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Seojeong Jay LeeView profile

Associate Professor

• Econometric methods
• Statistical inference
• Causal econometrics
• +7 more

Associate Professor Seojeong Jay Lee is a faculty member at the UNSW Business School , specializing in Econometrics and Statistical Methods . Joining the University of New South Wales (UNSW) in 2012 after completing a PhD at the University of Wisconsin-Madison, they focus on developing robust statistical tools for economic models where traditional experimental controls are impractical. Research Interests Generalized Method of Moments (GMM) Instrumental Variables (IV) and Two-Stage Least Squares (2SLS) Robust inference under model misspecification Handling invalid/many/weak instruments in econometrics Heterogeneous treatment effects and clustered sampling analysis Grants & Funding Australian Research Council (ARC) Discovery Project DP210101440 (AUD 336,939), 2021-2023 Health@Business UNSW Grant (AUD 3,500), 2020 ARC Discovery Early Career Researcher Award (DECRA) DE170100787 (AUD 331,000), 2017-2019 UNSW Business School Special Research Grant (AUD 17,699), 2013-2014 Awards & Recognition Zellner Thesis Award (Honorable Mention), American Statistical Association, 2014 Non-Professorial Research Achievement Award, UNSW Business School, 2017 Academic Leadership Currently supervising PhD candidates Wei Tian and Fangzhou Yu in econometric theory and applications.

Özlem İlk DağView profile

Professor

• Longitudinal Data Analysis
• Multilevel Modeling
• Interactive Graphics
• +5 more

Prof. Dr. Özlem İlk Dağ is a Professor of Statistics at the Middle East Technical University (METU), Ankara, Turkey. She holds the position of Department Head of Statistics and serves on advisory boards for TUBITAK and the Journal of Biostatistics. Her academic journey includes roles from Research Assistant (1997) to Professor (2018), with affiliations in Actuarial Sciences and Biostatistics. Education: Ph.D. in Statistics (Iowa State University, 2004), M.S. and B.S. from METU. Research focuses on longitudinal data analysis, multilevel modeling, Bayesian methods, and biostatistics. She authored three editions of R Yazilimina Giris and a foundational book on multivariate longitudinal data analysis. Key contributions include developing marginalized transition random effects models (MTREM) and R packages like 'mmm'. Awards include METU's 20-year service recognition and teaching excellence. She has supervised numerous research projects and co-authored over 50 peer-reviewed articles across biostatistics, genomics, and statistical computing. Her work bridges statistical theory and application, with notable contributions to gene expression clustering, maternal antibody studies in veterinary science, and computational methods for longitudinal data. She maintains active collaborations in genomics and biostatistics, and her lab focuses on integrating statistical computing with biomedical research.

• Extreme Value Theory
• Copulas
• Spatial Statistics
• +3 more

Professor Jennifer Wadsworth is a Professor in Statistics at the School of Mathematical Sciences, Lancaster University . Her research spans extreme value theory, copulas, spatial statistics, and environmental statistics, focusing on likelihood-based and nonparametric inference methods. Projects include Exploring and exploiting new representations for multivariate extremes (2023-2026) and STORi: Multivariate Extremes for Nuclear Regulation (2019-2023). Current PhD supervision interests: multivariate and spatial extremes, aiming to develop realistic models by relaxing assumptions. Research groups: Extreme Value Theory, STOR-i Centre for Doctoral Training. Her recent work, such as Modeling of spatial extremes in environmental data science (2025), highlights the need to move beyond max-stable processes for more accurate spatial extreme modeling. She supervises PhD students including Ryan Campbell, Kristina Grolmusova, and Lydia Kakampakou at Lancaster University.

Howard BondellView profile

Professor

• Statistical Data Science
• Model Selection
• Robust Estimation
• +5 more

Howard Bondell is a Professor of Statistical Data Science at the School of Mathematics and Statistics, University of Melbourne, since 2018. He serves as Head of School since 2021, Co-Director of the Melbourne Centre for Data Science, and holds an ARC Future Fellowship (2020-2024). Ph.D. in Statistics, Rutgers University (2005) Academic Career: North Carolina State University (2005-2018) His research focuses on model selection , robust estimation , regularisation , Bayesian methods , and uncertainty quantification in statistical and machine learning. His publications emphasize applications in regression analysis, quantile modeling, variable selection for high-dimensional data, and genetic data analysis. Scientific awards include: Fellow of the American Statistical Association (2017) ARC Future Fellow (2020-2024)

