Resume

Summary

  • Research Interests: Bayesian hierarchical model; Joint model of longitudinal and survival process; Dynamic prediction; Multivariate data analysis; Functional data analysis; Item response theory; Clinical trial.
  • Proficient in R, SAS, Stan, WinBugs, and implementing parallel computing on HPC clusters.
  • Rich experience in modeling and solving complex system problems using Agent-based modeling, Linear programming, Dynamic Programming, Markov Decision Process.
  • Willing to learn and accept constructive criticism.
  • Outstanding teamwork building ability and strong interpersonal skills.

Technical Strengths
Education
Employment
Certifications
Honers
Publications
Course Work


Technical Strengths


  • Statistical Packages: R, SAS, Stan, WinBugs, Rcpp, Shiny.
  • Programming Languages: Java, Python, Shell, Julia, SQL, VBA.
  • Platforms: Linux, Windows

Education


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Ph.D. in Biostatistics, The University of Texas Health Science Center at Houston

Mar. 2018

  • GPA: 4.0/4.0
  • Minor: Bioinformatics, Health economics

M.S. in Industrial Engineering & Operations Research, University of Pittsburgh

May. 2011

  • GPA: 3.9/4.0

B.S. in Electrical Engineering, Beijing Institute of Technology

Jul. 2009

  • GPA: 3.7/4.0

Employment


Sr. Scientist, Biostatistics, BARDS, Merck & Co., Inc.

Apr. 2018 – Present

  • Late stage statistician on oncology studies.


Graduate Research Assistant, Department of Biostatistics, The University of Texas Health Science Center at Houston

July 2015 – Mar. 2018

Dissertation: Functional Joint Models: an application to Alzheimer’s disease (AD)

  • Developed methods to incorporate longitudinal functional data in Bayesian joint models framework. [abstract]
  • Developed Bayesian longitudinal item response theory model to estimate AD progression. [abstract]
  • Investigated approaches to handle computing issues for large-scale data and compute-intensive models.

Project: Personalized Dynamic Prediction of Huntington’s disease (HD) using PREDICT-HD data

  • Analyzed HD progression using joint model of longitudinal and survival data. [abstract]
  • Conducted dynamic prediction of future health outcome and risk of HD progression for early diagnosis.
  • Developed Web-based App of HD prediction tool for clinical use.

Project: Longitudinal analyses of National Parkinson Foundation Quality Improvement Initiative data

  • Fitted multilevel linear/generalized linear mixed models to examine the effect of consistent exercise and physical therapy to mobility and health-related quality of life in people with PD.
  • Prepared statistical reports for non-statistical medical researchers and revised analysis based on their feedback accordingly.


Biostatistics Intern, Merck & Co., Inc.

May 2017 – Aug. 2017

Project: Continuous safety monitoring and benefit-risk analysis. [abstract]


Research Assistant, Department of Health Service, The University of Texas MD Anderson Cancer Center

Jan. 2014 – June 2015

Project: Treatment of Hepatitis C in Correctional Setting

  • Conducted survival analysis to estimate transition probability of HCV progression in a Markov model.
  • Developed large-scale agent-based simulation models for health economic evaluation of intervention strategies in Hepatitis C prevention. [abstract]


Teaching Assistant, Department of Biostatistics, The University of Texas Health Science Center at Houston

Fall 2013, Spring 2014, Fall 2016

  • Graduate-level courses: Linear Model; Categorical data analysis; Statistical Computing


Research Associate, Center for Public Health Practice, University of Pittsburgh

Sep. 2011 - Aug. 2013

Project: Social Mixing and Respiratory Transmission in Schools

  • Served in multiple roles and cooperated with other researchers to achieve the project objectives of each phase, including data collection, data management, analyzing, and publication preparation.
  • Fitted logistic regression model for classification based on participants’ features and contact patterns.
  • Conducted simulation study of flu transmission on parameterized social networks. [abstract]


Graduate Research Assistant, Department of Industrial Engineering, University of Pittsburgh

Jan. 2010 - Aug. 2011

Project: Vaccine Modeling Initiative

  • Applied linear programming and Markov decision process models to optimize the performance of vaccine supply chain in resource allocation and capabilities-based planning.
  • Developed Excel VBA based spreadsheet tools for decision-making in vaccine administration.

