• Data privacy and differential privacy
• Statistical learning and machine learning
• Regularization techniques for empirical risk minimization in complex and large models
  such as deep neural networks and graphical models
• Bayesian methods and modelling
• Statistical analysis of missing data
• Epidemiological, biostatistical, and social science applications

PI Fang Liu, Ph.D.
Professor & Director of Graduate Studies
Prof Liu joined Notre Dame and the ACMS in the fall of 2011. Her statistical methodological work focuses on data privacy, Bayesian methods and modelling, and statistical learning and machine learning (refer to for her publications). Dr. Liu is funded as PI on 3 NSF grants on differential privacy theory, methods and applications. In addition, she serves as the lead statistician on a Gates Foundation funded project (Click here for an introduction of the research program) and a UNITAID-funded project (Click here for the Press Release) to assess the efficacy of promising malaria control methods, and the Notre Dame liaison on the Design and Biostatistics Program in the Indiana Clinical and Translational Sciences Institute to provide investigators centralized access to the biostatistics and bioinformatics programs among several Indiana research universities (ND, Purdue, IUPUI, IU-Bloomington). Prof Liu's works is/was supported by
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Current members

Yu Wang (Ph.D. candidate in ACMS) : works on machine learning; 2018 ~

Bingyue Su (Ph.D. candidate in ACMS) : works on data privacy; 2018 ~

Tian Yan (Ph.D. student in ACMS) : TBD; 2019 ~

Xingyuan Zhao (Ph.D. student in ACMS) : TBD; 2019 ~

Dong Wang (Visiting Ph.D. student) : works data privacy 2019 ~

Alumni (with the 1st job upon graduation)

Yinan Li (Ph.D. in ACMS) 2020 (JP Morgan Chase & Co.)

Evercita Eugenio (Ph.D. in ACMS), 2019 (Sandia National Labs)

Claire Bowen (Ph.D. in ACMS), 2018 (Los Alamos National Lab)

Bide Xiong (Ph.D. candidate in ACMS), 2019

Xiao Liu (Research M.S. in Statistics; Ph.D. in Quantitative Psychology), 2020

Bao Khanh Cu (Professional M.S. student), 2020

Qimin Liu (Research M.S. in Statistics; Ph.D. in Quantitative Psychology), 2019 (Vanderbilt)

Brenna Gomer (Research M.S. in Statistics; Ph.D. in Quantitative Psychology), 2019

Yushan Zhang (Research M.S. in Statistics; Ph.D. in Chemical Engineering), 2019 (McKinsey)

Kaiwei Chen (Research M.S. in Statistics; Ph.D. in Electrical Engineering), 2019 (Microsoft)

Xin Mu (Research M.S. in Statistics; Ph.D. in AME), 2017 (BMO Harris Bank)

JiXin Si (Research M.S. in Statistics; Ph.D. in Physics), 2017

Richard GibbonPrice (Research M.S. in Statistics; Ph.D. in Political Science), 2017

Nathanael Sumaktoyo (Research M.S. in Statistics; Ph.D. in Political Science), 2016

Han Du (Research M.S. in Statistics; Ph.D. in QuantPsy), 2015 (UCLA)

Rachel Baird (Research M.S. in Statistics; Ph.D. in QuantPsy), 2015 (UPMC)

Daniel McArtor (Research M.S. in Statistics; Ph.D. in QuantPsy), 2015 (Google)

Patrick Miller (Research M.S. in Statistics; Ph.D. in QuantPsy), 2015

Evan Claudeanos (Research M.S. in Statistics; Ph.D. in Philosophy), 2015

Ling Sun (Research M.S. in Statistics; Ph.D. in Bioengineering), 2014 (ND)

Lu Li (Professional M.S. in Applied Statistics), 2013

Jocob Chang (undergraduate researcher), 2020

Ashley Ahimbisibwe (undergraduate researcher), 2016 ~ 2017

Rachael Quest (undergraduate researcher), 2017

YiKun Qian (undergraduate researcher), 2017

Zhaoyu Cai (undergraduate researcher), 2016

Yilan He (undergraduate researcher), 2016

Bojia Qiu (undergraduate researcher), 2016

Nick Troetti (undergraduate researcher), 2015 (senior actuarial assistant at AIG)

Colleen Pinkleman (undergraduate researcher), 2014 (business analyst at Amazon)

Yunchuan Kong (undergraduate researcher), 2013 (doctoral biostatistics student at Emory)

• ACMS 30600 Statistical Methods and Data Analysis
• ACMS 40852/60852 Advanced Biostatistical Methods
• ACMS 60786 (60784/60785): Applied Regression Models (I/II)
• ACMS 60885 Applied Bayesian Statistics

R package "zoib" for Bayesian Inference of zero-one inflated beta regression

• R package
• Documentation
• FAQ
• Report bugs to fang DOT liu DOT 131 AT nd DOT edu

R package "gset" for group sequential designs of equivalence studies

• R package
• Documentation

Construction of ROC and Calculatio of AUC (as referenced in "Controversies and Conundrums in Hydrogen Sulfide Biology" by Olsen, DeLeon, and Liu; Nitric Oxide (2014) doi: 10.1016/j.niox.2014.05.012.)

Supplemental materials to "Model-based Differentially Private Data Synthesis" by Liu (2016), arxiv:1606.08052

Supplemental materials to "Statistical Properties of Sanitized Results from Differentially Private Laplace Mechanisms with Bounding Constraints" by Liu (2016), arxiv:1607.08554v2

Supplemental materials to "A Bayesian “fill in” method for correcting for publication bias in meta-analysis" by Du, Liu, and Wang (2017), Psychological Methods, 22(4):799-817. doi: 10.1037/met0000164

Supplemental materials to "Assessment of Bayesian Expected Power via Bayesian Bootstrap" by Liu in Statistics in Medicine 2018 https://doi.org/10.1002/sim.7826

Supplemental materials to "A Review and Comparison of Bayesian and Likelihood-Based Inferences in Beta Regression and Zero-or-One Inflated Beta Regression" by Liu and Eugenio in Stat Methods Med Res. 2018 Apr;27(4):1024-1044. doi: 10.1177/0962280216650699

R codes for simulating data BiRepeated in R package zoib and in paper "A Review and Comparison of Bayesian and Likelihood-Based Inferences in Beta Regression and Zero-or-One Inflated Beta Regression" by Liu and Eugenio in Stat Methods Med Res. 2018 Apr;27(4):1024-1044. doi: 10.1177/0962280216650699