Please check out the Applied Reinforcement Learning online seminar series.

• Associate Editor: Annals of Statistics 2022-
• Associate Editor: Journal of the American Statistical
• Association: Theory & method, 2021-
• Guest Associate Editor: A special issue for Journal of Econometrics. 2021-2022.
• Associate Editor: Journal of the American Statistical Association: Book review, 2018-
• Associate Editor: Journal of Computational and Graphical Statistics, 2020-
• Associate Editor: Biometrics, 2018-2020.

• NSF grant DMS-2113637, 2021-2024, $200,000, “Offline Statistical Reinforcement Learning with Applications in Precision Health,” PI.
• NSF grant DMS-1555244, 2016-2021, $400,000, “CAREER: Semiparametric and Machine Learning Approaches for Big Data Challenges in Precision Medicine,” PI.
• NCSU Research and Innovation Seed Funding Program, 2014-2015, $15,000, “Machine Learning Methods for Annual Influenza Vaccine Update,” co-PI (PI, Dr. Osman Ozaltin from ISE department).
• NCI grant P01 CA142538, 2012-2021, “Statistical Methods for Cancer Clinical Trials,” Co-I.
• NSF grant DMS-1309465, 2013-2016, $109,999, “High-dimensional Multi-stage Statistical Learning with Application to Dynamic Treatment Regimens,” PI.
• NCSU Faculty Research and Professional Development Award Grant, 2013-2014, $4,000, “Statistical Models, Methodologies and Related Theory For Developing Dynamic Treatment Regimens,” PI.
• NSF grant DMS-1007698, 2010-2013, $100,000, “Variable Selection Methods in High Dimensional Feature Space,” PI.

Preprints

• Gunn, K., Lu, W., and Song, R. “Adaptive Semi-Supervised Inference for Optimal Treatment Decisions with Electronic Medical Record Data”.
• Wan, R., Ge, L. and Song, R. “Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework”.
• Ge, L., An, X., Zeng, D., McLean, S., Kessler, R. and Song, R. “Exploratory Hidden Markov Factor Models for Longitudinal Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae”.
• Chen, H., Lu, W., Song, R. and Ghosh, P. “On Learning and Testing of Counterfactual Fairness through Data Preprocessing”.
• Shi, C., Luo, S., Zhu, H. and Song, R. “Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons”.
• Shi. C., Zhu, J., Shen, Y., Luo, S., Zhu, H. and Song, R. “Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process”.
• Shi, C., Wan, R., Song, R., Luo, S., Zhu, H. and Song, R. “A multi-agent Reinforcement Learning Framework for Off-policy Evaluation in Two-sided Markets”.
• Chen, Y, Song, R. and Jordan, MI. “Reinforcement Learning with Heterogeneous Data: Estimation and Inference”.
• Ding, Y., Li, Y. and Song, R. “Statistical Learning for Individualized Asset Allocation”.
• Cai, H., Shen, S. and Song, R. “Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning”.
• Cai, H., Lu, W. and Song, R. “CODA: Calibrated Optimal Decision Making with Multiple Data Sources and Limited Outcome”.
• Cai, H., Shi, C., Lu, W. and Song, R. “Jump Interval-Learning for Individualized Decision Making”.
• Ghosh, T., Ma, Y., Song, R. and Zhong, S. “Flexible Inference of Optimal Individualized Treatment Strategy in Covariate Adjusted Randomization with Multiple Covariates”.

2022

• Wan, R., Kveton, B. and Song, R. “Safe Exploration for Efficient Policy Evaluation and Comparison”. Accepted at ICML 2022.
• Zhou, Y., Wang, L., Song, R. and Zhao, T. “Transformation-Invariant Learning of Optimal Individualized Decision Rules with Time-to-Event Outcomes”. Accepted at Journal of American Statistical Association.
• Zhang, S., Wang, J., Jiang, H. and Song, R. “Locally Aggregated Feature Attribution on Natural Language Understanding”. Accepted at NAACL 2022.
• Wang, J., Zhang, S., Xiao, Y.and Song, R. “A Review on Graph Neural Network Methods in Financial Applications”. Accepted at Journal of Data Science.
• Chen, L., Jiang, S., Liu, J., Wang, C., Zhang, S., Xie, C., Liang, J., Xiao, Y. Song, R. “Rule Mining over Knowledge Graphs via Reinforcement Learning”. Accepted at Knowledge-Based Systems.
• Shi, C.,Wang, X. Luo, S., Ye, J., Zhu, H. and Song, R. “Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework”. Accepted at Journal of American Statistical Association.

