May 2024
Newsletter Editor: Ashif Iquebal
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Dear Members of the IISE Data Analytics & Information Systems (DAIS) Division,
Thank you for a successful 2023-24 year! DAIS brings together members from academia, industry, and government who share a common interest in topics related to research and practice of data analytics and information systems. Over the past year, we have shown that the Division can stay strong and keep providing high-quality services to the community through both established programs and new ideas. As of April 2024, the total number of DAIS members has reached 2,284.
The 2024 IISE Annual Conference & Expo will take place from May 18–21 at the Montreal Convention Centre, Montreal, Canada, and DAIS received 144 abstracts with 49 full papers. Thanks to the DAIS co-Chairs (Adam Meyers, Arda Vanli and Cesar Ruiz), Tech Coordinators, session chairs and authors, the Division will organize 34 sessions including 10 special sessions as listed below.
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DAIS Competitions
- Best Student Paper Competition
- Data Analytics Competition
- Mobile/Web App Competition
- Best Track Paper Competition
DAIS Special Sessions
- Meet the Editors & Research Highlights from IISE Transactions on Healthcare Systems Engineering
- AI Accelerated Sustainability: How ISEs Using AI Can Support Achievement of the UN
- Generative AI and Chat GPT - Where are we now?
- A Gallery of Network Science Projects in ISE
- Artificial Intelligence & Machine Learning in the Financial Technology (FinTech) Industry
- Town Hall
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We are also excited to share our new LinkedIn profile. Follow the page to stay up to date on our activities and announcements. We are also thrilled to announce our new mentor-mentee program. Details will be forthcoming.
I would like to express my gratitude to our president-elect for 2023–2024, Chenang Liu (Oklahoma State University), and current board directors, Yu Jin (Binghamton University), Ashif Iquebal (Arizona State), Adam Meyers (University of Miami), Xiaowei Yue (Tsinghua University) and Cesar Ruiz (University of Oklahoma), Arda Vanli (Florida A&M University), Xiaoyu Chen (University of Buffalo), and Yinan Wang (Rochester Polytechnic Institute) for their contributions to the Division.
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I would also like to share our election results. Nathan Gaw (Air Force Institute of Technology) has been elected as the president-elect for the 2024–2025 academic year, and our new board directors include Kiran Kumar Dandu (Moderna), Nan Kong (Purdue University), Alexander Krall (Penn State University), Hyeong Suk Na (University of Missouri). I have no doubt that 2024–2025 will be another fantastic year.
We are also excited to announce the winner of this year’s professional achievement award, Dr. Jing Li from Georgia Institute of Technology and the teaching award, Dr. Anahita Khojandi from the University of Tennessee. Finally, you are cordially invited to join the DAIS Town Hall meeting to learn about the DAIS’ current status, many of its exciting activities, and future plans (4:30pm, Monday, May 20, Room 513A). The recipients of various competitions will be recognized at the DAIS town hall meeting. We look forward to seeing you at the 2024 IISE Annual Conference and Expo in Montreal and greatly appreciate your feedback on our current and future initiatives.
Sincerely,
Na Zou (University of Houston)
IISE DAIS Division President, 2023-2024
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Introducing the 2024-2025 DAIS Board Members |
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Chenang Liu
President
2024-2025
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Nathan Gaw
President Elect
2024-2025
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Na Zou
Past President
2024-2025
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Yinan Wang
Board Director
2023-2025
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Cesar Ruiz
Board Director
2023-2025
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Xiaoyu Chen
Board Director
2023-2025
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Arda Vanli
Board Director
2023-2025
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Kiran Kumar Dandu
Board Director
2024-2026
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Nan Kong
Board Director
2024-2026
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Alexander Krall
Board Director
2024-2026
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Hyeong Suk Na
Board Director
2024-2026
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DAIS's Student Board Members
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Data Analytics and Information Systems (DAIS) Track Highlights at the 2024 IISE Annual Conference & Expo
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As the IISE Annual Conference & Expo 2024 is just around the corner, we would like to present some highlights from the Data Analytics and Information Systems (DAIS) Track.
The DAIS Track has a total of 34 sessions, including 10 special sessions (including four competitions, 5 special topic/joint sessions, and Town Hall). The presentations will cover various topics including Machine Learning & Artificial Intelligence, Data Analytics & Informatics, and Modeling & Methodological Advancements and a wide range of applications including Manufacturing, Healthcare & Disease, Transportation, Energy & Environment, Social Systems, and more. We would like to share highlights of the DAIS track sessions at the 2024 IISE Annual Conference & Expo.
