AI isn’t just speeding up research—it’s changing how discoveries are made. By improving experiments, revealing hidden patterns in massive datasets, automating tasks, and making it easier to model complex systems, AI is transforming the way science is done. And to further unlock AI’s potential in research, Daniela Ushizima of Lawrence Berkeley National Laboratory and Christopher Henry of Argonne National Laboratory co-chaired the 2025 Workshop on Envisioning Frontiers in AI and Computing for Biological Research last month in Washington, D.C. 

The event brought together scientists from diverse fields across academia, industry, and U.S. Department of Energy (DOE) National Labs to explore how AI can advance biological research. In addition to Ushizima, numerous Berkeley Lab researchers participated, with several having their position papers accepted. The goal of the workshop was to identify grand challenges for the next 5-10 years to help uncover new biological processes and systems essential for DOE projects, while supporting broader efforts to strengthen the nation’s bioeconomy.

According to Ushizima, who also serves as the Machine Learning Lead in DOE’s Center for Advanced Mathematics for Energy Research Applications (CAMERA), this workshop showcased cutting-edge computational technologies from the DOE’s Office of Advanced Scientific Computing Research (ASCR) for applications within the Biological and Environmental Research (BER) domain. Key themes included data-driven discovery, system understanding, and the development of algorithms for tackling complex biological challenges like molecular structure deciphering, DNA analysis, and phenotype understanding. 

She and Henry, who is a principal investigator on DOE’s Systems Biology Knowledgebase (KBase) program, were both asked to co-chair the workshop because of their decades of experience bridging the gap between cutting-edge computational science tools and BER research challenges.

“Berkeley Lab has a tradition of cross-disciplinary collaboration to accomplish scientific breakthroughs, and this workshop is an example of that” successfully uniting experts to address Biology and Mathematics research in the context of AI and Exascale computing,” said Ushizima. “DOE’s National Labs have deep expertise in both high-performance computing and biological research, and by fostering collaboration between computational and biological scientists, we will accelerate the next generation of AI-driven discoveries.”

A workshop report is forthcoming. Read the pre-workshop report. 

In addition to Ushizima’s role in co-organizing the workshop, a significant number of Berkeley Lab researchers had position papers accepted for presentation. Of the 55 position papers accepted, 41 Berkeley Lab researchers were co-authors on 15 different papers, including: Adam P. Arkin, Alexander Hexemer, Aditi Krishnapriyan, Aeron Tynes Hammack, Anna Giannakou, Antonio Camargo, Aydın Buluc, Benjamin P. Bowen, Camilo Valdes, Crysten Blaby-Haas, Dan Gunter, Daniela Ushizima, Dinesh Kumar, Emiley Eloe-Fadrosh, Harinarayan Krishnan, Harshini Mukundan, Hector Garcia Martin, James Sethian, Jeffery Donatelli, Kanupriya Pande, Katherine Yelick, Leonid Oliker, Marcus Noack, Marcin Joachimiak, Mingfei Chen, Natalia Ivanova, Nikos Kyrpides, Oguz Selvitopi, Paramvir S. Dehal, Peter Nugent, Prachi Gupta, Robert Riley, Rob Egan, Romy Chakraborty, Ronald Pandolfi, Simon Roux, Steven Hofmeyr, Trent R. Northen, William Riehl, Zineb Sordo, Zixi Hu, and Petrus Zwart.