Invited Talks and Tutorials

• Molecule Representation Learning and Discovery: A Perspective from Topology, Geometry, and Textual Description
  University of California, Berkeley, March 16th, 2023
  

• ProteinDT: A Text-guided Protein Design Framework
  NRC NLP seminar, March 10th, 2023
  

• Molecule Representation Learning and Discovery: A Perspective from Topology, Geometry, and Textual Description
  M2D2, Feb 14th, 2023
  

• GraphCG: Unsupervised Discovery of Steerable Factors in Graphs
  LOG, Meetup @ Mila, Dec 12th, 2022
  [Slides]

• Self-Supervised Pretraining with 3D Molecular Geometry
  Huawei, July 18th, 2022
  [Slides]

• Pre-training Molecular Graph Representation with 3D Geometry – Rethinking Self-Supervised Learning on Structured Data
  AI Times, March 31st, 2022
  [Slides]

• Pre-training Molecular Graph Representation with 3D Geometry – Rethinking Self-Supervised Learning on Structured Data
  Graph Reading Group, Mila, Feb 24th, 2022
  [Slides]

• Pre-training Molecular Graph Representation with 3D Geometry – Rethinking Self-Supervised Learning on Structured Data
  NLP Reading Group, University of Oxford, Feb 11th, 2022
  [Slides]

• Molecular Representation Learning with Limited Data
  Predoc-3 Presentation, Mila, Dec 9th, 2021
  [Slides]

Group Meetings

• Foundation Model for Drug Discovery, Mila, Feb 2023
• Transformer and Self-Supervised Learning, Mila, Jan 2022
• Representation Learning Theory with Unlabeled Data, Mila, April 2021
• Contrastive (Self-Supervised) Leanring on Graphs and Some Thoughts, Mila, July 2020
• Multi-View Learning, Mila, June 2020
• Meta Learning, Mila, April 2020
• Drug Discovery: PriA-SSB Aldrich, UW2020, Apr 2019
• Negative Transfer in Deep Multi-task Learning, UW2020, Aug 2018
• Intro to Feature Representation in Virtual Screening, UW2020, Jan 2018