News Archive

A systematic review of time-series algorithms in predictive maintenance was accepted in Decision Analytics Journal.

Our promptable, label-free porosity segmentation work was accepted in npj Advanced Manufacturing.

A comprehensive statistical and time-series analysis of CO2 emissions in high-human-development-index countries was published in Sustainability.

Our physics-infused neural-network study for enhanced marine vessel tracking was published in the Journal of Marine Science and Engineering.

Our CTGAN-based data augmentation study for predicting paper breaks in pulp-and-paper production was published in Manufacturing Letters.

Our threshold-driven hybrid acquisition policy for accelerating material discovery was published in Manufacturing Letters.

Hamed Khosravi earned third place in the INFORMS Best Poster Competition for work on Binary Gaussian Copula Synthesis for early prediction of chronic kidney disease.

Hamed Khosravi and his team were selected as finalists in the INFORMS QSR Manufacturing AI Competition and presented at the 2024 INFORMS Annual Meeting.

Research led by Ahmed Shoyeb Raihan was selected as a finalist in the NAMRC 52 Best Student Research Paper Competition.

An augmented surprise-guided sequential learning framework for predicting melt-pool geometry was published in the Journal of Manufacturing Systems.

Our N3C study of new schizophrenia-spectrum and psychotic-disorder diagnoses following SARS-CoV-2 infection was published in PLOS ONE.

Our study of bottleneck management through strategic sequencing in smart manufacturing systems was published in Smart and Sustainable Manufacturing Systems.

A collaborative WVU and UTSA study of aerial drone fleet deployment with endogenous battery replacement was published in Expert Systems with Applications.

Hamed Khosravi received an NSF travel award to attend NAMRC 2024 in Knoxville, Tennessee.

Ahmed Shoyeb Raihan and Hamed Khosravi received Researcher of the Year awards from the Statler College.

A cluster-based human-resources analytics study for predicting employee turnover using optimized neural networks and data augmentation was published in Decision Analytics Journal.

A data-driven sequential learning framework for multi-objective manufacturing decisions was published in the Journal of Intelligent Manufacturing.

Our bibliometric and systematic review of chatbots and ChatGPT was accepted in the International Journal of Data Mining, Modelling and Management.

A CNN-LSTM architecture for marine vessel track association using Automatic Identification System data was published in Sensors.

A Bi-LSTM autoencoder framework for anomaly detection in wind-power data was accepted at the IEEE Conference on Automation Science and Engineering.

Four students from the Systems Analytics Lab presented their research at the 2023 IISE Annual Conference in New Orleans.

Our review of federated learning in manufacturing and Industry 4.0 and 5.0 challenges was accepted at the 8th IEOM North American Conference.

WVU Research and Scholarship Grants funded the project Psychiatric Sequelae of COVID-19: Identifying Contributing Factors and Developing a Data-Driven Predictive Framework.

Tasmiah Haque successfully defended her MS thesis.

Two lab papers were accepted for the 2023 IISE Annual Conference Proceedings, covering Bayesian optimization for autonomous experimentation and multi-model LSTM track association.

The U.S. Environmental Protection Agency funded the project A Novel and Integrated Approach to Enhance Adoption of Pollution Prevention Practices, led by Dr. Ashish Nimbarte with Imtiaz Ahmed as Co-I.

A poster presented by Ahmed Shoyeb Raihan won first place at the 2022 INFORMS Annual Meeting poster competition.

Hamed Khosravi and Maryam Ramazani joined the Systems Analytics Lab as PhD students.

Our marine-vessel tracking paper was accepted in IEEE Transactions on Intelligent Transportation Systems.

Ahmed Shoyeb Raihan received a $500 travel award to present autonomous experimentation research during WVU Statler College Research Week.

Organized the invited session Advanced Machine Learning Techniques in Manufacturing Systems at the 2021 INFORMS Annual Meeting.

Ahmed Shoyeb Raihan joined the Systems Analytics Lab as a PhD student.

Organized the invited session Sequential Decision Making under Uncertainty in the QCRE track at the 2021 IISE Annual Conference.