2021

• Cai, H., Cen, Z. and Song, R. “[MAGNET: Multi-Agent Graph Cooperative Bandits”. Accepted at NeurIPS 2021 Workshop on Causal Inference Challenges in Sequential Decision Making: Bridging Theory and Practice.
• Shi, C., Luo, S., Zhu, H. and Song, R. “An Online Sequential Test for Qualitative Treatment Effects”. Accepted at Journal of Machine Learning Research.
• Liu, Y., Song, R., Lu, W. and Xiao, Y. “A Probit Tensor Factorization Model For Relational Learning”. Accepted at the Journal of Computational and Graphical Statistics.
• Wan, R., Ge, L. and Song, R. “Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models”. Accepted at NeurIPS 2021.
• Cai, H., Shi, C., Song R. and Lu, W. “Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings”. Accepted at NeurIPS 2021.
• Yu, M., Lu, W. and Song, R. “Online Testing of Subgroup Treatment Effects Based on Value Difference”. Accepted at ICDM IEEE 2021.
• Chen, X. Song, R., Zhang, J., Adams, S.A., Sun, L. and Lu, W. “On Estimating Optimal Regime for Treatment Initiation Time Based on Restricted Mean Residual Lifetime”. Accepted at Biometrics.
• Wan, R., Giannakakis, I., Gu, J. and Song, R. (2021) “[Reinforcement Learning for Replaceability Index Estimation and Assortment Optimization]”. Accepted at Amazon Consumer Science Summit (CSS) (among 12 selected presentations from over 250 submissions)
• Cai, H., Song, R. and Lu, W. “GEAR: On Optimal Decision Making with Auxiliary Data”. Accepted at STAT.
• Cai, H., Cen, Z., Leng, L, and Song, R. “Periodic-GP: Learning Periodic World with Gaussian Process Bandits”. Accepted for presentation at IJCAI-21 Reinforcement Learning for Intelligent Transportation Systems Workshop 2021. (Spotlight presentation)
• Shi, C., Zhang, S., Lu, W. and Song, R. “Statistical Inference of the Value Function for Reinforcement Learning in Infinite-Horizon Settings”. Accepted at Journal of the Royal Statistical Society, Series B.
• Wan, R., Zhang, S., Shi, C., Luo, S. and Song, R. “Pattern Transfer Learning for Reinforcement Learning in Order Dispatching”. Accepted for presentation at IJCAI-21 Reinforcement Learning for Intelligent Transportation Systems Workshop 2021. (Best paper, spotlight presentation)
• Wan, R., Zhang, X. and Song, R. “Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control”. Accepted for presentation in the research track of SIGKDD 2021. (Runzhe Wan won the Norman Breslow Young Investigator Award by the ASA Section on Statistics in Epidemiology based on this paper.)
• Shi, C., Wan, R., Chernozhukov, V. and Song, R., “Deeply-debiased Off-policy Interval Estimation”. Accepted at ICML 2021.
• Cai, H., Song, R. and Lu, W. “ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning”. Accepted at ICLR 2021.

