Mangal Prakash is a Research Scientist in the AI & Robotics team at EIT, where he conducts foundational research across multiple scientific domains, with a primary focus on developing foundation models and machine learning methods for healthcare.

Previously, he was a Senior Principal Scientist in the Machine Learning team at Johnson & Johnson, where he worked on geometric Deep Learning and multimodal representation learning for biomolecular systems, including RNA language modelling, molecular structure learning, and gene regulatory network inference. He led teams building scalable machine learning solutions across omics, molecular structure, gene regulation, and clinical data to support therapeutic design and delivery. Prior to this, he was at Exscientia (now Recursion), where he led efforts on machine learning methods for small molecule property prediction.

Mangal holds a PhD in Computer Science from TU Dresden and the Max Planck Institute in Germany, and an MS in Electrical Engineering from the University of Minnesota in USA. His doctoral research focused on developing deep learning and graphical model–based methods for unsupervised image denoising, object segmentation, and tracking in biomedical images.

Outside of work, Mangal enjoys playing cricket and tennis and reading non-fiction books on topics ranging from history and economics to politics and beyond.