About

Kai Tan, PhD, is a Professor of Pediatrics (Oncology) at the Perelman School of Medicine at the University of Pennsylvania. He is a member of several institutes including the Abramson Cancer Center, the Institute for Regenerative Medicine, the Institute for Immunology, the Institute for Diabetes, Obesity and Metabolism, and the Epigenetics Institute. Dr. Tan holds the Richard & Sheila Sanford Endowed Chair at the Children's Hospital of Philadelphia and is affiliated with the Department of Pediatrics and multiple graduate groups such as Epidemiology and Biostatistics, Immunology, Genomics and Computational Biology, Cell and Molecular Biology, and Pharmacology. His research expertise centers on gene regulation, cellular development, and cancer. His lab employs genomics and systems biology approaches to understand gene regulatory factors underlying cellular processes, utilizing bulk and single-cell omics and imaging assays. Dr. Tan's work investigates the molecular basis of oncogenesis and therapy resistance in cancer, including generating multi-omic atlases of pediatric cancer, identifying novel combination therapies, and studying cellular adaptations to targeted therapy and immunotherapy. Additionally, his research explores gene regulatory networks controlling the embryonic origin of hematopoietic stem cells, T-cell differentiation, and pediatric cancers, with a focus on key regulators, cis-regulatory elements, 3D genome organization, and mutations conferring disease risk. He also develops computational methods to interpret high-dimensional and single-cell transcriptomics, proteomics, and epigenomics data, integrating multi-omics data through network biology and machine learning. His ongoing projects aim to identify disease-perturbed pathways, characterize gene regulatory events leading to differentiation and cancer, and understand the impact of non-coding somatic variations in pediatric cancer. Dr. Tan's contributions advance understanding of gene regulation in development and disease, with a particular focus on cancer biology and therapeutic resistance.

Research topics

• Biology
• Computational biology
• Genetics
• Immunology
• Computer Science
• Medicine
• Cancer research
• Pathology
• World Wide Web
• Bioinformatics
• Cell biology
• Anatomy

Selected publications

• Towards a universal model of tissue cellular architecture

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-11

  articleOpen access1st authorCorresponding

  Presented at the “2026 NAIRR Annual Meeting” (NSF Award #2536728), Arlington, VA, USA, March 10-13, 2026. https://zenodo.org/communities/nairr2026

  PublisherDOI

• Precise asymptotics of bagging regularized M-estimators

  The Annals of Statistics · 2026-04-01

  article

  We characterize the squared prediction risk of ensemble estimators obtained through subagging (subsample bootstrap aggregating) regularized M-estimators and construct a consistent estimator for the risk. Specifically, we consider a heterogeneous collection of M≥1 regularized M-estimators, each trained with (possibly different) subsample sizes, convex differentiable losses, and convex regularizers. We operate under the proportional asymptotics regime, where the sample size n, feature size p, and subsample sizes km for m∈[M] all diverge with fixed limiting ratios n/p and km/n. Key to our analysis is a new result on the joint asymptotic behavior of correlations between the estimator and residual errors on overlapping subsamples, governed through a (provably) contractive nonlinear system of equations. Of independent interest we also establish convergence of trace functionals related to degrees of freedom in the nonensemble setting (with M=1) along the way, extending previously known cases for squared loss with ridge and lasso regularizers. When specialized to homogeneous ensembles trained with a common loss, regularizer, and subsample size, the risk characterization sheds some light on the (implicitly) induced regularization effect due to the ensemble and subsample sizes (M,k). For any ensemble size M, optimally tuning subsample size yields samplewise monotonic risk. For the full-ensemble estimator (when M→∞), the optimal subsample size k⋆ tends to be in the overparameterized regime (k⋆≤min{n,p}), when explicit regularization is vanishing. Finally, joint optimization of subsample size, ensemble size, and regularization can significantly outperform regularizer optimization alone on the full data (without any subagging).