Accepted a tenure-track Assistant Professor position in Industrial and Management Systems Engineering at West Virginia University, beginning Fall 2021.

Our graph-regularized autoencoder paper was accepted in IEEE Transactions on Pattern Analysis and Machine Intelligence.

The paper containing a major portion of my doctoral research was accepted in the Journal of Machine Learning Research.

Welcomed my daughter, Ereshva.

Presented research at the virtual 2020 INFORMS Annual Meeting.

Presented research at the virtual 2020 IISE Annual Conference and Expo.

Began work as a Postdoctoral Research Associate in the Department of Industrial and Systems Engineering at Texas A&M University.

Graduated from Texas A&M University with a PhD in Industrial Engineering.

Successfully defended my PhD dissertation.

Started a new project on surprise observations and their relationship with Bayesian optimization.

Started a project using NSF ATD Challenge datasets with Dr. Mikyoung Jun from the Texas A&M Statistics Department.

Completed work on graph-regularized deep-learning methods for anomaly detection.

Won first place in the INFORMS Poster Competition at the 2019 INFORMS Annual Meeting in Seattle.

Presented research at the 2019 INFORMS Annual Meeting in Seattle.

Participated in the QSR Student Interaction and Poster Session at the 2019 INFORMS Annual Meeting.

Received the Best Poster Presentation Award at the 2019 Texas A&M Conference on Energy.

Selected as a finalist in the 2019 INFORMS Best Student Poster Competition.

Received a doctoral student colloquium travel grant from the Texas A&M Department of Industrial and Systems Engineering.

Presented a poster at the 2019 Foundations of Data Science Summer School at Georgia Tech.

Selected to participate in the NSF-sponsored 2019 Foundations of Data Science Summer School at Georgia Tech.

Won the 2019 QCRE Best Student Paper Competition at the IISE Annual Conference in Orlando.

Presented research in a QCRE invited session at the 2019 IISE Annual Conference in Orlando.

A conference paper was accepted for the 15th IEEE Conference on Automation Science and Engineering.

Selected as one of four finalists in the 2019 QCRE Best Student Paper Competition.

Received a $1,000 Texas A&M Institute of Data Science Graduate Travel Grant.

Started work on dimensionality reduction using an autoencoder with neighborhood regularization.

Completed research on online algorithms for anomaly detection.

Submitted O-LoMST: An Online Anomaly Detection Approach and Its Application in a Hydropower Generation Plant to IEEE CASE.

Passed the PhD preliminary examination and defended my dissertation proposal.

Presented my second doctoral research project at the 2018 INFORMS Annual Meeting in Phoenix.

Started work on online algorithms for anomaly detection.

Completed and submitted work on neighborhood-structure-assisted nonnegative matrix factorization for unsupervised point anomaly detection.

Our journal paper was accepted in IEEE Transactions on Automation Science and Engineering after two rounds of revision.

A conference paper was accepted for publication in the conference proceedings.

Submitted a conference paper on outlier detection in hydropower generation plants to IEEE CASE.

Presented my first doctoral research project at the 2017 INFORMS Annual Meeting in Houston.

Started a collaborative research project with Dr. Xia Ben Hu from the Texas A&M Department of Computer Science and Engineering.

Completed a summer internship project at ABB Corporate Research Center and returned to Texas A&M.

Submitted research on minimum-spanning-tree-based unsupervised anomaly detection for hydropower turbines to IEEE Transactions on Automation Science and Engineering.

Joined ABB Corporate Research Center as a summer research intern.

Completed the first research project of my doctoral program.

Started an ABB and Texas A&M collaborative project on algorithms for unsupervised anomaly detection.

Completed a summer internship project at ABB Corporate Research Center and returned to Texas A&M.

Passed the Texas A&M Industrial and Systems Engineering PhD qualifying examination.

Joined ABB Corporate Research Center as a summer research intern.

Joined Dr. Yu Ding's research group as a graduate research assistant in Industrial Data Science.

Joined the PhD program in Industrial and Systems Engineering at Texas A&M University.