AI

Control microscopes in plain language — LLM agents that turn scientific intent into imaging, segmentation, and analysis.

A decision-theoretic framework (RMPMAB) for smart microscopy that decides when and where to image — modelling each region as an ensemble of Markov chains and using scalable Whittle-index policies to capture 93% more biologically relevant events under fixed acquisition, compute, and photobleaching budgets.

An open, highly configurable, general-purpose automated microscopy platform for low-cost, high-throughput diagnostics — including malaria screening, sickle cell anemia, and antibiotic resistance — designed for use in low-resource settings.

A domain adaptation of video diffusion models that generates realistic time-lapse microscopy videos of dividing HeLa cells — conditioned on phenotype measurements or an initial frame — capturing mitosis and migration for in-silico hypothesis testing and data augmentation.

A community-driven, AI-powered assistant that helps researchers navigate bioimaging tools, databases and services — and autonomously runs analysis tasks from plain-language prompts. Published in Nature Methods.

AICell Lab joyfully welcomes Yanhong Su as our new postdoctoral researcher. With her expertise in oncology, Yanhong will be a key contributor to our self-driving microscopy project.

We are ecstatic to welcome Gabriel, a Postdoc Researcher with a rich background in automated scientific discovery, computational biology, AI, and lab robotics. His addition to the AICell Lab team promises new avenues in our mission of advancing data-driven life sciences.

We’re excited to introduce Linh, a Researcher with dual expertise in molecular biology and machine learning. His arrival opens new avenues for AICell Lab in automated cell manipulation and imaging.

We are delighted to welcome Jeremy, a Research Engineer with diverse expertise in computer vision, computational biology, and modelling. His arrival marks an exciting chapter in AICell Lab’s journey towards advancing data-driven life sciences.

The AICell Lab is thrilled to welcome our first PhD student, Songtao Cheng, this coming October. His multidisciplinary expertise in machine learning, lab automation, and biological wet lab skills will be instrumental in advancing our research projects.