I am a Ph.D. student in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, advised by Emily Fox. I was fortunate to be funded by an IGERT fellowship in Big Data and Data Science from 2017-2019. My undergraduate research was in the Brain-Machine Interface Systems Lab at UC Berkeley, advised by Jose Carmena.
My research interests are in developing novel methods in statistical machine learning, applied to artificial intelligence and data science. Currently I'm researching new machine learning models for time series and applying them to neuroimaging data to learn more about the brain.
A hierarchical state-space model with Gaussian process dynamics for functional connectivity estimation
Rahul Nadkarni, Nicholas J. Foti, Adrian KC Lee, and Emily B. Fox
NeurIPS Workshop on Learning Meaningful Representations of Life, 2019
Learning dynamic functional connectivity networks from infant magnetoencephalography data
Rahul Nadkarni, Nicholas J. Foti, and Emily B. Fox
NeurIPS BigNeuro Workshop, 2017
Sparse plus low-rank graphical models of time series for functional connectivity in MEG
Nicholas J. Foti, Rahul Nadkarni, Adrian KC Lee, and Emily B. Fox
SIGKDD Workshop on Mining and Learning from Time Series, 2016
paper talk slides
Software Engineering Intern, Ph.D.
June – September 2017
|Autumn 2015||Statistical Inference (STAT 512)|
|Winter 2016||Graphical Models (CSE 515), Natural Language Processing (CSE 517)|
|Spring 2016||Machine Learning for Big Data (CSE 547)|
|Autumn 2016||Convex Optimization (EE 578), Databases (CSE 544)|
|Winter 2017||Computational Neuroscience (CSE 528), Algorithms (CSE 521)|
|Spring 2017||Computer Vision (CSE 576)|
|Winter 2018||Online and Adaptive Methods for Machine Learning (CSE 599I)|
|Autumn 2015||Teaching Assistant, Data Structures and Algorithms (CSE 373)|
|Winter 2016||Teaching Assistant, Data Structures and Algorithms (CSE 373)|
|Spring 2016||Teaching Assistant, Introduction to Artificial Intelligence (CSE 415)|