Ping Wang (pwang44)

Ping Wang

Assistant Professor

Charles V. Schaefer, Jr. School of Engineering and Science

Computer Science


  • PhD (2021) Virginia Tech (Computer Science)


Machine learning
Natural language processing
Information retrieval and knowledge discovery
Healthcare informatics

Professional Service

  • AAAI 2023 Program Committee Member
  • EMNLP 2022 Program Committee Member
  • Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Reviewer
  • AACL 2022 Program Committee Member
  • Transactions on Neural Networks and Learning Systems (TNNLS) Reviewer
  • CIKM 2022 Program Committee Member
  • COLING 2022 Area Chair
  • Management Information Systems (MIS) Quarterly Reviewer
  • Journal of the Royal Statistical Society: Series B (JRSSB) Reviewer
  • Journal of Machine Learning Research (JMLR) Reviwer
  • AMIA Annual Symposium 2022 Program Committee Member
  • NSF Panelist 2022
  • Transactions on Knowledge and Data Engineering (TKDE) Reviewer 2022
  • NSF Panelist 2021
  • ACL Open Reviewer 2021
  • AAAI 2022 Program Committee Member
  • Reviewer for ACM Transactions on Knowledge Discovery from Data (TKDD)
  • Reviewer for ACM Computing Surveys (CSUR)
  • EMNLP 2021 Program Committee Member
  • ACL 2021 Program Committee Member


Assistant Professor, Department of Computer Science, Stevens Institute of Technology, 2021 - present

Professional Societies

  • ACM – Association for Computing Machinery (ACM) Member Member
  • IEEE – The Institute of Electrical and Electronics Engineers (IEEE) Member Member

Selected Publications

Conference Proceeding

  1. Wang, P.; Shi, T.; Agarwal, K.; Choudhury, S.; Reddy, C. K. (2022). Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes. ACM International Conference on Bioinformatics, Computational Biology and Health Informatics.
  2. Wang, P.; Agarwal, K.; Ham, C.; Choudhury, S.; Reddy, C. K. (2021). Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks. Proceedings of the Web Conference 2021 (pp. 2946--2957).
  3. Shi, T.; Li, L.; Wang, P.; Reddy, C. K. (2020). A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
  4. Wang, P.; Shi, T.; Reddy, C. K. (2020). Text-to-sql generation for question answering on electronic medical records. Proceedings of The Web Conference 2020 (pp. 350--361).
  5. Shi, T.; Wang, P.; Reddy, C. K. (2019). LeafNATS: An open-source toolkit and live demo system for neural abstractive text summarization. Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL).
  6. Wang, P.; Padthe, K. K.; Vinzamuri, B.; Reddy, C. K. (2016). Crisp: Consensus regularized selection based prediction. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (pp. 1019--1028).

Journal Article

  1. Shi, T.; Zhang, X.; Wang, P.; Reddy, C. K. (2020). A Concept-based Abstraction-Aggregation Deep Neural Network for Interpretable Document Classification. ACM Transactions on Knowledge Discovery from Data (TKDD).
  2. Shi, T.; Wang, P.; Reddy, C. K. (2020). Deliberate Self-Attention Network with Uncertainty Estimation for Multi-Aspect Review Rating Prediction. arXiv preprint arXiv:2009.09112.
  3. Wang, P.; Shi, T.; Reddy, C. K. (2020). Tensor-based Temporal Multi-Task Survival Analysis. IEEE Transactions on Knowledge and Data Engineering. IEEE.
  4. Wang, P.; Li, Y.; Reddy, C. K. (2019). Machine learning for survival analysis: A survey. ACM Computing Surveys (CSUR) (6 ed., vol. 51, pp. 1--36). ACM New York, NY, USA.
  5. Fard, M. J.; Wang, P.; Chawla, S.; Reddy, C. K. (2016). A bayesian perspective on early stage event prediction in longitudinal data. IEEE Transactions on Knowledge and Data Engineering (12 ed., vol. 28, pp. 3126--3139). IEEE.
  6. Shi, T.; Wang, P. (2016). GPView: A program for wave function analysis and visualization. Journal of Molecular Graphics and Modelling (vol. 70, pp. 305--314). Elsevier.


CS 559 Machine Learning: Fall 2021, Spring 2022