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Assistant Professor
Estefanía Talavera Martínez is an Assistant Professor specializing in Datamanagement & Biometrics , with research spanning artificial intelligence, computer vision, and health informatics. Her work addresses surveillance, emotion recognition, and egocentric data analysis.
Lecturer
Mehrdad Naderi is a Lecturer in Statistics at the Department of Mathematics, Physics, and Electrical Engineering , Northumbria University . His academic journey includes a PhD in Mathematical Statistics from Shahid Bahonar University of Kerman (2017) and postdoctoral research at National Chung Hsing University (Taiwan), Ferdowsi University of Mashhad (Iran), and University of Pretoria (South Africa). Education: PhD in Mathematical Statistics, Shahid Bahonar University of Kerman (2017) His research focuses on applied statistical inference with emphasis on classification , cluster analysis , factor analysis , finite mixture models , and EM algorithm for robust estimation. He has contributed to multivariate and matrix-variate analysis, particularly in handling outliers and asymmetrical data structures. Recent work includes three-way data clustering using matrix-variate normal distributions and robust Bayesian inference for censored mixture models. His publications demonstrate expertise in distribution theory, statistical computation, and applications to financial data, environmental modeling, and astrophysics. Current collaborations span multiple institutions, focusing on heavy-tailed distributions and computational methods for complex data structures.
Assistant Professor
Ramya Korlakai Vinayak is the Dugald C. Jackson Assistant Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison's College of Engineering. Her research bridges theoretical machine learning with human-centered AI applications. Education: Postdoctoral Researcher, Paul G. Allen School of Computer Science and Engineering, University of Washington Ph.D. and M.S. in Electrical Engineering, California Institute of Technology (Caltech) B.Tech. in Electrical Engineering, Indian Institute of Technology Madras (IIT M) Research Focus: Her work centers on machine learning foundations with emphasis on statistical inference, crowdsourcing, and human-AI collaboration. Key themes include modeling heterogeneous human preferences, developing robust out-of-distribution detection systems, and creating culturally-aware generative models. She pioneers methods for pluralistic alignment where AI systems accommodate diverse human values through ideal point modeling and adaptive feedback mechanisms. Publication Trends: Recent publications (2023-2025) reveal three converging trajectories: (1) Human-in-the-loop frameworks for reducing false positives in safety-critical systems, (2) Metric learning innovations using limited preference data, and (3) Critical examinations of cultural limitations in generative AI. Her work consistently addresses real-world challenges like cognitive overload in crowdsourcing and biases in face generation systems. Research Infrastructure: She leads an active research group with a dedicated lab website (https://ramyakv.github.io/) focusing on developing theoretically-grounded yet practically-deployable AI systems that respect human plurality.
Assistant Professor
Maoran Xu serves as an Assistant Professor in the Department of Statistics at Indiana University Bloomington, where he develops advanced probabilistic models for high-dimensional data analysis. His academic credentials include: Ph.D. in Statistics, University of Florida (2018-2022) B.S. in Mathematics and Applied Mathematics, Fudan University (2014-2018) Professor Xu's research program centers on Bayesian inference frameworks with emphasis on efficient sampling and low-dimensional structure discovery. His work tackles critical challenges in high-dimensional data through innovative approaches to latent factors, clustering, and sparse models. He specifically develops optimization-enhanced Bayesian methods for building likelihood-based models on geometric spaces, enabling robust uncertainty quantification for network data and complex structured datasets. His methodologies bridge theoretical statistics with practical applications in modern data science challenges. Contact information is available through email (mx13@iu.edu) and his professional website (https://xumaoran.com/), with office location in Swain Hall East 229 at Indiana University.
Professor
Wang You-Gan is an accomplished academic researcher with over three decades of publication history spanning from 1991 to 2024. His work demonstrates a strong foundation in statistical methodology with applications across multiple domains including environmental science, fisheries management, machine learning, and computational statistics. His research has been published in high-impact journals across statistics, computer science, environmental science, and biology. Wang You-Gan's primary research interests include statistical modeling, regression analysis (particularly support vector regression), longitudinal data analysis, machine learning applications, and environmental statistics. His work shows a clear evolution from early fisheries and environmental applications to broader computational statistical methods with applications in energy systems, genomics, and cloud computing. His research demonstrates strong methodological development coupled with practical applications. His recent publications (2022-2024) show a strong focus on advanced regression techniques, particularly support vector regression with innovations in handling heterogeneous variances, autoregressive processes, and automatic hyperparameter selection. He has also expanded into machine learning applications for energy demand forecasting, air quality prediction, and data center optimization. His work often bridges theoretical statistical development with practical implementation across diverse domains. Wang You-Gan has received significant citation impact for his methodological contributions, with several papers accumulating over 100 citations. His collaborations span multiple institutions and disciplines, indicating his work's broad relevance across fields. His research has important applications in environmental monitoring, energy systems management, computational biology, and cloud computing infrastructure. The consistent publication record over more than 30 years demonstrates his sustained contribution to statistical methodology and its applications.
Professor
Prof. Aurélie Lemmens is a Full Professor of Customer Analytics at Rotterdam School of Management (RSM), Erasmus University Rotterdam, where she chairs the Department of Marketing Management. She concurrently serves as Academic Director of the Expert Practice on Customer Analytics at the Erasmus Center for Data Analytics. Previously, she held academic positions at Erasmus School of Economics and Tilburg University, and was a visiting scholar at Harvard Business School. She obtained her PhD from K.U. Leuven (Belgium) following an MSc in Business Engineering from Solvay Business School. Her research develops prescriptive analytics methodologies leveraging consumer data for customer-centric decision-making, organized around three core lifecycle stages: (1) customer acquisition and new product introduction, (2) customer development and engagement, and (3) customer retention. Her work extensively employs machine learning and large-scale computing. Prof. Lemmens' publication portfolio demonstrates consistent focus on quantitative marketing, customer analytics, and methodological innovation, with recent work emphasizing personalization algorithms, donor behavior modeling, and churn optimization techniques. Her research consistently bridges theoretical rigor with practical business applications. Awards and honors include: 2012 IJRM Best Paper Award Marie Curie Grant (European Research Council) Veni and Vidi grants (Dutch Science Foundation) ERC Consolidator Grant Finalist Best Teaching Awards (2017, 2019) for Conjoint Analysis course She teaches Customer Analytics (Business Analytics MSc), Customer Centricity (Marketing Management MSc), and Creating Customer Value (Executive MBA). She leads the Expert Practice on Customer Analytics, coordinating industry-academia collaborations in applied data analytics. Additional information is available via her personal website .
