Internships

• Data Scientist Intern, Adobe Research (San Jose, CA, USA), May 2015 to August 2015.
  As an intern in the Big Data Experience and Imagination Labs, I used deep learning techniques along with multitask learning and stochastic subsampling for classification of highly imbalanced data. I implemented some custom mods to Caffe for the neural net workflow, and used it in conjunction with some Python wrappers.
  My mentors were Hung Bui and Trung Bui, and I also worked with Hailin Jin.

• Software Engineering Intern, Google (Kirkland, WA, USA), May 2016 to August 2016.
  As an intern in the Ads team, I wrote multi-head deep neural nets along with data extraction and post-processing pipelines for classification of large scale data. The pipeline stages were written in C++, and I used Tensorflow with certain wrapper libraries (TF-Learn, TF-Serving) for the deep learning workflow.
  My mentor was Li He, and I also worked with Arthur Asuncion, Alex Chen, and Zakaria Haque.

• Research Intern, IBM T. J. Watson Research Center (Yorktown Heights, NY, USA), May 2017 to August 2017.
  I am working on a deep reinforcement learning project at the Center for Computational and Statistical Learning in the Mathematical Sciences division.
  My manager is Dr. Naoki Abe.

Patents

• Digital Content Interaction Prediction and Training that Addresses Imbalanced Classes.
  Anirban Roychowdhury, Hung Bui, Trung Bui, Hailin Jin. (Filed 1/2016)

• Content Presentation Based on a Multi-Task Neural Network.
  Anirban Roychowdhury, Hung Bui, Trung Bui, Hailin Jin, John Kucera. (Filed 3/2016)