• Structural Equation Modeling
• Psychometrics
• Statistics
• +5 more

Yves Rosseel is a Professor at Ghent University (UGent) specializing in Structural Equation Modeling (SEM) , Psychometrics , and Statistical Methodology . With over 15 recent publications (2024-2025), he focuses on small-sample SEM solutions, factor score regression, measurement error, and Bayesian extensions. His work bridges Statistics with applications in Psychology , Education , and Neuroimaging . Research Trends Developed Mixture Multigroup SEM for cross-group comparisons Proposed Information-Theoretic Hypergraphs in psychometrics Advanced Two-Stage Estimation for round-robin data Created blavaan R package for Bayesian SEM Investigated Measurement Error in hypothesis testing Scientific Contributions Published 84 Social Sciences papers, 37 Statistics works, and 11 Neuroimaging studies Promoted 8 PhDs including Sara Dhaene and Julie De Jonckere Co-authored 12+ works with Marijke Welvaert and 10+ with Stijn Vanheule

• Nonparametric Statistics
• High-Dimensional Statistics
• Inverse Problems
• +3 more

Cristina Butucea is a Professor of Statistics and Machine Learning at ENSAE-CREST, Institut Polytechnique de Paris . She was nominated an IMS Fellow (2019) for her contributions to nonparametric and high-dimensional statistics. She has co-organized major conferences like Fréjus 2018 , Luminy 2019-2020 , and Oberwolfach 2021 , and serves as Associate Editor for ALEA . Fields of interest: Nonparametric statistics, quantum statistics, differential privacy, inverse problems, machine learning. Awards: IMS Fellowship, conference organization leadership. Research trends: Focuses on optimal estimation under privacy constraints, quantum state reconstruction, high-dimensional inference, and adaptive nonparametric methods. Email: Cristina.Butucea@ensea.fr | Cristina.Butucea@ip-paris.fr

Neil LawrenceView profile

Professor

• Machine Learning
• Artificial Intelligence
• Uncertainty Quantification
• +3 more

Neil Lawrence is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge's Department of Computer Science and Technology. He also holds positions as a Senior AI Fellow at The Alan Turing Institute and a Visiting Professor of Machine Learning at the University of Sheffield. After three years as Director of Machine Learning at Amazon, Lawrence recently returned to academia, bringing extensive industry experience to his academic work. Lawrence's research focuses on the intersection of machine learning with the physical world, particularly in uncertainty quantification and end-to-end solutions for real-world applications. His work was initially inspired by deploying machine learning systems in African contexts, where comprehensive solutions are often required. His technical expertise spans over two decades in machine learning methods, with a growing interest in public understanding of machine learning, policy decisions, and data governance implications. His recent publications reveal a diverse research portfolio spanning climate science, healthcare applications, systems engineering for AI deployment, data governance, and theoretical machine learning. Lawrence's work demonstrates a consistent theme of bridging theoretical machine learning with practical applications across multiple domains, with particular attention to uncertainty quantification and the societal implications of AI systems. Lawrence serves on the board of the AISTATS conference and the ELLIS Foundation, and acts as the founding and series editor for the Proceedings of Machine Learning Research. He is also the co-host of the Talking Machines podcast, demonstrating his commitment to public engagement with machine learning concepts. At Cambridge, Lawrence teaches Advanced Data Science (Part II) and Machine Learning and the Physical World (MPhil ACS, Part III), contributing to both undergraduate and postgraduate education in computer science. His work with the Accelerate Programme for Scientific Discovery and the Data Trusts Initiative positions him at the forefront of developing frameworks for responsible and effective AI deployment in scientific and societal contexts.

Erik HjalmarssonView profile

Professor

• Empirical Finance
• Financial Econometrics
• Empirical Asset Pricing
• +4 more

Erik Hjalmarsson is a Professor of Banking and Financial Economics at the Department of Economics, University of Gothenburg. He holds the Felix Neubergh Chair and previously served as Director of the Centre for Finance. His research focuses on empirical asset pricing, financial econometrics, and long-run stock returns. Hjalmarsson earned his PhD from Yale University and has held roles at the Federal Reserve Board and Winton Capital Management. Education: PhD (Yale University, 2005), M.Sc. in Econometrics (London School of Economics, 2000), B.Sc. in Mathematical Statistics (University of Gothenburg, 1999). Research interests include stock return predictability, high-frequency trading, and econometric methods. His work has been published in top journals like the Journal of Finance and Journal of Financial Economics . Key grants include funding from the Swedish Research Council and Marianne and Marcus Wallenberg Foundation. Awards include the Carl Anderson Prize (2004) and multiple stipends for doctoral education. He supervises PhD students and advises on central bank policies. His recent studies explore long-horizon returns, inflation expectations, and portfolio strategies. Teaching includes PhD courses in econometrics and financial economics. He serves on editorial boards and as a referee for leading journals.

