Reading and interpreting the genome with algorithms and AI.
We build novel combinatorial algorithms and artificial intelligence (AI) to study genomes and uncover the biomolecular causes of complex human disorders.
At the intersection of computer science, AI, and genomics
The Hormozdiari Lab develops innovative combinatorial algorithms and artificial intelligence (AI) methods—including combinatorial optimization, machine learning, and deep learning—to analyze large-scale biological data. Our primary goal is to uncover how genetic variation, particularly understudied structural variation, influences human health, evolution, and disease.
We design computational tools to discover and genotype variants from whole-genome sequencing, assemble genomes de novo, model how variants reshape the three-dimensional structure of the genome, and predict complex phenotypes from genomic data. We apply these methods to complex disorders ranging from autism to cancer.
The lab is based at the UC Davis Genome Center, with affiliations across the Department of Biochemistry & Molecular Medicine, the MIND Institute, and the graduate groups in Computer Science and Integrated Genetics & Genomics.
What we work on
Our research spans algorithm design and AI for sequence analysis, together with systems-biology approaches to predicting disease.
Structural variation discovery
Novel computational methods to discover and genotype structural variants from whole-genome sequencing, and to link them to complex disorders such as autism and cancer.
Algorithms · WGSPangenome graph analysis
Building, augmenting, and analyzing pangenome graphs that capture variation across many genomes — moving beyond a single linear reference for more complete variant discovery and genotyping.
Graphs · PangenomesStructural variation & the 3D genome
Modeling how structural variants disrupt higher-order chromatin organization — TADs, boundaries, and loops — to alter gene regulation in disease.
Hi-C · ChromatinModules & pathways in disorders
Network algorithms that identify gene modules and biological pathways contributing to neurodevelopmental and other complex disorders.
Networks · Systems biologyComplex-disorder prediction
Machine-learning classifiers that predict phenotype from rare and common variants and other -omics data, toward accurate disorder prediction.
Machine learningOpen methods & software
We release tools the community can build on — from read mapping and variant discovery to gene-network and prediction frameworks.
Open sourceSelected publications
Recent and landmark work across structural variation, the 3D genome, neurodevelopmental disorders, and machine learning. Bold indicates lab members.
Recent preprints
Open positions
We are always looking for curious, motivated people who enjoy working at the boundary of algorithms and genomics.
Postdoctoral Research Fellow in AI & Computational Genomics
We are seeking a motivated postdoc to spearhead the development of advanced computational methods and next-generation AI architectures for genomics and multi-omics — at the intersection of algorithmic computer science, machine learning, and large-scale multi-omics, with direct impact on understanding complex human disease.
To apply, email a cover letter, CV (with full publication list), and contact information for 3 references to fhormozd@ucdavis.edu.
Ph.D. students
Prospective students with a background in computer science, statistics, or quantitative biology can apply through the Computer Science or Integrated Genetics & Genomics graduate groups.
Postdoctoral scholars
We welcome postdocs interested in structural variation, the 3D genome, or machine learning for disease prediction. Reach out with your CV and research interests.
Undergraduate researchers
UC Davis undergraduates eager to gain hands-on experience in computational genomics are encouraged to inquire about project openings.
Interested in working with us?
Email Dr. Hormozdiari with a short note about your background and interests, along with a CV. Prospective graduate students should also apply to the relevant UC Davis graduate group.