In agriculture, our lab mainly stands for leveraging AI to advance crop BREeding during Agricultural Digitalization (AI4BREAD). Specifically, Crops, as natural systems, consist of genetic elements governed by a molecular language with unknown syntax and semantics. By interacting with the environment, agronomic traits like yields emerge from the crop system. Facing population growth and climate change, optimizing crop systems digitally, or intelligent crop breeding, is vital for achieving sustainable agriculture and global food security. Our AI4Bread team enhances crop breeding using AI, IT/BT, and big data following systems theory. This can be divided into three research directions, including genomics, phenomics, and large-molecular language modeling (LMLM). In genomics, novel computational methods are developed based on cutting-edge sequencing technologies to decode complete genomes and thus the full genetic makeup. In phenomics, novel computational methods are developed based on sensor networks, IoT, UAVs, and computer vision etc to gather and analyze phenotypic data efficiently. In LMLM, AI models are trained using the genomic and phenomic data to understand the molecular language and the molecular mechanisms behind crop traits. To generate data for these studies, crops are grown in greenhouses and farms.

In healthcare, our lab mainly stands for leveraging AI to advance these fields on Bioinformatics Research Exploration and heAlth Development (AI4BREAD). Since Deep learning and AI are beneficial for bioinformatics, enhancing our ability to handle multi-omics data and decipher biological rules. Integrating micro-level (e.g. single-cell transcriptome, epigenetics), and macro-level (e.g. medical images, text corpus) data reveals cellular heterogeneity and disease complexity. Furthermore, it also improves medical image analysis, single-cell multi-omics, spatial transcriptomics, and drug discovery, enabling personalized treatments. We focus on integrating deep learning algorithms with multi-omics data to uncover cellular heterogeneity and the mechanism of life and health. By digging deep into the interactions from the micro to the macro levels, we aim to systematically explore the processes that govern life—from cells and tissues to animals, humans, and crops. Through constructing advanced AI models, we seek to reveal the underlying principles that drive biological functions and development. This comprehensive approach allows us to contribute to the significant mission of improving human health and advancing our understanding of life.

In order to better realize the above two research branches about 'BREAD', the lab emphasizes fundamental theories and methods. The main topic is to study AIfoundation theory, models and optimization methods to conduct Big data REpresentation And moDeling (AI4BREAD), especially for the multi-modal, dynamic and complex data involved in real-world agriculture and healthcare. Overall, our AI4Bread lab is committed to carrying out interesting, useful, and heart-warming research about the betterment of livelihoods for the long term.