I'm currently working as a Research Software Engineer at Google Brain. I graduated in 2017 with a M.S. in Machine Learning from Carnegie Mellon University's Machine Learning Department. Before that, I completed my B.S. in Computer Science (minor in ML) also at CMU. Over the past few summers, I've worked at Google in Seattle, Google in Mountain View, and Dropbox in San Francisco.

My research interests are deep learning, computer vision, and large scale machine learning. I'm currently working on research in efficiently utilizing extremely large amounts of compute to accelerate and improve current deep learning techniques.

Outside of work and academia, I'm an amateur photographer and a fan of anything nature-related (hiking, camping, and anything new!).

• Adaptive Depth Computational Policies for Visual Tracking
  
  Multi-depth fully convolutional Siamese network with conditional computation to adaptively determine network depth during computation, applied to visual tracking on VOT2016 dataset. Published in EMMCVPR 2017.[research poster]
• Improving Event Coreference using Knowledge Bases
  
  Utilize knowledge bases like YAGO and DBpedia to improve event coreference. [research poster].
• ParaBDD: Parallel Binary Decision Diagrams for Efficient Model Checking
  
  Parallel BDD library using Intel CilkPlus and lockless hash tables. [writeup].
• Hypercube: Breaking the 4th Wall in Minecraft
  
  Minecraft mod which adds a block that interacts with a real-world light. [video demo].
• PacAttack
  
  PaperJS-based web game that utilizes flocking behavior to create interesting mechanics. [play game].
• Pulsr: Audience Sentiment Tracking App
  
  Audience sentiment tracking web app using Firebase and Google Graph API. [code].

Coming soon!

Coming soon!

Please email me at: .

My Github profile: chrisying.

My résumé can be found here (currently out-of-date).