Liping LiuView profile

Associate Professor

• Machine Learning
• Data Science
• Deep Learning
• +3 more

Liping Liu is an Associate Professor in the Department of Computer Science at Tufts University's School of Engineering. He holds a Ph.D. from Oregon State University and has held postdoctoral positions at Columbia University and Tufts. His research focuses on machine learning, generative models, graph learning, and their applications in biochemical data analysis and fluid dynamics simulation. His work on graph generative methods earned the NSF CAREER Award. Education: Ph.D. (Oregon State University, 2016), M.Sc. (Nanjing University, 2009), B.S. (Hebei University of Technology, 2006). Research Interests: Machine Learning, Deep Learning, Generative Models, Time Series, Graph Learning. Dr. Liu's research emphasizes probabilistic modeling and neural networks, addressing challenges in graph generation, data-driven physics simulation, and biochemical analysis. His recent work includes advancements in graph-based recommendation systems, turbulence modeling, and enzymatic reaction prediction. His publications span top AI conferences like NeurIPS, ICML, and ICLR. He has secured grants totaling over $9 million, including the NSF CAREER Award and NIH funding for metabolomics and enzymatic promiscuity studies. His teaching includes courses on generative models, deep learning, and machine learning for graph analytics. Awards: NSF CAREER Award (2023), NIH grants, DARPA ACT-NOW project (2019). Service: NSF panelist, program committee member for AAAI, NeurIPS, and IJCAI.

Sinan YıldırımView profile

Researcher

• Bayesian Statistics
• Monte Carlo Methods
• Data Privacy
• +3 more

Sinan Yıldırım is a Researcher in the Faculty of Engineering and Natural Sciences at Sabancı University, Turkey. His primary research focuses on Bayesian Statistics, Monte Carlo methods, and data privacy, with interdisciplinary applications in machine learning and signal processing. He holds a BSc and MSc in Electrical and Electronics Engineering from Boğaziçi University, followed by a PhD in Mathematical Statistics from the University of Cambridge. Postdoctoral research (2013-2015) at the University of Bristol’s School of Mathematics involved the EPSRC-funded project 'Intractable Likelihood: New Challenges from Modern Applications (i-like).' His work bridges theoretical statistics with practical problems in privacy, control systems, and energy optimization. Research interests emphasize Bayesian methodologies for privacy-preserving data analysis, dynamic modeling of complex systems, and stochastic optimization algorithms. Recent publications explore differential privacy in machine learning, Monte Carlo techniques for high-dimensional inference, and applications of Bayesian methods in robotics and energy systems. Advising and grants include contributions to multi-party resource sharing frameworks and privacy-aware algorithms. His work integrates computational methods with real-world challenges in engineering and policy modeling.

NG Hui KhoonView profile

Associate Professor

• Quantum Information Theory
• Quantum Computation
• Quantum Error Correction
• +3 more

NG Hui Khoon is an Associate Professor at the National University of Singapore , affiliated with Yale-NUS College and the Centre for Quantum Technologies . She holds a PhD in Physics from the California Institute of Technology (Caltech), USA (2009). Research Interests: Her work focuses on theoretical aspects of quantum information and computation, particularly quantum error correction and fault tolerance , quantum noise modeling , and quantum tomography . She investigates how resource constraints limit quantum computing and develops adaptive methods for quantum state estimation using neural networks. Publication Trends: Her recent articles (2021–2013) emphasize quantum error correction frameworks, tomography techniques, and statistical methods for quantum systems. Key themes include fault tolerance under amplitude-damping noise, randomized benchmarking for time-correlated dephasing, and Bayesian approaches for prior-data conflict checking. Scientific Awards: Early Career Teaching Award (2019, Inaugural recipient) CQT Fellowship (2019 – current) Advising & Grants: No explicit advising or grant details are provided. She collaborates with institutions like the Centre for Quantum Technologies and Yale-NUS College. Labs & Teams: She is associated with the Centre for Quantum Technologies, a leading research center in quantum information science.

