Publications and Presentations

Please click a title in blue to view the abstract.  Names with underlines are current or former students under my supervision.

*** Author's post print for preview.  Copyright owned by the journal.  For official academic use, please refer to the journal's copy.

Refereed Journals and Transactions (Published or Accepted)

1. Jin, R., Li, J. and Shi, J., 2007, “Quality Prediction and Control in Rolling Processes using Logistic Regression,” NAMRI/SME Transactions, No.35, pp. 113-120.

2. Izquierdo, L., Hu, J., Du, H., Jin, R., Jee, H. and Shi, J., 2009, “Robust Fixture Layout Design for a Product Family Assembled in a Multistage Reconfigurable Line,” ASME Transactions, Journal of Manufacturing Sciences and Engineering, Vol. 131, pp. 041008.

3. Zhao, H., Jin, R., Wu, S. and Shi, J., 2011, “PDE-constrained Gaussian Process Model on Material Removal Rate of Wire Saw Slicing Process,” ASME Transactions, Journal of Manufacturing Sciences and Engineering, Vol. 133, 2, pp. 021012.

4. Jin, R. and Shi, J., 2012, “Reconfigured Piecewise Linear Regression Tree for Multistage Manufacturing Process Control,” IIE Transactions, Vol. 44, 4, pp. 249-261. ***Author's post-print copy

5. Jin, R., Chang, C.J. and Shi, J., 2012, “Sequential Sensing Strategy of Wafer Profiles Using Gaussian Process Model,” IIE Transactions, Vol. 44, 1, pp. 1-12. (Best Applied Paper Award in IIE Transactions Quality and Reliability Engineering 2012) ***Author's post-print copy

6. Jin, R. and Liu, K., 2013, “Multimode Variation Modeling and Process Monitoring for Serial-Parallel Multistage Manufacturing Processes,” IIE Transactions, Special Issue on Integration of Manufacturing System Design and Quality Management, Vol. 45, pp. 617-629. ***Author's post-print copy          

7. Plumlee, M., Jin, R., Joseph, R.V. and Shi, J., 2013, “Gaussian Process Modeling for Engineered Surfaces with Applications to Si Wafer Production,” Stat, Vol. 2, pp. 159-170. ***Author's post-print copy

8. Bao, L., Wang, K. and Jin, R., 2014, “A Hierarchical Model for Characterizing Spatial Wafer Variations,” International Journal of Production Research, Vol. 52 (6), pp. 1827-1842. ***Author's post-print copy

9. Zhang, J., Li, W., Wang, K., and Jin, R., 2014, “Process Adjustment with an Asymmetric Quality Loss Function,” Journal of Manufacturing Systems, Vol. 33(1), pp. 159-165.

10. Dai, C., Wang, K. and Jin. R., 2014, “Monitoring Profile Trajectories with Dynamic Time Warping Alignment,” Quality and Reliability Engineering International, Vol. 30(6), pp. 815-827. ***Author's post-print copy

11. Jin, R. and Deng, X., 2015, “Ensemble Modeling for Data Fusion in Manufacturing Process Scale-up,” IIE Transactions, Vol. 47 (3), pp. 203-214. ***Author's post-print copy

12. Deng, X. and Jin, R., 2015, “QQ Models: Joint Modeling for Quantitative and Qualitative Quality Responses in Manufacturing Systems,” Technometrics, Vol. 57 (3), pp. 320-331. ***Author's post-print copy

13. Xu, Z., Hong, Y. and Jin, R., 2015, “Nonlinear General Path Models for Degradation Data with Dynamic Covariates,” Applied Stochastic Models in Business and Industry, Vol. 32 (2), pp. 153-167.

14. Sun, H., Luo, S., Jin, R. and He, Z., 2015, “A Multi-Task Lasso Model for Investigating Multi-module Design Factors, Operational Factors and Covariates in Tubular Microbial Fuel Cells,” ACS Sustainable Chemistry and Engineering, Vol. 3 (12), pp. 3231–3238.

