Hongjun’s research centers on developing and applying statistical and deep learning methods to study gene regulation in cell differentiation and cancer progression. His work integrates diverse multi-omics datasets, including single-cell/spatial multi-omics as well as 3D genomics data, to decipher the complex regulatory networks underlying cellular identity and tumor evolution. By combining advanced computational models with systems biology principles, he aims to build predictive, mechanistic frameworks that can simulate, predict, and manipulate cellular behaviors driven by gene regulatory dynamics.

Chengcheng Liu’s research aims to combine single-cell sequencing (scRNA-seq, scATAC-seq), bulk transcriptomics, and whole genome sequence approaches to study cancer development.

Maithri’s research is centered on computationally studying the developmental systems of C. elegans. Its inherent invariance leads to exceptional experimental reproducibility, making it an ideal organism for exploring fundamental biological processes. Currently, her work involves predicting and modelling critical developmental events in the worm gut.

Xinxin’s research focuses on developing statistical and machine learning frameworks to study early embryonic development. Her work centers on predicting cell–cell contact maps in C. elegans using single-cell RNA-seq data, aiming to understand how gene expression patterns shape spatial organization in early embryos. By integrating computational modeling with developmental biology principles, she seeks to build robust analytical methods that infer cellular relationships, improve cell identity prediction, and provide mechanistic insight into early-stage developmental systems.

Dr. Ahmed Abbas Elmahdi interested in applying machine learning techniques to analyze and predict three-dimensional genome organisation.

Dr. Ben Niu studies Systems Biology with mathematical and machine learning approaches. He is currently focusing on understanding how gene expression regulation controls early embryogenesis using C. elegans as the model organism. His is specialised in data mining, image pattern recognition, bioinformatics, mathematical modelling and software engineering.

Khyati Raghunath Chandratre is a Doctoral candidate and GRACE Fellowship awardee in the Zhang Lab. She investigates the changes in 3D genome organisation in different prostate cancer cell lines to study transcription dysregulation by integrating chromatin data,  multi-omics data (ChIP-Seq & RNA-Seq) and machine learning.

Xingyu’s research focuses on using applied machine learning / deep learning models to investigate and simulate the intricate process of tissue and organ boundary formation during the early developmental stages of C.elegans worms, uncovering critical insights into gene regulated cellular morphology.