About me
I’m a third-year Ph.D. student at McGill University and Mila - Quebec AI Institute, where I’m supervised by Doina Precup and William L. Hamilton. I completed my M.S by Research (Thesis) at the Indian Institute of Technology Madras (IIT M) under the guidance of Balaraman Ravindran. I have 5 years of experience as a research staff working at IIT Madras, where I was associated with the Robert Bosch Centre for Data Sciences and AI (RBCDSAI) and RISE Interactive Intelligence Lab.
My research interests lie in the broad areas of Machine Learning and Complex Networks, especially at their intersection, where I design Deep Neural Network models for relational representation and reasoning tasks in irregularly structured domains.
Updates
2022
- Our work on Benchmarking and Analysing Unsupervised Network Representation Learning and the Illusion of Progress by
S Gurukar
*, P Vijayan*, S Parthasarathy, B Ravindran, A Srinivasan, G Bajaj, C Cai, M Keymanesh, S Kumar, P Maneriker, A Mitra and V Patel is available in the Proceedings ofTransactions on Machine Learning ResearchFull Paper | Code - Our work on Scaling Graph Propagation Kernels for Predictive Learning by P Vijayan, Y Chandak, M Khapra, S Parthasarathy, and B Ravindran is available in the Proceedings of the
Frontiers in Big Data, section Data Mining and Management (Frontiers 2022). Paper | Code
2021
- Recognized as an Outstanding Reviewer at ICLR’2021
- Our work on Semi-Supervised Deep Learning for Multiplex Networks by M Anasua, P Vijayan, R Sanasam, D Goswame, S Parthasarathy, and B Ravindran is available in the Proceedings of the
ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD'21.Paper | Code - Our work on Unsupervised Network Representation Learning and the Illusion of Progress by
S Gurukar
*, P Vijayan*, S Parthasarathy, B Ravindran, A Srinivasan, G Bajaj, C Cai, M Keymanesh, S Kumar, P Maneriker, A Mitra and V Patel will be presentated at theKDD deep learning day, KDD'21.Full Paper | Code - Our work on EGO-GNNs: Exploiting EGO Structures in Graph Neural Networks by D Sandfelder, P Vijayan and W L Hamilton is available in the Proceedings of the
International Conference on Acoustics, Speech, and Signal Processing, ICASSP'21.Paper
2020
- Our work on Understanding Dynamic Scenes using Graph Convolution Networks by S Mylavarapu, M Sandhu, P Vijayan, M Krishna, B Ravindran, and A Namboodiri is available in the Proceedings of the
International Conference on Intelligent Robots and Systems, IROS'20.Paper | Video | Code - Our Work on On Incorporating Structural Information to Improve Dialogue Response Generation by
N Moghe, P Vijayan, B Ravindran, and M Khapra is available in the Proceedings of
NLP for Conversational AI workshop, ACL'20.Paper | Code - Our work on Influence Maximization in Unknown Social Networks: Learning Policies for Effective Graph Sampling by H Kamarthi, P Vijayan, B Wilder, B Ravindran, and M Tambe is available in the Proceedings of the
International Conference on Autonomous Agents and Multiagent Systems, AAMAS'20.[Nominated for Best Paper Award] Paper | Code - Our work on A Unified Non-Negative Matrix Factorization Framework for Semi-Supervised Learning on Graphs by A Mitra, P Vijayan, S Parthasarathy, and B Ravindran is available in the Proceedings of
SIAM International Conference on Data Mining, SDM'20.Paper | Code | Supplement - Our work on Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks by S Mylavarapu, M Sandhu, P Vijayan, M Krishna, B Ravindran, and A Namboodiri will appear in the Proceedings of
IEEE Intelligent Vehicles Symposium, IV'20.Paper | Code
Service
- Program Commitee Member: SIAM SDM (2022), EMNLP (2021), SIAM SDM (2021), NAACL-HLT (2021), GCLR Workshop AAAI (2021), ADCOM (2018), CODS-COMAD (2018)
- Reviewer: ICLR (2022-Declined, 2021, 2020), NeurIPS (2021-Declined), JDSA Jounral (2021), DMKD Journal (2019), ACL(2018)
Teaching Assistant
- INF8953DE: Reinforcement Learning (Fall’21)
- COMP596-001: Network Science (Fall’20)
- COMP598-001: Introduction to Data Science (Fall’20)
- COMP767-001: Graph Representation Learning (Winter’20)
Courses
McGill University
- COMP597-002: Automated Reasoning with Machine Learning (on-going)
- COMP767-001: Reinforcement Learning
- COMP550-001: Natural Language Processing
- COMP767-002: Probabilistic Graphical Models
Indian Institute of Technology Madras
- CS5011: Introduction to Machine Learning
- CS6012: Social Network Analysis
- CS7015: Deep Learning
- CS6720: Data Mining
- CS6310: Deep Learning for Computer Vision
- CH5440: Multivariate Data Analysis