15. Luo, S., Sun, H., Ping, Q., Jin, R. and He, Z., 2016, “A Review of Modeling Bioelectrochemical System: Engineering and Statistical Aspects,” Energies, Vol. 9 (2), pp. 111. ***Author's post-print copy

16. Sun, H., Deng, X., Wang, K., and Jin, R., 2016, “Logistic Regression for Crystal Growth Process Modeling through Hierarchical Nonnegative Garrote based Variable Selection,” IIE Transactions, Vol. 48 (8), pp. 787-796. (Best Student Paper Award Finalist, Quality Control and Reliability Engineering Track, Industrial and Systems Engineering Research Conference 2014) ***Author's post-print copy

17. Zang, Y., Wang, K. and Jin, R., 2016, “Unaligned Profile Monitoring using Penalized Methods,” Quality and Reliability Engineering International, Vol. 32 (8), pp. 2761-2776. ***Author's post-print copy

18. Tian, W., Jin, R., Huang, T., and Camelio, J., 2017, “Statistical Process Control for Multistage Processes with Non-repeating Cyclic Profiles,” IISE Transactions, Vol. 49 (3), pp. 320-331. (Best Student Paper Award Finalist, Process Industries Track, Industrial and Systems Engineering Research Conference 2015)

19. Chen, X. and Jin, R., 2017, “Statistical Modeling for Visualization Evaluation through Data Fusion,” Applied Ergonomics, Special Issues on New Technologies in Human Factors and Ergonomics Research and Practice, 65: 551-561.

20. Sun, H., Wang, K., Li, Y., Zhang, C. and Jin, R., 2017, “Quality Modeling of Printed Electronics in Aerosol Jet Printing based on Microscopic Images,” ASME Transactions, Journal of Manufacturing Sciences and Engineering, Vol. 139(7), 071012.

21. Sun, H., Luo, S., Jin, R., and He, Z., 2017, “Ensemble Engineering and Statistical Modeling for Parameter Calibration towards Optimal Design of Microbial Fuel Cells,” Journal of Power Sources, 356: 288-298. 

22. Sun, H., Huang, S. and Jin, R., 2017, “Functional Graphical Models for Manufacturing Process Modeling,” IEEE Transactions on Automation Science and Engineering, Vol. PP(99), pp.1-10.

23. Li, Y., Mohan, K., Sun, H., and Jin, R., 2017, “Ensemble Modelling of in situ Feature Variables for Printed Electronics Manufacturing with in situ Process Control Potential,” IEEE Robotics and Automation Letters 2(4): 1864–1870. 

24. Sun, H., Rao, P., Kong, Z., Deng, X., and Jin, R., 2017, “Functional Quantitative and Qualitative Models for Quality Modeling in a Fused Deposition Modeling Process,” IEEE Transactions on Automation Science and Engineering, Vol 15(1): 393-403.

25. Li, J., Jin, R., and Yu, H., 2018, “Integration of Physically-based and Data-driven Approaches for Thermal Field Prediction in Additive Manufacturing,” Materials and Design, Vol. 139:473-485.***Author's post-print copy

26. Liu, J., Jin, R., and Kong, Z., 2018, “Wafer Quality Monitoring using Spatial Dirichlet Process based Mixed-Effect Profile Modeling Scheme,” Journal of Manufacturing Systems, Vol.48 A: 21-32.

27. Lan, Q., Sun, H., Robertson, J., Deng, X., and Jin, R., 2018, “Non-invasive Assessment of Liver Quality in Transplantation based on Thermal Imaging Analysis,” Computer Methods and Programs in Biomedicine, Vol. 164: 31-47.

28. Kang X., Kang L., Deng X. and Jin, R., 2018, “Bayesian Hierarchical Model for Quantitative and Qualitative Responses,” Journal of Quality Technology, Vol. 50 (3): 290-308.

29. Jin, R., Deng, X., Chen, X., Zhu, L. and Zhang, J., 2019, “Dynamic Model for Quality-Process Relationship Considering Equipment Degradation,” Journal of Quality Technology, Vol. 51.(3): 217-229.

30. Li, Y., Jin, R., and Luo, Y., 2018, “Classifying Relations in Clinical Narratives using Segment Graph Convolutional and Recurrent Neural Networks (Seg-GCRNs),” Journal of the American Medical Informatics Association, Vol. 26 (3), 262-268.

31. Sun, H., Jin, R., and Luo, Y., 2020, “Super-SANMF: Supervised Subgraph Augmented Non-Negative Matrix Factorization for Manufacturing Time Series Data Analytics,” IISE Transactions, Vol. 52.(1): 120-131.

