Faculty Directory

Lee, Jay

Lee, Jay

Clark Distinguished Chair
Director of Industrial Artificial Intelligence Center
A. James Clark School of Engineering
Mechanical Engineering
Maryland Robotics Center
Center for Risk and Reliability
2181 Glenn L. Martin Hall

Dr. Jay Lee is Clark Distinguished Chair and Director of Industrial Artificial Intelligence Center in the Mechanical Engineering Dept. of the Univ. of Maryland College Park. 

Previously, he served as an Ohio Eminent Scholar, L.W. Scott Alter Chair and Univ. Distinguished Professor at Univ. of Cincinnati.  He was Founding Director of National Science Foundation (NSF) Industry/University Cooperative Research Center (I/UCRC) on Intelligent Maintenance Systems (www.imscenter.net) during 2001-2019.  IMS Center has developed research memberships with over 100 global company since 2000 and was selected as the most economically impactful I/UCRC in the NSF Economic Impact Study Report in 2012. He is also the Founding Director of Industrial AI Center (www.iaicenter.com ).  He mentored his students and developed a number of start-up companies including Predictronics through NSF iCorp in 2013 and has won 1st Place for PHM Society Data Challenges competition 5 times. He has developed Dominant Innovation® methodology for product and service innovation design.

  He was on leave from UC to serve as Vice Chairman and Board Member for Foxconn Technology Group (ranked 26th in Global Fortune 500) during 2019-2021 to lead the development of Foxconn Wisconsin Science Park (~$1B investment) in Mt. Pleasant, WI (www.foxconnwiofficial.com). In addition, he advised Foxconn business units to successfully receive five WEF Lighthouse Factory Awards since 2019. 

 He is a member of  Global Future Council on Advanced Manufacturing and Production of the World Economics Council (WEF), a member of Board of Governors of the Manufacturing Executive Leadership Council of National Association of Manufacturers (NAM), Board of Trustees of MTConnect, as well as a senior advisor to McKinsey. Previously, he served as Director for Product Development and Manufacturing at United Technologies Research Center (now Raytheon Technologies Research Center) as well as Program Director for a number of programs at NSF.

He was selected as 30 Visionaries in Smart Manufacturing in by SME in Jan. 2016 and 20 most influential professors in Smart Manufacturing in June 2020, SME Eli Whitney Productivity Award, and SME/NAMRC S.M. Wu Research Implementation Award in 2022.  His new book on Industrial AI was published by Springer in 2020.

For publication citations, highlights, and recent news:

Google Scholar https://scholar.google.com/citations?user=g9GtqgQAAAAJ&hl=en&oi=ao 

ResearchID:  https://researchid.co/jay.lee   

ResearchGate https://www.researchgate.net/profile/Jay_Lee10

Mechanical and Aerospace Engineering Leader Award 2023-2024.  World Best Mechanical and Aerospace Professors (based on https://research.com/)


Top 2% of World Scientists by Stanford Univ. 2020-2023, by Stanford Univ..2023 link  https://elsevier.digitalcommonsdata.com/datasets/btchxktzyw/6 

      WEF Davos 2023 Featured video on AI for Smart Manufacturing. https://app.frame.io/presentations/c6b734dd-9db2-4900-bab2-903ace86979f

     WEF interview article https://theinnovator.news/interview-of-the-week-jay-lee-industrial-ai-expert/






Industrial AI, Industrial Big Data, Prognostics and Health Management (PHM), Smart Manufacturing, Digital Twin, etc.

1. Intelligent Metrology Systems of Advanced Semiconductor Manufacturing.

2. Smart Electrification Analytics for EV systems.

3. AI Augmented ICU and Medical Analytics (TBI)

4. Cyber Phyiscal Systems and Digital Twin for Smart Manufacturing.

5. Prognostics of Highly Connected and Complex Systems including Wind Turbine/Wind Farm, High Speed Train, Aircraft Fleet, Factoty Automation and Robot Lines, Semiconductor Fab., etc.

6. Intelligent Maintenance Systems for Mechanical Components (bearing, ball screw, pump, valve, motors, etc) and sensors/sensory systems.

Partial List of Current Research Projects:

Winbond Electronics Corporation

Winbond Electrostatic (ESC) Chuck Remaining Useful Life

Hitachi High-Technologies Corporation

Phase V: Chamber Matching based on Etching Digital Twin Model

Applied Materials, Inc.

