Ph.D. Candidate, Computer Science
Machine Learning and Intelligence Lab (Engineering 2, 489)
University of California, Santa Cruz (Transferred from Purdue University
Email: params AT ucsc DOT edu
I am in the job market this academic year
I am a final year Ph.D. candidate in Computer Science at University of
California, Santa Cruz advised by Prof. S.V.N. Vishwanathan. My
research interests lie at the intersection of Optimization and
Large-Scale Machine Learning, with applications in areas such as
Ranking, Recommender Systems, Extreme Classification (multi-class or multi-label
involving huge number of data points and classes/labels), Extreme
Clustering (huge number of
data points and clusters) and Deep Learning. Other interests
include Scalable Bayesian Inference for Graphical Models.
My thesis work has focussed on:
Developing reformulations for a wide spectrum of frequentist and bayesian models
to help distribute computation across machines more efficiently (de-centralize both data as well as
model parameters simultaneously to achieve Hybrid Parallelism) by using novel algorithmic/statistical/computational techniques,
Developing and implementing "asynchronous" distributed stochastic optimizers to solve the reformulations.
During the course of my Ph.D., I have also spent wonderful summers interning at
LinkedIn (2014), Microsoft Research CISL Lab (2015), Adobe
Research (2016) and Amazon AI (2017) working on diverse interesting problems in machine learning.
Before joining my Ph.D., I worked at Yahoo! (2011-2013) in the Personalization
Group on applying machine learning and NLP to Entity Matching, Entity Extraction and Knowledge Graph problems.
I got my masters degree in Computer Science at Georgia
Tech (2011). I worked in the Sonification Lab of Georgia Tech, with
Prof. Bruce Walker in developing auditory and non-traditional interfaces for Human Computer Interaction.