Ya Ju Fan


Email: fan4@llnl.gov
Phone: 925-423-4438


Ya Ju Fan is a computational scientist in the Center for Applied Scientific Computing. She specializes in practical optimization models and machine learning algorithms. Her work combines mathematics, statistics and computer science for applying advanced analytical methods on real-world data.

Educational Background

Ph.D.     Industrial and Systems Engineering, Rutgers University, New Brunswick, NJ, 2010
M.S.       Industrial Engineering, University of Wisconsin-Madison
B.B.A.    Production & Operations Management, Fu Jen Catholic University, Taiwan

Selected Publications

Y. J. Fan and C. Kamath. “Anomaly Detection for Streaming Data with an Application to Wind Ramp Events”. Under Review. 2014.

Y. J. Fan and C. Kamath. “Identifying and Exploiting Diurnal Motifs in Wind Generation Time Series Data”. Accepted. IJPRAI, 2014.

C. Kamath and Y. J. Fan. "Incremental SVD for Insight into Wind Generation". 13th International Conference on Machine Learning and Applications ICMLA 2014. December 2014.

C. Kamath and Y. J. Fan. “Finding Motifs in Wind Generation Time Series Data”. 11th International Conference on Machine Learning and Applications ICMLA 2012. December 2012.
 

C. Kamath and Y. J. Fan. “Using Data Mining Techniques to Enable Integration of Wind Energy on the Power Grid”.  Statistical Analysis & Data Mining. Volume 5, Issue 5, pp 410-427, October 2012.

Y.  J. Fan and C. Kamath. “On the Selection of Dimension Reduction Techniques for Scientific Applications”.  Annals of Information Systems. To Appear. August 2012.

Y. J. Fan, and W. A. Chaovalitwongse, "Optimizing Feature Selection to Improve Medical Diagnosis". Annals of Operations Research on Data Mining, 174(1): 169-183, 2010.

W. A. Chaovalitwongse, Y. J. Fan, and R. C. Sachdeo. "Novel Optimization Models for Abnormal Brain Activity Classification". Operations Research, 56(6): 1450-1460, December, 2008.

W. A. Chaovalitwongse, Y. J. Fan, and R. C. Sachdeo. "On the Time Series K-Nearest Neighbor Classification of Abnormal Brain". IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 37(6): 1005-1016, November, 2007.

W. A. Chaovalitwongse, Y. J. Fan, and R. C. Sachdeo. Support Feature Machine for Classification of Abnormal Brain Activity. The Thirteenth ACM SIGKDD International Conference On Knowledge Discovery and Data Mining (SIGKDD 2007), pp. 113-122.

Presentations

"Incremental SVD for Insight into Wind Generation", 13th International Conference on Machine Learning and Applications, Detroit, MI. December 3-5, 2014.

“Exploiting Motifs and Anomalies in Streaming Data”, Department of Energy Applied Mathematics Program Meeting, Albuquerque, NM. August 5-8, 2013.

“Detecting Changes in Weather Data Streams for Wind Energy Prediction”, SIAM Annual Meeting, San Diego, CA. July 8-12, 2013.

“Determining Number of Motifs in Wind Generation Time Series Data”, SIAM Annual Meeting, San Diego, CA. July 8-12, 2013.

“Finding Motifs in Wind Generation Time Series Data”, 11th International Conference on Machine Learning and Applications, Boca Raton, FL. December 12-15, 2012.

 “A Heuristic for the Local Region Covering Problem”, 21st International Symposium on Mathematical Programming, Berlin, Germany. August 19-24, 2012.

“A Comparison of Dimensionality Reduction Techniques in Scientific Applications”, Center for Advanced Signal and Image Sciences, Livermore, CA. May 23, 2012.

 “A Comparison of Dimensionality Reduction Techniques in Scientific Applications”, SIAM Conference on Uncertainty Quantification, Raleigh, NC. April 2-5, 2012.

 “Intrinsic Dimensionality Using Non-linear Dimension Reduction Techniques”, Institute for Operations Research and Management Sciences Annual Meeting, Charlotte, NC. November 13-16, 2011.

“A Comparison of Non-linear Techniques for Dimension Reduction”, LLNL Postdoc Poster Symposium. June 1, 2011.

“Medical Data Classification Via Optimizing Feature Selection”, Institute for Operations Research and the Management Sciences (INFORMS) Annual Meeting, Washington, D.C. October 2008.

“Classification of Normal and Abnormal EEG Signals Using K-Nearest Neighbor Rule in Support Feature Machine”, Conference on Computational Neuroscience, University of Florida, Gainesville, FL. February 2008.

“On the Time Series K-Nearest Neighbor Classification of Abnormal Brain Activity”, Institute for Operations Research and the Management Sciences (INFORMS) Annual Meeting, Pittsburgh, PA. November 2006.

Honors & Awards

  • Pierskalla Best Paper Award, INFORMS Annual Meeting, Washington, D.C., October 2008.
  • Transportation Coordinating Council / Federal Transit Administration (TCC/FTA) Fellowship, Fall 2007 and Spring 2008
  • Kuhl Memorial Engineering Fellowship, Rutgers Graduate School, Fall 2006