The Jiang lab website: https://sites.google.com/view/jiang-lab/
My research is centered on developing statistical methods and data-driven approaches to leverage massive genomic and transcriptomic data to investigate the temporal-spatial variation and dynamics of gene regulation, understanding and tackling the heterogeneity of cellular populations, complex traits and diseases. One unique aspect of my research is that I integrate a variety of statistical/bioinformatic methods, such as network modeling, machine learning, and genomic and transcriptomic data analysis, to leverage complex/large-scale omics datasets. These integrative computational approaches allow us to maximize the knowledge learned from the data to gain novel insights into the fundamental and translational aspects of human diseases. Another unique aspect of my research is that I have developed several comparative genomic strategies that allow us to analyze high-throughput sequencing data generated from species lacking sequenced genomes or with poorly annotated genes, such as Nile grass rat.
We are always seeking inspired, highly motivated, and curious graduate students to join our group. We set very ambitious goals for students:
We expect each PhD student will have 1-2 collaborative projects and one independent project.
· Year 1: Python & R programming training, Computational Biology training, 1-2 collaborative projects or one independent project;
· Year 2: Focusing on independent projects
· Year 3-4: PhD project & Thesis/Defense
Examples of currently active collaborative projects:
- Exosomes & tissue regeneration (transcriptomic study) – Collaboration with UW-Madison;
- Mouse digit/bone regeneration (RNA regulation) - Collaboration with UW-Madison
- Nile grass rat model to study metabolic disorders (diet induced type 2 diabetes & retinopathy) - Collaboration with UCSB
- Volumetric muscle loss & tissue regeneration - Collaboration with U of Pitt.
- Nile grass rat model to study light response (brain activity & transcriptomic study) - Collaboration with Michigan State University
- Single-cell RNA-seq data analysis (vascular disease) – Collaboration with UW-Madison & Morgridge Institute For Research
- Multiple internal collaborations with CSU labs
Examples of independent projects with computational biology focus:
- Develop pipelines for Ribo-seq data analysis
- Develop network-based data prioritization system
- Develop computational framework for multi-omics data integration
- Develop computational methods and software for single-cell RNA-seq data analysis
The students will be supported by a mixture of Teaching Assistant (TA), Research Assistant (RA) and Fellowships (a. CMMS 2-year fellowship: https://sciences.csuohio.edu/cmms/no-title-333; b. NSF 3-year Graduate Research Fellowship Program (https://www.csuohio.edu/research/nsf-grfp-workshop-application).
Our lab has limited RA positions (one or two) for PhD students. Starting from August 2022, all RA applicants need to pass our lab programming test (Python & R) and basic computational biology knowledge test. The RA positions are yearly renewed. A minimal expectation (renew criteria) for RA is that at least one co-author manuscript submitted per year and at least one first author manuscript submitted every two years. The authorship is based on the contribution to the projects.