2020

• Liu, Y., Zhang, S., Song, R., Feng, S. and Xiao, Y., “Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning”. Accepted at EMNLP 2020.
• Chen, H., Lu, W. and Song, R., (2020) “Statistical Inference for Online Decision Making via Stochastic Gradient Descent”. Accepted at Journal of American Statistical Association.
• Shi, C, Lu, W. and Song, R. (2020) “Breaking the curse of nonregularity with subagging - Inference of the mean outcome under optimal treatment regimes”. Accepted at Journal of Machine Learning Research.
• Dong, L., Laber, E., Goldberg, Y., Song, R. and Yang, S. (2020) “Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness”. Accepted at Statistics in Medicine.
• Cai, H., Mandaviya, C., Levkin, R., & Song, R. (2020). “[Marketing Experiment Bridging: Time Inverse Bayesian Learning (TIBL)]”. Accepted at the Amazon Machine Learning Conference, 2020.
• Zhu, L., Lu W. and Song, R. “Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health”. Accepted at ICML 2020.
• Shi, C., Wan, R., Song, R., Lu, W. and Leng, L. “Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making”. Accepted at ICML 2020.
• Cai, H., Lu, W. and Song, R. “On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies”. Accepted at ICML 2020.
• Zhu, L., Lu, W., Kosorok, M. R. and Song, R. “Kernel Assisted Learning for Personalized Dose Finding”. Accepted at KDD 2020.
• Chen, H., Lu, W. and Song, R., (2020) “Statistical Inference for Online Decision-Making: In a Contextual Bandit Setting”. Accepted at Journal of American Statistical Association.
• Shi, C., Song, R., Lu, W. and Li, R. (2020) “Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation”. Accepted at Journal of American Statistical Association.

2019

• Yang, S., Kim, J. and Song, R. (2019) “Doubly Robust Inference when Combining Probability and Non-probability Samples with High-dimensional Data”. Journal of the Royal Statistical Society, Series B (Statistical Methodology) 82.2 (2020): 445-465.
• Yu, M., Lu, W. and Song, R. (2019) “A New Framework for Online Testing of Heterogeneous Treatment Effect”. Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 06. 2020.
• Pan, M, Li, Y., Zhou, X., Liu, Z., Song, R. , Liu, H. and Luo, J. (2019) “DHPA: Dynamic Human Preference Analytics Framework— A Case Study on Taxi Drivers’ Learning Curve Analysis”. ACM Transactions on Intelligent Systems and Technology (TIST) 11.1 (2020): 1-19.
• Shi, C., Song, R. and Lu, W. (2019) “Concordance and Value Information Criteria for Optimal Treatment Decision”. The Annals of Statistics 49.1 (2021): 49-75.
• Su, L,, Lu, W. Song, R. and Huang, D. (2019) “Testing and Estimation of Social Network Dependence with Time to Event Data”. Accepted at Journal of American Statistical Association.
• Shi, C., Lu, W. and Song, R. (2019) “A Sparse Random Projection-based Test for Overall Qualitative Treatment Effects”. Accepted at Journal of American Statistical Association.
• Su, L,, Lu, W., and Song, R. (2019) “Modeling and Estimation for Optimal Treatment Decision with Interference”. Stat 8.1 (2019): e219.
• Pan, M, Li, Y., Zhou, X., Liu, Z., Song, R. , Liu, H. and Luo, J. (2019) “Dissecting the Learning Curve of of Taxi Drivers: A Data-Driven Approach”. Proceedings of the 2019 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2019. (Acceptance rate 22.7%). Won the award for Best Applied Data Science Paper.

2018

• Shi, C., Lu, W. and Song, R. (2018+) “Determining the Number of Latent Factors in Statistical Multi-Relational Learning”. Journal of Machine Learning Research 20 (2019): 1-38.
• Jiang, B., Song, R., Zeng, D. and Li, J. (2018+) “Entropy Learning for Dynamic Treatment Regimes”, with discussions. Statistica Sinica 29.4 (2019): 1633. Rejoinder
• Shi, C., Song, R., Chen, Z. and Li, R. (2018+) “Linear hypothesis testing for high dimensional generalized linear models”. Annals of statistics 47.5 (2019): 2671.
• Liang, S., Lu, W. and Song, R., (2018+) “Deep advantage learning for optimal dynamic treatment regime”. Statistical theory and related fields 2.1 (2018): 80-88.
• Shi, C., Lu, W. and Song, R., (2018+) “On Testing Conditional Qualitative Treatment Effects”. Annals of statistics 47.4 (2019): 2348.
• Zhu, W., Zeng, D. and Song, R., (2018+) “Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes”. Journal of the American Statistical Association 114.527 (2019): 1404-1417.
• Shi, C., Song, R., Lu, W., (2018+) “Discussion of ’Optimal treatment allocations in space and time for on-line control of an emerging infectious disease’”. Accepted at Journal of the Royal Statistical Society, Series C.
• Shi, C., Song, R., Lu, W., and Fu, B., (2018+) “Maximin-Projection Learning for Optimal Treatment Decision with Heterogeneous Data”. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 80.4 (2018): 681-702.
• Liang, S., Lu, W., Song, R., and Wang, L. (2018+). “Sparse concordance-assisted learning for optimal treatment decision”. The Journal of Machine Learning Research 18.1 (2017): 7375-7400.
• Luo, S., Song, R., Styner, M., Gilmore, J. and Zhu, H., (2018) “FSEM: Functional Structural Equation Models for Twin Functional Data”. Journal of the American Statistical Association 114.525 (2019): 344-357.

