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Publications
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Harnessing the Power of Machine Learning to Solve Global Problems
Harnessing the Power of Machine Learning to Solve Global Problems
This work was presented as part of the honorary symposium for Professor Winston (Wole) Soboyejo at TMS 2024.
Price, S., Soboyejo, W., Neamtu, R., Harnessing the Power of Machine Learning to Solve Global Problems, In: Materials Science for Global Development—Health, Energy, and Environment: An SMD Symposium in Honor of Wole Soboyejo. (2024)
Data-Driven Optimization of Wire Arc DED Manufacturing Conditions for Improved Bead Shape Prediction
Data-Driven Optimization of Wire Arc DED Manufacturing Conditions for Improved Bead Shape Prediction
This work won first place in the Graduate Poster Competition (Additive Manufacturing) at TMS 2024!
This work won first place in the Graduate Poster Competition (Additive Manufacturing) at TMS 2024!
Price, S., Tsaknopoulos, K., Cote, D.L. and Neamtu, R., 2024. Data-Driven Optimization of Wire Arc DED Manufacturing Conditions for Improved Bead Shape. In: TMS 2024, March 3-7, Orlando, FL. (2024)
Price, S., Tsaknopoulos, K., Cote, D.L. and Neamtu, R., 2024. Data-Driven Optimization of Wire Arc DED Manufacturing Conditions for Improved Bead Shape. In: TMS 2024, March 3-7, Orlando, FL. (2024)
Advancing Wire Arc Directed Energy Deposition: Analyzing Impact of Materials and Parameters on Bead Shape
Advancing Wire Arc Directed Energy Deposition: Analyzing Impact of Materials and Parameters on Bead Shape
This work was accepted and published in 2024 by Metals.
This work was accepted and published in 2024 by Metals.
Price, S., Judd, K., Gleason, M., Tsaknopoulos, K., Cote, D. L., & Neamtu, R. (2024). Advancing Wire Arc Directed Energy Deposition: Analyzing Impact of Materials and Parameters on Bead Shape. Metals, 14(3), 282.
Price, S., Judd, K., Gleason, M., Tsaknopoulos, K., Cote, D. L., & Neamtu, R. (2024). Advancing Wire Arc Directed Energy Deposition: Analyzing Impact of Materials and Parameters on Bead Shape. Metals, 14(3), 282.
Using machine learning for prediction of spray coated perovskite solar cells efficiency: From experimental to theoretical models
Using machine learning for prediction of spray coated perovskite solar cells efficiency: From experimental to theoretical models
This work was accepted and published in 2023 by Materials and Design.
This work was accepted and published in 2023 by Materials and Design.
Ichwani, R., Price, S., Oyewole, O. K., Neamtu, R., & Soboyejo, W. O. (2023). Using machine learning for prediction of spray coated perovskite solar cells efficiency: From experimental to theoretical models. Materials & Design, 233, 112161.
Ichwani, R., Price, S., Oyewole, O. K., Neamtu, R., & Soboyejo, W. O. (2023). Using machine learning for prediction of spray coated perovskite solar cells efficiency: From experimental to theoretical models. Materials & Design, 233, 112161.
Multiple-Instance Regression for Metal Powder Hall Flow Rate Prediction Using Augmented Particle Size and Shape Data
Multiple-Instance Regression for Metal Powder Hall Flow Rate Prediction Using Augmented Particle Size and Shape Data
This work was accepted and published in 2023 by Powders.
This work was accepted and published in 2023 by Powders.
Schuliger, A., Price, S., Sousa, B. C., Cote, D. L., & Neamtu, R. (2023). Multiple-Instance Regression for Metal Powder Hall Flow Rate Prediction Using Augmented Particle Size and Shape Data. Powders, 2(1), 189-204.
Schuliger, A., Price, S., Sousa, B. C., Cote, D. L., & Neamtu, R. (2023). Multiple-Instance Regression for Metal Powder Hall Flow Rate Prediction Using Augmented Particle Size and Shape Data. Powders, 2(1), 189-204.
