Name

Christian Dallago


Academic title

Dr. Rer. Nat.


Current role & affiliations

Senior Research Scientist in Digital Biology, NVIDIA.

Visiting Assistant Professor in Biostatistics & Bioinformatics, and Cell Biology, Duke University.


Bio

(longest)

Chris earned his PhD in Informatics (summa cum laude) from the Technical University of Munich in 2023. During his doctoral studies, he made significant advances in bio-sequence representation learning, helping to establish the field—particularly through pioneering work on transformer models for proteins and nucleotides. His research also encompassed a wide range of applied bioinformatics challenges, resulting in impactful software and data solutions that have enabled new scientific discoveries. Notably, Chris played a key role in launching rigorous evaluation standards for protein models in protein design and engineering, introducing gold-standard benchmarking datasets and innovative data splitting analyses.


In his first two years at NVIDIA, Chris was instrumental in shaping the company’s digital biology strategy. He contributed to product development, ecosystem building, and leadership initiatives, helping NVIDIA become a leading community contributor in digital biology. His work has driven the release of influential open-source software, addressing challenges from accelerated homology search to biological design.


As of 2025, Chris holds a dual appointment: he is a Visiting Assistant Professor at Duke University and leads an applied research group in Digital Biology at NVIDIA. At Duke, he focuses on exploratory research in biological machine learning (BioML), while at NVIDIA, he leads applied research in accelerated drug discovery.


You can find Chris’s Google Scholar profile here, and more information about his background and achievements in his CV.


(long, 110 words)

Chris is a computer scientist turned bioinformatician with a passion for systematically modelling biological mechanisms through machine learning. His path towards reaching this goal led him to contribute and push the community of learned protein sequence representations in order to find new, principled ways to describe biological entities. Bio-sequence representation learning, for instance through transformer models, is today an established field in bioinformatics with flourishing frameworks and impactful research applications, like the prediction of protein 3D structure from just an input sequence. Chris remains focused on trying to address problems for which data and intuition remain scarce, for instance those allowing to design new proteins with therapeutic or industrial use.


(short, 35 words)

Chris, a computer scientist turned bioinformatician, passionately models biological mechanisms using machine learning. He's advanced bio-sequence representation learning, contributing to its establishment, notably in transformer models. Chris is dedicated to solving scarce data problems, such as designing proteins for therapeutic and industrial applications.


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