Hello! I am currently a master’s candidate at SEAS in Harvard University. I spend time as a graduate researcher at DASlab, advised by Prof. Stratos Idreos, where I think about how to design neural network architectures. We want to determine if there exists a best formulation of parameters to solve a given problem subject to budget, speed, and accuracy constraints. Understanding the space of such architectures will give us better understanding of picking which model to solve the emerging class of cognitive tasks in machine learning, and help us avoid the curse of bigger models = better generalization.
Very recently, I’m also interested in how we can design efficient datasets. I’m exploring ideas from computational neuroscience to inform how we can achieve better generalization with fewer examples.
Broadly, I’d like to understand these questions:
- What is the design space of efficient neural network models?
- Can we effectively incorporate knowledge from other scientific theories (biologically plausible learning, physical systems) into our artifical models?
Before coming to graduate school at Harvard, I had a career as a full-stack software engineer at Academia.edu and at Abbvie Stemcentrx. I obtained my bachelor’s degree in computer science from Caltech in sunny Pasadena.
I started this website to improve my writing and to keep track of important trends in my research interests. I’m also interested in applying for a PhD for the 2023 academic year! Please feel free to write me at /< albertge /> at g.harvard.edu to get in touch.