Emanuele Rossi

I’m a postdoctoral researcher in the Gladia group at Sapienza University of Rome, working with Emanuele Rodolà. My research explores how AI can help decode animal communication, focusing on how multimodal models can reveal what wild animals communicate to one another, what this tells us about their intelligence and consciousness, and how such understanding can reshape our relationship with the natural world.

Before turning my attention to animal communication, I worked at Vant AI, developing generative models for structural biology and drug discovery, and earned my PhD between Imperial College London and Twitter, supervised by Michael Bronstein and focusing on Graph Neural Networks. Earlier, I was part of Fabula AI, a deep learning startup for fake news detection that was acquired by Twitter. I hold degrees in Computer Science from Imperial College London and the University of Cambridge.

Outside research, I love scuba diving, hiking, and quiet moments in nature.

News

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 📝

Selected Publications

bioRxiv

Preprint

PINDER: The Protein Interaction Dataset and Evaluation Resource

Daniel Kovtun, Mehmet Akdel, Alexander Goncearenco, and 16 more authors

bioRxiv, 2024

bioRxiv Bib Code Docs

@article{kovtun_pinder_2024,
  title = {PINDER: The Protein Interaction Dataset and Evaluation Resource},
  author = {Kovtun, Daniel and Akdel, Mehmet and Goncearenco, Alexander and Zhou, Guoqing and Holt, Graham and Baugher, David and Lin, Dejun and Adeshina, Yusuf and Castiglione, Thomas and Wang, Xiaoyun and Marquet, Céline and McPartlon, Matt and Geffner, Tomas and Corso, Gabriele and Stärk, Hannes and Carpenter, Zachary and Kucukbenli, Emine and Bronstein, Michael and Naef, Luca},
  year = {2024},
  journal = {bioRxiv},
  biorxiv = {2024.07.17.603980v4},
}

ICML

Workshop

PLINDER: The Protein-Ligand Interactions Dataset and Evaluation Resource

Janani Durairaj, Yusuf Adeshina, Zhonglin Cao, and 20 more authors

ICML ML for Life and Material Science Workshop, 2024

bioRxiv Bib Code Dataset Docs

@article{durairaj_plinder_2024,
  title = {PLINDER: The Protein-Ligand Interactions Dataset and Evaluation Resource},
  author = {Durairaj, Janani and Adeshina, Yusuf and Cao, Zhonglin and Zhang, Xuejin and Oleinikovas, Vladas and Duignan, Thomas and McClure, Zachary and Robin, Xavier and Kovtun, Danny and Rossi, Emanuele and Zhou, Guoqing and Veccham, Srimukh and Isert, Clemens and Peng, Yuxing and Sundareson, Prabindh and Akdel, Mehmet and Corso, Gabriele and Stärk, Hannes and Carpenter, Zachary and Bronstein, Michael and Kucukbenli, Emine and Schwede, Torsten and Naef, Luca},
  year = {2024},
  journal = {ICML ML for Life and Material Science Workshop},
  biorxiv = {2024.07.17.603980v4},
}

PhD Thesis

Thesis

Deep Learning on Real-World Graphs

Emanuele Rossi

Imperial College London, 2024

Proceedings Bib DOI

NeurIPS

Conference

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, and 7 more authors

Advances in Neural Information Processing Systems, 2023

arXiv Proceedings Bib Code Website

@article{tgb_huang_2023,
  title = {Temporal Graph Benchmark for Machine Learning on Temporal Graphs},
  author = {Huang, Shenyang and Poursafaei, Farimah and Danovitch, Jacob and Fey, Matthias and Hu, Weihua and Rossi, Emanuele and Leskovec, Jure and Bronstein, Michael M. and Rabusseau, Guillaume and Rabbany, Reihaneh},
  year = {2023},
  journal = {Advances in Neural Information Processing Systems},
  arxiv = {2307.01026},
}

LoG

Conference

Edge Directionality Improves Learning on Heterophilic Graphs

Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, and 3 more authors

Learning on Graphs Conference (LoG), 2023

arXiv Proceedings Bib Blog Code

@article{dirgnn_rossi_2023,
  title = {Edge Directionality Improves Learning on Heterophilic Graphs},
  author = {Rossi, Emanuele and Charpentier, Bertrand and Giovanni, Francesco Di and Frasca, Fabrizio and Günnemann, Stephan and Bronstein, Michael M.},
  year = {2023},
  journal = {Learning on Graphs Conference (LoG)},
  arxiv = {2305.10498},
}

ICML

Workshop

Temporal Graph Networks for Deep Learning on Dynamic Graphs

Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, and 3 more authors

ICML Workshop on Graph Representation Learning, 2020

arXiv Proceedings Bib Blog Code Slides

@article{rossi2020tgn,
  title = {Temporal Graph Networks for Deep Learning on Dynamic Graphs},
  author = {Rossi, Emanuele and Chamberlain, Ben and Frasca, Fabrizio and Eynard, Davide and Monti, Federico and Bronstein, Michael},
  year = {2020},
  journal = {ICML Workshop on Graph Representation Learning},
  arxiv = {2006.10637},
}

ICML

Workshop

SIGN: Scalable Inception Graph Neural Networks

Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, and 3 more authors

ICML Workshop on Graph Representation Learning, 2020

arXiv Proceedings Bib Blog Code

@article{rossi2020sign,
  title = {SIGN: Scalable Inception Graph Neural Networks},
  author = {Rossi, Emanuele and Frasca, Fabrizio and Chamberlain, Ben and Eynard, Davide and Bronstein, Michael and Monti, Federico},
  year = {2020},
  journal = {ICML Workshop on Graph Representation Learning},
  arxiv = {2004.11198},
}