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I am a Postdoctoral Associate in the image-based profiling team. I'm interested in developing machine learning methods for studying cellular organization and improving disease diagnosis and treatment.

I am currently a graduate student in Computer Science at the University of California, Los Angeles (UCLA), specializing in Deep Learning and Software Engineering. My interests include analyzing and programming biological systems through the application of deep learning tools and methods.

My interest lies in applying machine learning methods to analyze and interpret the Cell Painting assay for predicting drug efficacy, safety, and toxicity. I am passionate about education outreach, advocating for a better understanding of Artificial Intelligence and delivering seminars about its role...

My interests lie in the interface of computer science, math, image analysis, and biology. I aim to develop tools and workflows to process high throughput imaging datasets, with special interest on applying computational methods on live microscopy data. I am also keen on extracting biological...

I am interested in trying to predict and gain insights into adverse health outcomes in living organisms from rich cellular profiling data collected from in vitro experiments.

I am the Administrative Assistant for the Imaging platform, holding dual master’s degrees in Digital Economy and Business from Zagreb University of Applied Sciences, as well as Cultural and Fashion studies from the University of Zagreb. My interests lie in the financial aspect of nonprofit...

I am interested in hacking biological systems for fun and profit. This includes (non-exhaustively) using tools from mathematics, computer science, and engineering disciplines to probe, understand, and program biological systems.

I lead the image-based profiling team. We develop machine learning and statistical methods to create fingerprints of genes, chemicals, and diseases from microscopy images of cells.

I'm a 2nd year PhD student at the Theislab in Munich, Germany. There, my primary focus is development of tools and resources for spatial multi-omics. At the Broad to learn more about xxx terabyte scale data and how we can use phenomics to better understand perturbations.