| Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media D Tao, D Zhang, R Hu, E Rundensteiner, H Feng Scientific reports 11 (1), 21678, 2021 | 11 | 2021 |
| Depression predicts chronic pain interference in racially diverse, income-disadvantaged patients BC Nephew, AC Incollingo Rodriguez, V Melican, JJ Polcari, KE Nippert, ... Pain Medicine 23 (7), 1239-1248, 2022 | 7 | 2022 |
| TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks R Hu, D Zhang, D Tao, T Hartvigsen, H Feng, E Rundensteiner In Proceedings of the Thirteenth Language Resources and Evaluation …, 2022 | 6 | 2022 |
| A novel foodborne illness detection and web application tool based on social media D Tao, R Hu, D Zhang, J Laber, A Lapsley, T Kwan, L Rathke, ... Foods 12 (14), 2769, 2023 | 3 | 2023 |
| Uce-fid: Using large unlabeled, medium crowdsourced-labeled, and small expert-labeled tweets for foodborne illness detection R Hu, D Zhang, D Tao, H Zhang, H Feng, E Rundensteiner 2023 IEEE International Conference on Big Data (BigData), 5250-5259, 2023 | 2 | 2023 |
| Epidemiological data mining for assisting with foodborne outbreak investigation D Tao, D Zhang, R Hu, E Rundensteiner, H Feng Foods 12 (20), 3825, 2023 | 1 | 2023 |
| CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality Guidance D Zhang, R Hu, E Rundensteiner Proceedings of the 2024 SIAM International Conference on Data Mining (SDM …, 2024 | | 2024 |
| (v) Crowdsourcing and Machine Learning Approaches for Extracting Information Indicating Potential Foodborne Outbreak from Social Media D Tao, D Zhang, R Hu, E Rundensteiner, H Feng IAFP 2021, 2021 | | 2021 |
Elke RundensteinerThe William Smith Dean's Professor, Computer Science; Founding Head, Data Science Program; WorcesterVerified email at wpi.edu
