Yingyan Zeng
Ph.D. Candidate
Virginia Tech
Biography
Hello, I am a Ph.D. candidate in Industrial and Systems Engineering at Virginia Tech, working with Prof. Ran Jin. I'm simultaneously pursing a Master's degree in Computer Engineering at Virginia Tech under the advice of Prof. Ruoxi Jia.
My research focuses on creating a data quality assurance paradigm to enable effetcive AI modeling perofmance in Manufacturing Industrial Internet. Data generation, data acquisition, and data valuation methods have been investigated for AI models with applications in additive manufacturing, bio-manufacturing, semiconducting, and IoT infrastructure design, where statistical and machine learning models as well as optimization methods are heavily used. I also conducted research projects in interpretable machine learning methods, predictive modeling with statistical models, and supply chain optimization by applied operations research.
- Data Quality
- Data Valuation
- Manufacturing Industrial Internet
- Statistical Learning and Machine Learning
Interests
Education
- PhD in Industrial and Systems Engineering, 2019 - 2024 (Exp.)
Virginia Tech - Master in Compter Engineering, 2021 - 2024
Virginia Tech - Bachelor in Industrial and Systems Engineering, 2015-2019
Shanghai Jiao Tong University
Publications
- Refereed Journals and Transactions
[1] Zeng, Y., Chen, X., Jin, R., Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing. (Accepted by ACM Transactions on Intelligent Systems and Technology (TIST)), 2023.
[2] Zeng, Y.+, Shohan, S.+, Chen, X., Jin, R., Shirwaiker, R., Investigating Dielectric Spectroscopy and Soft Sensing for Nondestructive Quality Assessment of Engineered Tissues. Biosensors and Bioelectronics 114286, 2022.
[3] Chen, X., Zeng, Y., Jin, R., INN: An Interpretable Neural Network for AI Incubation in Manufacturing. ACM Transactions on Intelligent Systems and Technology (TIST), 2022.
[4] Zeng, Y., Chen, X., Jin, R., Deng, X., A Prediction-Oriented Optimal Design for Visualization Recommender Systems. Statistical Theory and Related Fields, 5(2), 134-148, 2022. - Peer-Reviewed Conference Proceedings
[1] Zeng, Y., Thiyagarajan, P., Chan, B., Jin, R., Synthetic Data Generation and Sampling for Online Training Of DNNs in Manufacturing Supervised Learning Problems. 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), 2023.
[2] Shojaee, P., Zeng, Y., Wahed, M., Seth, A., Jin, R., Lourentzou, I., Task-Driven Privacy-Preserving Data-Sharing Framework For Industrial Internet. IEEE International Conference on Big Data, 2022.
[3] Zeng, Y., Wang, T., Chen, S., Just, HA., Jin, R., Jia, R., ModelPred: A Framework for Predicting Trained Model from Training Data. 2023 IEEE 1st Conference on Secure and Trustworthy Machine Learning (SaTML), 2023.
[4] Zeng, Y., Shojaee, P., Faruqui, SHA., Alaeddini, A., Jin, R., Contextual Bandit Guided Data Farming for Deep Neural Networks in Manufacturing Industrial Internet. 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), 2022.
[5] Shojaee, P., Zeng, Y., Chen, X., Jin, R., Deng, X., Zhang, C., Deep Neural Network Pipelines for Multivariate Time Series Classification in Smart Manufacturing. 2021 IEEE 4th International Conference on Industrial Cyber-Physical Systems (ICPS), 2021.
+ equal contribution