Industrial Engineering, Materials Science, Operations Research, Sensors and Sensor Networks
Causal Inference, Heterogeneous Data Integration, Machine Learning, Pattern Analysis and Classification, Spatio-Temporal Data Analysis, Statistics, Tensor Analysis
Relevant Projects:

NSF, AFOSR, US-DOT, Arizona State-DOT, DOE, Michigan Economic Development Corporation (MEDC)

Forging Industry Educational and Research Foundation

SME Education Foundation

U.S. Civilian Research & Development Foundation (CRDF)

TARDEC (US Army Tank Automotive Research Development Engineering Center)

GM, FORD, OG Technologies, Global Solar Energy


Data Mining

Quality, Statistics & Reliability (QSR), data Analytics in INFORMS

Quality Control and Reliability Engineering (QCRE) in IIE

International Statistics Institute

Formal Vice President of INFORM, President of QCRE, Chairman of QSR

Judy Jin


Industrial and Operations Engineering, College of Engineering
Integrative Systems and Design, College of Engineering

Judy Jin, PhD, is Professor of Industrial and Operations Engineering in the College of Engineering at the University of Michigan, Ann Arbor.

Prof. Jin’s research focuses on the development of new data fusion methodologies for improving system operation and quality with the emphasis on fusion of data and engineering knowledge collected from disparate sources by integrating multidisciplinary methods. Her research has been widely applied in both manufacturing and service industry by providing techniques for knowledge discovery and risk-informed decision making. Key research issues are being pursued:

  1. Advanced quality control methodologies for system monitoring, diagnosis and control with temporally and spatially dense operational/sensing data.
  2. Multi-scale data transform and high order tensor data analysis for modeling, analysis, classification, and making inferences of multistream sensing signals.
  3. Optimal sensor distribution and hierarchical variable selection methods for system abnormal detection and sensor fusion decisions, which integrates the causal probability network model, statistical change detection, set-covering algorithm, and hierarchical lasso regression.
  4. A unified approach for variation reduction in multistage manufacturing processes (MMPs) using a state space model, which blend the control theory with advanced statistics for MMPs sensing, monitoring, diagnosis and control, integrative design of process tolerance and maintenance policy considering the interaction between product quality and tool reliability.

Data science applications: (a) Smart manufacturing with sensor fusion, process monitoring, diagnosis and control (e.g., metal forming including stamping, forging, casting and rolling), assembly, ultrasonic welding, photovoltaic thin film deposition. (b) Travel time estimation and traffic prediction for intelligent transportation systems. (c) Multi-stream data analysis of human motion/vehicle crash testing data for improving vehicle design and safety. (d) Risk informed decision support for healthcare and clinical decisions. (e) Customer behavior modeling for fraud detection in healthcare and telecommunication. (f) Human decision-making behavior modeling in a dynamic/emergency environment.