Industrial Engineering

Faculty

Ying Lin
Ying Lin, Ph.D.
Assistant Professor
Office Location: Room E211
Phone: 713-743-6674   |   Fax: 713-743-4190
Email: ylin53 [at] central [dot] uh [dot] edu

Education: 

Ph.D., Industrial and Systems Engineering (ISE), University of Washington, 2017
M.S., Industrial and Management Systems Engineering (IMSE), University of South Florida, 2014
B.S., Statistics, University of Science and Technology of China, P. R. China, 2012

Courses Taught: 

INDE 7397 Engineering Analytics, IE, UH, Fall 2017
INDE 4349 Facilities Planning and Design, IE, UH, Spring 2018

Research Interests: 

Research interests of Dr. Lin’s group lie at the interactions of data analytics, quality engineering and healthcare. Her group focuses on the following research directions:

  • Statistical learning and data mining: biomedical and sensing data analysis.
  • Quality engineering: system modeling and prognosis, prognostic-based monitoring.
  • Medical decision making: personalized medicine, disease prevention and monitoring.

Awards & Honors: 

Finalist of Student Paper Competition in Quality Control and Reliability Engineering (QCRE) Division, Institute of Industrial and Systems Engineering (IISE) Annual Conference, Pittsburg, PA, May. 2017
The Lee B. Lusted Research Prize, Society for Medical Decision Making (SMDM) Annual Conference, Vancouver, Canada, Oct. 2016
Student Travel Scholarship, Society for Industrial and Applied Mathematics (SIAM) Data Mining Conference, Vancouver, BC, Canada, May. 2015
Outstanding Student Scholarship, USTC, China, 2011
Alumni Foundation Excellent Student Award, USTC, China, 2008

Professional Activities: 

Society for Medical Decision Making (SMDM) Membership Committee, 2017 – 2018

IISE DAIS board director, 2018 – 2019

Institute of Industrial and Systems Engineers (IIE)

Institute for Operation Research and the Management Sciences (INFORMS)

Society for Medical Decision Making (SMDM)

Selected Publications

  • Lin, Y., Liu, S., and Huang, S., “Selective Sensing of A Heterogeneous Population of Units with Dynamic Health Conditions”, IIE Transactions, accepted

    , 2018.
  • Lin, Y., Huang, S., Simon, G.E., and Liu, S., “Data-based Decision Rules to Personalize Depression Follow-up”, Scientific Reports, 5064

    , 2018.
  • Lin, Y., Liu, K., Byon, E., Liu, S., and Huang, S., “A Collaborative Learning Framework for Estimating Many Individualized Regression Models in A Heterogeneous Population”, IEEE Transactions on Reliability, 99: 1-14

    , 2017.
  • Li, M., Lin, Y., Huang, S., and Crossland, C., “The Use of Sparse Inverse Covariance Estimation for Relationship Detection and Hypothesis Generation in Strategic Management”, Strategic Management Journal, 37: 86-97

    , 2016.
  • Lin, Y., Huang, S., Simon, G.E., and Liu, S., “Analysis of Depression Trajectory Patterns using Collaborative Learning”, Mathematical Biosciences, 282: 191-203

    , 2016.
  • Lin, Y., Qian, X., Krischer, J., Vehik, K., Lee, H.S. and Huang, S, “A Rule-Based Prognostic Model for Type 1 Diabetes by Identifying and Synthesizing Baseline Profile Patterns”, PLOS one, 9(6): e91095

    , 2014.

Recent Presentations

  • “Patient-specific Depression Monitoring by Selective Sensing”, INFORMS Annual Conference, Nashville, TN

    , 2016.
  • “Adaptive Monitoring of Depression Treatment Population: A Data-driven Approach”, INFORMS Annual Conference, Nashville, TN

    , 2016.
  • “A Longitudinal Pattern based Prognostic Model for Depression Monitoring via Rulebased Method”, SMDM 38th Annual Meeting, Vancouver, CA

    , 2016.
  • “Adaptive Monitoring of Depression Patient Population: A Selective Sensing Approach”, SMDM 38th Annual Meeting, Vancouver, CA

    , 2016.
  • “A Longitudinal Pattern based Prognostic Model for Depression Monitoring via Rulebased Method”, Group Health Research Institute, Seattle, WA

    , 2016.
  • “Large-Scale Personalized Health Surveillance by Collaborative Modeling and Selective Sensing”, INFORMS Annual Conference, Philadelphia, NJ

    , 2015.
  • “Analysis of Electronic Health Record based Depression T rajectory and Monitoring”, INFORMS Annual Conference, Philadelphia, NJ

    , 2015.
  • “Collaborative Alerts Ranking for Intrusion Detection”, NEC Laboratories America, Princeton, NJ

    , 2015.
  • “Trajectory Modeling via Collaborative Learning Approach”, NEC Laboratories America, Princeton, NJ

    , 2015.
  • “Cognitive Degradation Modeling for Alzheimer’s Disease via A Collaborative Degradation Modeling Approach,” SDM 2015, Vancouver, CA

    , 2015.
  • “Domain-Knowledge Driven Cognitive Degradation Modeling for Alzheimer’s Disease,” INFORMS Annual Conference, San Francisco, CA

    , 2014.

Conference Publications

  • Lin, Y., Chen, Z., Cao, C et al., “Collaborative Alert Ranking for Enterprise Security System”, in Conference on Information and Knowledge Management 2018 (CIKM 2018), Oct. 22 - Oct.26, 2018, Turin, Italy. (historical paper acceptance rate < 25%)

    , 2018.
  • Lin, Y., Liu, S., and Huang, S., “A Longitudinal Pattern Based Prognostic Model for Depression Monitoring via Rule-based Method”, in 38th Annual Meeting of the Society for Medical Decision Making (SMDM), Oct 23 - Oct 24, 2016, Vancouver, CA. (Abstract)

    , 2016.
  • Lin, Y., Liu, S., and Huang, S., “Adaptive Monitoring of Depression Patient Population: A Selective Sensing Approach”, in 38th Annual Meeting of the Society for Medical Decision Making (SMDM), Oct 23 - Oct 24, 2016, Vancouver, CA. (Abstract)

    , 2016.
  • Lin, Y., Huang, S., and Liu, S., “Analysis of Depression Trajectory Patterns Using Collaborative Learning”, in 37th Annual Meeting of the Society for Medical Decision Making (SMDM), Oct 18 - Oct 21, 2015, St. Louis, MO. (Abstract: invited for oral presentation)

    , 2015.
  • Lin, Y., Liu, K., Byon, E., Qian, X., and Huang, S., “Domain-knowledge Driven Cognitive Degradation Modeling for Alzheimer’s Disease”, in SIAM International Conference on Data Mining 2015 (SDM 2015), Apr 30 - May 2, 2015, Vancouver, CA. (historical paper acceptance rate < 25%)

    , 2015.