Data Analytics and Information Systems – Webinar archive
Sparse Regression Analysis of Mixed Multi-Responses
Presented by the DAIS and QCRE divisions
Speaker: Dr. Xiaoyu Chen, University of Buffalo
Monday, April 8, 1 p.m. ET
In this talk, Dr. Chen will present studies on (1) a multivariate regression of mixed responses (MRMR) model with application to a visualization evaluation problem; and (2) a Bayesian MRMR model that considers both individual and group variable selection with application to a runtime performance metrics prediction problem in Fog computing network.
In addition, future research opportunities will be discussed.
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Data Science Enabled Decision-making in Advanced Manufacturing and Personalized Safety
Presented jointly by the IISE DAIS and QCRE Divisions
Speaker: Dr. Hongyue Sun, University of Georgia
Tuesday, March 26, 12 noon ET
Dr. Sun presented data science enabled decision-making to address the above challenge, with applications in advanced manufacturing and personalized safety. He will introduce the consideration of high dimensional and streaming data for inkjet printing additive manufacturing process modeling, monitoring, and control. He will also speak about the work on occupational
worker fatigue assessment based on wearable sensors, to model the personalized, dynamic, and heterogeneous information of workers and inform the optimal system management. Finally, the future plans will be discussed.
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Advancing and Accelerating Qualification and Characterization through Stochastic Inverse ModelingMy Webinar
Presented jointly by the IISE Data Analytics and Quality Control and Reliability Divisions
November 28, 2023
Presenter: Dr. Ashif Iquebal
This webinar explores the existing research on inverse problems and how they are limited in accurately estimating the QoIs and their variabilities.
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Assessing the Calibration and Performance of Attention-based Spatiotemporal Neural Networks for Lightning Prediction
Presented jointly by the IISE Data Analytics and Quality Control and Reliability Divisions
November 14, 2023
Presenter: Dr. Nathan Gaw
This webinar will discussed the recent neural networks developed to produce accurate lightning nowcasts, using various types of satellite imagery, past lightning data, and other weather parameters as inputs to their model.
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Digital Twin for Quality Innovations: Manufacturing and Health Applications
Presented jointly by the IISE Data Analytics and Quality Control and Reliability Divisions
October 31, 2023
Presenter: Dr. Hui Yang
This webinar discusses our continuous research efforts on the “sensing-modeling-optimization” framework to build new cyber-physical digital twins in disparate disciplines of manufacturing and healthcare.
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Do Reliability and Machine Learning Models With Time Delays and Model Selection Matter?
Presented by the IISE QCRE and DAIS Divisions
1 p.m. ET March 2
Presenter: Dr. Hoang Phan
With the development of various machine learning models, both model-driven and data-driven approaches in recent years, the task of determining an appropriate model which satisfies certain criteria from a set of candidate models given a set of data has become more challenging for engineers and analysts. In this talk, I will discuss various model selection criteria and some recent modeling studies
of systems such as system degradation and body's immune system with time delays.
Machine Learning for Digital Transformation of Clinical Trials and Legal Review
Presented jointly by the IISE Data Analytics and Quality Control and Reliability Divisions
10 a.m. PT/1 p.m. ET Feb. 27
Presenter: Cao (Danica) Xiao
The digital transformation of healthcare and legal data presents remarkable opportunities for machine learning (and deep learning) technologies to revolutionize the healthcare and legal industries. To ensure research will be useful and thus have real world impact, it is desirable to study the industry expert workflow and identify meaningful tasks that machine learning can help automate or augment.
This talk will cover examples of research works and products to show some initial success of machine learning in improving the success and efficiency of patient recruitment in clinical research and privilege and privacy redaction in legal review.
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Dynamic Characterization and Optimal Self-Management of the Emergence Trajectories of Multiple Chronic Conditions
Presented jointly by the IISE Data Analytics and Information Systems (DAIS) Division Quality Control and Reliability Engineering (QCRE) Division
1 p.m. ET Dec. 6
Presenter: Dr. Adel Alaeddini
More than a quarter of all Americans and two out of three older Americans are estimated to have at least two chronic health problems. Treatment for people living with multiple chronic conditions (MCC) consume an estimated 66 percent of U.S. healthcare costs, and as the population ages, the number of MCC patients will increase. However, fundamental knowledge gaps remain in our understanding of how MCC evolves at the individual and population levels.
his presentation introduces functional and deep continuous time Bayesian networks to model the relationship among MCC and non/modifiable risk factors to characterize major patterns of MCC emergence in individuals based on a dataset from the US Department of Veteran Affairs.
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Online Monitoring of Big Data Streams -- Roadmap and Recent Advances
A Webinar presented by the Online Monitoring of Big Data Streams -- Roadmap and Recent Advances
November 17, 2022
Presenter: Dr. Kaibo Liu, Associate Professor, University of Wisconsin-Madison
The rapid advancements of internet of things (IoT) technology and cyber-physical infrastructure have resulted in a temporally and spatially dense data-rich environment, which provides unprecedented opportunities for performance improvement in various complex systems. Meanwhile, it also raises new research challenges on process monitoring,
such as heterogeneous data formats, high-dimensional and big data structures, inherent complexity of the target systems, and potential lack of complete a priori knowledge.
