We are interested in the exploration of single-cell dynamics within microbial communities, leveraging advanced analytical techniques such as Raman spectroscopy and flow cytometry to obtain high-resolution phenotypic information. Complementing these phenotypic analyses, we incorporate multi-omics approaches, including metagenomics, transcriptomics, and metabolomics, to furnish a comprehensive understanding of microbial functionality and microbial interaction. This integrative framework allows for the elucidation of complex microbial interactions at single cell level and their implications for ecological and public health objectives
The microbial interactions are a central axis for both ecosystem functionality and the assembly of microbial communities. Recognizing the continuous and dynamic nature of microbial interactions, we focus on how these interactions modulate responses to environmental stimuli, thereby influencing biogeochemical cycles and virulence regulation. Our research approach extends beyond phenotypic and transcriptomic traits, it also encompasses the spatial patterns of metabolites within microbial interactions. These allow us to offer practical insights into the implications of microbial interactions in ecological engineering and regulating pathogenesis.
We are interested in the deployment of artificial intelligence (AI) methodologies to predict environmental contaminant levels with high accuracy using large datasets related to water quality and geospatial information. We also emphasize rigorous data treatment procedures and the automation of these predictive algorithms, contributing to both public health and environmental management through timely and accurate contaminant forecasts. Overall, we aim to revolutionize environmental risk assessment and management strategies by integrating state-of-the-art AI techniques with environmental engineering.