• Ph.D. student at CMU CSD since Fall, 2012.
  • Advised by Garth A. Gibson and Eric P. Xing.
  • Developing distributed frameworks for scaling out machine learning training applications. My work on the Bösen parameter server is part of the Petuum project, which is now a Pittsburgh-based startup.
  • Generally interested in distributed systems, database systems and machine learning applications.
  • B.S. in Computer Engineering, Minor in Mathematics, with Distinction from Purdue University, West Lafayette.
  • CV
Email: jinlianw at cs dot cmu dot edu
Cell: (765) 427-5257
Gates-Hillman Center (GHC) 7505
Computer Science Department
Carnegie Mellon University

Research Summary

My doctoral research is focused on enabling machine learning researchers and practitioners to efficiently train large and complex models with big data on distributed clusters. I spent a few years on developing parameter server systems to make machine learning programs run fast and efficiently and worked on automating dependence-aware parallelization of serial, imperative ML programs for distributed training. I am now working on dynamic scheduling (i.e., distributed device placement) for neural network training to complete my thesis.

Selected Publications

Teaching Experience (CMU)

Work Experience

  • Summer 2017 -- Microsoft Research, Redmond
  • Summer 2015 -- HP Labs