2017

• Shi, C., Fan, A, Song, R. and Lu, W. (2017) “High-dimensional A-learning for Dynamic Treatment Regimes”. Annals of statistics 46.3 (2018): 925.
• Shi, C., Song, R., Lu, W., (2017) “Discussion of ‘Random Projection Ensemble Classification’ ”. Journal of the Royal Statistical Society, Series B, 79, 959-1035.
• Kang, S., Lu, W. and Song, R., (2017) “Subgroup Detection and Sample Size Calculation with Proportional Hazards Regression for Survival Data”, Statistics in Medicine. DOI: 10.1002/sim.7441
• Jiang, R., Lu, W., Song, R., Hudgens, M.G. and Naprvavnik, S, (2017) “Doubly Robust Estimation of Optimal Treatment Regimes for Survival Data”. The Annals of Applied Statistics, 11, 1763– 1786.
• Shi, C., Lu, W. and Song, R., (2017) “A massive data framework for M-estimators with cubic-rate”. Journal of the American Statistical Association 113.524 (2018): 1698-1709.
• Wang, L., Zhou, Y. Song, R., and Sherwood, (2017) “Quantile-Optimal Treatment Regimens”. Journal of the American Statistical Association 113.523 (2018): 1243-1254.
• Liu, Z, Song, R., Zeng, D and Zhang, J., (2017) “Principal Component Adjusted Screening for High-dimensional Data”. Computational Statistics and Data Analysis. 110:134-144.
• Song, R., Luo, S., Zeng, D., Zhang, H. H., Lu, W. and Li, Z, (2017) “Semiparametric single-index model for estimating optimal individualized treatment strategy”. Electronic Journal of Statistics, 11(1) 364-384.

2016

• Fan, C., Lu, W., Song, R. and Zhou, Y, (2016) “Concordance-assisted learning for estimating optimal individualized treatment regimes”. Journal of the Royal Statistical Society, Series B : doi:10.1111/rssb.12216
• Bai, X, Tsiatis, Lu, W and Song, R., (2016) “Optimal treatment regimes for survival endpoints using locally-efficient doubly-robust estimator from a classification perspective”. Lifetime Data Analysis. 1–20.
• Shi, C. Song, R. and Lu, W. (2016) “Robust Learning for Optimal Treatment Regimes with NPDimensionality”. Electronic Journal of Statistics. 10(2), 2894–2921.
• Jiang, R, Lu, W, Song, R., and Davidian, M., (2016) “On estimation of optimal treatment regimes for maximizing t-year survival probability”. Journal of the Royal Statistical Society, Series B. DOI: 10.1111/rssb.12201
• Chen, J., Liu Y., Zeng, D., Song, R., Zhao, Y. and Kosorok, M.R., (2016) “Comment on ‘Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times.’ ” Accepted at Journal of American Statistical Association.
• Fan, A., Song, R. and Lu, W., (2016) “Change-Plane Analysis for Subgroup Detection and Sample Size Calculation”. Journal of American Statistical Association. DOI:10.1080/01621459.2016.1166115
• Laber, EB, Zhao, Y., Regh, T., Davidian, M., Tsiatis, A., Stanford, J.B., Zeng, D., Song, R. and Kosorok MR., (2016) “Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy”. Statistics in Medicine. DOI: 10.1002/sim.6783
• Song, R., Banerjee, M, and Kosorok, MR, (2016) “Asymptotics for change-point models under varying degrees of mis-specification”. The Annals of Statistics. 44(1), 153-182
• Fan, A, Lu, W. Song, R., (2016) “Sequential Advantage Selection for Optimal Treatment Regimens”. The Annals of Applied Statistics. 10(1), 32-53.

