Ting He

AI Foundations

• coreset-based learning
• cost-efficient distributed/decentralized optimization
• privacy attacks on machine learning

Applied AI

• machine learning for intrusion detection in smart grids

Externally Funded AI Projects

• “Agile Analytics Enabled by Decentralized Continuous Learning in Coalitions” (Army Research Laboratory – Distributed Analytics and Information Science (DAIS) ITA: dais-ita.org/pub)

AI-related Courses

• Learning in Networks, Spring 2021
• Inferential Network Monitoring, Fall 2016

Web Page

nsrg.cse.psu.edu/members/ting-he

Publications

1. Hanlin Lu, Ming-Ju Li, Ting He, Shiqiang Wang, Vijay Narayanan, and Kevin S. Chan, Robust Coreset Construction for Distributed Machine Learning, IEEE JSAC Special Issue on Advances in Artificial Intelligence and Machine Learning for Networking, vol. 38, no. 10, pp. 2400-2417, October 2020. [arXiv version] [Code]
2. Stephen Pasteris, Ting He, Fabio Vitale, Shiqiang Wang, and Mark Herbster, Online Learning of Facility Locations, Algorithmic Learning Theory (ALT), March 2021.
3. Hanlin Lu, Changchang Liu, Ting He, Shiqiang Wang, and Kevin S. Chan, Sharing Models or Coresets: A Study based on Membership Inference Attack, International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2020, long paper presentation, July 2020.
4. Hanlin Lu, Ting He, Shiqiang Wang, Changchang Liu, Mehrdad Mahdavi, Vijay Narayanan, Kevin S Chan, and Stephen Pasteris, Communication-efficient k-Means for Edge-based Machine Learning, IEEE ICDCS, July 2020.
5. Hanlin Lu, Changchang Liu, Shiqiang Wang, Ting He, Vijay Narayanan, Kevin S Chan, and Stephen Pasteris, Joint Coreset Construction and Quantization for Distributed Machine Learning, IFIP Networking, June 2020.
6. Sebastian Stein, Mateusz Ochal, Ioana-Adriana Moisoiu, Enrico Gerding, Raghu Ganti, Ting He, and Tom La Porta, Strategyproof Reinforcement Learning for Online Resource Allocation, AAMAS, May 2020.