About me
I’m a second-year Ph.D. scholar at McGill University and Mila - Quebec AI Institute, where Prof. William Hamilton advises me. I completed my M.S by Research (Thesis) at the Indian Institute of Technology Madras (IIT M) under the guidance of Prof. 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
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 will appear 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 will appear 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
2019
- An extended abstract of our work, On Incorporating Structural Information to Improve Dialogue Response Generation by
N Moghe, P Vijayan, B Ravindran, and M Khapra was presented at
EurNLP'19.Poster - Our Work on Learning policies for Social network discovery with Reinforcement learning by
H Kamarthi, P Vijayan, B Wilder, B Ravindran, M Tambe was presented at the
Graph Representation Learning Workshop, NeurIPS'19. - Check out our Arxiv Pre-print on Network Representation Learning: Consolidation and Renewed Bearing by
S Gurukar
*, A Srinivasan*, P Vijayan*, G Bajaj, C Cai, M Keymanesh, S Kumar, P Maneriker, A Mitra, V Patel, B Ravindran, S Parthasarathy. Full Paper | Code
2018
- Our work on Learning semi-supervised cluster invariant node representations with NMF by A Mitra, P Vijayan, S Parthasarathy, and B Ravindran was presented at the
Relational Learning Workshop (R2L), NeurIPS'18. - Our work on Higher Order Propagation for Deep Collective Classification by P Vijayan, Y Chandak, M Khapra, and B Ravindran was presented at the eighth workshop on
Statistical relational learning for AI (StarAI), IJCAI'18.Full Paper | Code - Our work on Fusion Graph Convolutional networks by P Vijayan, Y Chandak, M Khapra, and B Ravindran was presented at the
14th workshop on Mining and Learning with Graphs (MLG), KDD'18.Full Paper | Code
Service
- Program Commitee Member: SIAM SDM (2021), NAACL-HLT (2021), GCLR Workshop AAAI (2021), ADCOM (2018), CODS-COMAD (2018)
- Reviewer: ICLR (2021, 2020), DMKD Journal (2019), ACL(2018)
Teaching Assistant
- COMP596-001: Network Science (Fall’20)
- COMP598-001: Introduction to Data Science (Fall’20)
- COMP767-001: Graph Representation Learning (Winter’20)
Courses
McGill University
- 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