2015

• Song, R., Zeng, D, Laber, E, Zhao, Y, Yuan M, and Kosorok, MR, (2015) “On Sparse Representation for Outcome-Weighted Learning”. STAT DOI: 10.1002/sta4.78.
• Bradic, J and Song, R., (2015) “Gaussian Oracle Inequalities for Structured Selection in NonParametric Cox Model”. Electronic Journal of Statistics. 9(1) 492-534.

2014

• Song, R., Wang, W, Zeng, D and Kosorok, MR, (2014) “Penalized Q-learning for Dynamic Treatment Regimes”. Statistical Sinica. 25(3):901-920.
• Zhao, Y, Zeng, D, Laber, E, Song, R., Yuan M, and Kosorok, MR, (2014) “Doubly Robust Learning for Estimating Individualized Treatment with Censored Data”. Biometrika. doi: 10.1093/biomet/asu050
• Goldberg, Y, Song, R., Zeng, D. and Kosorok, MR (2014), “Comment on ‘Dynamic treatment regimes: technical challenges and applications’ ”. Electronic Journal of Statistics.
• Song, R., Lu, W, Ma, S and Jeng, J, (2014) “Censored Rank Independence Screening for High-dimensional Survival Data”. Biometrika. doi: 10.1093/biomet/asu047
• Song, R., Kosorok, M.R. and Fine, J.P. (2014) Comment on ‘Multiscale change point inference’ by Frick, Munk and Sieling. Journal of the Royal Statistical Society, Series B. 76(3), 564.
• Song, R., Yi, F and Zou, H, (2014) “On Varying-coefficient Independence Screening for High-dimensional data”. Statistical Sinica. 24(4), 1735-1752.

2012

• Goldberg, Y, Song, R. and Kosorok, MR (2012), “Adaptive Q-learning”, IMS Collections: From Probability to Statistics and Back: High-Dimensional Models and Processes 9: 150-162.
• Song, R., Huang, J and Ma, S (2012), “Integrative Prescreening in Analysis of Multiple Cancer Genomic Studies”. BMC Bioinformatics 13:168.

2011

• Fan, J., Feng, Y. and Song, R. (2011), “Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models”. Journal of American Statistical Association, 106, 544-557.
• Zhou, H., Song, R., and Qin, J. (2011), “Statistical inference for a two-stage outcome-dependent sampling design with a continuous outcome”. Biometrics, 67, 194-202.

2010

• Fan, J and Song, R. (2010) “Sure independence screening in generalized linear models with npdimensionality”. The Annals of Statistics, 38(6), 3567-3604.
• Song, R. and Cai, J. (2010). “Joint covariate-adjusted score test statistics for recurrent events and a terminal event”. In Special issue: Recurrent Events of Lifetime Data Analysis, 16(4), 491-508.

2009

• Anand, I., Carson, P., Galle E., Song, R. , Boehmer, J., Ghali, K.J., Jaski, B., Lindenfeld, J., O’Connor, C., Steinberg, R.J., Leigh, J., Yong, P., Kosorok, M. R., Feldman, A.M., DeMets, D. and Bristow, M. R. (2009). “Cardiac Resynchronization Therapy Reduces the Risk of Hospitalizations in Patients With Advanced Heart Failure: Results From the Comparison of Medical Therapy, Pacing and Defibrillation in Heart Failure (COMPANION) Trial”. Circulation, 119: 969-977.
• Song, R., Zhou, H. and Kosorok, M. R. (2009). “On Semiparametric efficient inference for Two-stage outcome-dependent-sampling with a continuous outcome”. Biometrika, 96, 221-228.
• Song, R., Kosorok, M. R. and Fine, J. P. (2009). “On Asymptotically optimal tests under loss of identifiability in semiparametric models”. Annals of Statistics, 37, 2409-2444.

