Fall 2022 Seminars

• Tagbo Aroh & Emmanuel Otubo (Auburn University) Experiences from Data Science Internships
• Kelly Dunning (Auburn University) Data Science for Conservation of North American Wildlife
• Maarten Jansen (Universite' Libre de Bruxelles) The use of information criteria in high dimensional graph and tree model selection
• Ephraim Hanks (Penn State University) An Introduction to the Quantum Monte-Carlo Algorithm
• Yuhang Xu (Bowling Green State University) Random Forests: Why They Work and Why That's a Problem
• Jordan Awan (Purdue University) Bayesian Inference from Privatized Data
• Tobia Boschi (IBM Research) FAStEN: an efficient adaptive method for feature selection and estimation in high-dimensional functional regressions
• Marco Riani (University of Parma) Robust and efficient regression analysis with applications
• Mia Hubert (KU Leuven) Outlier detection in non-elliptical data by kernel MRCD
• Ioannis Sgouralis (University of Tennessee) Bayesian nonparametric modeling of biophysical and biochemical data
• Andrea Angiuli (Amazon Prime Science) Bridging the gap of reinforcement learning for mean field games and mean field control problems

Spring 2022 Seminars

• Melinda Lanius (Auburn University) Developing a Heart Rate Variability Statistic for Measuring Math Anxiety in the Undergraduate Classroom
• Fekadu Bayisa (Auburn University) Semiparametric Lasso-like Elastic-net Regularized Spatial Point Process Modelling of Ambulance Call Risk
• Yuming Zhang (University of Geneva) A General Approach for Simulation-based Bias Correction in High Dimensional Settings
• Frederic Holweck (University of Technology Belfort-Montbéliard) An Introduction to the Quantum Monte-Carlo Algorithm
• Lucas Mentch (University of Pittsburgh) Random Forests: Why They Work and Why That's a Problem
• Pulong Ma (Clemson University) Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions
• Amanda Muyskens (Lawrence Livermore National Laboratory) MuyGPs: Scalable Gaussian Process Model Estimation with Uncertainty Quantification
• Christian Sampson (UCAR) A Study of Disproportionately Affected Populations by Race/Ethnicity During the SARS-CoV-2 Pandemic Using Multi-Population SEIR Modeling and Ensemble Data Assimilation
• Matthias Sachs (University of Birmingham) Non-reversible Markov chain Monte Carlo for sampling of districting maps
• Luke Oeding (Auburn University) What do Tensors and Geometry have to do with Deep Neural Networks?
• Nedret Billor & Mark Uzochukwu (Auburn University) Data Science Capstone Project: Building Confidence Model for the Prediction of Flight Modes
• Simon Mak (Duke University) A graphical Gaussian process model for multi-fidelity emulation of expensive computer codes
• Yawen Guan (University of Nebraska) A spectral adjustment for spatial confounding

Fall 2021 Seminars

• Yao Xie (Georgia Institute of Technology) Statistical Inference for Spatio-Temporal Point Processes
• Chenang Liu (Oklahoma State University) Data-Driven Anomaly Detection and Blockchain-Enabled Security Protection for Smart Manufacturing
• Xiongtao Dai (Iowa State University) Exploratory Data Analysis for Data Objects on a Metric Space via Tukey's Depth
• Xiaowei Yue (Virginia Tech) Stochastic Surrogate Models: Method, Algorithm, and Engineering Applications
• Fushing Hsieh (UC Davis) The geometry of colors in van Gogh's Sunflowers
• Yanzhao Cao (Auburn University) Uncertainty quantification of deep neural networks
• Gaetan Bakalli (Auburn University) A penalized two-pass regression to predict stock returns with time-varying risk premia
• Stéphane Guerrier (University of Geneva) Assessing Coronavirus Disease 2019 Prevalence with Sample Surveys and Census Data with Participation Bias
• Yao Li (UNC at Chapel Hill) On the Robustness of Machine Learning Systems
• Da Yan (University of Alabama at Birmingham) Large-Scale Graph Mining: From "Think Like a Vertex" to "Think Like a Task"
• Paromita Dubey (USC Marshall Business School) Fréchet Change Point Detection
• Xuan Cao (University of Cincinnati) Bayesian Group Selection in Logistic Regression with Application to MRI Data Analysis
• Lei Li (FDA) Robust Divergence Based Inference for Finite Mixture Models
• Luca Insolia (Sant'Anna School of Advanced Studies) Parasitic Mites, Pesticides and Extreme Weather Linked to Honey Bee Loss: a Study Across the United States Through Multiple Open Data Sources

Spring 2021 Seminars

• Xinyi Li (Clemson University) Sparse Learning and Structure Identification for Ultrahigh-Dimensional Image-on-Scalar Regression
• Steven Nixon (Penn State University) Condition Based Maintenance in the Big Data Era
• Mucyo Karemera (University of Geneva) A General Approach for Simulation-based Bias Correction in High Dimensional Settings
• Shuoyang Wang (Auburn University) Estimation of the Mean Function of Functional Data via Deep Neural Networks
• Antony Pearson (Auburn University) Quantifying Structure within Unstructured Symbolic Data
• Hans-Werner van Wyk (Auburn University) Stochastic Optimization in Data Analysis and Design under Uncertainty
• Michael A. Alcorn (Auburn University) baller2vec : A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling
• Andrea Apolloni (CIRAD (France)) Modelling and Predicting National and Regional Animal Mobility in North/West Africa
• Marco Avella-Medina (Columbia University) Differentially Private Inference via Noisy Optimization
• Dave Zhao (U of I at Urbana Champaign) Perfect is the Enemy of Good: New Shrinkage Estimators for Genomics
• Debashis Mondal (Oregon State University) H-likelihood Methods in Spatial Statistics
• Mikhail Zhelonkin (University of Rotterdam) Robust Estimation of Probit Models with Endogeneity

Fall 2020 Seminars

• Roberto Molinari (Mathematics and Statistics, Auburn University) SWAG: A Sparse Wrapper Algorithm with Applications in Gene Selection
• Luke Oeding (Mathematics and Statistics, Auburn University) Stochastic Alternating Least Squares for Tensor Decomposition
• Artur Manukyan (University of Massachusetts Medical School & Broad Institute of MIT and Harvard) Graph-based Learning for Class Cover Problem and Adaptive Clustering Algorithms using Statistical Tests of Spatial Data Analysis
• Todd Steury (Wildlife Ecology, Auburn University) Confounding effects: the most devilish problem in the sciences
• Alex Vinel (Industrial and Systems Engineering, Auburn University) Explaining and predicting unsafe driving events among commercial truck drivers: lessons learned from observing 20 million driving miles using IoT sensors
• Santu Karmaker (Computer Science and Software Engineering, Auburn University) Data Science for "All"
• Whitney Huang (School of Mathematical and Statistical Sciences, Clemson University) Airflow Recovery from Thoracic and Abdominal Movements using Synchrosqueezing Transform and Locally Stationary Gaussian Process Regression
• Hyungsuk (Tak) Tak (Statistics & Astronomy and Astrophysics, Penn State) Time Delay Cosmography Towards The Hubble Constant (Part 1, Part 2)
• Yang Zhou (Computer Science and Software Engineering, Auburn University) Adversarial Machine Learning for Robust Prediction
• Ernest Fokoué (School of Mathematical Sciences, Rochester Institute of Technology) On the Ubiquity of Kernels in Statistical Machine Learning
• Mikael Kuusela (Statistics and Data Science, Carnegie Mellon University) Objective Frequentist Uncertainty Quantification for Atmospheric Carbon Dioxide Retrievals