Using Machine Learning to Address Real-World Problems
Using Machine Learning to Address Real-World Problems
This work was accepted and presented at the 2022 Nobel Symposium "Predictability in Science in the Age of AI" by Professor Winston (Wole) Soboyejo.
Ichwani, R., Price, S., Oyewole, K.O., Neamtu, R., Soboyejo, W., Using Machine Learning to Address Real-World Problems, Nobel Symposium: Predictability in Science in the Age of AI, Royal Swedish Academy of Science, 2022.
Application of Convolutional Neural Network Modeling to Metallic Powder Particle Satellite Detection and Segmentation
Application of Convolutional Neural Network Modeling to Metallic Powder Particle Satellite Detection and Segmentation
This work was accepted and presented as a poster presentation 2022 Material Science and Technology Conference (MS&T) in the Undergraduate Poster Competition.
This work was accepted and presented as a poster presentation 2022 Material Science and Technology Conference (MS&T) in the Undergraduate Poster Competition.
Price, S., Neamtu, R., Cote, D.: Application of Convolutional Neural Network Modeling to Metallic Powder Particle Satellite Detection and Segmentation. In: Material Science and Technology 2022, October 9-12, Pittsburgh, PA. (2022)
Price, S., Neamtu, R., Cote, D.: Application of Convolutional Neural Network Modeling to Metallic Powder Particle Satellite Detection and Segmentation. In: Material Science and Technology 2022, October 9-12, Pittsburgh, PA. (2022)
Identifying, Evaluating, and Addressing Nondeterminism in Mask R-CNNs
Identifying, Evaluating, and Addressing Nondeterminism in Mask R-CNNs
This work was accepted and presented at the 3rd International Conference on Pattern Recognition and Artificial Intelligence in June of 2022, and published in Lecture Notes of Computer Science.
This work was accepted and presented at the 3rd International Conference on Pattern Recognition and Artificial Intelligence in June of 2022, and published in Lecture Notes of Computer Science.
Price, S., Neamtu, R.: Identifying, evaluating, and addressing nondeterminism in mask r-CNNs. In: Pattern Recognition and Artificial Intelligence, pp. 3–14. Springer International Publishing (2022)
Price, S., Neamtu, R.: Identifying, evaluating, and addressing nondeterminism in mask r-CNNs. In: Pattern Recognition and Artificial Intelligence, pp. 3–14. Springer International Publishing (2022)
Clustering Algorithms for Nanomechanical Property Mapping and Resultant Microstructural Constituent and Phase Quantification
Clustering Algorithms for Nanomechanical Property Mapping and Resultant Microstructural Constituent and Phase Quantification
This work was accepted and published in January of 2022 by The Minerals, Metals & Materials Series:
This work was accepted and published in January of 2022 by The Minerals, Metals & Materials Series:
Investigating the Suitability of Tableau Dashboards and Decision Trees for Particulate Materials Science and Engineering Data Analysis
Investigating the Suitability of Tableau Dashboards and Decision Trees for Particulate Materials Science and Engineering Data Analysis
This work was accepted and published in January of 2022 by The Minerals, Metals & Materials Series:
This work was accepted and published in January of 2022 by The Minerals, Metals & Materials Series:
Automated and Refined Application of Convolutional Neural Network Modeling to Metallic Powder Particle Satellite Detection
Automated and Refined Application of Convolutional Neural Network Modeling to Metallic Powder Particle Satellite Detection
This work was accepted and published in November of 2021 by Integrating Materials and Manufacturing Innovation:
This work was accepted and published in November of 2021 by Integrating Materials and Manufacturing Innovation:
Classifying Powder Flowability for Cold Spray Additive Manufacturing Using Machine Learning
Classifying Powder Flowability for Cold Spray Additive Manufacturing Using Machine Learning
This publication was accepted by the 2020 IEEE Big Data Conference.
This publication was accepted by the 2020 IEEE Big Data Conference.
Link to video presentation: https://wpi0.sharepoint.com/sites/gr-Materials-DataScience/Shared%20Documents/General/BigDataPresentation.mp4
Link to video presentation: https://wpi0.sharepoint.com/sites/gr-Materials-DataScience/Shared%20Documents/General/BigDataPresentation.mp4
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