Multivariate Simulation Dataset Generation for Fault Detection with Semi-Automated Feature Extraction and Semi-supervised Limits Setting Applied to Semiconductor Manufacturing Processes

Mitsubishi Electric Corporation

Health Assessment & Fault Detection for Industrial Robots

Hitachi High-Technologies Corporation

Phase 2021-1: Development of digital twin model for Hitachi plasma etching tool and calibration of tool performance shift

United Microelectronics Corporation (UMC)

Applications of Machine Learning Operation Techniques

National Institute of Standards and Technology

Industrial Artificial Intelligence Consortium to Advance High-Mix Production Systems


Predictive Maintenance Analytics of Roll-to-Rolll Manufacturing






















Industrial AI (Course Number ENME 691 or ENME 485)


Term: Fall/2023

Professor: Jay Lee

Pronouns: He/His/Him

Office Phone: 301-405-5205

Email: leejay@umd.edu

Office Hours: Monday 9 am- 11am, Jeong H. Kim Engineering Building 3226

Teaching Assistant: Dai-Yan Ji, Takanobu Minami

Pronouns: He/His/Him

Email: jidn@umd.edu & minamitu@umd.edu

Office Hours: Tuesday&Thursday 2pm- 3pm, Jeong H. Kim Engineering Building 1210

Credits: 3

Course Dates: From August 28, 2023 - December 11, 2023 

Course Times: Monday 5 pm-7:40 pm

Classroom: CHE 2136

Course Description

In today’s competitive business environments, companies have urgent needs to use advanced analytical tools to manage their industrial data to gain more insights of their operations. Those insights include machine health condition, system remaining useful life, system performance, and other key performance indicators. With the advent of networked devices, an abundance of data now exists in virtually every level of industry, from the individual manufacturing asset to the manufacturing facility as well as the entire organization. This data is often not used to its greatest potential. It is often acquired, stored and forgotten. Most organizations understand the need for acquiring data, but do not understand how to leverage the data to enhance their system design, condition monitoring and decision-making capabilities. This course will introduce students to advanced technologies—such as machine learnings and tools, prognostics and health management (PHM), data-centric engineering analytics—that ultimately enable the conversion of industrial big data into decision-ready information that can be used to improve the design, the productivity and the efficiency of industrial systems.

Learning Outcomes

After successfully completing this course you will be able to:

  • Learn the fundamental issues in data quality engineering for machine learning as well as utilize intelligent AI tools for machine monitoring, performance assessment, and prognostics of complex industrial systems.
  • Understand the systematic approach in developing, validating, and deploying machine learning tools in using the real-world industrial datasets.
  • Apply the learned Industrial AI tools to the assigned team project through an integrated learning of domain, data, and discipline system approach.

Required Resource Course Website: elms.umd.edu There will be no formal textbook for this class, but all lecture notes will be posted online. The reference below is strongly recommended to use on a regular basis throughout the semester.

  • Text Book:

1. Jay Lee, Industrial AI: Applications with Sustainable Performance, Springer, 2020.

  • Reference Books:

1. Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.

2. Gelman, Andrew, et al. Bayesian data analysis. Vol. 2. London: Chapman & Hall/CRC, 2014.

3. Randall, Robert Bond. Vibration-based condition monitoring: industrial, aerospace and automotive applications. John Wiley & Sons, 2011.

  • Software: MATLAB or Python

Prerequisite and Criteria for the Course

Abilities in using MATLAB or Python or Permission from the Instructor

Course Structure

This course belongs to in-person session (on-line Recording will be arranged for students to review). Students are expected to attend all classes (please be on time).  Even though most of the course materials are well covered by the posted lecture notes and text, there may be some information that is discussed exclusively in class.  If you missed class, you are still responsible for learning the materials taught during your absence.

Major Assignments

Homework Assignments

  • Assignment#1

1. Review the assigned publications, reports, and white papers.

2. Survey the literature to gain the understanding of the role of AI in industrial 4.0, digital transformation, and smart manufacturing.

3. Each student will develop a survey report and give a 5-minute presentation to share the findings and summary with the class.

  • Assignment#2

1. A bearing vibration dataset and specific homework requirements will be given to the student. The

dataset is generated from the vibration test in the IAI center.

2. Sample code for Fast Fourier Transform and logistic regression will be provided to the student to finish

the homework.

3. Students will be divided into working groups for homework.

4. Each group will give a 10-minute presentation in the class.

  • Assignment#3

1. The student groups are asked to use the Support Vector Machine (SVM) and other algorithms to detect and diagnose the bearing failures. Sample code for SVM will be provided.

2. The assignment will be finished by the group, and each group is asked to report their work in the class

through a 10-minute presentation.

  • Assignment#4

1. The student groups are asked to use the Self-Organizing Map (SOM) and SOM-MQE to detect and diagnose the bearing failure. Sample code for SOM and SOM-MQE will be provided to the class to finish the


2. Each group is asked to report their work in the class through a 10-minute presentation.

Final Projects

  • Project 1: Fault detection in semiconductor Etching

Project 2: Gearbox fault diagnosis and prognosis

Project 3: Bearing fault diagnosis and prognosis.

Project 4: Virtual metrology for critical semiconductor manufacturing processes

Project 5: Machine tool degradation prognosis and remaining useful life prediction.

