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General:
Daniel Wong is a final year PhD student in Carnegie Mellon University's Computer Science Department and Parallel Data Laboratory, and received a BA from the University of Cambridge. Daniel's research interests are in the intersection of ML and scalable distributed systems, and currently works on ML for flash caching. Daniel has worked on scheduling for model parallelism in TensorFlow at Google and consistency in large-scale distributed storage while interning at Dropbox and Facebook.
Last updated: June 2023
Academic (non-CMU) use:
Daniel Wong is a final year PhD student in Carnegie Mellon University's Computer Science Department. He is a member of the Parallel Data Laboratory and advised by Greg Ganger. He received his BA in Computer Science from the University of Cambridge. His thesis is on ML for flash caching. Previously, he worked on high performance distributed systems with next-generation storage, and systems for efficient machine learning.
CMU use:
Daniel Wong is a final year CSD PhD student advised by Professor Greg Ganger. He received his BA degree in Computer Science from the University of Cambridge. His current research focuses on ML for flash caching. Previously, he worked on high performance distributed systems with next-generation storage, and systems for efficient machine learning.
Last updated: Jun 2023