Liu’s Trustworthy & Ethical Statistical Learning Lab (TESLLa)
Research in differential privacy, synthetic data, trustworthy statistical learning, probabilistic machine learning, and Bayesain methods, with applications in biology, medicine, public health, engineering, and the social sciences.
Group news
Recent updates and announcements. Older items are in the news archive.
05/2026
Congratulations to Ruyu Zhou on succesfully defending her PhD dissertation on Topics in Statistical Inference Under Differential Privacy!
Ruyu will start as a tenure-track Assistant Professor in the Department of Computer Science and Statistics at the University of Rhode Island
in Fall 2026.
05/2026
Congratulations to Gina Mannino on receiving the Notre Dame-IBM Tech Ethics Lab Graduate Fellowship through the Notre Dame Institute for Ethics and the Common Good
for AY 2026–2027!
07/2025
Congratulations to Ruyu Zhou on receiving the Notre Dame Scientific Artificial Intelligence (SAI) Initiative Graduate Fellowship in Fall 2025!
07/2025
Congratulations to Spencer Giddens on successfully defending his doctoral dissertation on Advanced Topics in Differentially Private Statistical Learning. He will join the AI Trust and Reliability (AITAR) Lab in Lucy Family Institute of Data & Society at ND as a Postdoc Researcher.
Research
Our work focuses on the following areas.
Research focus
Data Privacy & Differential privacy
Research focus
Synthetic Data and Generative Models
Research focus
Trustworthy Machine Learning
Research focus
Probabilistic Machine Learning and Bayesian Methods
Research focus
Statistical Analysis of Missing Data
Research focus
Epidemiological, Biostatistical, and Social Science Applications
People
Principal Investigator
Dr. Fang Liu
Notre Dame Collegiate Professor · Associate Chair @
Applied and Computational Mathematics and Statistics (ACMS);
Director, Health Data Exploration & Analytics Lab (HEAL) @ the Lucy Family Institute of Data & Society
Publications @ Google Scholar.
Thanks to support from NSF · NIH · Gates Foundation · UNITAID · Ara Parseghian Medical Research Fund · Notre Dame
Recent Awards
Current lab members
- Xiaoan (Shawn) Lang (Ph.D. student in ACMS): working on trustworthy machine learning; 2024 ~
- Gina Mannino (Ph.D. student in ACMS): working on differentially private synthetic data generation; 2024 ~
Lab alumni (job upon graduation)
-
Ph.D. in ACMS
- Ruyu Zhou , Ph.D. in ACMS, 2026 (Assistant Professor, Univ of Rhode Island)
- Spencer Giddens , Ph.D. in ACMS, 2025 (postdoc @ Lucy Family Institute of Data & Society --> Research Data Scientist @ Google)
- Tian Yan , Ph.D. in ACMS, 2024 (TikTok)
- Xingyuan Zhao , Ph.D. in ACMS, 2024 (Wells Fargo)
- Yu Wang , Ph.D. in ACMS, 2023 (Epsilon)
- Bingyue Su , Ph.D. in ACMS, 2023 (Wells Fargo)
- Dong Wang , visiting Ph.D. student from 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 (postdoc @ Los Alamos National Lab --> Lead Data Scientist @ Urban Institute)
- Bide Xiong , Ph.D. candidate in ACMS, 2019 Research M.S. in ACMS, Ph.D. in non-ACMS
- Sijing Shao (Ph.D. in Quantitative Psychology), 2020
- Xiao Liu (Research M.S. in Statistics; Ph.D. in Quantitative Psychology), 2020 (Assistant Professor, UT Austin)
- Qimin Liu (Research M.S. in Statistics; Ph.D. in Quantitative Psychology), 2019 (Vanderbilt --> Assistant Professor, Boston U)
- Brenna Gomer (Research M.S. in Statistics; Ph.D. in Quantitative Psychology), 2019 (Assistant Professor, U of Utah)
- 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 (Assistant Professor, 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 Professional M.S. students
- Bao Khanh Cu, 2020
- Lu Li, 2013 Undergraduate researchers
- Leyang Li (undergraduate researcher in ACMS), spring 2026 (Ph.D. student in CS @ Johs Hopskins)
- Sofia Li-Harezlak (undergraduate Researcher) spring/summer, 2024
- Rui Guan (undergraduate Researcher) summer, 2021 (MS in Statistics @ Yale)
- Jocob Chang (undergraduate researcher), 2021 (Ph.D. studnet in biomedical data science @ Stanford)
- Ashley Ahimbisibwe (undergraduate researcher), 2016 ~ 2017 (M.S in Statisitcs @ University College Dublin)
- Rachael Quest (undergraduate researcher), 2017
- YiKun Qian (undergraduate researcher), 2017 (MS in Statistics @ U of Minnesota)
- Zhaoyu Cai (undergraduate researcher), 2016 (MS in CS @ U of Berkeley)
- Yilan He (undergraduate researcher), 2016 (MS in Financial Engineering @ UCLA)
- Bojia Qiu (undergraduate researcher), 2016 (MS in CS @ McGill Univ)
- Nick Troetti (undergraduate researcher), 2015 (senior actuarial assistant @ AIG)
- Colleen Pinkleman (undergraduate researcher), 2014 (business analyst @ Amazon)
- Yunchuan Kong (undergraduate researcher), 2013 (Ph.D. student in Biostatistics @ Emory)
Teaching
Courses taught at Notre Dame.
- ACMS 80870 Topics in Statistics (Trustworthy Machine Learning) (Spring 2026)
- ACMS 60885 Applied Bayesian Statistics (2014 ~ )
- ACMS 40852/60852 Advanced Biostatistical Methods (2012 ~ )
- ACMS 80695 Research Master's Project (2020, 2021)
- ACMS 60786 (60784/60785): Applied Regression Models (I/II) (2016, 2017)
- ACMS 30600 Statistical Methods and Data Analysis (2012)
Contact
Office
201B Crowley
Email
fang DOT liu DOT 131 AT nd DOT edu
·
Phone
574-631-0895