I am currently an M.S./Ph.D. student in the Manning College of Information & Computer Sciences at University of Massachusetts Amherst. I was an undergraduate student working in Prof. Ralf Haefner lab working closely with one of his graduate students, Anton Platenev. I’ve worked on several projects in the Haefner Lab. One project, I developed computational methods with the goal of tracking, identifying, and labelling individual neurons over discrete intermittent recording sessions. My current project, I examined the neuron responses in artificial neural networks trained on object recognition and compared these responses to what we find in the brain.
My research interest lies in understanding the relationship between human inference processes and machine learning processes, and how we can utilize understanding of our brain to build more human-like machines. This is including but not limited to:
How can computational models of human inference processes be used to build more complex AI?
How do computational models developed in the field of Computer Science relate to computational models of the brain?
Are there important distinctions between biological intelligence and synthetic/artificial intelligence? If so, what are the core mechansisms/differences?
Current Work
My current work is examining the neuron responses in artificial neural networks (ANNs) and comparing these responses to what we find in the brain. This work is ongoing with a heavy focus on how injecting different sources of noise can affect the distribution of neuron responses in these networks.
