South SFV Today

Stanford University has announced the appointment of Laura Gwilliams and Brian Hie as the first faculty members of Stanford Data Science, a unit within the dean of research dedicated to data-driven discovery and expanding data science education. Gwilliams is an assistant professor of psychology studying how the human brain makes language possible, while Hie is an assistant professor of chemical engineering developing large AI neural networks to understand molecular evolution.

Gwilliams explained that data science can connect diverse fields such as neuroscience and weather patterns through complex spatio-temporal patterns in data. She emphasized its interdisciplinary nature, stating, "The field of data science is at the core of – I would say – all scientific endeavors, making it a true interdisciplinary pursuit."

Hie defined data science as an interdisciplinary field that uses scientific methods to extract knowledge from structured and unstructured data. He noted its transformative impact on his research in biology, allowing for advances in designing new biological systems involving DNA, RNA, and proteins.

Both professors highlighted significant changes in data science over the past decade. Gwilliams pointed out that recognizing the importance of data has pushed all scientific fields to leverage it appropriately. In linguistics, this has led to artificial systems generating language at a quality level previously thought exclusive to humans.

Hie discussed how genomic databases containing millions of sequences have enabled large language models to process information from DNA, RNA, and proteins. This fusion of big data and advanced modeling is revolutionizing approaches to complex biological systems and accelerating medical applications.

The interdisciplinary nature of data science is crucial for both researchers. Gwilliams mentioned that understanding how the human brain implements language requires insights from multiple disciplines combined with powerful modeling tools from data science. Hie added that major progress in biology often requires deep computational expertise due to the explosion of biological data.

Looking ahead, Gwilliams' lab is developing a dataset spanning single neurons to region-wide structures using a new brain recording device called the optically pumped magnetometer system. Hie's team aims to push boundaries in biological design guided by machine learning, with goals including reprogramming biological systems for climate change mitigation or improved therapeutics.

Both professors shared their hopes for future advancements. Hie expressed his desire to contribute to more effective disease prevention and cures through computational biology. Gwilliams advocated for collaboration between academia and industry towards open-source models and algorithms, emphasizing understanding model success and failure modes over performance alone.

Gwilliams holds multiple roles at Stanford including faculty fellow at Stanford Data Science and member of Stanford Bio-X. Hie is also involved with Bio-X and Sarafan ChEM-H as a faculty fellow.