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2024 IISE DAIS Best Track Paper Competition
Session chairs – Yu Jin, Arda Vanli
Sunday, May 19 from 4:30-5:50 PM
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Solution Path Algorithm for Kernel Distributionally Robust Support Vector Machines by Neng Fan (University of Arizona) , Guangrui Tang
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Oral-Anatomical Knowledge-driven Semi-supervised Semantic Segmentation for Dental CBCT Image by Yeonju Lee (Georgia Tech), Mingu Kwak, Rui Qi Chen, Hao Yan, Mel Mupparapu, Fleming Lure, Frank Setzer, Jing Li
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Heterogeneous Recurrence Network Analysis: Unveiling Complex Dynamics for Emotion Recognition in EEG Signals by Yujie Wang (University of Miami) and Cheng-Bang Chen, Diane Lim, Toshihiro Imamura
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Integrating Neural Controlled Differential Equations and Neural Flow for Comprehensive Irregularly-sampled Time Series Analysis by YongKyung Oh (Ulsan National Institute of Science & Technology), Dongyoung Lim, Sungil Kim
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2024 IISE DAIS Best Student Paper Competition
Session chairs: Cesar Ruiz, Xiaoyu Chen, Ashif Iqubeal
Sunday, May 19 12:00PM-1:20PM
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Asymptotic Behavior of Adversarial Training Estimator under l∞ Perturbation by Yiling Xie (Georgia Institute of Technology), Xiaoming Huo
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Boosting Discriminability of Transferable Features in Unsupervised Domain Adaptation by Abdur Rahman (Mississippi State University), Mohammad Marufuzzaman, and Haifeng Wang
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Optimal Sensor Allocation for Emission Source Detection with Linear Atmospheric Dispersion Processes by Xinchao Liu (Georgia Institute of Technology), Dzung Phan, Youngdeok Hwang, Levente Klein, Kyongmin Yeo, and Xiao Liu
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One Step Beyond Linear: An Integrated Prediction-and-Optimization Framework with Rectified-Linear Objectives by Haoran Guo (Tsinghua University), Meng Qi, Wei Qi
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2024 DAIS Mobile/Web App Competition
Session chair: Xiaowei Yue
Sunday, May 19 3:00PM- 4:20PM
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MTrip, Your Personalized Multi-Modal Trip Planner, Angineh Keshishian (University of Pennsylvania), Xiaoyu (Lareina) Liu, Lingchao Mao, Wenting Zheng
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Go-Repair: Resilient Infrastructure Learning Game to Evaluate Restoration Decisions, Nischal Newar (George Mason University), Shima Mohebbi, Pavithra Sripathanallur Murali
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TextGenius: Generative AI App for a Seamless Conversation with Text Data, Mohammed-Khalil Ghali (Binghamton University), Yu (Chelsea) Jin
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The SmartSHOTS App: Reducing Barriers to Immunizations, Aliza Sharmin (University of Tennessee, Knoxville), Jose Tupayachi, Xudong Wang, Xueping Li, Victoria Niederhouser
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2024 DAIS Data Analytics Competition
Session chair: Yinan and Xiaoyu
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nnY-Net: Swin-NeXt with Cross-Attention for 3D Medical Images by Haixu Liu (The University of Sydney), Zerui Tao, Wenzhen Dong, Qiuzhuang Sun
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A Holistic Weakly Supervised Approach for Liver Tumor Segmentation by Hairong Wang (Georgia Institute of Technology), Lingchao Mao, Zihan Zhang
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Liver Tumor Segmentation by Arushi Agarwal (State University of New York at Binghamton), Rahul Gupta, Shreya Agarwal
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Liver Tumor Segmentation by Galla Shashank (Texas A&M University), Hanchate Abhishek Karthikeyan Adithyaa, Nakkina Ganatma
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Other Joint and Special Sessions |
Meet the Editors & Research Highlights from IISE Transactions on Healthcare Systems Engineering
Sunday, May 19, 8:00AM-9:20AM, Room 513B
Session Speakers/Panelists/Moderator: Dr. Hui Yang, The Pennsylvania State University; Dr. Jing Li, Georgia Institute of Technology; Dr. Paul Griffin, The Pennsylvania State University; Dr. Nathan Gaw, Air Force Institute of Technology; Dr. Murat Erkoc, University of Miami; Dr. Adam Meyers, University of Miami
AI Accelerated Sustainability: How ISEs Using AI Can Support Achievement of the UN Sustainable Development Goals -- Panel Discussion
Sunday, May 19, 3:00PM-4:20PM, Room 518B
Session Speakers/Panelists/Moderator: Dr. Chen Zhou, Georgia Institute of Technology; Dr. Joe Wilck, Bucknell University; Dr. Yisha Xiang, University of Houston; John M. Corliss, Jr., PE, PEER Consultants, P.C. ; Dr. Adam Meyers, University of Miami.
Generative AI and Chat GPT - Where are we now?
