Presentations
These are the selected presentation slides that I have used in internal reading groups, workshops, and conferences.
External presentations
Seminar series @ GIST
This is a series of seminars that I gave at Gwangju Institute of Science and Technology (GIST) as a guest speaker from March ~ June 2025. The seminar series was about the basics of graph neural networks, several fundamental concepts, and applications of GNNs.
Session 1: Introduction to graph mining and graph neural networks
Session 2: On the representational power of graph neural networks
Session 3: A graph signal processing viewpoint of graph neural networks
Session 4 From label propagation to graph neural networks
Seminar @ Ehwa Womans University
This is a seminar that I gave at Ehwa Womans University as a guest speaker in April 2025.
Slides: A practical introduction to (explainable) graph learning
Faithful and Accurate Self-Attention Attribution for Message Passing Neural Networks via the Computation Tree Viewpoint
This is the poster for my paper “Faithful and Accurate Self-Attention Attribution for Message Passing Neural Networks via the Computation Tree Viewpoint” that was accepted at AAAI 2025. The arxiv version of the paper can be found here.
PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks
This presentation slides were submitted as an official supplementary material after being accepted as the best academic paper at the Graduate School of Yonsei University.
Slide: PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks
Also, this is the poster that I presented for the short version, accepted at AAAI 2022.
Poster: PAGE (AAAI 2022)
Edgeless-GNN: Unsupervised Inductive Edgeless Network Embedding
This presentation was given during the 28th Samsung HumanTech award, which eventually led to the award of the bronze prize.
Slide: Edgeless-GNN: Unsupervised Inductive Edgeless Network Embedding
Internal presentations
Causal learning
This was a two-part presentation where I introduced the basics of causal learning in an internal seminar at my lab, which I got a LOT of help from “Introduction to Causal Inference” by Brady Neal. Thank you!!
Slide 1: Causal learning: Part 1
Slide 2: Causal learning: Part 2
Self-supervised learning
This was a presentation made when I was interested in understanding the SimCLR framework.
Slide: Introduction to SimCLR
Knowledge distillation
Towards understanding knowledge distillation: This was a presentation made when I was interested in understanding knowledge distillation, which I eventually applied to my research on efficient graph learning.
Slide: Towards understanding knowledge distillation
Physics-informed machine learning (PIML)
A review of the paper: Vector Neurons: A General Framework for SO(3)-Equivariant Networks by Dent et al. This was a presentation made when I was interested in understanding the physics-informed machine learning landscape.
Slide: Vector Neurons: A General Framework for SO(3)-Equivariant Networks
Unsupervised disentanglement
A review of the paper: Challenging common assumptions in the unsupervised learning of disentangled representations by Locatello et al.
Slide: Challenging common assumptions in the unsupervised learning of disentangled representations