| Mar 21, 2025 | Excited to finally share what we’ve been working on at Vant AI for the past year and a half: Neo-1, a unified model for all-atom structure prediction and generation of all biomolecules 🔬 |
| Jul 19, 2024 | The lack of large, high-quality datasets and robust evaluation is holding back ML in Drug Discovery. We are releasing Pinder (Protein-Protein) and Plinder (Protein-Ligand) to help bridge this gap and drive meaningful progress 🧬 |
| Jan 16, 2024 | I’m thrilled to announce that I’ve joined Vant AI as a Machine Learning Researcher. Vant combines a compelling ML vision with a proprietary data generation platform, focusing on the novel field of molecular glues. I’ll be developing generative models for structural biology to advance the drug discovery process 🚀 |
| Jul 31, 2023 | We are excited to release the Temporal Graph Benchmark, a collection of seven realistic, large-scale and diverse benchmarks for learning on temporal graphs! The accompanying paper has been accepted to NeurIPS 2023 Datasets and Benchmark track 📝 |
| Jun 08, 2023 | Our new paper on Graph Neural Networks for Directed Graphs, and how they improve learning on heterophilic graph, is out along with its associated blog post 📝 |
| Apr 20, 2023 | New blog posts on our recent paper on Learning to Infer Structures of Network Games 📝 |
| Jan 24, 2023 | Our paper “Graph Neural Networks for Link Prediction with Subgraph Sketching” has been accepted at ICLR 2022 as an oral presentation (top 5%) 🎉 |
| Nov 24, 2022 | Our paper “On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features” has been accepted at the new Learning on Graphs Conference 🎉 |
| Nov 19, 2022 | Our paper “Provably Efficient Causal Model-Based Reinforcement Learning for Environment-Agnostic Generalization” has been accepted at AAAI 2023 🎉 |
| Jun 01, 2022 | Published a blog posts on our collaboration with GraphCore on “Accelerating and scaling Temporal Graph Networks on the Graphcore IPU” 📝 |
| May 01, 2022 | Our paper “Learning to Infer Structures of Network Games” has been accepted as a spotlight at ICML 2022 🎉 |
| Feb 01, 2022 | Published a blog posts on our new paper on Learning on Graphs with Missing Node Features 📝 |
| Sep 01, 2021 | I moved permanently to Barcelona (from London), where I will continue working remotely in my current role at Twitter, as well as learning Spanish 🇪🇸 |
| May 01, 2021 | Our paper “GRAND: Graph Neural Diffusion” has been accepted as a spotlight at ICML 2021 🎉 |
| Oct 01, 2020 | Started a PhD at Imperial College London, supervised by Prof. Michael Bronstein. The PhD will largely overlap with my current research at Twitter, and I will continue working on GNNs 🎓 |
| Aug 08, 2020 | Published blog posts on our new papers on Temporal Graph Networks and scalable GNNs 📝 |
| Jul 10, 2020 | I’ve attended MLSS 2020 Tuebingen 🏫 |
| Mar 01, 2020 | Forbes Italy published an article about LeadTheFuture, the mentoring non-profit which I co-founded. Soon after, my cofounders and I were also included in the 100 under 30 by Forbes Italy on the most talented young leaders in the country. |
| Jul 20, 2019 | Graduated with distinction from an MPhil in Advanced Computer Science at Cambridge. |
| Jun 01, 2019 | Fabula AI is acquired by Twitter. We join the machine learning team in London (including Magic Pony) to work on fundamental and applied research around graph neural networks. |
| Apr 29, 2019 | Moved to UCLA as a visiting researcher during IPAM long program on learning from geometric data. |
| Mar 11, 2019 | Started working part-time as a data scientist for Fabula AI, a start-up using geometric deep learning to solve fake news detection. |
| Oct 01, 2018 | Started an MPhil in Advanced Computer Science at Cambridge. |
| Jun 30, 2018 | Graduated with a BEng in Computing from Imperial College London. |
