermany

Prof. Dr. Dietmar Bauer holds the Chair of Econometrics at the Faculty of Business Administration and Economics at Bielefeld University. He maintains multiple affiliations including the Department of Empirical Methods, Center for Statistics, Institute for Technological Innovation, Market Development and Entrepreneurship, BIGSEM (Bielefeld Graduate School of Economics and Management), and the Bielefeld Graduate School in Theoretical Sciences. His academic credentials include a Habilitation in Econometrics (2007), Doctorate in Technical Mathematics (1998), and Diploma in Technical Mathematics (1995), all from TU Wien, Austria. Prior to his position at Bielefeld, he served as Senior Scientist at the Austrian Institute of Technology (2005-2014) and Assistant at TU Wien (1995-2005), with additional postdoctoral positions at Yale University, University of Linköping, and University of Newcastle. Prof. Bauer's research focuses on two primary areas: time series analysis and discrete choice models. His work in time series investigates subspace methods for state space models, particularly for integrated and seasonally integrated processes. In discrete choice modeling, he examines computational efficiency in Probit models and transportation mode choice, with special attention to the MACML approach. His publications demonstrate consistent contributions to both theoretical econometrics and applied transportation research. Multa Scripsit Award from Econometric Theory (2013) Best paper awards at UrbComp 2012 (with Jameson Toole, Marta Gonzalez, and Michael Ulm) Oskar Morgenstern Award of IHS, Vienna (with Martin Wagner) Prof. Bauer teaches numerous courses across four institutes including Software Applications for Economists, Data Analysis, Statistical Methods, and Econometrics. He supervises diploma theses, master's theses, and doctoral dissertations. His research has been supported by funding from DFG, FWF, FFG, and EU-level projects, reflecting his strong record in securing competitive research funding. He is actively involved with the Center for Statistics and the Bielefeld Center for Data Science, contributing to interdisciplinary research initiatives.

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Jiangang LiaoView profile

Professor

Pennsylvania State UniversityUnited States

Jiangang "Jason" Liao is a Professor in the Department of Public Health Sciences at Pennsylvania State University, with affiliations including the Penn State Cancer Institute and the Penn State Center for Research on Tobacco and Health. His roles include leading the Biostatistics and Data Management Core and serving as Principal Investigator (PI) on multiple research projects funded by the National Institute on Drug Abuse and the National Cancer Institute. His research focuses on Biostatistics, Public Health, and Oncology, with an emphasis on tobacco product composition, nicotine content, and their impacts on health. Key areas include statistical methodologies, cancer prevention, metabolic disorders, and health disparities. Recent articles highlight advancements in statistical confidence intervals, cancer biomarkers, migraine treatments, and adolescent metabolic health. Liao has received three Outstanding Collaborative Research Team awards for his contributions to interdisciplinary projects. He leads grants totaling over $[unspecified] in funding, addressing tobacco regulatory science and clinical trial design. His work is integral to Penn State’s Cancer Institute and Next-Generation Therapies initiatives, with a focus on translational research and public health outcomes.

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Zhongjun QuView profile

Professor

Boston UniversityUnited States

Zhongjun Qu is a Professor and Director of Graduate Studies at Boston University. His research focuses on theoretical and applied econometrics, with expertise in dynamic stochastic general equilibrium (DSGE) models, regime switching, structural change analysis, and quantile regression. He holds a PhD from Boston University and has contributed extensively to econometric methodology, financial economics, and macroeconomic modeling. Education: PhD in Economics from Boston University. Research Interests: Econometric theory and applications DSGE model identification and estimation Regime switching and structural change analysis Quantile regression methodologies Financial econometrics Recent work emphasizes quantile treatment effects, option-implied state price density estimation, and high-dimensional regime switching models. His contributions include developing R packages for regression discontinuity designs and advancing sieve estimation techniques. No scientific awards explicitly listed in the provided materials. Advising and grants sections remain underdeveloped in the current data. Active in journal editing for Journal of Econometric Methods .

