Schmidt Center, Helmholtz Munich launch AI and machine learning in genomics collaboration

Helmholtz Munich and the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard today announce the launch of a collaboration to bridge a gap in health research with AI and machine learning.

In the past decade, the field of genomics has accelerated to a point where we can now both measure and perturb biological systems at massive, unprecedented scales, holding huge potential for disease treatment. However, the computational tools needed to take advantage of all this data have not kept pace. By leveraging machine learning methods, the partnership between Helmholtz Munich and the Eric and Wendy Schmidt Center seeks to gain valuable insights into important genomics problems while simultaneously advancing the foundations of machine learning through novel research inspired by genomics questions.

Leading this joint initiative are Caroline Uhler, co-director of the Eric and Wendy Schmidt Center at the Broad Institute, and Fabian Theis, head of the Computational Health Center (CHC) at Helmholtz Munich and Director of Helmholtz AI. Both Caroline Uhler and Fabian Theis have backgrounds in machine learning, statistics, data science, biology, and human biology. “This exchange model between the Broad Institute and Helmholtz Munich will merge our expertise on machine learning and genomics to foster innovative ways to address major challenges in biomedical research,” said Fabian Theis.

The collaboration will encompass a range of activities, including the exchange of graduate students, postdoctoral fellows, and other research staff between the two research centers. These individuals will undertake short research stays, enabling them to benefit from the expertise and resources available at both centers. In addition, the research centers will co-organize workshops and conferences to facilitate knowledge exchange and foster collaboration in the field of AI and genomics.

“Despite an explosion in biological data, the technology sector remains the key driver of machine learning advances today,” said Caroline Uhler. “Both Helmholtz Munich and the Broad Institute are seeking to change that by developing foundations of machine learning that are geared specifically to biological problems, and we’re excited for this collaboration to amplify our efforts.”