I am a PhD student in the Machine Learning Department
of the School of Computer Science
at Carnegie Mellon University
. My advisor is Tom Mitchell
and I work on Never-Ending Learning
. My current research is motivated by the fact that real-world problems require integrating multiple, distinct modalities of information (e.g., image, audio, language, etc.) in ways that machine learning models cannot currently handle well. Most deep learning approaches are not able to utilize information learned from solving one problem to directly help in solving another. They are also not capable of never-ending learning
, failing on problems that are dynamic, ever-changing, and not fixed a priori, which is true of problems in the real world due to the dynamicity of nature. With my research, I aim to bridge the gap between UTCs, deep learning, and never-ending learning, by proposing neural cognitive architectures (NCAs)
that are inspired by human cognition and that can learn to continuously solve multiple problems that can grow in number over time, across multiple distinct perception and action modalities, and from multiple noisy sources of supervision combined with self-supervision. Their experience from learning to solve past problems can also be leveraged to learn to solve future ones. If you are interested to read more about NCAs, my thesis proposal
would be a good place to start. Throughout my PhD I have also worked on multiple other projects
related to artificial intelligence and machine learning.
Before I joined CMU, I graduated with an M.Eng. in Electrical and Electronic Engineering
from Imperial College London
. For my Master's thesis I proposed a way to use topic modelling methods in order to perform human motion classification.