Thien Quy Pham

I am a PhD candidate in Biostatistics and Health Data Science at the University of Pittsburgh, where I work in the Bioinformatics and Statistical Machine Learning Group led by Dr. George Tseng. I develop statistical methods for circadian biology and multi-omics data, with additional work in causal and longitudinal inference for biomedical studies.

My work sits between method development and collaborative science. I am especially interested in models for structured, high-dimensional biological data that remain interpretable across studies, tissues, and data modalities.

I began my training as a medical student in Vietnam, where the questions I cared about most kept turning out to be quantitative ones — why diseases unfold the way they do, why some patients respond to treatment and others don't, why the same intervention seems to work in one population and fail in another. The clinical setting taught me which questions matter; it didn't give me the tools to answer them. I followed those tools to UNC–Chapel Hill for statistics and mathematics, and then to Pittsburgh for biostatistics. The methodological work I do now remains motivated by the patients and biological questions that first drew me in. That early training still helps me act as a bridge between clinical questions and statistical methodology — both in the methods I develop and in the collaborations I take on.

I expect to graduate in November 2026 and am fortunate to be joining the Department of Statistics and Data Science at Carnegie Mellon University in December 2026 as Special Faculty (Postdoctoral Researcher) under the guidance of Dr. Kathryn Roeder.

Research themes

My current research has three connected threads: Bayesian models for circadian rhythms, integrative methods for multi-omics data, and longitudinal or causal methods for applied biomedical research. Across these areas, I aim to build methods that are statistically principled, scientifically interpretable, and useful in real collaborative settings.

Theme 01

Bayesian methods for circadian biology

I develop hierarchical Bayesian and meta-analytic models for detecting, aligning, and comparing circadian programs across tissues, studies, and species.

Theme 02

Multi-omics integration

I work on probabilistic and deep-learning approaches, including sparse multi-CCA, VAEs, and graph neural networks, to integrate RNA-seq, single-cell, DNA methylation, and proteomics data.

Theme 03

Causal & longitudinal inference

In collaborative work, I apply mixed-effects models, causal mediation, and related methods to longitudinal and correlated data from clinical trials and cohort studies spanning HIV, women's health, and rehabilitation research.

Selected publications

An asterisk (*) denotes first or co-first author.

Method BayesRC
circadian 2026

Bayesian · CircadianIn preparation

BayesRC: a comparative Bayesian multilevel framework for circadian synchrony

A methodological project on comparing circadian synchrony across conditions through a multilevel Bayesian meta-analytic framework.

Pham TQ*, Tseng GC, et al. In preparation.

PNAS Sex differences
in stress 2026

Multi-omics · NeurosciencePublished, 2026

Impact of Sex Chromosomes and Gonad Type in Stress Susceptibility in Corticostriatal Brain Regions

Multi-omics analysis of how sex chromosomes and gonadal hormones shape stress-related transcriptional programs in corticostriatal brain regions.

Barko KN*, Shelton MA, Kropp DR, Pham TQ, Rainville JR, Xue X, Tseng GC, Hodes GE, Seney ML. Proceedings of the National Academy of Sciences (PNAS), 2026. doi:10.1073/pnas.2531920123

QoL Res HIV
quality of life 2026

Clinical trial · Longitudinal2026

Tailored interventions for men living with HIV

First-author analysis of a multilevel intervention and its association with quality-of-life outcomes among men living with HIV in India.

Pham TQ*, Wu Q, Pan C, Ha L, Bui TC, Sujan MSH, Schensul SL, Shrestha R, Le NT, Ha T. Quality of Life Research, 35(2):27.

See the full publication list →

Methods and applications

Collaborative work

What I work with

Across projects, I work with bulk and single-cell RNA-seq, DNA methylation, proteomics, longitudinal clinical data, and electronic health records. I am most useful when the analysis requires both statistical rigor and close attention to the scientific question.

I have also worked on pharmaceutical trials in nephrology and oncology, including projects at Otsuka and Merck. Those experiences strengthened my interest in study design, survival analysis, longitudinal endpoints, and the practical side of collaborative statistical work.

I am always glad to talk with collaborators in biomedicine, public health, and related areas, especially when the analytic strategy is still being developed.

Get in touch