Vijay Prakash Dwivedi

Interests. Machine Learning, Deep Learning on Graphs

I am a Postdoctoral Scholar at Stanford University working with Jure Leskovec in SNAP group on graph representation learning.

Prior to this, I was an AI Researcher at ASUS Intelligent Cloud Services (AICS) in Singapore developing advanced AI capabilities using machine learning, large language models and multimodal deep learning for healthcare applications. I completed my PhD at Nanyang Technological University (NTU), Singapore, where I was advised by Luu Anh Tuan and Xavier Bresson and received one of the Outstanding PhD Thesis Awards.

Nanyang Technological University (NTU), Singapore Doctor of Philosophy (PhD), 2019-2024
College of Computing and Data Science
Motilal Nehru National Institute of Technology (MNNIT), Allahabad Bachelor of Technology (BTech), 2014-2018
Department of Computer Science and Engineering

Postdoctoral Scholar, Stanford University, USA, Nov 2024-now
AI Researcher, ASUS Intelligent Cloud Services (AICS), Singapore, Dec 2023-Oct 2024
Research Intern, Snap Inc., Seattle, USA, Jun-Aug 2023
Research Intern, Sea AI Lab, Singapore, Dec 2022-May 2023
Research Intern, Sony Group Corporation, Tokyo, Japan, Jun-Aug 2022
Research Assistant, Media & Interactive Computing Lab, NTU Singapore, Feb-Jul 2019
Solution Consultant, Francium Technologies Pvt Ltd, Chennai, Dec 2018-Feb 2019 [blog]
Research Intern, Language Technologies Research Center (LTRC), IIIT Hyderabad, May-Jul 2017 [code]
Research Intern, CogBID Lab, University of Stirling, May-Jul 2017

Master of Computer Science (MCS) Program at Lincoln International College, Kathmandu, Nepal, Feb 2024 Guest Lecture: Learning on Graphs for Artificial Intelligence
ML on Graphs (MLoG) Workshop at the 16th ACM International WSDM Conference, Singapore, Mar 2023 Keynote: Transformers for Graph Structured Data
NTU Course MH6812: Advanced Natural Language Processing with Deep Learning, Feb 2023 Guest Lecture: Introduction to Graph Neural Networks
4th NAAMII Nepal Winter School in AI, Kathmandu, Nepal, May 2022 Introduction to GNNs
3rd NAAMII Nepal Winter School in AI, Kathmandu, Nepal, Dec 2021 Introduction to GNNs
LoGaG: Learning on Graphs and Geometry Reading Group, Virtual, Nov 2021 Graph Neural Networks with Learnable Structural and Positional Representations
[Video] [Slides]
4th National Workshop on Machine Learning and Data Science, Kathmandu, Nepal, Sep 2021 Introduction to GNNs and Application Prospects
The DECAL Lab, UC Davis, Apr 2021 Benchmarking Graph Neural Networks
NTU SCSE-GSC Student Lecture Series, Mar 2021 A Generalization of Transformer Networks to Graphs
[Video] [Slides]

ACL Rolling Review (ARR), 2024
AAAI Conference on Artificial Intelligence (AAAI), 2025, 2024
Learning on Graphs Conference (LoG), 2024, 2023, 2022
Neural Information Processing Systems (NeurIPS), 2024, 2023, 2022
International Conference on Learning Representations (ICLR), 2025, 2024, 2023, 2022. (Highlighted Reviewer)
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024, 2023
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023
IEEE International Conference on Robotics and Automation (ICRA), 2022
AAAI Workshop on Graphs and More Complex Structures for Learning and Reasoning (GCLR), 2022
Neural Networks, Elsevier, 2022, 2021
MLSys Workshop on Graph Neural Networks and Systems (GNNSys), 2021
ICML Workshop on Graph Representation Learning and Beyond (GRL+), 2020
Multiple NeurIPS, ICLR and ICML Workshops on graphs and geometric learning domains, 2022-now