I am currently working as a postdoc in computational biology at the University of Washington, Seattle, WA. There, I’ve joined a multi-disciplinary group led by Roger Bumgarner, Adrian Raftery and Ka Yee Yeung, among many scientists from the Microbiology and Statistics Departments as well as the Fred Hutchinson Cancer Research Center, to participate in exciting projects of constructing gene regulatory networks using gene expression data, proteomics data and documented evidence. I can say, this work is really promising and I’m looking forward to seeing the outcome in the near future!

In the future, I am interested in pursuing my career as a Statistician / Statistical Scientist in the Technology or Bioinformatics industry.

If you want to get a more complete profile of mine, please take a look at my resume.

Prior to joining the University of Washington, I graduated from the University of British Columbia in 2009, after four years of my PhD study in Statistics. My research supervisors were Raphael Gottardo at the Clinical Research Institute of Montreal, and Ryan Brinkman at the British Columbia Cancer Research Center. My PhD research was to devise statistical methods for high-throughput data of genomics, including microarray and flow cytometry. In addition, I have been working on both algorithm and software development for the automated analysis of flow cytometry data.

Before coming to North America, I completed my Bachelor’s and Master’s studies in Actuarial Science at the University of Hong Kong. My Master’s research, under the supervision of Prof. Tony W. K. Fung, was to devise statistical models to handle panel data in Credibility, a popular field in insurance for determining the premium level.

At leisure, I love to play and watch badminton and volleyball. My favorite teams include the Serbian and Bulgarian national volleyball teams. Regarding music, my iTunes statistics show that I listen to OneRepublic, Leona Lewis, and Shayne Ward (when is his third album released, or will it?!) the most recently.

Publications

Refereed journal articles:

1. J. Baudry, A. E. Raftery, G. Celeux, K. Lo, R. Gottardo (2010). Combining mixture components for clustering. To appear in Journal of Computational and Graphical Statistics.
2. K. Lo, F. Hahne, R. R. Brinkman, R. Gottardo (2009). flowClust: a Bioconductor package for automated gating of flow cytometry data. BMC Bioinformatics, 10:145. 
3. A. Bashashati, K. Lo, R. Gottardo, R. D. Gascoyne, A. Weng, R. R. Brinkman (2009). A pipeline for automated analysis of flow cytometry data: preliminary results on lymphoma sub-type diagnosis. Conference Proceedings of IEEE Engineering in Medicine and Biology Society, 1:4945-4948.
4. K. Lo, R. R. Brinkman, R. Gottardo (2008). Automated gating of flow cytometry data via robust model-based clustering. Cytometry Part A, 73:321-332.
5. K. Lo, W. K. Fung, Z. Y. Zhu (2007). Structural parameter estimation using generalized estimating equations for regression credibility models. ASTIN Bulletin, 37(2):323-343.
6. K. Lo, R. Gottardo (2007). Flexible empirical Bayes models for differential gene expression. Bioinformatics, 23:328-335.
7. K. Lo, W. K. Fung, Z. Y. Zhu (2006). Generalized estimating equations for variance and covariance parameters in regression credibility models. Insurance Mathematics and Economics, 39:99-113.

Submitted manuscripts:

1. A. Bashashati, N. A. Johnson, A. H. Khodabakhshi, M. Whiteside, K. Lo, S. Bakovic, R. Gottardo, F. S. L. Brinkman, J. M. Connors, G. W. Slack, R. D. Gascoyne, A. P. Weng, R. R. Brinkman (2010). B-cells with high side scatter parameter by flow cytometry correlate with inferior survival in diffuse large B cell lymphoma. Under review.
2. K. Lo, R. Gottardo (2009). Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: an alternative to the skew t distribution. Under review.

Presentations

Invited presentations:

• ENAR Meeting, San Antonio TX, USA, March 2009
  Automated Gating of Flow Cytometry Data via Robust Model-based Clustering
• ISAC International Congress, Budapest, Hungary, May 2008
  Automated Gating of Flow Cytometry Data via Robust Model-based Clustering
• SFU/UBC Statistics and Actuarial Science Workshop, Vancouver BC, Canada, November 2006
  Model-based Clustering for Flow Cytometry Data

Contributed presentations:

• SSC Meeting, Vancouver BC, Canada, June 2009
  Flexible Empirical Bayes Models for Differential Gene Expression
• Joint Meeting of the SSC and the SFdS, Ottawa ON, Canada, May 2008
  Model-based Clustering using t Mixtures with Box-Cox Transformation
• IME Conference, Laval QC, Canada, July 2005
  Generalized Estimating Equations for Variance and Covariance Parameters in Regression Credibility Models

Poster Presentations:

• PIMS Meeting on Recent Advances in Modeling Biological Processes, Seattle WA, USA, December 2008
  flowClust: A Clustering Software for Automated Gating of Flow Cytometry Data
• ISAC International Congress, Budapest, Hungary, May 2008
  flowClust: A Clustering Software for Automated Gating of Flow Cytometry Data
• Pacific Northwest Statistics Meeting, Vancouver BC, Canada, September 2007
  Model-based Clustering for Flow Cytometry Data
• NW Regional Cytometry Meeting, Seattle WA, USA, March 2007
  Model-based Clustering for Flow Cytometry Data

Software

I am the author and active maintainer of an R package called flowClust available at Bioconductor. This package is for automated analysis of flow cytometry data to identify cell populations, implementing a robust model-based clustering methodology proposed by Lo, Brinkman and Gottardo (2008) . It can also serve as a general-purpose, standalone tool for cluster analysis. Please go and check it out!