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Vineet GandhiView profile

Researcher

University of StrasbourgFrance

Vineet Gandhi is a Researcher at INRIA Rhone Alpes, France. He completed his PhD in the Imagine team under Dr. Remi Ronfard, funded by the CIBLE scholarship under the Scenoptique project. His research focuses on computer vision applications for video production, including actor detection, multi-clip editing, and sensor fusion for depth estimation. He holds a Masters from the CIMET consortium with an Erasmus Mundus scholarship and a B.Tech from Indian Institute of Information Technology, Jabalpur. Education: PhD: INRIA Rhone Alpes (2014), supervised by Dr. Remi Ronfard Masters: CIMET consortium (2011), thesis supervised by Dr. Radu Horaud and Dr. Jan Cech Bachelor: Indian Institute of Information Technology, Jabalpur (thesis with Prof. Challa S Sastry and Prof. Amit Ray) Research Interests: Visual detection/tracking, computational videography, sensor fusion, and cinematography automation. His work emphasizes practical applications like theatre performance analysis and automated video editing. Publications: Focus on optimizing video editing workflows, generative models for actor detection, and high-resolution depth mapping via sensor fusion. His methods combine computer vision techniques with cinematic best practices. Labs/Teams: Active in the Imagine and Perception teams at INRIA. Collaborated with University of Wisconsin-Madison (Michael Gleicher) on cinematography automation projects.

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Joann JasiakView profile

Professor

York UniversityCanada

Joann Jasiak is a Professor in the Department of Economics at York University, affiliated with the Faculty of Liberal Arts & Professional Studies. She holds a PhD from the University of Montreal. Her research focuses on econometrics and time series analysis, with recent work emphasizing noncausal processes, stationary martingales, and applications to financial and economic data, including cryptocurrency markets and bubble detection. She has also contributed to cybersecurity analysis in Canadian businesses and epidemiological modeling using stochastic methods. Her research interests span econometric theory, financial econometrics, and the analysis of time series data with noncausal structures. Key areas include developing methodologies for forecasting cryptocurrency returns, modeling common bubbles in asset prices, and optimizing covariance estimation techniques for noncausal processes. Her work integrates advanced statistical tools with real-world applications in finance, economics, and public health. Recent publications (2023–2025) highlight her contributions to topics such as cryptocurrency dynamics, stochastic tree models for asset pricing, and generalized covariance-based inference. Her articles frequently address methodological challenges in econometrics and their practical implications for understanding market behaviors and economic phenomena. Dr. Jasiak’s work has not been explicitly linked to awards or major grants in the provided texts, though her extensive publication record reflects sustained academic engagement. No lab or team affiliations are mentioned in the available data.

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Ganggang XuView profile

Associate Professor

University of MiamiUnited States

Ganggang Xu is an Associate Professor (with tenure) in the Department of Management Science at the Miami Herbert Business School , University of Miami . He specializes in advanced statistical methodologies, particularly in nonparametric and semiparametric modeling, spatial statistics, and point process theory. Education: Ph.D. in Statistics, Texas A&M University (2011) B.S. in Statistics, Zhejiang University (2006) Research Interests: His research spans several key areas in modern statistics and data science. He has made significant contributions to nonparametric and semiparametric regression , particularly in the context of functional data analysis and spatial-temporal modeling . His work on point processes includes marked, multivariate, and clustered point processes, with applications ranging from neuroscience to social media behavior. He also explores Bayesian hierarchical models and model selection techniques, often integrating computational efficiency with theoretical rigor. Publications Overview: His recent publications (2023–2025) reflect a strong focus on machine learning-enhanced statistical modeling , including tree-based estimation of intensity functions, network autoregressive models, and quantized inference. He has also contributed to applied domains such as medical imaging and inventory control , demonstrating the broad applicability of his methodological work. Grants & Collaborations: While specific grants are not listed in the provided text, his extensive publication record with multiple co-authors across institutions suggests active collaboration and possible funding from NSF or NIH-equivalent bodies in statistics and data science. Labs & Teams: Though no specific lab is mentioned, his affiliations and co-authorships imply involvement in interdisciplinary research teams at the University of Miami, especially within the business analytics and statistical modeling domains.

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Tobias Kasper Skovborg RitschelView profile

