Hi! I’m Udit Saxena.
I am a machine learning engineer at Sumo Logic with the Advanced Analytics team. I’ve completed my Master’s in Computer Science from UMass, Amherst. My interests lie in the field of Machine Learning - specifically Natural Language Processing and Deep Learning.
At Sumo, I work on building distributed fast approximate strucutured and unstructured streaming clustering algorithms for text streams, time series models and clustering algorithms for peer-based component/workload modeling and security event analysis. Previously I’ve interned with Sumo Logic, working on Distributed Tracing, in the Bay Area with the metrics team led by David Andrzejewski. Before this I worked as a Product Engineer with the Core team at Sprinklr and have interned with Adobe on User Analytics with the Adobe Captivate team.
During Spring 2018, I was working with Microsoft Research team at Montreal and Cambridge on Active Learning for transferring knowledge for Reading Comprehension systems with T.J. Hazen. I have also interned at Lexalytics as a Machine Learning intern where my focus was on Graph Convolutional Networks for Text Classification. I’ve worked on a Pytorch implementation of End to End Memory Networks, which is currently open as a pull request on the official Pytorch examples repository. In Spring 2017, I worked with Prof. Andrew McCallum on serving pre-trained Tensorflow models for the JVM as part of IESL. I was a Google Summer of Code intern, working with MLPACK (along with Ryan Curtin), a C++ based open source machine learning library.
Check out my resume