32. Li, Y., Sun, H., Deng, X., Zhang, C., Wang, H., and Jin, R., 2020, “Manufacturing Quality Prediction Using Smooth Spatial Variable Selection Estimator with Applications in AerosolJet® Printed Electronics Manufacturing,” IISE Transactions, Vol. 52.(3): 321-333.

33. Gao, Z., Du, P., Jin, R., and Robertson, J.L., “Surface Temperature Monitoring in Liver Procurementvia Functional Variance Change Point Analysis,” Annals of Applied Statistics,14(1), pp.143-159.

34. Huang, W., Chen, X., Jin, R.,  and Lau, N., 2020, “Detecting Cognitive Hacking in Visual Inspection with Physiological Measurements”, Applied Ergonomics, Vol. 84: 103022.

35. Wang, L., Chen, X., Kang, S., Deng, X., and Jin, R., 2020 "Meta-modeling of High-fidelity FEA Simulation for Efficient Product and Process Design in Additive Manufacturing," Additive Manufacturing (2020): 101211.

36. Kang, X., Chen, X., Jin, R., Wu, H., and Deng, X., 2020 "Multivariate Regression of Mixed Responses for Evaluation of Visualization Designs," IISE Transactions (2020), 1-13.

37. Chen, X., Lau, N., and Jin, R., 2021 "PRIME: A Personalized Recommender System for Information Visualization Methods via Extended Matrix Completion," ACM Transactions on Interactive Intelligent Systems, 11(1), 1-30.

38. Chen, X. and Jin, R., 2021 "AdaPipe: A Recommender System for Adaptive Computation Pipelines in Cyber-Manufacturing Computation Services," IEEE Transactions on Industrial Informatics, vol. 17, no. 9, pp. 6221-6229.

39. Wang, L., Chen, X., Daniel, H., and Jin, R., 2020 "Family Learning: A Process Modeling Method for Cyber-Additive Manufacturing Network," IISE Transactions (2020), 1-20. 

40. Wang, L., Du, P., and Jin, R., 2020. “ MOSS: Multi-modal Best Subset Modeling in Smart Manufacturing”, Sensors, Accepted. ***Author's post-print copy

41. Li, Y., Deng, X., Ba, S., Myers, W., Brenneman, W., Lange, S., Zink, R., and Jin, R., 2021 "Cluster-based Data Filtering for Manufacturing Big Data Systems," Journal of Quality Technology(2021): 1-13. ***Author's post-print copy

42. Zeng, Y., Chen, X., Deng, X., and Jin, R., 2021 "A Prediction-Oriented Optimal Design for Visualization Recommender Systems," Statistical Theory and Related Fields, 5(2), 134-148. ***Author's post-print copy

43. Wang, L., Chen, X., Henkel, D., and Jin, R., 2021 "Pyramid Ensemble Convolutional Neural Network for Virtual Computed Tomography Image Prediction in a Selective Laser Melting Process," ASME Journal of Manufacturing Science and Engineering, 143(12), 121003. 

44. Kang, S., Deng, X., and Jin, R., 2021 "A Cost-Efficient Data-Driven Approach to Design Space Exploration for Personalized Geometric Design in Additive Manufacturing," ASME Journal of Computing and Information Science in Engineering 21(6), 061008. 

45. Shojaee, P., Chen, X., and Jin, R., 2021 "Adaptively Weighted Top-N Recommendation for Organ Matching," ACM Transactions on Computing for Healthcare, 2021, 3(1):1-29.

46. Lan, Q., Li, Y., Robertson, J., and Jin, R., 2021 "Modeling of Pre-transplantation Liver Viability with Spatial-temporal Smooth Variable Selection", Computer Methods and Programs in Biomedicine 208 (2021): 106264.

47. Kang, S., Jin, R., Deng, X., and Kenett, Ron S., 2021 "Challenges of Modeling and Analysis in Cybermanufacturing: A Review from a Machine Learning and Computation Perspective", Journal of Intelligent Manufacturing, 1-14.  ***Author's post-print copy

48. Li, Y.**, Wang, L., Lee, D., and Jin, R., “Monitoring Runtime Metrics of Fog Manufacturing via a Qualitative and Quantitative (QQ) Control Chart,” ACM Transactions on Internet of Things,  3(2), 1-19, (IF: 3.135 2021).