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IIIoT, AI&ML - Network Science and The Future of Manufacturing in the USA
A Webinar presented by the Data Analytics and Information Systems (DAIS) Division and the IISE Body of Knowledge
March 16, 2022
Presenter: Dr. Soundar Kumara
Covid-19 has shown us the drawbacks of our supply chains and exposed our vulnerabilities. “self-sufficiency,” can only be realized by making SMMs an integral part of the larger manufacturing ecosystem. Local Manufacturing, a paradigm that relates the local skills, resources, and SMMs is needed for making our country self-sufficient. We show how IIoT, AI&ML, and Networks will become the backbone of such a futuristic manufacturing ecosystem and bring four different areas of
research together to pave the foundation for the next generation manufacturing.
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Tips on Transitioning from Academia to Industry
A Webinar presented by the Data Analytics and Information Systems (DAIS) Division
Feb. 11, 11 a.m. ET
Presenter: Andres Uribe-Sanchez, Ph.D.
After more than 8 years of working in academia, the transition to an industry environment was particularly difficult. In a blink of an eye, daily routines needed to be adjusted, the lens of my understanding had to change in to answer business related questions, as well as lost some freedom/time to explore/investigate novel approaches. In this seminar I would like to
share some good practices I have seen and tried to mimic from managers and successful data scientists.
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Visualizing
the Effects of Predictor Variables in Blackbox Supervised Learning Models
In many supervised learning applications, understanding and visualizing the effects of the predictor variables on the predicted response is of paramount importance. A short coming of black box supervised learning models (e.g. complex trees, neural networks, boosted trees, random forests, nearest
neighbors, local kernel-weighted methods and support vector regression) in this regard is their lack of interpretability or transparency. Partial dependence plots, which are the most popular approach for visualizing the effects of the predictors with black box supervised learning models, can produce erroneous
results if the predictors are strongly correlated. This talk discusses a new visualization approach, accumulated local effects (ALE) plot, which does not require this unreliable extrapolation with correlated predictors. It is substantially less computationally expensive and avoids the extrapolation
problem that can render PD plots unreliable when the predictors are highly correlated.
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An Introduction to the Fairness in Machine Learning, Fundamental Concepts, and Real-World Examples
Machine learning algorithms have achieved dramatic progress nowadays, and are increasingly being deployed in high-stake applications, including employment, criminal justice, personalized medicine etc. Nevertheless, fairness in machine learning remains a critical problem.
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Topological data analysis (TDA) is rapidly emerging as one of the most general-purpose methods for feature extraction and selection in a variety of predictive data analytics applications. Based on the core idea of characterizing topological structures in noisy and high-dimensional data sets
using their persistence information, TDA provides a robust framework to yield suitable features.
In the age of Big Data, one pressing challenge facing engineers is to perform reliability analysis for a large fleet of heterogeneous repairable systems with covariates.
To date, engineering product design relies significantly on computer simulation models (e.g., finite element analysis or FEA) to predict product performance of interest given a set of design configurations. Model prediction could be problematic without referring to the corresponding test data because all models are built to approximate the real physical systems with some assumptions and simplifications.
This presentation mostly focuses on building a statistical monitoring scheme for service systems that experience time varying arrivals of customers and have time varying service rates.
This webinar will survey the various approaches used for monitoring build quality in additive manufacturing (AM, 3D Printing) processes, including algorithms and sensing systems.
Obstructive sleep apnea (OSA) is a common sleep disorder that affects 24 percent of men and nine percent of women. It is caused by the collapse of upper airway during sleep, which subsequently leads to breath cessation and decrease of blood oxygen level that triggers arousals.
This webinar will focus on how IBM's cloud-based InBalance optimization solution can be utilized to minimize e-commerce shipping costs during order sourcing and scheduling process. We will discuss the usage of node specific carriers, carrier rate cards and transit days, regional carriers, node shipping capacity and order backlog and how InBalance enables Sterling OMS and other order management systems to make the best possible sourcing decisions.
This webinar will introduce the innovative wireless charging electric vehicle which charges the battery wirelessly from the charging infrastructure installed under the road. The technology is innovative in that the vehicle is charged while it is even in motion. One example of the commercialized system is the Korea Advanced Institute of Science and Technology (KAIST) wireless charging EV shuttle, which is called Online Electric Vehicle currently operating in the KAIST campus.
Wireless sensor network has emerged as a key technology for monitoring space-time dynamics of complex systems, e.g., environmental sensor network, battlefield surveillance network and body area sensor network. As a result, distributed sensing gives rise to spatially-temporally big data.
Assessing the current "state of health" of individual transit networks is a fundamental part of studies aimed at planning changes and/or upgrades to the transportation network serving a region. To be able to affect changes that benefit both the individual transit networks as well as the larger transportation system, organizations need to develop meaningful strategies guided by specific performance metrics. A fundamental requirement for the development of these performance metrics is the availability of accurate data regarding transit networks.
In today’s connected world, we have the ability to gather extensive quantities of data from many different sources. Making good use of this data is limited by our ability to understand the data content, interpret relationships in the data, and take action on what we find. Data visualization is a key part of this process and when done well, takes advantage of how humans process inputs to accelerate our understanding of the data content. This webinar will discuss the general topic of data visualization, cover key principles in human processing of charts, discuss criteria for selecting visualization tools, and demonstrate the construction of visualization for a sample data set.
How can computer and information systems overcome the difficulties of locating, integrating and sharing distributed knowledge for better decisions and better understanding? The integration of Collaborative Intelligence with Collaborative Control Theory are reviewed, explained and illustrated: They can enable significant improvement in exiting Internet and HUB based services by optimizing knowledge sharing and decisions. This webinar will describe applications in networks of cultural exchange, education and training, healthcare, manufacturing and supply networks.