GROUP NEWS

05/04/2021: Fang Liu is named a Fellow of American Statistical Association! Congratulations! Dr. Liu. Read the news here.

04/09/2021: Congratulations to Tian Yan on advancing to Ph.D. candidacy! Congratulations! Tian.

03/05/2021: Fang Liu is featured in Women Lead 2021 in celebration of the Womens History Month. Read her story here.

03/04/2021: Congratulations to Xingyuan Zhao on advancing to Ph.D. candidacy! Congratulations! Xingyuan.

News Archive     
RESEARCH: Our research focuses on the following areas
   
  • 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
with the joint efforts from a group of hard-working people:
   


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 Google Scholar for her publications). Dr. Liu is funded by multiple NSF grants on differential privacy theory, methods, and application research. 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
    --->

Current members
  • Bingyue Su (Ph.D. candidate in ACMS) : working on differential privacy; 2018 ~
  • Yu Wang (Ph.D. candidate in ACMS) : working on machine learning methodology; 2018 ~
  • Xingyuan Zhao (Ph.D. candidate in ACMS) : working on differential privacy; 2019 ~
  • Tian Yan (Ph.D. student in ACMS) : working on machine learning applications; 2019 ~
  • Spencer Giddes (Ph.D. student in ACMS) : working on differential privacy; 2020 ~
  • Sijing Shao (Ph.D. student in Quantitative Psychology) 2020 ~
  • Rui Guan (undergraduate Researcher from Wuhan University) summer, 2021

  • Alumni (with the 1st job upon graduation)
  • Dong Wang (Visiting Ph.D. student; Ph.D. in Computer Science at Wuhan University) 2021 (Assistant Professor, Hangzhou Dianzi Univ)
  • Yinan Li  (Ph.D. in ACMS), 2020 (JP Morgan Chase & Co.)
  • Evercita Eugenio  (Ph.D. in ACMS), 2019 (Sandia National Lab)
  • Claire Bowen   (Ph.D. in ACMS), 2018 (Los Alamos National Lab --> Urban Institute)
  • 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)
  • TEACHING: Prof. Liu teaches the following courses at Notre Dame. The course descriptions can be found here . Students who are registered for the courses can view the course materials on Sakai .
         
    • 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
    Software & Tools
     

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

       
     

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

       
     
     

    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

    Office: 201B Crowley

    Email: fang.liu.131@nd.edu

    Office phone: 574-631-0895