49. Jin, R., “Commentary on ‘Visualization in Operations Management Research’ (3 pages),” INFORMS Journal on Data Science, 1(2), 194-195.

50. Chen, X.**, Zeng, Y., Kang, S., and Jin, R., “INN: An Interpretable Neural Network for AI Incubation in Manufacturing,” ACM Transactions on Intelligent Systems and Technology, (IF: 4.654 2020), 2022, 13(5):1-23.

51. Li, Y.**, Yan, H., and Jin, R., “Multi-task Learning with Latent Variation Decomposition for Multivariate Responses in a Manufacturing Network,” IEEE Transactions on Automation Science and Engineering, 20(1), 285-295 (IF: 4.938 2020).

52. Zeng, Y., Shohan, S., Shirwaiker, R., and Jin, R., “Investigating Dielectric Spectroscopy and Soft Sensing for Nondestructive Quality Assessment of Engineered Tissues", Biosensors and Bioelectronics, 2022, 216. ***Author's post-print copy

53. Li, Y.**, Wang. L., Chen, X., Jin, R., "Distributed Data Filtering and Modeling for Fog and Networked Manufacturing",  IISE Transactions (2023), 1-21.

54. Li, Y., Yan, H., and Jin, R.**, “Multi-task Learning with Latent Variation Decomposition for Multivariate Responses in a Manufacturing Network,” IEEE Transactions on Automation Science and Engineering, (IF: 4.938 2020), 20 (1), February 14, 2022. DOI: 10.1109/TASE.2022.3148977

55. Zeng, Y. +, Shohan, S. +, Chen, X., Jin, R.**, and Shirwaiker R.A.**, “Investigating Dielectric Spectroscopy and Soft Sensing for Nondestructive Quality Assessment of Engineered Tissues,” Biosensors and Bioelectronics, (IF: 10.618 2021), 215, Nov. 15, 2022.

56. Chen, X., Kang, X., Jin, R., and Deng, X.**, “Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing,” Technometrics, (IF: 2.988 2020), 65, 206-219, Oct 31, 2022, https://doi.org/10.1080/00401706.2022.2134928

57. Kang, L., Deng, X., and Jin, R., “Bayesian D-Optimal Design of Experiments with Quantitative and Qualitative Responses,” the New England Journal of Statistics in Data Science, 1, 3, 1-15, April 21, 2023, https://doi.org/10.51387/23-NEJSDS30.

58. Li, Y.**, Wang, L., Chen, X., and Jin, R., “Distributed Data Filtering and Modeling for Fog and Networked Manufacturing,” IISE Transactions, (IF: 2.90 2020), published online: April 5, 2023.

59. Zeng, Y., Chen, X., and Jin, R.**, “Ensemble Active Learning by Contextual Bandits for Artificial Intelligence Incubation in Manufacturing,” ACM Transactions on Intelligent Systems and Technology, (IF: 4.654 2020), 15, 1, 1-26, February 29, 2024.  

60. Lan, Q., Chen, X., Li, M., Robertson, J., Lei, Y., and Jin, R. **, “Improving Assessment in Kidney Transplantation by Multitask General Path Model,” Computer Methods and Programs in Biomedicine Update, Accepted on 11/14/2023.

61. Wang, L., Wang, X., Ji, Q., Wang, L., and Jin, R.**, “Mutual Active Learning for Engineering Regulated Statistical Digital Twin Models,” IEEE Transactions on Industrial Informatics, (IF: 10.215 2020), Accepted on 11/29/2023.

62. Shen, S., Jin, R., and Deng, X.**, “Efficient Estimation and Selection for Regularized Dynamic Logistic Regression,” IISE Transactions, Accepted on 4/17/2024.

63. Chen, X., and Jin, R.**, “Lori: Local Low-rank Response Imputation for Automatic Configuration of Contextualized Artificial Intelligence,” IEEE Transactions on Industrial Informatics, (IF: 10.215 2020), Accepted on 07/08/2024.

