Kun Deng

Resume

Computer Scientist, Data Architect · Ann Arbor, MI ·
[email protected]

Employment

University of Michigan Health System — Splunk Data Architect and Data Scientist

Ann Arbor, MI · 10/2013 – present

  • Time-series analysis of machine and health metrics with Splunk, Tableau, and Python.
  • Adjunct Lecturer for the Master’s Degree Program in Data Science.

University of Michigan — Postdoctoral Research Fellow

Ann Arbor, MI · 09/2010 – 09/2013

  • Advanced clinical-trial recruitment through active and budgeted learning algorithms.
  • Data transformation and analysis in R and SAS.
  • Taught Programming and Numerical Methods in Statistics (Stats 607) in Python.

Arizona State University — Postdoctoral Research Fellow

Tempe, AZ · 09/2009 – 09/2010

  • Led R&D for computer-aided diagnosis of pulmonary embolism.
  • Machine-learning methods built on cascaded AdaBoost.

University of Nebraska — PhD Research Assistant

Lincoln, NE · 09/2004 – 09/2009

  • R&D of machine-learning algorithms in MATLAB, C++, and Python.
  • Taught the undergraduate MATLAB programming course.

Education

  • Ph.D., Computer Science — University of Nebraska, 2009
  • M.Sc., Computer Science — National University of Singapore, 2003
  • M.Eng., Electrical Engineering — Beijing Normal University, 2001

Skills

  • Languages: Python, R, MATLAB, Java, SQL, PowerShell
  • Statistical modeling: active learning, budgeted learning, predictive modeling
  • Data pipelines: Splunk, NiFi, Cribl, Spark
  • Visualization: Splunk, Tableau, Power BI
  • Certifications: Splunk Certified Architect, Tableau Certified Professional,
    RapidMiner Certified Professional

Selected Publications

  • Deng, Zheng, Bourke, Scott, Masciale. “New Algorithms for Budgeted Learning.”
    Machine Learning 90(1), 2013.
  • Deng, Pineau, Murphy. “Active Learning for Personalizing Treatment.” IEEE ADPRL, 2011.
    doi:10.1109/ADPRL.2011.5967348
  • Wu, Deng, Liang. “Machine Learning Based Automatic Detection of Pulmonary Trunk.”
    Proc. SPIE 7963, 2011.
  • Zheng, Scott, Deng. “Active Learning from Multiple Noisy Labelers with Varied Costs.”
    IEEE ICDM, 2010.
  • Deng, Bourke, Scott, Sunderman, Zheng. “Bandit-Based Algorithms for Budgeted Learning.”
    IEEE ICDM, 2007.
  • Bourke, Deng, Scott, Schapire, Vinodchandran. “On Reoptimizing Multi-Class
    Classifiers.” Machine Learning 71(2–3), 2008.
  • Culver, Deng, Scott. “Active Learning to Maximize Area under the ROC Curve.”
    IEEE ICDM, 2006.