Talks and presentations

  • Invited Tutorial “Neural and Symbolic Logical Reasoning on Knowledge Graphs”, at the Summer School of Chinese Information Processing Society of China (CIPS), November, 2020. slides.
  • Invited Talk “Deep Generative Models for Molecular Conformation Generation”, at the AI Cures Drug Discovery Conference. October, 2020. video,slides.
  • Invited Talk “Graph Representation Learning for Drug Discovery”, Mila/WeBank/DiDi Webinar, October, 2020.slides.
  • Invited Talk “Graph Representation Learning and Applications to Drug Discovery”, at the McGill Seminar Series in Quantitative Life Sciences and MedicineOctober, 2020. slides.
  • Invited Talk “Towards combining System I and System II Reasoning” at the Workshop on “Graph Neural Networks” in the second annual conference of Beijing Academy of Artificial Intelligence, June, 2020.
  • Guest Lecture “Logical Reasoning with Graph Neural Networks”, Tsinghua, 2020.
  • Mila tea talk “Graph Representation Learning: Algorithms and Applications” May, 2020
  • Invited Talk “Reasoning with Text, Knowledge Graphs, and Logical Rules “, Mila-Industrial NLP Workshop, Feb, 2020.
  • Guest Lecture “Graph Representation Learning and Applications”, Mcgill University, November, 2019
  • Guest Lecture “Graph Representation Learning and Applications”, University of Montreal, November, 2019
  • Invited Talk “Graph Representation Learning and Applications” at National Research Council Canada, 13rd, November, 2019
  • Invited Talk “Graph Representation Learning and Applications to Drug Discovery”, Midi Recherche, HEC Montreal, 6th, November, 2019.
  • Invited Talk “Graph Representation Learning and Applications to Drug Discovery” at the Clinical Research Association of Canada, 29th, October, 2019.
  • Invited Talk “Towards Combining Statistical Relational Learning and Graph Neural Networks for Reasoning” at the Deep Learning Summit, Montreal, 24th, October, 2019. Slides
  • Talk “Towards Combining Statistical Relational Learning and Graph Neural Networks for Reasoning” at the annual Mila-Microsoft Workshop. 16th, October, 2019.
  • Invited Talk “Graph Representation Learning and Applications to Drug Discovery” at the first annual conference of Canada Chapter of Chinese Biopharmaceutical Association, 5th, October, 2019
  • Invited Talk “Towards Combining Statistical Relational Learning and Graph Neural Networks” at IBM New York, August, 2019.
  • Talk “Graph Representation Learning and Reasoning” at Peking University, Tsinghua University, Shanghai Jiaotong University, University of Science and Technology, July, 2019.
  • Keynote Speaker: “Graph Representation Learning: Algorithms, Applications, Systems” at the invitation-only AI Experts Workshop in AI for Good Global Summit, Geneva, Swithland.
  • Invited Speaker: “GMNN: Graph Markov Neural Networks” at IPAM Workshop “Deep Geometric Learning of Big Data and Applications”, UCLA, United States, 2019.5 video, slides
  • Talk “Graph Representation Learning: Algorithms, Applications, Systems” in Computer Science Department at UCLA, United States, 2019.5
  • Talk “Knowledge graph embedding and alignment”, Mila-Samsung Workshop, 2019.5, slides
  • Talk “Graph Representation Learning: Algorithms, Applications, Systems” in the Statistics Department at McGill University, 2019.4
  • Talk “Graph Representation Learning and Applications in Healthcare and Biomedical Applications” Faculty Seminar on AI and healthcare at McGill University, 2019.4.
  • Talk “RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space” at Peking Univeristy,2019.1.
  • Talk “RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space” at Huawei inhouse workshop,2018.12.
  • Talk “Graph representation learning and applications” at Université du Québec à Montréal. 2018.11
  • Talk “Graph Representation Learning for Natural Language Understanding and Reasoning”, Natural Language Processing Workshop for MILA Sponsors and Partners. 2018. 9
  • Talk “Graph Representation Learning and Applications”, Tsinghua University, 2018.7.
  • Talk “Learning Representations of Graphs” at Google Brain, Montreal, 2018.05.07.
  • Invited talk “Progress and Future Directions of Network Representations” at Machine Intelligence Frontier Seminar 2017, CCF special topic on knowledge graph, 2017.10
  • Invited talk “Towards combining information retrieval and reasoning for natural language understanding”, at Tsinghua University, 2017.9.
  • Tutorial “Learning representations of large-scale networks” at KDD 2017, Halifax, Canada, 2017.8 slides
  • Talk “Introduction to Deep Learning \& How to Do Research in Machine Learning”, at Peking University, 2017.6
  • Talk “Visualizing large-scale and high-dimensional data”, at PKU-UCLA Symposium. 2017. 7
  • Talk “Learning representations of large-scale networks”, at Peking University, Tsinghua University, JingDong, iFlytek, TianYanCha, Toutiao AI Lab, 2017.6-7.
  • Talk “Learning representations of large-scale networks”, at HEC Montreal.
  • Talk “Learning representations of large-scale networks”, at University of Montreal.
  • Talk “Visualizing large-scale and high-dimensional data” at School of Information, Central University of Finance and Economics, 2016.
  • Talk “Visualizing large-scale and high-dimensional data” at MOE-Microsoft Key Laboratory of Statistics and Information Technology of Peking University, 2016.
  • Invited talk “Learning text embedding via network embedding” at 9th National R Meeting, 2016. slides.
  • Invited talk “Study on the limiting factors of topic modeling” at the China National Computer Congress (CNCC) 2015.
  • Invited talk “LINE: large-scale information network embedding” at Beijing Institute of Technology, Alibaba Technical Forum.
  • Oral presentation at KDD 2013, ICML 2014, WWW 2015, KDD 2015.
  • Invited talk “Look Ma, No hands! A parameter-free topic model” at student seminar of statistics department in University of Michigan.
  • Talk “Look Ma, No hands! A parameter-free topic model” at the 4th Michigan data mining workshop.
  • 2009.7 programming language summer school, in University of Oregon.