Wolfson alumni honoured in King's New Year's List
BEng PhD
Kangjie is a machine learning scientist at the Wellcome Sanger Institute. His research lies at the intersection of AI and biology, aiming to develop generalisable AI foundation models for large-scale multi-omics data to elucidate the principles of gene regulation.
Kangjie is a computer science researcher with a long-standing interest in machine learning and its applications to the life sciences. He completed his BEng degree in Computer Science at the Honors School of the Harbin Institute of Technology in 2020, where he developed a strong interest in machine learning and its ability to extract structure and insight from complex, high-dimensional data. After that, he continued his academic training at the School of Computer Science at Peking University from 2020 to 2025, pursuing a PhD under the supervision of Professor Ming Zhang. His doctoral research focused on designing pre-trained models for molecular representation learning and exploring their applications in areas such as small-molecule modeling and multi-scale protein structure understanding.
Building on this interdisciplinary foundation, Kangjie is now a Postdoctoral Fellow at the Wellcome Sanger Institute, working with Dr Mo Lotfollahi. In this role, Kangjie develops scalable AI foundation models for large-scale, multi-omics datasets. By integrating expertise in machine learning, molecular modeling, and computational biology, he seeks to bridge data-driven AI methodologies with fundamental questions in biology.
Biological systems perform highly coordinated tasks, yet the molecular instructions behind them are extraordinarily complex. During embryonic development, for example, a single fertilised egg gives rise to hundreds of specialised cell types. These transformations are driven by gene regulation, a multilayered system in which DNA sequences, chromatin structure, transcription factors, and the spatial organisation of the genome work together to control when genes are switched on or off. This complexity has left our understanding of how gene regulatory mechanisms shape cell-type development still limited.
Artificial intelligence models offer a powerful capability to uncover patterns hidden within complex and large-scale datasets. Building on this potential, Kangjie’s research applies advanced AI models to decipher the regulatory logic encoded in genomic, transcriptomic, and epigenomic data. His work aims to construct computational frameworks that explain how molecular programs give rise to distinct cellular states and dynamic biological processes.
Yet a central challenge persists: gene regulation is inherently multi-scale. Regulatory outcomes emerge from the integration of DNA sequences, chromatin accessibility, transcription factor binding, and the intricate interactions among these layers. Understanding how such multi-scale regulatory mechanisms shape cellular development and differentiation remains a fundamental yet difficult question in modern biology.
To address this, Kangjie is developing a multilevel AI foundation model for gene regulation. By developing novel model architectures and training them on large multi-omics datasets, his research aims to identify previously unrecognised transcription factor activities, infer their context-specific regulatory roles, and uncover how combinations of factors cooperate to control gene expression.
Through this work, Kangjie aims not only to deepen our mechanistic understanding of life’s regulatory architecture but also to enable transformative applications in genomics, developmental biology, and precision medicine.
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