T32 ODSi Training Program
T32 ODSi Training Program
The Oncological Data Science (T32 ODSi) Training Program is a two-year predoctoral program that develops skills in biomolecular-anchored cancer data science by integrating bioinformatics, molecular population health, and cancer data science within a strong Huntsman Cancer Institute educational and research environment.
The T32 ODSi program requires a mentoring team that includes a computational/data mentor and a mentor from either cancer biology or cancer population sciences (domain expert). See Figure below for the ODSi mentorship model. The list of current T32 ODSi mentors is here.
Program Goal
Prepare young scientists to become data ambassadors and data innovators in the use of cancer data science techniques and methodologies to advance the understanding, prevention, diagnosis, and treatment of cancer.
For more information, contact Aik Choon Tan at Aikchoon.Tan@hci.utah.edu.
Eligibility
- Full-time 2nd or 3rd year U of U PhD-seeking student who has initiated a research
program in the laboratory of an ODSi faculty member - US citizen or permanent resident, and meet all other NIH eligibility criteria
- Agree to attend and participate in all program components for the entirety of the program
Accepts
Predoctoral Only
Important Dates
Applications Open: February 13, 2026
Applications due: August 31, 2026
Funding begins: October 1, 2026
Contact Us
Principal Investigators
Aik Choon Tan, PhD
Yves Lussier, MD, FACMI, FAMIA
Jen Doherty, PhD, MS
Program Coordinator:
Kelly Chanthapanya
Trainees
Cohort 2 (2026)
Tessa Bass
Tessa’s project aims to develop an integrated computational framework to characterize a novel phenotype known as androgen receptor–indifferent (ARi) prostate cancer. By integrating bulk DNA and RNA sequencing, spatial transcriptomics, digital pathology, multiplexed immunofluorescence, and clinical data, she seeks to create a framework capable of identifying ARi regions within prostate cancer tissue and generating phenotype-specific molecular and spatial characterizations. This work will also investigate the transcriptional programs underlying ARi development and establish clinically relevant correlates associated with the phenotype. Ultimately, she hopes to translate these findings into clinically actionable biomarkers that can guide treatment strategies for patients with ARi prostate cancer.
Tessa's Mentors:
- Data Mentors: Yi Qiao, PhD
- Cancer Biology Mentor: Beatrice S. Knudsen, MD, PhD
Samuel Coleman
Samuel Coleman develops computational and statistical methods for analyzing single-cell genomic data, with a particular focus on CRISPR-based perturbation experiments and single-cell DNA sequencing technologies. His research aims to improve the resolution and scalability of approaches for characterizing cellular heterogeneity, lineage relationships, and genome editing outcomes at the single-cell level. By integrating novel computational methodologies with high-throughput sequencing data, he seeks to better understand mechanisms underlying cancer progression, therapeutic response, and immune regulation. His long-term interests lie in advancing single-cell technologies and applying them to oncology, immunotherapy, and precision medicine.
Samuel's Mentors:
- Data Mentors: Kendell Clement, PhD
- Cancer Biology Mentor: Ami B. Patel, MD
Cohort 1 (2025)
Sophie Huebler
Sophie’s research focuses on accelerating the discovery of microbiome-based therapies for patients undergoing allogeneic stem cell transplants, who are at high risk of developing Graft-versus-Host Disease (GVHD). To overcome inconsistent findings from small studies and inadequate statistical methods, Sophie is building a large harmonized multi-study database and applying a novel Bayesian meta-analysis framework. This approach will enable robust identification of microbial signatures that drive GVHD, guiding targeted therapies to improve patient survival.
Sophie’s Mentors:
- Data Mentors: Xi Qiao, PhD
- Cancer Biology Mentor: Allison Weis, PhD
Elena Nazarenko
Elena's project aims to develop new strategies for identifying effective therapies for rare cancers such as acral melanoma, where treatment options remain limited. Using a Bayesian variable selection framework, she will analyze high-dimensional multi-omics data to propose rational combination therapies and construct graphical models to infer cell–cell interactions within the tumor microenvironment. To maximize impact, she will also design interactive visualization tools to share our findings openly with researchers and clinicians.
Elena’s Mentors:
- Data Mentor: Ann Chen, PhD
- Cancer Biology Mentor: Robert Judson-Torres, PhD
Carrie Vanty
Carrie’s project involves mathematical modeling and data analysis to study the Hedgehog pathway, a gene regulatory network that is essential for vertebrate development. Mutations within the pathway are associated with various cancers, such as medulloblastoma, the most common pediatric brain cancer. In collaboration with Dr. Adler and Dr. Myers, Carrie aims to use data-driven modeling to uncover influential genes involved in medulloblastoma and investigate their role in the Hedgehog pathway.
Carrie’s Mentors:
- Data Mentor: Fred Adler, PhD
- Cancer Biology Mentor: Ben Myers, PhD