UCLA Tanigawa Lab is recruiting (2026)
The Tanigawa Lab at UCLA is recruiting students and postdoctoral researchers in statistical genetics, computational genomics, and precision medicine.
Assistant Professor
Mapping the biological basis of heterogeneity in disease.
Yosuke Tanigawa is an Assistant Professor in the Department of Bioengineering at UCLA. He leads the Tanigawa Lab, which studies why individuals with the same diagnosis differ in disease onset, progression, and treatment response.
The lab develops statistical and computational methods for large-scale genetic, genomic, and phenotypic data. The work spans disease heterogeneity dissection, ancestry-aware polygenic prediction, and therapeutic target discovery from human genetics.
Before joining UCLA in July 2025, Yosuke trained at the MIT Computational Biology Lab with Manolis Kellis. He received his PhD in Biomedical Informatics at Stanford University, where he worked with Manuel Rivas and Gill Bejerano.
The lab values scientific rigor, open science, and clear communication. We aim to make complex human genetics accessible without losing scientific precision.
The Tanigawa Lab at UCLA is recruiting students and postdoctoral researchers in statistical genetics, computational genomics, and precision medicine.
How I think about mentorship, scientific training, expectations, and professional growth across career stages.
In this study, led by Xiaohe (Lucy) Tian, we showed that ancestry-aware integration of tissue-specific genomic annotations enhances the transferability of polygenic scores (PGS).
Yosuke received a JST PRESTO award to support research on mathematical modeling of interindividual differences in disease.
The Tanigawa Lab at UCLA is recruiting students and postdoctoral researchers in statistical genetics, computational genomics, and precision medicine.
Yosuke Tanigawa joined UCLA Bioengineering in July 2025 to launch a research program on disease heterogeneity.
We introduce "hypometric genetics," an approach to investigate the genetic basis of binarized traits representing the presence of below-the-limit-of-quantification (BLQ) quality …
We developed GenoBoost, a polygenic score modeling approach, incorporating both additive and non-additive genetic dominance effects.
We developed a polygenic score training approach that allows direct inclusion of admixed individuals without the need for local ancestry inference and showed ancestry-diverse …
We develop an ontology-guided approach to ranking tissue-/cell-type-specific transcription factors (TFs) from chromatin accessibility data.