Certifications


  • SAS Advanced Programming Certificate for SAS 9

July 2013

  • SAS Base Programming Certificate for SAS 9

May 2013


Honers


  • JSM 2018 Biopharmaceutical Student Paper Award, American Statistical Association

July. 2018

  • R.Hardy and C. Morton Hawkins Endowed Scholarship, The University of Texas Health Science Center

May. 2016

  • Doctoral Outstanding New Student Scholarship, The University of Texas Health Science Center

Aug. 2013

  • Outstanding Graduating Student (Top 5%), Beijing Institute of Technology

Jun. 2009

  • National Scholarship (Top 1%), Chinese Ministries of Education

Dec. 2007


Publications


  • Li, K, Luo, S., 2018. “Bayesian Functional Joint Models for Multivariate Longitudinal and Time-to-Event data” Computational Statistics & Data Analysis. [Paper]

  • Li, K, Yuan, S., Wang, W., et al., 2018. “Periodic Benefit-Risk Assessment using Bayesian Stochastic Multi-criteria Acceptability Analysis.” Contemporary Clinical Trials. [Paper]

  • Li, K, O’Brien, R., Lutz, M., Luo, S., 2018. “A Prognostic Model of Alzheimer’s Disease Relying on Multiple Longitudinal Measures and Time-to-Event Data.” Alzheimer’s & Dementia. [Paper]

  • Li, K., Luo, S., 2017. “Dynamic Predictions in Bayesian Functional Joint Models for Longitudinal and Time-to-Event Data.” Statistical Methods in Medical Research. [Paper]

  • Li, K., Luo, S., 2017. “Functional Joint Model for Longitudinal and Time-to-Event Data: An Application to Alzheimer’s Disease.” Statistics in Medicine. [Paper]

  • Li, K., Stimming, E. F., Luo, S., 2017. “Dynamic Predictions of motor diagnosis in Huntington’s disease using a joint modeling approach.” Journal of Huntington’s Disease. [Paper]

  • Li, K., Chan, W., Doody, R.S., Luo, S., the ADNI, 2017. “Prediction of Conversion to Alzheimer’s Disease with Longitudinal Measures and Time-To-Event Data.” Journal of Alzheimer’s Disease. [Paper] [Media]

  • Csencsits, K., Suescun, J., Li, K., Luo, S., Bick, D., 2017. “Serum Lymphocyte-Associated Cytokine Concentrations Change More Rapidly over Time in Multiple System Atrophy Compared to Parkinson Disease.” Neuroimmunomodulation. [Paper]

  • Rafferty, M. R., Schmidt, P. N., Luo, S. T., Li, K., Marras, C., Davis, T. L., … & Simuni, T., 2016. “Regular Exercise, Quality of Life, and Mobility in Parkinson’s Disease: A Longitudinal Analysis of National Parkinson Foundation Quality Improvement Initiative Data.” Journal of Parkinson’s Disease. [Paper]

  • He, T., Li, K., Roberts, M.S., Spaulding, A.C., Ayer, T., Grefenstette, J.J. and Chhatwal, J., 2015. “Prevention of Hepatitis C by Screening and Treatment in US Prisons.” Annals of Internal Medicine. [Paper]


Course Work


  • Multiple Regression Analysis (SAS, R)
  • Correlate data Analysis (R)
  • Survival Analysis (SAS, R)
  • Bayesian Data Analysis (R, WinBugs)
  • Multivariate Statistical Analysis (SAS, R)
  • Categorical Data Analysis (SAS, STATA)
  • Statistical Computing (R, WinBugs, LaTex)
  • Data Mining (R)
  • Nonparametric Regression (R)
  • Distribution free methods (R)
  • Linear Model (R)
  • Sampling Techniques (R)
  • Theory of Statistics I, II
  • Time Series Analysis (R)
  • Stochastic Process (R)
  • Design of Experiments
  • Practical Bioinformatics (R, Python)