Bio

Tip

Great Data Science is built on great engineering.

The path from neuroscience to technology leadership is well-traveled. Consider the creators of Scikit-Learn, who were all neuroscientists. They needed a robust machine learning library to analyze their MRI and MEG data, and their solution became one of the most widely-used tools in data science today.

This is no coincidence. Neuroscience demands mastery across multiple technical domains:

• To process neuroimaging data, you need a strong foundation in linear algebra, calculus, and signal processing.

• To handle large datasets, you must know how to write efficient code and manage memory.

• To understand your data, you need to design experiments and employ both classical statistical and modern machine learning techniques.

• To communicate your findings, you need to convey the narrative in your data, for audiences ranging from technical specialists to practitioners.

A neuroscientists intersection.

I am very grateful for my doctoral training in neuroscience, which provided hands-on experience with each of these challenges. Now, at USC, I research at the intersection of machine learning, scientific computing, and neuroscience.