Talks and Tutorials
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Physics-Inspired Geometric Pretraining for Molecule Representation
AAAI 2025 Tutorial, Philadelphia, PA, USA, Feb 2025
[Website] -
Multi-modal Foundation Model for Scientific Discovery: With Applications in Chemistry, Material, and Biology
AAAI 2025 Tutorial, Philadelphia, PA, USA, Feb 2025
[Website] -
Molecule Representation Learning and Discovery: A Perspective from Topology, Geometry, and Textual Description
Westlake University, July 2024 -
Molecule Representation Learning and Discovery: A Perspective from Topology, Geometry, and Textual Description
AI for Chemistry and Materials Science (AI4CM) Symposium, March 2024 -
AI for Molecule Discovery with Multi-Modal Knowledge
Ph.D. Defense, Mila-UdeM, 20th July, 2023
[Slides] [Paper] -
Molecule Representation Learning and Discovery: A Perspective from Topology, Geometry, and Textual Description
University of California, Berkeley, March 2023 -
ProteinDT: A Text-guided Protein Design Framework
NRC NLP seminar, March 2023 -
Molecule Representation Learning and Discovery: A Perspective from Topology, Geometry, and Textual Description
M2D2, Feb 2023 -
GraphCG: Unsupervised Discovery of Steerable Factors in Graphs
LOG, Meetup @ Mila, Dec 2022
[Slides] -
Self-Supervised Pretraining with 3D Molecular Geometry
Huawei, July 2022
[Slides] -
Pre-training Molecular Graph Representation with 3D Geometry – Rethinking Self-Supervised Learning on Structured Data
AI Times, March 2022
[Slides] -
Pre-training Molecular Graph Representation with 3D Geometry – Rethinking Self-Supervised Learning on Structured Data
Graph Reading Group, Mila, Feb 2022
[Slides] -
Pre-training Molecular Graph Representation with 3D Geometry – Rethinking Self-Supervised Learning on Structured Data
NLP Reading Group, University of Oxford, Feb 2022
[Slides] -
Molecular Representation Learning with Limited Data
Predoc-3 Presentation, Mila-UdeM, Dec 2021
[Slides]