2008

• Song, R., Kosorok, M. R. and Cai, J. (2008). “Robust covariate-adjusted log-rank statistics and corresponding sample size formula for recurrent events data”. Biometrics, 64, 741-750.
• Meunier, J., Song, R., Scott, R. L., Doherty, E. K., David O. E., Andersen, E. and Bruggink, G. J. (2008). “Proximate cues for a short distance migratory species: A new application of survival analysis”. Journal of Wildlife Management, 72(2): 440–448.

2007

• Song, R., Cook, T. D., Kosorok, M. R. (2007). “What we want Versus what we can get: A closer look at endpoints for cardiovascular studies”. Journal of Biopharmaceutical Statistics, 18(2), 370-381.
• Kosorok, M. R. and Song, R. (2007). “Inference under right censoring for transformation models with a change-point based on a covariate threshold”. Annals of Statistics, 35, 957-989.

Students I am currently (co-)advising/working with at NCSU:

• Jianian Wang (expected to graduate in 2023)
• Han Wang (expected to graduate in 2023, co-advised with Dr. Wenbin Lu)
• Ye Shen (expected to graduate in 2024)
• Lin Ge (expected to graduate in 2024)
• Dale Gao (expected to graduate in 2024)
• Richard Watson (expected to graduate in 2024)
• Yang Xu (expected to graduate in 2025)
• Haoyu Wei (expected to graduate in 2025)
• Mohsen Sahraei Ardakani (Department of ECE)
• Hunter Jiang

Former graduate students:

• Runzhe Wan (graduated in 2022, first job at Amazon)
• Hengrui Cai (graduated in 2022, co-advised with Dr. Wenbin Lu, first job is assistant professor at UC Irvine)
• Haoyu Chen (co-advised with Dr. Wenbin Lu, graduated in June - 2021, first job at Pinterest)
• Sheng Zhang (graduated in June 2021, first job at Amazon)
• Kevin Gunn (co-advised with Dr. Wenbin Lu, graduated in Jul 2020, first job at Liberty Mutual)
• Ye Liu (graduated in June 2020, first job at Google)
• Chaowen Zheng (co-advised with Dr. Yichao Wu, graduated in March 2020, first job at Bank of America)
• Liangyu Zhu, PhD (co-advised with Dr. Wenbin Lu, graduated in Dec 2019, first job at Google)
• Chengchun Shi, PhD (co-advised with Dr. Wenbin Lu, graduated in Jun 2019, first job is assistant professor at LSE)
• Shuhan Liang, PhD (co-advised with Dr. Wenbin Lu, graduated in May 2018, first job at Google)
• Ailin Fan, PhD (co-advised with Dr. Wenbin Lu, graduated in May 2016, first job at Chase)
• Shikai Luo, PhD (co-advised with Dr. Subhasis Ghoshal, graduated at May 2016, first job at Quantlab)
• Runchao Jiang, PhD (co-advised with Dr. Wenbin Lu, graduated in May 2015, first job at Facebook)
• Neal Jorgensen, MS (graduated in December 2010 at Colorado State University)

Visiting undergraduate students:

• Yunan Gao, BS (Tsinghua University, May 2017-August 2017)
• Cheng Ma, BS (Tsinghua University, May 2017-August 2017)
• Lantian Xu, BS (University of Science and Technology of China, May 2017-August 2017)

At North Carolina State University:

• ST 361 Introduction to Statistics for Engineers
• ST 508 Statistics For the Behavioral Sciences II
• ST 511 Introduction to Statistics for Biological Sciences
• ST 563 Introduction to Statistical Learning
• ST 745 Analysis of Survival Data
• ST 790 Financial Statistics
• ST 810 Topics in High-dimensional Statistical Inference

At Corolado State University:

• ST 305 Sampling Techniques
• ST 501 Statistical Science
• ST 640 Design and Linear Modeling
• ST 740 Introduction to Empirical Processes and Semiparametric Inference