Assistant Professor

Max Planck Institute for Dynamics of Complex Technical SystemsGermany

Tobias Kasper Skovborg Ritschel serves as Assistant Professor (Tenure Track) in the Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark. His research bridges theoretical advances in control systems with practical implementation across energy systems, bioreactors, epidemiology, and medical applications. Ritschel directs a productive research program focusing on computational methods for complex dynamical systems with rigorous thermodynamic foundations. His educational background demonstrates deep technical training: PhD in Applied Mathematics (2015-2018), Technical University of Denmark MSc in Mathematical Modeling and Computation (2013-2015), Technical University of Denmark BSc in Mathematics and Technology (2010-2013), Technical University of Denmark Ritschel's research expertise spans multiple domains of control theory and mathematical modeling. His core competencies include stochastic adaptive control, model predictive control, and optimal control frameworks applied to nonlinear dynamical systems. He specializes in numerical methods for differential equations (stochastic, partial, delay, and differential-algebraic) with computational implementations in MATLAB, C/C++, and Python. His application areas demonstrate remarkable breadth: from oil reservoir management and nuclear power systems to bioreactor operations, epidemiological modeling, and diabetes management technologies. Analysis of his publication trajectory reveals a strategic evolution from foundational work on thermodynamically rigorous reservoir simulation toward increasingly diverse applications. Early publications (2017-2019) focused on oil and gas applications with rigorous phase equilibrium modeling, while recent work (2020-2024) addresses pressing societal challenges including pandemic response strategies, power grid flexibility for renewable integration, and biomedical control systems. This demonstrates his ability to transfer core methodological expertise across disparate application domains while maintaining mathematical rigor. Dr. Ritschel actively supervises numerous students across all academic levels, with recent BSc projects focusing on molten salt reactors and demand-side flexibility in power systems. His teaching portfolio includes advanced graduate courses in stochastic adaptive control, dynamical systems, and time series analysis. He maintains strong industry connections through EU-funded projects including COCOP (Horizon 2020) and OPTION (Innovation Fund Denmark).

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Fabian H. SinzView profile

Professor

University of GöttingenGermany

Fabian H. Sinz is a Professor at the University of Tuebingen, leading the Neuronal Intelligence Group . His research focuses on understanding how biological neuronal networks leverage model biases through architecture, nonlinearities, and dynamics to enhance robust inference and accelerate learning. He employs deep learning and system identification techniques on large-scale neurophysiological and anatomical data. His work spans theoretical and applied domains, including system identification , neuroscience , reinforcement learning , and medical AI . Recent studies explore bidirectional coding in visual cortical neurons, contrastive learning for neuroscience time-series, and foundational models predicting neural responses to novel stimuli. Key publications highlight advancements in functional connectomics , invariance manifold learning , and neural likelihood estimation . Collaborations with institutions like the University of Texas and Max Planck Institute underscore his interdisciplinary impact. Tools like LAMINR (Learning and Aligning Manifolds of Single-Neuron Invariances) demonstrate his contributions to open-source neuroscience research.

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Peter KassonView profile

Professor

Georgia Institute of TechnologyUnited States

Peter Kasson is a Professor in the School of Chemistry & Biochemistry at the Georgia Institute of Technology , where he leads the Kasson Laboratory . The lab operates internationally, with active collaborations and postdoctoral positions based in both the United States and Sweden. Education PhD/MD via the Stanford University MSTP program Research Interests The Kasson group integrates biophysical experiments with large-scale molecular simulations and machine learning to dissect how enveloped viruses—such as Influenza , SARS-CoV-2 , and Zika —enter host cells. By combining single-virus spectroscopy , cryo-electron microscopy , and computational modeling , the team explores: Membrane fusion mechanisms and lipid determinants that control viral entry. Drug-resistance evolution in viral proteins and antibiotic uptake in bacteria. Methods development for adaptive ensemble simulations (e.g., gmxapi , ProGuide ). Global health applications, including pandemic-risk prediction and vaccine design. The group also contributes to public science initiatives such as Folding@Home , leveraging citizen computing to study viral dynamics worldwide. Recent Research Trends Over the past five years, Prof. Kasson’s publications have converged on coronavirus and influenza entry kinetics, membrane lipid control of fusion, and integrative structural biology of viral proteins. A notable 2025 suite of studies clarifies how sphingomyelin , caveolin-1 , and lipid curvature modulate viral infection, while computational work refines experimental data via advanced statistical models. Awards & Honors 2021 Pinn Scholar – recognition of outstanding research contributions. Training & Funding The Kasson Lab is currently supported by NIGMS R01 grants targeting heterogeneous conformational ensembles in viral proteins. The group actively recruits PhD students through Georgia Tech programs (Biomedical Engineering, Chemistry & Biochemistry, Bioengineering, Quantitative Biosciences) as well as postdoctoral fellows in both experimental and computational tracks. Labs & Teams Research is conducted in the Kasson Laboratory located in IBB 1305 on the Georgia Tech campus, with satellite activities at Uppsala University in Sweden. The team comprises a diverse, international cohort of PhD students, postdocs, and undergraduates working together on global infectious-disease challenges.

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