Peer Reviewed Conference Proceedings

1. Izquierdo, L., Du, H., Hu, J., Jin, R., Shi, J. and Jee, H., 2006, “Robust Fixture Layout Design for a Product Family Assembled in a Multistage Reconfigurable Line,” ASME International Conference on Manufacturing Sciences and Engineering, October 8-11, 2006, Ypsilanti, Michigan, MSEC2006-21082, pp. 693-702.

2. Zhu, L., Dai, C., Sun, H., Li, W., Jin, R., and Wang, K., 2014. “Curve Monitoring for a Single-crystal Ingot Growth Process,” in Proceedings of the 5th International Asia Conference on Industrial Engineering and Management Innovation (IEMI2014), pp. 227-232. Atlantis Press.

3. Lan, Q., Jin, R. and Robertson, J., 2015, “Quantitative and Qualitative Evaluation for Organ Preservation in Transplant,” in Proceedings of Industrial and Systems Engineering Research Conference 2015. 

4. Sun, H., Jin, R. and Zimmerman, B., 2015, “Process Modeling and Mapping for a Plasma Spray Coating Process,” in Proceedings of Industrial and Systems Engineering Research Conference 2015. (Best Student Paper Award Finalist, Process Industries Track, Industrial and Systems Engineering Research Conference 2015) 

5. Chen, X., Sun, H. and Jin, R., 2016, “Variation Analysis and Visualization of Manufacturing Processes via Augmented Reality,” in Proceedings of Industrial and Systems Engineering Research Conference 2016. 

6. Li, Y., Mohan, K., Sun, H., and Jin, R., 2017, “Ensemble Modelling of in situ Feature Variables for Printed Electronics Manufacturing with in situ Process Control Potential,” IEEE CASE 2017. 

7. Chen, X., Wang, L., Wang, C., and Jin, R., 2018, “Predictive Offloading in Mobile-Fog-Cloud enabled Cyber-Manufacturing Systems,” IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) 2018.

8. Wang, L., Jin, R., and Henkel, D., 2018, “Data Fusion for in situ Layer-wise Modeling and Feedforward Control of Selective Laser Melting Processes,” in Proceedings of IISE Annual Conference 2018. ***Author's post-print copy

9. Chen, X., and Jin, R., 2018, “Data Fusion Pipelines for Autonomous Smart Manufacturing,” in Proceedings of IEEE CASE 2018. 

10. Zhang, Y., Wang, L., Chen, X., and Jin, R., 2019, “Fog Computing for Distributed Family Learning in Cyber-Manufacturing Modeling,” IEEE International Conference on Industrial Cyber-Physical Systems (ICPS).

11. Kang, S.**, Taylor, V., Okwei, M., Schultz, B., and Jin, R., “Rule Extraction to Identify Export Regulation Compliance of AM Parts,” in Proceedings of IISE Annual Conference 2020.

12. Zhang, Y., Wang, L., Chen, X., Lee, D., and Jin, R., 2020, “System Informatics and Hypothesis Tests of Significant Factors to Performance in A Fog Manufacturing System,” IISE Annual Conference & Expo 2020.

13. Wang, L., Zhang, Y., Chen, X., and Jin, R., 2020, “Online Computation Performance Analysis for Distributed Machine Learning Pipelines in Fog Manufacturing,” IEEE 16th International Conference on Automation Science and Engineering.

14. Wang, L., Zhang, Y., and Jin, R., 2020, “A Monitoring System for Anomaly Detection in Fog Manufacturing,” IEEE 3rd Conference on Industrial Cyberphysical Systems.

15. Shojaee, P., Zeng, Y., Chen, X., Jin, R., Deng, X., Zhang, C., 2021, "Deep neural network pipelines for multivariate time series classification in smart manufacturing," IEEE 4th International Conference on Industrial Cyberphysical Systems (ICPS). 

16. Nallendran, R.V., Wang, L., and Jin, R., 2021, “Predictive Offloading in Fog Manufacturing for Computational Pipelines using Multi-task Learning,” in Proceedings of IEEE CASE 2021.

17. Zeng, Y., Shojaee, P., Faruqui, S., Alaeddini., A., Jin, R., 2022, "Contextual Bandit Guided Data Farming for Deep Neural Networks in Manufacturing Industrial Internet," IEEE 5th International Conference on Industrial Cyberphysical Systems (ICPS).

