Kevin K. Tsia

MorphoGenie is an unsupervised deep-learning framework for morphological profiling of cells that learns interpretable, generalizable features without manual annotation or labeled data.

The paper introduces FACED 2.0, a high-speed imaging technique that enables simultaneous voltage and calcium activity recording in vivo across large neuronal populations.

This paper introduces SpAD, a high-throughput label-free imaging platform for live-cell drug and genetic screens, enabling rapid, unbiased profiling of cellular responses.

StaVia integrates spatial and temporal data with higher-order random walks to improve cell lineage analysis and visualization in developmental atlases.

CytoMAD improves label-free morphological analysis of cell heterogeneity across batches by distilling morphological information and enabling drug response prediction without manual annotation.

The paper introduces DIF, a method to focus polydisperse micro-particles without size-dependent dispersion, achieving high efficiency and uniformity in microfluidic applications.

VIA is a scalable trajectory inference method that accurately reconstructs complex cellular trajectories in large single-cell datasets, including cyclic and disconnected structures.

starmapVR provides an immersive visualization tool for single-cell and spatial omic data using VR, supporting 3D and 2.5D views with multiple interaction methods.

This paper demonstrates ultrafast two-photon fluorescence microscopy for in vivo neural activity imaging at high spatiotemporal resolution in awake mice.

PARC is a scalable graph-based clustering algorithm for large single-cell datasets, outperforming existing methods in speed and rare population detection.