Outreach
Talks, panels, community work, and research mentoring.
Talks
2023
- 2023/09/14: “Edge Directionality Improves Learning on Heterophilic Graphs” at the Temporal Graph Reading Group [Slides][Video]
- 2023/09/07: “Edge Directionality Improves Learning on Heterophilic Graphs” at the Explainability and Applicability of Graph Neural Networks Workshop in Kassel
2022
- 2022/09/28: “Improving Machine Learning at Twitter using Graphs” at Privacy Week [Video (in Italian)]
- 2022/09/09: “Learning on Graphs with Missing Node Features” at the Tiger Graph Reading Group
- 2022/03/01: “Learning on Graphs with Missing Node Features” at the Learning on Graphs and Geometry Reading Group [Slides][Video]
- 2022/02/24: “Learning on Graphs with Missing Node Features” at the Machine Learning Research Group of the University of Oxford
2021
- 2021/11/23: “Learning on Graphs with Missing Node Features” at University of Cambridge AI Research Group Talks
- 2021/11/17: “Graph Neural Networks with Almost No Features” at the Toronto Machine Learning Summit
- 2021/06/16: “Machine Learning on Dynamic Graphs: Temporal Graph Networks” at MLOps World 2021
- 2021/04/26: “Machine Learning on Dynamic Graphs: Temporal Graph Networks” at Intel ML Seminar [Slides]
- 2021/04/09: “Machine Learning on Dynamic Graphs: Temporal Graph Networks” at the GNNSys’21 workshop
- 2021/01/22: “Dynamic Graphs and Temporal Graph Networks” at the University of Liverpool, Department of Computer Science. [Slides]
2020
Conversations
- 2021/03/02: “Scaling Graph Neural Networks to Twitter-scale” with Zak Jost [Video]
- 2021/01/14: “My journey, my research at Twitter and Imperial College, and my work at LeadTheFuture” with The Smarter Podcast [Video (in Italian)]
Panels
2022
- 2022/12/03: Temporal Graph Learning Workshop
Workshop Program Committee
- 2022/12: NeurReps Workshop – Symmetry and Geometry in Neural Representations (at Neurips 2022)
- 2022/02: GCLR 22 – 2nd Workshop on Graphs and more complex structures for learning and reasoning (at AAAI 2022)
- 2021/10: GReS – Workshop on Graph Neural Networks for Recommendation and Search (at ACM RecSys 2021)
- 2021/04: GNNSys’21 – Workshop on Graph Neural Networks and Systems (at MLSys 2021)
- 2021/02: AAAI-21 GCLR (at AAAI 2021)
Supervision and Mentoring
Mentoring bright and motivated students is one of the most rewarding aspects of my research. I am grateful for their creativity and curiosity, and for how much I learn from them in return.
Below are the students I have had the pleasure to supervise. If you are a prospective student interested in working with me, feel free to contact my former mentees to learn more about my mentoring style and what collaboration with me looks like.
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Davide Marincione — 2025-05 — Present — PhD Student @ Sapienza University of Rome
Project: Model Merging Enables In-Context Learning for Bioacoustics Foundation Models (Under Review) -
Harrison Rush — 2024-08 — Present — ML Engineer @ Amboss Technologies
Project: MP-Flow: A Deep Graph Reinforcement Learning Agent for Maximizing Throughput in the Lightning Network (Under Review) -
Simone Antonelli - 2024-10 — Present — ML Research Intern @ Amboss Technologies
Project: Predicting Link Closure in the Lightning Network with Temporal Graph Networks (Work in Progress) -
Marco Pegoraro - 2024-10 — 2025-04 — ML Research Intern @ VantAI
Project: Neo 1: a unified model for all-atom structure prediction and generation of all biomolecules -
Thomas Castiglione - 2024-03 — 2025-04 — ML Researcher @ VantAI
Project: Neo 1: a unified model for all-atom structure prediction and generation of all biomolecules -
Andy Huang - 2024-03 — 2025-04 — PhD Student @ Mila
Projects:
• TGB: Temporal Graph Benchmark for Machine Learning on Temporal Graphs
• UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs -
Vincent — 2023-11 — 2024-05 — ML Engineer @ Amboss Technologies
Project: Channel Balance Interpolation in the Lightning Network via Machine Learning