18. Shojaee, P., Zeng, Y., Wahed, M., Seth, A., Jin, R., Lourentzou, I., 2022, "Task-Driven Privacy-Preserving Data-Sharing Framework for the Industrial Internet," 2022 IEEE International Conference on Big Data (Big Data) (pp. 1505-1514).

19. Zeng, Y., Wang, T.J., Chen, S., Just, HA., Jin, R., Jia, R., 2023, "ModelPred: A Framework for Predicting Trained Model from Training Data ," 2023 IEEE 1st Conference on Secure and Trustworthy Machine Learning (SaTML), accepted.

20. Zeng, Y., Shojaee, P., Faruqui, S., Alaeddini, A., and Jin, R.**, 2022, “Contextual Bandit Guided Data Farming for Deep Neural Networks in Manufacturing Industrial Internet,” in Proceedings of IEEE 5th International Conference on Industrial Cyberphysical Systems (ICPS) 2022.

21. Shojaee, P., Zeng, Y., Wahed, M., Seth A., Jin, R., Lourentzou, I., 2022, “A Task-Driven Privacy-Preserving Data-Sharing Framework For Industrial Internet,” 2022 IEEE International Conference on Big Data, Dec. 17, 2022

22. Zeng, Y., Wang, T.J., Chen, S., Just, H.A., Jin, R., and Jia, R.**, 2023, “ModelPred: A Framework for Predicting Trained Model from Training Data,” 2023 IEEE 1st Conference on Secure and Trustworthy Machine Learning (SaTML), 2023, Doi: 10.1109/SaTML54575.2023.00037.

23. Zeng, Y., Thiyagarajan, P., Chan, B., and Jin, R.**, 2023, “Synthetic Data Generation and Sampling for Online Training of DNNs in Manufacturing Supervised Learning Problems,” 2023 IEEE International Conference on Automation Science and Engineering.

24. Chilukuri, P. K., Song, B., Kang, S., and Jin, R.**, 2024, “Generating Optimized 3D Designs for Manufacturing Using a Guided Voxel Diffusion Model,” in Proc. ASME 2024 Int. Manuf. Sci. Eng. Conf., MSEC 2024, Knoxville, TN, USA.

25. Zhou, X., Zeng, Y., Jin, R., and Lourentzou, I., 2024, “Hierarchical Bayesian Dataset Selection for High-Quality Data Sharing,” 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, Idaho, USA, Submitted on 5/202/2024.

Papers Submitted

1. Kang, L., Deng, X. and Jin, R., “Bayesian D-Optimal Design of Experiments with Quantitative and Qualitative Responses,” submitted to Journal of Statistical Science and Application, December, 2015.

Invited Presentations

1. INFORMS Conference, “On-line Surface Defect Detection in Rolling Process for Quality Improvement,” Nov. 2007, Seattle, WA

2. INFORMS Conference, “Variability Modeling and Analysis in Wafer Manufacturing Process,” Oct. 2008, Washington, D.C.

3. INFORMS Conference, “Sequential Sensing Strategy Based on Gaussian Process Model for Wafer Geometric Profile Estimation,” Oct. 2009, San Diego, CA

4. INFORMS Conference, “Reconfigured Piecewise Linear Regression Tree for Multistage Manufacturing Process Control,” Oct. 2009, San Diego, CA

5. INFORMS Conference, “PDE-constrained GP Model for Thickness Profile Modeling and Optimization in Slicing Processes,” Nov. 2010, Austin, TX

6. INFORMS Conference, “Multistage Multimode Process Monitoring using PLRTs Considering Modeling Uncertainty,” Nov. 2010, Austin, TX

7. INFORMS Conference, “Engineered Surface Modeling Using Gaussian Process Models,” Nov. 2011, Charlotte, NC

8. ISERC Conference, “Engineering Driven Reconfigured Tree based Variation Reduction Considering Uncertainties,” May 2012, Orlando, FL

9. International Symposium on Business and Industrial Statistics, “Wafer Geometric Profile Modeling and Control in Lapping Processes using Gaussian Process Models,” June 2012, Bangkok, Thailand

