The interrogation of cellular states and interactions in immunology research is an ever-evolving task, requiring adaptation to the current levels of high dimensionality. Cytometry enables high-dimensional profiling of immune cells, but its analysis is hindered by the complexity and variability of the data. We present MAESTRO, a self-supervised set representation learning model that generates vector representations of set-structured data, which we apply to learn immune profiles from cytometry data. Unlike previous studies only learn cell-level representations, whereas MAESTRO uses all of a sample's cells to learn a set representation. MAESTRO leverages specialized attention mechanisms to handle sets of variable number of cells and ensure permutation invariance, coupled with an online tokenizer by self-distillation framework. We benchmarked our model against existing cytometry approaches and other existing machine learning methods that have never been applied in cytometry. Our model outperforms existing approaches in retrieving cell-type proportions and capturing clinically relevant features for downstream tasks such as disease diagnosis and immune cell profiling.

Ocular mobility disorders such as strabismus af-fect millions of people. Patients' descriptions of their symptoms, such as what they see and how their vision has changed, are important for ophthalmologists to diagnose, monitor pro-gression, and evaluate treatment effectiveness. However, such verbal depiction may be vague and Subjective. A data-driven simulator that visualizes abnormal vision experienced by a strabismic patient can be helpful to objectively illustrate each individual's vision condition and thus to better understand and manage strabismus. To fulfill this technical void, this paper presents the first vision visualization robot that uses human eye movement data to simulate strabismic vision. We developed a robotic binocular eye platform, which is capable of displaying simulated visual scenes using its onboard cameras. Based on the hypothesis that a human's binocular vision fusion process can be mimicked as a homography transformation from one view to another view, we developed a pipeline to estimate the time-varying homography matrix, and generate the fused view of a human's binocular vision. The effectiveness of the proposed method is demonstrated through experiments with eye movement data from both healthy individuals and strabismic patients.