Project 6: Aero-engine remaining useful life prediction

Project 7: EV Batteries

Project 8: Spacecraft propulsion system

  • In the final project, each student group will focus on one of the 8 projects that are listed above. The dataset and detailed requirements will be given to students to start the project. TA will be assigned to each group to provide support and answer questions. Each group will work independently to fulfill the requirements in the final project. In the exam week, each group will make a 15-minute presentation to report their work and document all the details in a report. When promising results are achieved or novel methods are proposed, the instructor will advise the student group to publish papers at conferences or research journals.
  • The concept of design these projects is to motivate students to learn by address real-world problems independently. The datasets used in these final projects are from IAI past research projects or open data competitions. They are all real-world datasets generated from machines or production lines.  Although each student group will focus on one of the 8 projects in this class, the datasets of all these projects will be made accessible to all the students for their future use.Grading Structure


Percentage %

Homework & Presentation


Midterm Test


Final Term-Project Report





Course Outline

Week #



(8/28) 1

Industrial AI Introduction (Part 1):

Data Issues on Industrial Big Data System:

Data Source, Data Quality, and Data Context


(9/11) 2

Invited Lecture: Prof. Alex Jia, Univ. of Cincinnati

Fundamentals of Signal Processing and Feature Extraction and

Commonly used tools and Case studies in Industrial AI and Industrial AI (Part 2)


(9/18) 3

Industrial AI Introduction (Part 3):

Machine Learning tools (Part 1): Logistic Regression & Student HW 1 Presentations and Discussions

HW2 (Bearing data sets)

(9/25) 4

Machine Learning tools (Part 2): Support Vector Machine, Self-Organizing Map, K-Means and Others.

and Student HW 2 Presentations and Discussions

HW3 (Bearing – SVM)

(10/2) 5

Review ML Tools and Case Studies

Machine Health Monitoring using Industrial Big Data:

Case Study I: Machine Level Heath Monitoring

Case Study II: System Level Health Monitoring

and Student HW 3 Presentations and Discussions

HW4 (Bearing – SOM)

(10/9) 6

Invited Speaker: David Siegel, CTO of Predictronics (TBD)

Industrial AI tool for Prognostics and Health Management and Introduction to PHM Data Challenge and Case Studies

and Student HW 4 Presentations and Discussions,

Preview Midterm Preparation


(10/16) 7



(10/23) 8

Midterm Review

and Industrial AI for Networked and Complex Engineering Systems:

Case Study III: Wind Turbine and Wind Farm

Case Study IV: Smart EV Battery & Mobility


(10/30) 9

Introduction of Group Projects and Assignments

Project 1: Fault detection in semiconductor Etching

Project 2: Gearbox fault diagnosis and prognosis

Project 3: Bearing fault diagnosis and prognosis.

Project 4: Virtual metrology for critical semiconductor manufacturing processes

Project 5: Machine tool degradation prognosis and remaining useful life prediction.

Project 6: Aero-engine remaining useful life prediction

Project 7: EV Batteries

Project 8: Spacecraft propulsion system


(11/6) 10

Group Project Presentation 1


(11/13) 11

Invited Guest Lecture

Group Project Presentation 2



Group Project Presentation 3



Invited Lecture: Prof. Kiritsis Dimitris, EPFL, Switzerland

Group Project Presentation 4


(12/4) 14

Final Group Project Presentation


(12/11) 15

Final Report Due





Selected Books, Book Chapters, and Special Issues for Journal  (for the past five years):
  1. Lee, Jay. Industrial AI: Applications with Sustainable Performance, Springer, ISBN 978-981-15-2143-0, 2020.

Book Chapter:

  1. Book Chapter, Lee, J. etc. 'Cyber-Physical System Framework of AI in Manufacturing and Maintenance,” Book on “Artificial Intelligence in Manufacturing,” Elsevier, 2023.
  2. Book Chapter, Analyzing Data Obtained via Wind Farm Supervisory Control and Aata Acquisition,” Book on Utility-Scale Wind Turbines and Wind Farms, Institution of Engineering and Technology (IET), ISBN: 9781839530999, e-ISBN: 9781839531002, 2020.

Special Issues:

  1. Guest Editor, Special Issue on "Artificial Intelligence for Cyber-Enabled Industrial Systems" Journal of Machines, 2018.
  2. Guest Editor, Data-Driven Cognitive Manufacturing - Applications in Predictive Maintenance and Zero Defect Manufacturing, Organized by: Dimitris Kiritsis, Melinda Hodkiewicz, Oscar Lazaro, Jay Lee, Journal of Frontiers in Computer Science - Mobile and Ubiquitous Computing, 2020.
Selected Journal Papers (for the past five years)

UMD Hosts 3D Maryland Expert Group Meeting

Event Highlights 3D Printing, Additive Manufacturing Technologies at UMD

Jaydev Desai wins seed grant for diagnostic robotic system

Research will aid in prevention and treatment of atrial fibrillation.

S.K. Gupta and teammates win 2007 'Invention of the Year' physical science award

Gupta also finalist in information science category; Gary Rubloff finalist in physical science.

  • American Society of Mechanical Engineers (ASME), Society of Manufacturing Engineers (SME), Prognostics and Health Management (PHM) Society, International Society of Engineering Asset Management (ISEAM)