Research
We develop computational methods for measuring, interpreting, and modeling complex biological and neural systems.
AQuA2 (Activity Quantification and Analysis) is a tool for quantifying spatiotemporal signals across biosensors, cell types, organs, animal models, and imaging modalities of biological fluorescence imaging data.
Building methods for large-scale electron microscopy data analysis, including neural structure reconstruction, organelle and synapse identification, connectome mapping, and scalable data processing.
Developing methods for accurate cell tracking and lineage reconstruction in 3D+t imaging data.
Developing foundation models for generalizable 3D cell and nuclei instance segmentation across organisms, tissues, and imaging modalities.
Developing high-resolution-reference-guided registration, motion correction, and spatiotemporal activity analysis methods for sparse, low-resolution, and dynamic functional imaging data.
Combining neural signal analysis, computational modeling, and intelligent algorithms to study neural representation, decoding, and interaction in brain-computer interfaces.