Research Areas

Unraveling the genomic complexity of cancer through computational biology.

Tumor Heterogeneity

We study the diversity of cancer cells within tumors to understand how cellular states, spatial organization, and transcriptional programs influence disease progression, metastasis, and therapeutic response.

Tumor Heterogeneity Visualization
Clonal Hematopoiesis Visualization

Clonal Hematopoiesis

We investigate how genetic and epigenetic alterations drive the expansion of blood cell clones, contributing to leukemia, cardiovascular disease, and age-related health outcomes.

Computational Genomics

We develop open-source computational methods and software tools to enhance and ease the interpretation of diverse data modalities, addressing topics such as error modeling and mutation calling.

Computational Tools Visualization

Genomic Analysis

Multi-omic deep sequencing to uncover clonal evolution from pre-leukemic stages. Decoding how somatic mutations drive disease.

  • Clonal selection tracking
  • Evolutionary dynamics

Computational Models

State-of-the-art machine learning algorithms to estimate risk and detect somatic mutations with high precision.

  • Longitudinal risk modeling
  • Clinical-grade ML

Early Detection

Identifying key molecular biomarkers in healthy individuals to predict disease onset and risk classification.

  • Biomarker discovery
  • Pre-clinical risk screening

Genomic Analysis

Multi-omic deep sequencing to uncover clonal evolution from pre-leukemic stages. Decoding how somatic mutations drive disease.

  • Evolutionary dynamics
  • Single-cell multi-omics

Computational Models

State-of-the-art machine learning algorithms to estimate risk and detect somatic mutations with high precision.

  • Clinical-grade ML
  • Somatic mutation callers