Sunday, May 19, 4:30PM-5:50PM, Room 513B
Session Speakers/Panelists/Moderator: Dr. Joe Wilck, Bucknell University
A Gallery of Network Science Projects in ISE
Monday, May 20, 8:00AM-9:20AM, Room 513B
Session Speakers/Panelists/Moderator: Dr. Na Zou, University of Houston; Dr. Amro Farid, Stevens Institute of Technology; Dr. Dan Li, Clemson University; Dr. Cheng-Bang Chen, University of Miami; Jinming Wan, Binghamton University; Dr. Adam Meyers, University of Miami
Artificial Intelligence & Machine Learning in the Financial Technology (FinTech) Industry
Monday, May 20, 12:00PM-1:20PM, Room 513B
Session Speakers/Panelists/Moderator: Dr. Xue Han, Plaid
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Call for Participants: DAIS & QCRE Mentoring Program |
The Data Analytics & Information Systems (DAIS) Division and Quality Control & Reliability Engineering (QCRE) Division of the Institute of Industrial and Systems Engineers (IISE) are pleased to announce the launch of our mentorship program, specifically tailored for professionals in the fields of Data Science, Information Systems, Quality Science and Reliability Engineering. This program has been established with the vision to bridge knowledge gaps, foster collaboration, and drive personal and professional growth. The program's specific goals are to connect those who may need mentoring (such as students or early-career engineers) with those who are willing to mentor and facilitate those interactions.
How to Apply? If you are interested in serving as a mentor or being a mentee, please fill out the application form linked. Application Form Link: https://forms.gle/gfM4Uy6U8q5sjzXo6.
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We have had many great webinars this year! We appreciate the time and effort from the webinar committee and the wonderful presentations given by the speakers below.
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Dr. Mostafa Reisi Gahrooei, Tensor Data Analysis for Modeling High-Dimensional Distributed Data
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Dr. Hongyue Sun, Data Science Enabled Decision-making in Advanced Manufacturing and Personalized Safety
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Dr. Xiaoyu Chen, Sparse Regression Analysis of Mixed Multi-Responses
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Dr. Ashif Iquebal, Advancing and Accelerating Qualification and Characterization through Stochastic Inverse Modeling
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Dr. Nathan Gaw, Assessing the Calibration and Performance of Attention-based Spatiotemporal Neural Networks for Lightning Prediction
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Dr. Hui Yang, Digital Twin for Quality Innovations: Manufacturing and Health Applications
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Research Gallery (Graduate Students)
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Integrating HIV and HPV: A Novel Approach with Hybrid Agent-Based and Compartmental Simulation Methods by Xinmeng Zhao, Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst
Summary: The traditional single-disease model might lead to inaccurate estimations of an intervention's effectiveness, as it fails to consider the syndemic nature of diseases. This oversight is particularly evident in sexually transmitted diseases (STDs) like human papillomavirus (HPV) and human immunodeficiency virus (HIV), where co-infection can compromise the immune response to either virus, or shared behavioral factors influence their transmission. Often, it is the underlying social conditions, such as mental health challenges, poverty, and housing instability, that drive these risky behaviors. Recognizing these complexities and existing computational challenges, a new mixed agent-based network and compartmental (MAC) simulation framework has been developed. This model simulates individuals with less common diseases (here HIV, and thus HPV and cervical cancer in persons with HIV) in a network illustrated on the left side of the graph, while modeling all others (including those with only HPV) are represented within a compartment model shown on the right side. After calibrating and validating the model with U.S. disease burden data, we quantified the fraction of biological and behavioral risks contributing to the HPV disease burden among HIV-positive women. The findings highlight the necessity for comprehensive interventions: biomedical measures like vaccination, screening, and treatment are crucial for mitigating biological risks, while addressing behavioral risks calls for structural changes through enhanced social support, educational programs, and access to affordable housing.
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ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems by Yue Zhao, Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute
Summary: Machine learning (ML) methods are widely used in manufacturing applications, which usually require a large amount of training data. However, data collection needs extensive costs and time investments in the manufacturing system, and data scarcity commonly exists. With the development of the industrial internet of things (IIoT), data-sharing is widely enabled among multiple machines with similar functionality to augment the dataset for building ML models. Despite the machines being designed similarly, the distribution mismatch inevitably exists in their data due to different working conditions, process parameters, measurement noise, etc. However, the effective application of ML methods is built upon the assumption that the training and testing data are sampled from the same distribution. Thus, an intelligent data-sharing framework is needed to ensure the quality of the shared data such that only beneficial information is shared to improve the performance of ML methods. In this work, we propose an Active Data-sharing (ADs) framework to ensure the quality of the shared data among multiple machines. It is designed as a self-supervised learning framework by integrating the architecture of contrastive learning (CL) and active learning (AL). A novel acquisition function is developed for active learning by integrating the information measure for benefiting downstream tasks and the similarity score for data quality assurance. To validate the effectiveness of the proposed ADs framework, we collected real-world in-situ monitoring data from three 3D printers, two of which share identical specifications, while the other one is different. The results demonstrated that our ADs framework could intelligently share monitoring data between identical machines while eliminating the data points from the different machines when training ML methods. With a high-quality augmented dataset generated by our proposed framework, the ML methods can achieve a better performance of accuracy 95.78% when utilizing 26% labeled data. This represents an improvement of 1.41% compared with benchmark methods which used 100% labeled data.
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Have you considered a corporate membership? If not, why? Companies large and small enjoy the benefits. Include your entire team and receive all the benefits of individual membership, plus lowest rates to attend the Conferences and much more.
Contact Doug Long IISE Global Director for Strategic Alliances for more details.
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