Debdas Paul

Debdas started his academic journey as a computer science engineer in India, where he worked primarily on the application of spectral graph theory and complex systems.

Later on, he pursued his second master’s in computational and systems biology from the department of computer science, Aalto University in Finland and the Royal Institute of Technology (KTH) in Sweden as an Erasmus scholar funded by the European Commission. After completing his master’s degree in Nordics, Debdas moved to Germany where he completed his doctorate (Dr.-Ing.) in 2019 from the Institute for Systems Theory and Automatic Control (IST) of the University of Stuttgart under Prof. Nicole Radde. His doctoral dissertation explores the origin of robustness and its characterization in biological signalling networks as well as in gene regulation from systems theoretic point of view.

From pure theory and simulation-based research, Debdas moved to data-driven science during his two postdoctoral ventures at the Max Planck Institutes (MPI) for Biophysical Chemistry, Göttingen and at the University Hospital, Tübingen.

At MPI, he worked on developing Bayesian approaches to learn the posterior distributions of the conversion factors (a proportionality constant that relates mass spectrometry ion peak area of peptides to the absolute amount of the peptides) for the peptide products without further experimentation. In Tübingen. with collaborators from the University Hospital in Zürich and the University of Colorado, School of Medicine, Debdas worked on the problem of finding novel biomarkers from multi-omics data using machine learning approaches with a focus on cancer immunotherapy and auto-immune disease such as Systemic Lupus Erythematosus (SLE), respectively. During his time in Tübingen, he developed a keen interest in immunology.

Debdas is continuing his academic journey at the Zielinski lab, where he is addressing some of the fundamental questions regarding human T cells such as memory and tissue residence using bioinformatic and machine learning-based approaches.

Apart from immunology, Debdas possesses a great interest in quantum mechanics, cognitive science, and human history.