Charalampos Chelmis

Nazar, I., Liyanage, Y. W., Zois, D.-S., & Chelmis, C. (2019). Automated Optimal Online Civil Issue Classification using Multiple Feature Sets. In 2019 53rd Asilomar Conference on Signals, Systems, and Computers (p. 5). http://doi.org/10.1109/IEEECONF44664.2019.9048766

Yong, C., Chelmis, C., Lee, W., & Zois, D.-S. (2019). Understanding online civic engagement: a multi-neighborhood study of SeeClickFix. In 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (p. 8). http://doi.org/10.1145/3341161.3345330

Chelmis, C., Yao, M., & Lee, W. (2019). Web and society: a first look into the network of human service providers. In 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (p. 8). http://doi.org/10.1145/3341161.3345331

Liyanage, Y. W., Zois, D.-S., Chelmis, C., & Yao, M. (2019). Automating the Classification of Urban Issue Reports: an Optimal Stopping Approach. In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (p. 5). http://doi.org/10.1109/ICASSP.2019.8682778

Chelmis, C., & Yao, M. (2019). Creating Public Value by Democratizing the Ecosystem of Human Service Providers. In Companion Proceedings of The 2019 World Wide Web Conference (p. 6). http://doi.org/10.1145/3308560.3316487

Liyanage, Y., Yao, M., Yong, C., Zois, D.-S., & Chelmis, C. (2018). WHAT MATTERS THE MOST? OPTIMAL QUICK CLASSIFICATION OF URBAN ISSUE REPORTS BY IMPORTANCE. In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (p. 5). http://doi.org/10.1109/GlobalSIP.2018.8646639

Zois, D.-S., Yong, C., Chelmis, C., Kapodistria, A., & Lee, W. (2018). Improving Monitoring of Participatory Civil Issue Requests through Optimal Online Classification. In 52nd Asilomar Conference on Signals, Systems, and Computers (p. 5). http://doi.org/10.1109/ACSSC.2018.8645177

Srivastava, A., Kannan, R., Chelmis, C., & Prasanna, V. K. (2018). FActCheck: Keeping Activation of Fake News at CHeck. In AAMAS ’18 Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems (p. 3). Retrieved from https://par.nsf.gov/biblio/10067599

Chelmis, C., & Qi, W. (2021). Hierarchical MultiClass AdaBoost. In 2021 IEEE International Conference on Big Data (Big Data) (p. 8). http://doi.org/10.1109/BigData52589.2021.9671291

Qi, W., & Chelmis, C. (2021). Improving Algorithmic Decision–Making in the Presence of Untrustworthy Training Data. In 2021 IEEE International Conference on Big Data (Big Data) (p. 7). http://doi.org/10.1109/BigData52589.2021.9671677