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An Adaptive Sampling Strategy for Online Monitoring and Diagnosis of High-dimensional Streaming Data
Prof. Kamran Paynabar
Date:  July 6th, 2021.

Abstract: Statistical process control techniques have been widely used for online process monitoring and diagnosis of streaming data in various applications, including manufacturing, healthcare, and environmental engineering. In some applications, the sensing system that collects online data can only provide partial information from the process due to resource constraints. In such cases, an adaptive sampling strategy is needed to decide where to collect data while maximizing the change detection capability. This paper proposes an adaptive sampling strategy for online monitoring and diagnosis with partially observed data. The proposed methodology integrates two novel ideas: (i) the recursive projection of the high-dimensional streaming data onto a low-dimensional subspace to capture the spatio-temporal structure of the data while performing missing data imputation; and (ii) the development of an adaptive sampling scheme, balancing exploration and exploitation, to decide where to collect data at each acquisition time. Through simulations and two case studies, the proposed framework's performance is evaluated and compared with benchmark methods.

Short Biography: Kamran Paynabar is the Fouts Family Early Career Professor and Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He received his B.Sc. and M.Sc. in Industrial Engineering from Iran and his Ph.D. in Industrial and Operations Engineering from The University of Michigan in 2012. He also holds an M.A. in Statistics from The University of Michigan. His research interests comprise both applied and methodological aspects of machine-learning and statistical modeling integrated with engineering principles for predictive modeling, system monitoring, diagnosis and prognosis. He is a recipient of the INFORMS Data Mining Best Paper Award, the Best Application Paper Award from IIE Transactions, the Best QSR Refereed Paper from INFORMS, and the Best Paper Award from POMS. He has been recognized with the CETL/BP Junior Faculty Teaching Excellence Award and the Provost Teaching and Learning Fellowship. He served as the chair of Quality, Statistics, and Reliability of INFORMS, and the president of Quality Control and Reliability of IISE. He is Associate Editor for Technometrics, IEEE-TASE, INFORMS Journal of Computing, and INFORMS Journal of Data Science, a Department Editor for IISE-Transactions and a member of editorial board for Journal of Quality Technology. He is a co-founder of ProcessMiner an AI/ML startup company for manufacturing improvement.
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Bayesian Optimization of Expected Quadratic Loss for Multiresponse Computer Experiments with Internal Noise
Prof. Matthias Hwai Yong Tan
Date:May 10th, 2021

Abstract: Design of systems based on computer simulations is prevalent. An important idea to improve design quality, called robust parameter design (RPD), is to optimize control factors based on the expectation of a loss function so that the design is robust to noise factor variations. When computer simulations are time consuming, optimizing the simulator based on a Gaussian process (GP) emulator for the response is a computationally efficient approach. For this purpose, acquisition functions (AFs) are used to sequentially determine the next design point so that the GP emulator can more accurately locate the optimal setting. Despite this, few articles consider AFs for positive definite quadratic forms such as the expected quadratic loss (EQL) function, which is the standard expected loss function for RPD with nominally-the-best responses. This paper proposes new AFs for optimizing the EQL, analyzes their convergence, and develops quick and accurate methods based on the characteristic function of the EQL to compute them. We apply the AFs to RPD problems with internal noise factors based on a GP model and an initial design tailored for such problems. Numerical results indicate that all four AFs considered have similar performance, and they outperform an optimization approach based on modeling the quadratic loss as a GP and maximin Latin hypercube designs.

Short Biography: Matthias Hwai Yong Tan is an associate professor in the School of Data Science at City University of Hong Kong. He received his B.Eng. degree in Mechanical-Industrial Engineering from the Universiti Teknologi Malaysia, an M.Eng. degree in Industrial and Systems Engineering from the National University of Singapore and a Ph.D. degree in Industrial and Systems Engineering from Georgia Institute of Technology. His research interests include uncertainty quantification, design and analysis of computer experiments, and applied statistics.

Affidabilità per componenti e sistemi (RELIA)
Prof. Marcantonio Catelani
Date: Sept. 2021

Abstract: Starting from the examples of "criticality" and "points of attention" that characterize the topic, the aim of the meeting is to provide some insights and discussion starting from simple basic concepts, methodological and experimental, of reliability. The proposed seminar, "Reliability for components and systems", is a first event that will be followed by further in-depth analysis useful, according to the speakers, to understand the wider context of RAMS requirements and performances - Reliability, Availability, Maintanability and Safety - typical of some industrial scenarios. The contents of the seminar cover:
- Terms and definitions in accordance with industry technical standards
- From product conformity to system reliability assessment
- Reliability Functions and Parameters: from Reliability Law to Statistical Parameters
- The impact of failure rate on components and systems: tubular curve and classification of failures
- Experimental and theoretical-predictive evaluation of failure rate; which points of attention?
- The impact of operating conditions in the calculation and evaluation of reliability requirements
- Canonical functional configurations and system reliability: what characteristics and constraints
- The use of redundancies: cost-benefit evaluation
- What not to do when a reliability assessment and statement is required
- Can reliability be simulated in the laboratory?
Questions on the topics and discussion

Short Biography: Graduated in Electronic Engineering, he is Full Professor of Reliability and Quality Control at the University of Florence, School of Engineering, Department of Information Engineering (DINFO). His research activity is diversified and concerns the fields of Reliability, Diagnostics, Qualification and Certification of electronic components and systems. He deals with RAMS techniques (Reliability, Availability,

Within the framework of the ENBIS Spring Meeting 2018 which will be held in Florence from 4 to 6 June, the SteEring Center organizes a seminar meeting entitled:

"Statistics and engineering for the business world"

4 June 2018
9:15 to 12:30
Via Gino Capponi, 9 - FLORENCE

Head office:
Dept. of Statistics, Informatics,
Applications "G.Parenti"- DiSIA
Viale Morgagni 59
50134 Firenze.
Interuniversity Research Center for "Statistics for Engineering: Design, Quality and Reliability"
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tel. +39 055 2751500
fax. +39 055 2751525
P. IVA 01279680480
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