  PublisherDOI

• Towards a universal model of tissue cellular architecture

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-11

  articleOpen access1st authorCorresponding

  Presented at the “2026 NAIRR Annual Meeting” (NSF Award #2536728), Arlington, VA, USA, March 10-13, 2026. https://zenodo.org/communities/nairr2026

  PublisherDOI

• Table S5 from Impact of Genetic Ancestry on Genomics and Survival Outcomes in T-cell Acute Lymphoblastic Leukemia

  2025-10-07

  preprintOpen access

  <p>Association of Genetic Ancestries (25% Interval) as Continuous Variables with Pathway Alteration using Logistic Regression Models for Each Pathway.</p>

  PublisherDOI

• Table S1 from Impact of Genetic Ancestry on Genomics and Survival Outcomes in T-cell Acute Lymphoblastic Leukemia

  2025-10-07

  preprintOpen access

  <p>T-cell Acute Lymphoblastic Leukemia Genomic Subtype Description.</p>

  PublisherDOI

• Table S7 from Impact of Genetic Ancestry on Genomics and Survival Outcomes in T-cell Acute Lymphoblastic Leukemia

  2025-10-07

  articleOpen access

  <p>Types of Reported Events by Genetic Ancestry.</p>

  PublisherDOI

• Supplementary Table 1 from Characterization of Leukemic Resistance to CD19-Targeted CAR T-cell Therapy through Deep Genomic Sequencing

  2025-11-26

  articleOpen accessSenior author

  <p>Clinical information regarding the 13 patients involved in this study, including passage numbers of PDX samples.</p>

  PublisherDOI

• Table S10 from Impact of Genetic Ancestry on Genomics and Survival Outcomes in T-cell Acute Lymphoblastic Leukemia

  2025-10-07

  preprintOpen access

  <p>Detailed clinical data</p>

  PublisherDOI

• Table S2 from Impact of Genetic Ancestry on Genomics and Survival Outcomes in T-cell Acute Lymphoblastic Leukemia

  2025-10-07

  articleOpen access

  <p>T-cell Acute Lymphoblastic Leukemia subtype by Categorical Genetic Ancestry</p>

  PublisherDOI

• Supplementary Table 2 from Characterization of Leukemic Resistance to CD19-Targeted CAR T-cell Therapy through Deep Genomic Sequencing

  2025-11-26

  articleOpen accessSenior author

  <p>Details of SNVs and small indels detected from deep whole-exome sequencing in CD19 and selected genes associated with genomic instability.</p>

  PublisherDOI

Recent grants

• A TOOLKIT FOR IDENTIFYING CAUSAL VARIANTS IN TRANSCRIPTIONAL ENHANCERS

  NIH · $1.2M · 2014–2020

• Mechanisms of endothelial-to-hemogenic transition mediated by Runx1

  NIH · $2.1M · 2017–2023

• A SYSTEMS APPROACH TO THE GENETIC STUDY OF ALCOHOL DEPENDENCE

  NIH · $1.6M · 2017–2022

• EPIGENETIC REGULATION OF STEM CELL FATE CHOICE

  NIH · $1.7M · 2016–2018

• Center for Pediatric Tumor Cell Atlas - Admin Supplement

  NIH · $16.2M · 2023–2025

Frequent coauthors

• Changya Chen

  Children's Hospital of Philadelphia

  94 shared

• Peng Gao

  90 shared

• Wenbao Yu

  Children's Hospital of Philadelphia

  70 shared

• Yangyang Ding

  69 shared

• Nancy A. Speck

  University of Pennsylvania

  68 shared

• Yasin Uzun

  Penn State Milton S. Hershey Medical Center

  67 shared

• Shovik Bandyopadhyay

  Children's Hospital of Philadelphia

  62 shared

• Stephen P. Hunger

  56 shared

Education

• PhD, Genetics

  Washington University in Saint Louis

  2004

• BS

  Beloit College

  1997