Xiaoqing "Ellen" Tan

Email: xit31 [at] pitt [dot] edu

About Me

I am a Research Scientist at Meta (GenAI Llama; previously FAIR). Prior to that, I obtained my Ph.D. in Biostatistics at University of Pittsburgh in 2022, advised by Lu Tang and Gong Tang. I was a visiting student in Computer Science at Carnegie Mellon University from 2019 to 2021. I obtained my B.S. in Pharmacy and Computer Science at Sun Yat-sen University in 2018.

My research interest lies in developing novel statistical and machine learning methods in causal inference, data integration, and decision fairness.

I had been a Graduate student researcher at NRG Oncology during my PhD. I interned at Eli Lilly and Company in summer 2021 working with Shu Yang and Ilya Lipkovich. I also worked with Timothy Girard at Department of Critical Care Medicine, University of Pittsburgh.

I enjoy volunteering. As well as being am a weekly cat care volunteer at Humane Animal Rescue, I was a pro-bono consultant at Fourth River Solutions for local businesses in Pittsburgh. I worked as a data science project reviewer in a global team at DataKind to help identify impactful and innovative proposals that will help spur inclusive growth during the pandemic in summer 2020.

RISE: Robust Individualized Decision Learning with Sensitive Variables
Tan, X., Qi, Z., Seymour, C., Tang, L.
Advances in Neural Information Processing Systems (NeurIPS) 2022
** Distinguished Student Paper Award for the ENAR 2023 Spring Meeting
[Paper] [Code] [Video]

A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
Tan, X., Chang, C., Zhou, L., Tang, L.
International Conference on Machine Learning (ICML) 2022
** Winner of the Student Research Award at the 35th New England Statistics Symposium
** Honorable Mention Award at JSM 2021 Student Paper Competition
[Paper] [Code] [Video]

Identifying Principal Stratum Causal Effects Conditional on a Post-treatment Intermediate Response
Tan, X., Abberbock, J., Rastogi, P., Tang, G.
Causal Learning and Reasoning (CLeaR) 2022
[Paper] [Code]

Invited Talks

A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
The 35th New England Statistics Symposium (NESS) 2022, Storrs, CT

Improving personalized causal inference with information borrowed from heterogeneous data sources
The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics) 2021, King's College London, UK

Selected Awards

Professional Services