International Statistical Engineering Association upcoming events

ISEA Short Course (10 Sep 2026)

Thu, 10 Sep 2026 14:00:00 GMT

STATISTICAL ENGINEERING: PROBLEM FORMULATION FOR SUCCESSFUL DATA PROJECTS

Don't start with the data. Start with the problem. Master the art of problem formulation in statistical practice.

Many data science, analytics, and improvement projects fail—not because of poor statistical methods, but because the wrong problem was defined in the first place.

This interactive short course introduces the principles of Statistical Engineering and teaches participants how to frame, structure, and formulate problems that lead to successful data-informed solutions.

Who Should Attend?

Experienced statisticians and data scientists who want to enhance project results
Early-career professionals involved in data-driven decision making
Graduate students in statistics, data science, analytics, or related disciplines

What You Will Learn

Participants will learn how to:

Apply the Statistical Engineering problem-solving framework
Recognize strengths and weaknesses of problem statements
Formulate clear, actionable, and measurable problem statements
Avoid common project pitfalls and framing errors
Develop strategies for tackling large, complex, and unstructured problems

Course Delivery Format

The course is split into two online live sessions

Session 1: September 10 at 10 am - 12 pm EST

Session 2: September 24 at 10 am - 12 pm EST

Each session will utilize interactive learning through:

Expert instruction
Individual reflection exercises
Group discussions
Real-world case studies
Practical problem-formulation activities

Meet Your Course Instructors

Roger W. Hoerl

Roger W. Hoerl is Brate-Peschel Professor of Statistics at Union College, in Schenectady, NY. Previously, he led the Applied Statistics Lab at GE Global Research. Dr. Hoerl has been named a Fellow of the American Statistical Association and the American Society for Quality, and has been elected to the International Statistical Institute and International Academy for Quality.

Caleb King

Dr. Caleb King is a senior developer for the DOE & Reliability group at JMP Statistical Discovery LLC. He received his PhD in statistics from Virginia Tech. His research interests included design of experiments and reliability. Prior to joining JMP, he worked as a senior statistician at Sandia National Laboratories. Dr. King currently also serves as Past Chair for ISEA.

Valeria Quevedo

Dr. Quevedo is an Associate Professor and researcher at the University of Piura. She holds a Ph.D. and an M.S. in Statistics from Virginia Tech (USA) and an M.S. in Operations Research from the University of British Columbia (Canada). She has professional experience in research and consulting projects within the fields of statistics and operations research. She is the current Chair of the Board of the International Statistical Engineering Association (ISEA).

Shilpa Gupta

Shilpa Gupta is an award-winning engineering educator and former data science leader whose work focuses on applied AI, statistical engineering education, authentic assessment, and the responsible use of LLM-based tools in engineering curricula. Dr. Gupta is an Adjunct Professor of Industrial and Systems Engineering at San Jose State University. She holds a doctorate in Industrial Engineering from Arizona State University.

Registration

Course Fee: US$50

Includes participation in both online sessions and all course materials. If you are a Silver or Gold corporate member, login to register for free.

For questions and general inquiry: info@isea-change.org

ISEA Short Course (24 Sep 2026)

Thu, 24 Sep 2026 14:00:00 GMT

STATISTICAL ENGINEERING: PROBLEM FORMULATION FOR SUCCESSFUL DATA PROJECTS

Don't start with the data. Start with the problem. Master the art of problem formulation in statistical practice.

Many data science, analytics, and improvement projects fail—not because of poor statistical methods, but because the wrong problem was defined in the first place.

Who Should Attend?

Experienced statisticians and data scientists who want to enhance project results
Early-career professionals involved in data-driven decision making
Graduate students in statistics, data science, analytics, or related disciplines

What You Will Learn

Participants will learn how to:

Apply the Statistical Engineering problem-solving framework
Recognize strengths and weaknesses of problem statements
Formulate clear, actionable, and measurable problem statements
Avoid common project pitfalls and framing errors
Develop strategies for tackling large, complex, and unstructured problems

Course Delivery Format

The course is split into two online live sessions

Session 1: September 10 at 10 am - 12 pm EST

Session 2: September 24 at 10 am - 12 pm EST

Each session will utilize interactive learning through:

Expert instruction
Individual reflection exercises
Group discussions
Real-world case studies
Practical problem-formulation activities

Meet Your Course Instructors

Roger W. Hoerl

Caleb King

Valeria Quevedo

Shilpa Gupta

Registration

Course Fee: US$50

Includes participation in both online sessions and all course materials. If you are a Silver or Gold corporate member, login to register for free.

For questions and general inquiry: info@isea-change.org

Reliability Study of Battery Lives: A Functional Degradation Analysis Approach (25 Sep 2026)

Fri, 25 Sep 2026 14:00:00 GMT

Topic: Reliability Study of Battery Lives: A Functional Degradation Analysis Approach

Speaker: Youngjin Cho

Date: September 25, 2026, 10:00 AM EST/ 7:00 AM PST/ 4:00 PM CEST

Abstract

Renewable energy is critical for combating climate change, whose first step is the storage of electricity generated from renewable energy sources. Li-ion batteries are a popular kind of storage units. Their continuous usage through charge-discharge cycles eventually leads to degradation. This can be visualized by plotting voltage discharge curves (VDCs) over discharge cycles. Studies of battery degradation have mostly concentrated on modeling degradation through one scalar measurement summarizing each VDC. Such simplification of curves can lead to inaccurate predictive models. Here we analyze the degradation of rechargeable Li-ion batteries from a NASA data set through modeling and predicting their full VDCs. With techniques from longitudinal and functional data analysis, we propose a new two-step predictive modeling procedure for functional responses residing on heterogeneous domains. We first predict the shapes and domain end points of VDCs using functional regression models. Then we integrate these predictions to perform a degradation analysis. Our functional approach allows the incorporation of usage information, produces predictions in a curve form and thus provides flexibility in the assessment of battery degradation. Through extensive simulation studies and cross-validated data analysis, our approach demonstrates better prediction than the existing approach of modeling degradation directly with aggregated data.

Speaker Bio

Youngjin Cho is an Assistant Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas. He received his Ph.D. in Statistics from Virginia Tech. His research focuses on the development of statistical methodology, with primary interests in smoothing splines, functional data analysis, survival analysis, and high-dimensional statistics. His recent research has focused on developing nonparametric testing and inference procedures in the smoothing spline framework.