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Spring 2011 (TA)

• Courses:
  • None.

Jan 31

• Examining how the main class clustering algorithms can be parallelized, respectively.
• Explaining spectral clustering from theoretical aspect, from graph cut viewpoint.
• Based on the understanding of main clustering algorithms, propose optimizing method for spectral clustering to deal with large data set.
• The proposed method makes use of LSH to do pre-precessing.

Jan 17

• Understanding Spectral clustering and distributed implementation.
• Mahout experimenting.

Jan 10

• Survey on main clustering algorithms and the distributed map-reduce method of these algorithms.
• Mahout experimenting.

Fall 2010 (FELLOWSHIP)

• Courses:
  • CMPT 771: Internet Architecture and Protcols
  • CMPT 741: Data Mining

• Worked on efficient approximation of gram matrix using map-reduce framework, focusing on LSH performance evaluation and network communication measurement.

Summer 2010 (RA)

• Courses:
  • None

• Worked on Approximation of gram matrices using Locality Sensitive Hashing on Cluster.

Spring 2010 (TA+RA)

• Courses:
  • CMPT 886: Special Topics in Operating Systems and Computer Architecture

• Worked on Band approximation of gram matrices (large high-dimensional dataset) using Hilbert curve on multicore.

Fall 2009 (TA)

• Courses:
  • CMPT 705: Design and Analysis of Algorithms
  • CMPT 726: Machine Learning

• Worked on Band approximation of gram matrices (large high-dimensional dataset) using Hilbert curve on multicore.