Ferdinando Fioretto

Research Assistant Professor

Electrical Engineering & Computer Science

Degree

  • Ph.D., Computer Science, New Mexico State University & University of Udine

Research interest

  • Artificial Intelligence
  • Machine Learning
  • Multiagent Systems
  • Data Privacy

Current Research

Ferdinando’s research interest is in Artificial Intelligence (AI) with an emphasis in developing single and multi-agent protocols to solve complex data-driven decision-making problems. He is particularly interested in the interface between optimization and learning(making better decisions), coordination (taking into account of agents’ goals and preferences), and privacy (while protecting the individual’s data from external attacks).

Teaching Interests

  • Artificial Intelligence
  • Data Privacy

Honors

  • Best AI Dissertation Award: Italian Artificial Intelligence Association, (AI*IA), 2017.
  • Most Visionary Paper Award: International Conference on Autonomous Agents and Multiagent Systems (AAMAS) Workshop series, 2017.
  • William Kluegel, Muhammad Aamir Iqbal, Ferdinando Fioretto, William Yeoh, and Enrico Pontelli. “A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs.”
  • NMSU Top 5% Graduate student honor’s cord: New Mexico State University, 2016.
  • 3 Years Ph.D. Scholarship Award: University of Udine, 2013-2016.
  • Outstanding Research Assistant Award: New Mexico State University, 2013.
  • Best Student Paper Award: Computational Methods in Systems Biology (CMSB), 2013.
  • Ferdinando Fioretto and Enrico Pontelli. “Constraint Programming in Community-based Gene Regulatory Network Inference.”
  • Outstanding Teaching Assistant Award: New Mexico State University, 2012.
  • Outstanding Graduate Assistantship Award, New Mexico State University, 2012.

Recent Publications

  • Ferdinando Fioretto, Pascal Van Hentenryck. “OptStream: Releasing Time Series Privately.” In Journal of Artificial Intelligence Research (JAIR), 2019.
  • Ferdinando Fioretto, Pascal Van Hentenryck. “Differential Privacy of Hierarchical Census Data: An Optimization Approach.” In Proceedings of the International Conference on Principles and Practice of Constraint Programming (CP), 2019.
  • Ferdinando Fioretto, Terrence W.K. Mak, Pascal Van Hentenryck. “Privacy-Preserving Obfuscation of Critical Infrastructure Networks.” In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2019.
  • Ferdinando Fioretto, Pascal Van Hentenryck. “Privacy-Preserving Federated Data Sharing.” In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2019.
  • Ferdinando Fioretto, Terrence W.K. Mak, Pascal Van Hentenryck. “Privacy-Preserving Obfuscation of Critical Infrastructure Networks.” CoRR abs/1905.09778 [cs.CR], 2019.
  • Ferdinando Fioretto, Terrence W.K. Mak, Pascal Van Hentenryck. “Differential Privacy for Power Grid Obfuscation.” CoRR abs/1901.06949 [cs.AI], 2019.
  • Ferdinando Fioretto, Agostino Dovier, Enrico Pontelli. “Distributed multi-agent optimization for smart grids and home automation”. In Intelligenza Artificiale (IA), 2019.
  • Ferdinando Fioretto, William Yeoh, Enrico Pontelli. “Distributed Constraint Optimization Problems and Applications: A Survey”. In Journal of Artificial Intelligence Research (JAIR), 2018.
  • Ferdinando Fioretto, William Yeoh. “AI buzzwords explained: distributed constraint optimization problems.” In AI Matters, 2018.
  • Ferdinando Fioretto, Enrico Pontelli, William Yeoh, Rina Dechter. “Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs.” In Constraints, 2018.
  • Ferdinando Fioretto, Hong Xu, Sven Koenig, TK Satish Kumar. ” Solving Multiagent Constraint Optimization Problems on the Constraint Composite Graph.” In International Conference on Principles and Practice of Multi-Agent Systems (PRIMA), 2018.
  • Khoi Hoang, Ferdinando Fioretto, William Yeoh, Enrico Pontelli, Roie Zivan. “A large neighboring search schema for multi-agent optimization.” In Proceedings of the International Conference on Principles and Practice of Constraint Programming (CP), 2018.
  • Ferdinando Fioretto, Chansoo Lee, Pascal Van Hentenryck. “Constrained-based Differential Privacy for Private Mobility.” In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2018.
  • Ferdinando Fioretto, Pascal Van Hentenryck. “Constrained-based Differential Privacy: Releasing Optimal Power Flow Benchmarks Privately.” In Proceedings of the International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR), 2018.