I consider myself a lifelong learner and believe in working hard. I recently graduated from UC San Diego with Master’s in Computer Science with specialization in Machine Learning. Currently, I’m working at Amazon with Amazon Global team.
My research interests are in the areas of Recommender Systems and User Behavior Modeling. I got my first exposure to these areas during my internship at IIT Madras where I was involved in the development of scalable Bayesian models for the rating prediction task.
At UC San Diego, I worked on several research projects under the guidance of Prof. Julian McAuley. Recently, one of my research papers got accepted at RecSys 2018, in which we proposed a new machine learning framework for predicting the fit of clothing products so that appropriate product sizes could be recommended to customers. The proposed approach improves upon a method proposed by Amazon by up to 18%. Other projects entailed uncovering interesting and practical insights on some novel datasets obtained by us and designing models to aid the prediction tasks.
Apart from this, I interned at Amazon in Summer 2017 where my project was to bring the support of primary key constraint, batch inserts/updates, transactions, and compaction in Hive while ensuring read consistency. This was particularly challenging as Hive is not designed to handle transactional data and operations. Previously, I also worked with Arcesium, a recently formed subsidiary of D.E. Shaw & Co, as a software developer. I was responsible for revamping their infrastructure to make it more scalable and robust.
My work experience has helped me in understanding the intricacies involved in developing critical and time-intensive software and my research experience is providing me in-depth knowledge of Machine Learning field.
Please reach out to me @ email@example.com