10. The Second International Conference on the Interface between Statistics and Engineering, “Reconfigured Piecewise Linear Regression Tree based Control Considering Modeling Uncertainty,” June 2012, Tainan, Taiwan

11. INFORMS Conference, “Ensemble Modeling for Manufacturing Scale-up through Experimental and Observational Data Fusion,” Oct. 2012, Phoenix, AZ

12. GE Global Research Center, Workshop on Sensor Enabled Adaptive Manufacturing,  “Engineering Driven Data Fusion for Manufacturing System Modeling and Performance Improvement,” April 2013, Schenectady, NY

13. ISERC Conference, “Non-negative Garrote based Logistic Regression Model for Crystal Growth Monitoring,” May 2013, San Juan, PR

14. INFORMS Conference, “Organ Preservation and Viability Evaluation in Transplant,” Oct. 2013, Minneapolis, MN

15. INFORMS Conference, “Broaching Process Monitoring based on Global and Cyclic Signals,” Oct. 2013, Minneapolis, MN

16. ISERC Conference, “Control of Qualitative and Quantitative Responses with Asymmetric Loss Functions,” May 2014, Montreal, QC, Canada

17. INFORMS Conference, “Dynamic Quality Models Considering Equipment Degradation in Manufacturing Systems,” Nov. 2014, San Francisco, CA

18. INFORMS Conference, “Ensemble Modeling for Data Fusion in Manufacturing Process Scale-up,” Nov., 2014, San Francisco, CA

19. INFORMS student chapter at Virginia Tech, “Data Fusion: from Mixed Types of Data to Mixed Types of Information,” Feb., 2015, Blacksburg, VA 

20. University of Washington, Dept. of Industrial and Systems Engineering, IND E 593, Industrial and Systems Engineering Seminar Series, “Ensemble Modeling via Design of Experimental and Observational Data Fusion in Manufacturing,” April, 2015, Seattle, WA

21. INFORMS Conference, “QQ Models: Joint Modeling for Quantitative and Qualitative Quality Responses in Manufacturing Systems,” Nov. 2015, Philadelphia, PA

22. International Conference for Advanced Manufacturing, “Soft Sensing and Process Modeling of Additive Manufacturing via Data Fusion,” April, 2016, Arlington, VA

23. Kansas State University, “Data Fusion in Smart Manufacturing,” October, 2016, Manhattan, KS

24. Georgia Institute of Technology, “Data-driven Modeling in Smart Manufacturing,” March, 2017, Atlanta, GA

25. University of Southern California, “Smart Manufacturing Modeling with Functional Data,” April, 2017, Atlanta, GA

26. IMS/ASA Spring Research Conference, “Modeling and Interpretation of Manufacturing Time Series Data via a Natural Language Processing Perspective”, May, 2017, Rutgers, NJ

27. IEEE CASE, “Ensemble Modelling of in situ Feature Variables for Printed Electronics Manufacturing with in situ Process Control Potential”, August, 2017, Xi’an, China

28. Fall Technical Conference, “Manufacturing Data Fusion”, October 11- October 13, 2017, Philadelphia, PA

29. INFORMS Conference, “Smart Manufacturing: Challenges, Lessons and Solutions,” October 23, 2017, Houston, TX

30. University of Wisconsin, Madison, “Modeling and Interpretation of Smart Manufacturing with Functional Data”, December 1, 2017, Madison, WI

31. Southern University of Science and Technology, “Functional Variable Modeling, Selection and Interpretation in Smart Manufacturing”, December 25, 2017, Shenzhen, China

32. The Procter and Gamble Company, “Modeling in situ Functional Data for Smart Manufacturing”, February 22, 2018, Mason, OH

33. The 1st IEEE International Conference on Industrial Cyber-Physical Systems, “Predictive Offloading in Mobile-Fog-Cloud enabled Cyber-manufacturing Systems”, May 18, 2018, St. Petersburg, RU

34. Tsinghua University / University of Science and Technology Beijing, “Computation Services in Cybermanufacturing Systems”, May 25, 2018, Beijing, China

35. Zhejiang University, “Recommendation and Predictive Offloading of Computation Services in Smart Manufacturing”, May 28, 2018, Hangzhou, China

36. Shanghai Jiaotong University, “Computation Services in Cybermanufacturing Systems”, June 1, 2018, Shanghai, China