We are entering a new era in genomics with exciting opportunities for computation-driven discovery. Our aim is to explore the new possibilities of what computation can do for biomedical science, from understanding sequence-based regulations to the evolution of genomes and their impact to diseases.
Lab news (2025/5): Congratulations to Yuchen for the manuscript “Inverse Flow and Consistency Models” accepted at ICML!
The epitome of life’s complexity is encoded in the simple form of genome sequence. We focus on decoding the regulatory programs encoded in the genomic sequence. With diverse genomic datasets and machine learning approaches, especially deep learning, we work on deciphering connections between sequence, chromatin organization, genome 3D architecture, gene expression, and phenotypes including diseases. We develop methods to predict, understand, and design genomic sequences.
The entanglement of sequence, function, and evolution shapes all genomes at both macro and micro scales. We develop computational methods to retrace the evolutionary history of biological circuits, combining data from model- and non-model organisms. We are especially interested in understanding the impact of evolutionary fine-tuning or remodeling of regulatory circuits on human health.
We believe in the instrumental value of machine learning, statistics, and AI method research for computational biology and other data-intensive natural sciences. We are interested in topics including deep learning, probabilistic graphical models, Bayesian inference including variational inference and MCMC, generative modeling, optimization, causal inference, and reinforcement learning. We are also interested in building softwares to enable rapid prototyping of research project-tailored machine learning models and enable automated statistical inference for robustness and reproducibility.
Please contact me at [email protected] if you are interested.
If you are interested in joining our lab as a graduate student, please apply through one of the relevant graduate programs at the University of Chicago, such as the GGSB gradaute program.
We will help students develop a strong analytical mindset and knowledge for conducting cutting-edge research with computational approaches.
We are looking for postdoctoral fellows to work at the intersection of genomics and AI. Ideal candidates should have Ph.D. or equivalent degrees in Computational Biology, Computer Science, Statistics, or a related field at the expected start time. Prior research experience in any areas including regulatory genomics, statistical or evolutionary genetics, single-cell genomics, computational structural biology, machine learning, or statistics is a plus but not required. This is a full research position, but teaching opportunities can be provided if desired. If interested, please email your CV, a brief description of your previous works, and your future research interests to [email protected].
We welcome motivated undergraduates to join our team. We are happy to train undergraduates in many aspects of computational biology and data science. Please contact Jian to discuss research opportunities.
The Zhou Lab is located in the University of Chicago campus in Hyde Park. Lab members also have access to considerable computational resources, including state-of-the-art GPU computing server clusters to support our deep learning research.
