Aditi Jha

Research Abstract:

Understanding how the brain performs complex computations and the resulting behavior of humans and animals has been a scientific goal for decades. We are at an exciting phase in this quest, when advancements in neural recording technologies and experimental techniques are yielding high-resolution neural and behavioral datasets. Consequently, there is an increasing demand for tools required to analyze these datasets, with machine learning fueling several modeling approaches in neuroscience. However, there are two crucial challenges in applying existing machine learning approaches to neuroscientific datasets: first, collecting data continues to be extremely expensive and tedious in neuroscience compared to mainstream machine learning applications such as vision and language; second, analyzing these datasets entails its own domain-specific challenges. My thesis focuses on solving these two challenges, thus improving the power and flexibility of machine learning tools for neuroscience.

Bio:

Aditi is a graduate student at Princeton, working jointly in Electrical and Computer Engineering and Neuroscience departments. She develops machine learning approaches to advance our understanding of decision-making and visual perception in humans and animals, using behavior as well as neural activity in the brain. Prior to this, Aditi studies electrical engineering as an undergrad at Indian Institute of Technology, Delhi. She is the recipient of Google's PhD fellowship, and Caltech's Young Investigator Lecturer award.