International Statistical Engineering Association upcoming events

Measuring Stability and Robustness of Autonomous Driving Perception Systems Under Dynamic Conditions (13 Aug 2026)

Thu, 13 Aug 2026 14:00:00 GMT

Topic: Measuring Stability and Robustness of Autonomous Driving Perception Systems Under Dynamic Conditions

Speaker: Liang Shi

Date: Thursday, August 13, 2026, 10:00 AM EST/ 7:00 AM PST/ 4:00 PM CEST

Abstract

Reliable perception is essential for autonomous driving systems, especially when vehicles operate under dynamic and adverse environmental conditions. Conventional evaluation metrics such as average precision, IoU, and F1 score are useful for static frame-level assessment, but they often fail to capture how perception reliability changes with distance, uncertainty, weather, and illumination. In this webinar, I will introduce Perception Characteristics Distance (PCD), an uncertainty-aware metric designed to quantify the maximum distance at which a perception system can consistently produce reliable detections under a specified decision rule. I will also discuss the SensorRainFall dataset, collected on the Virginia Smart Roads under controlled clear, rainy, daylight, night, and streetlight conditions, with manually annotated bounding boxes and segmentation masks for vehicle and pedestrian targets. Using benchmarks, I will show how PCD reveals important robustness characteristics that traditional metrics may overlook, and how this framework can support safer evaluation of perception systems for ADAS and autonomous driving applications.

Speaker Bio

Liang Shi is a Research Associate at the Virginia Tech Transportation Institute (VTTI), working in computer vision, machine learning, and transportation safety. He received his Ph.D. in Statistics from Virginia Tech and also holds a master’s degree in Computer Science. His research focuses on AI perception, vision-language models, naturalistic driving data, autonomous driving safety, and the evaluation of machine learning systems under real-world and safety-critical conditions. His recent work includes research on perception robustness for autonomous driving, synthetic and multimodal benchmarks for driving safety-critical events, and vision-language understanding of traffic safety scenarios. He is also an Adjunct Professor in the Department of Statistics at Virginia Tech.

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

Registration

Course Fee: US$50

Includes participation in both online sessions and all course materials. Registration fee is waived for Silver and Gold corporate members. For questions and general inquiry: info@isea-change.org

For Registration & Information: Coming Soon!

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

Registration

Course Fee: US$50

Includes participation in both online sessions and all course materials. Registration fee is waived for Silver and Gold corporate members. For questions and general inquiry: info@isea-change.org

For Registration & Information: Coming Soon!

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.