Theme 01
Structural Variation Detection
Developing computational methods to detect and characterize large-scale genomic variation across diverse sequencing technologies.
Bioinformatics · Machine Learning · Cancer Genomics
Computational methods for genomics, cancer evolution, and biological discovery.
The DeepBioMed Lab develops computational approaches for analyzing large-scale biological data, with a focus on structural variation detection, cancer heterogeneity, and single-cell genomics. Our work draws from machine learning, statistical inference, and optimization to better understand complex biological systems.
The Team
Overview
About the lab
We are interested in developing scalable computational tools that turn complex genomic and sequencing data into biological insight. The lab’s research spans algorithm development, machine learning, and integrative analysis for studying variation, tumor evolution, and heterogeneity in cancer.
Research
Current research directions
Our research combines computational rigor with biological relevance, emphasizing methods that can address real questions in genomics and cancer biology.
Theme 02
Cancer Heterogeneity and Evolution
Building tools to study tumor subclones, infer evolutionary history, and understand how cancer changes over time.
Theme 03
Machine Learning for Genomics
Applying machine learning and statistical inference to large-scale biological and single-cell data for more accurate and interpretable analysis.
Publications
Featured work
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People
Lab members
The lab is led by Prof. Xian Mallory and currently includes four PhD students.
Xian Mallory
Principal Investigator
Prof. Mallory develops computational techniques for analyzing biological data, with emphasis on cancer, structural variation, machine learning, and sequencing-based inference.
Current PhD Students
We currently have four PhD students in the lab:
- Pankaj Bhattarai
- Noor Mohammad Talukder
- Utkarsh Goyal
- Tanha Kabir Koly
Opportunities
Join the lab
We welcome students interested in bioinformatics, computational biology, cancer genomics, machine learning, and large-scale sequencing data analysis.
Prospective students
If you are interested in joining the lab, please reach out with your background, research interests, and CV. Strong preparation in programming, algorithms, statistics, and mathematics is especially valuable.
News
Latest updates
This section to have news and updates.
Apr, 2026
New paper!
scLongTree's revision has been updated on bioRxiv