Will FithianView profile

Associate Professor

• Theoretical Statistics
• Applied Statistics
• Post-Selection Inference
• +3 more

Will Fithian is an Associate Professor in the Department of Statistics at the University of California, Berkeley. He holds a position in the College of Letters & Science, specializing in theoretical and applied statistics. His research focuses on post-selection inference, scalable algorithms for big data, high-dimensional data analysis, and ecological statistics. Fithian has taught courses such as Theoretical Statistics (Stat 210A), Forecasting, and industry-relevant statistical methods. His work bridges statistical theory with applications in fields like genomics, ecology, and machine learning. Education and Career: While specific educational details are not explicitly provided, his academic rank and research focus suggest advanced training in statistics. He previously taught at Stanford University and has held roles such as Assistant Professor before his current position at Berkeley. Research Interests: His interests include developing robust statistical methods for handling modern data challenges, including false discovery rate control, selective inference, and computational efficiency in high-dimensional settings. He collaborates across disciplines, applying statistical tools to ecological and biomedical problems. Awards: Fithian received the Teaching Award from the Berkeley Statistics Department in 2012 and the Centennial Teaching Award (University-wide) in 2015, reflecting his dedication to pedagogy. His research contributions have been recognized through publications in top journals and conferences. Teaching and Service: He leads advanced courses like Stat 210A, a core PhD-level theoretical statistics course. His teaching emphasizes foundational concepts while addressing contemporary challenges. He also contributes to Berkeley’s Industry Alliance Program, fostering academic-industry partnerships.

• Statistics
• Machine Learning
• Psychology of Music
• +3 more

Dr. Linh Nghiem is a Lecturer in Statistics at the School of Mathematics & Statistics, University of Sydney. She specializes in both methodological and applied statistical research, focusing on measurement error modeling, dimension reduction, and graphical models. Her applied work involves collaborations with scientists exploring human perception of music and the societal impact of music on social empathy. Her research interests include longitudinal data analysis, privacy in data science, and experimental psychology of music at behavioral and neural levels. She is affiliated with the Sydney Southeast Asia Centre and actively contributes to interdisciplinary projects. Dr. Nghiem has secured grants such as the 2023 'Methodologies for complex datasets' under the Faculty Startup Scheme. She collaborates with institutions globally and maintains an active presence in academic communities through her ORCID profile and personal website.

Elliot HuiView profile

Associate Professor

• Biomedical Engineering
• Microfluidics
• Spatial Cell Biology
• +3 more

Elliot Hui, Ph.D., is an Associate Professor in the Department of Biomedical Engineering at the University of California, Irvine (UCI), within the Samueli School of Engineering. His research focuses on biological microtechnology, including spatial cell biology, microscale tissue engineering, global health diagnostics, and microfluidic computing. He leads the Hui Lab, which develops tools for automating biochemical reactions, controlling cellular organization, and understanding tissue development dynamics. Key achievements include pioneering microfluidic logic systems for autonomous laboratory automation and creating novel cell culture platforms to study intercellular communication in tissues. His work bridges engineering and biology, addressing challenges in diagnostics and regenerative medicine. Notable contributions include the development of a programmable finite state machine for microfluidic control and a SLAS Fellowship awarded to his student Erik. Research Interests: Microfluidic devices, cell-cell interaction modeling, tissue engineering, and lab-on-a-chip systems. Labs/Teams: Hui Lab at UCI, specializing in microscale biological systems and automation. Publications span topics such as microfluidic computing architectures, tissue dissociation devices, and Bayesian experimental design. His work emphasizes applications in global health diagnostics and mechanistic studies of cellular processes.

Suhasini Subba RaoView profile

Professor

• Time Series Analysis
• Nonstationary Processes
• Nonlinear Processes
• +3 more

Suhasini Subba Rao is a Professor of Statistics at Texas A&M University, affiliated with the Department of Statistics within the College of Arts & Sciences. Her research focuses on time series analysis, nonstationary processes, nonlinear processes, and spatio-temporal models. She holds a prominent academic position and contributes to both theoretical and applied statistical methodologies. Education details are not explicitly provided in the text, but her academic career includes extensive work in statistical theory and methodology. Her research interests emphasize developing robust statistical techniques for analyzing nonstationary and complex time series data, with applications in spatio-temporal modeling and recursive algorithms. Her recent articles highlight advancements in inverse covariance estimation, graphical models for nonstationary time series, and spectral methods for small sample data. She has also explored reconciliation of Gaussian and Whittle likelihoods, enhancing estimation accuracy in frequency domains. No scientific awards are explicitly mentioned. Her advising and grants are not detailed, but her prolific publication record suggests active research collaborations and funding. She maintains a lab/team focused on time series analysis, though specific team names are not provided.