I am a first-year PhD student in Computer Science at Stanford University, advised by Prof. Jure Leskovec.
I have also been fortunate to work with Prof. Carlos Guestrin.
Before this, I spent a lovely year learning under Prof. Zachary Lipton as a visiting researcher at Carnegie Mellon University.
I did my undergrad in CS at IIT Delhi,
where I was fortunate to work with Profs. Mausam, Parag Singla, Sayan Ranu, and Subodh Sharma.
RelBench (Beta) released at Learning on Graphs (LoG) conference.
Sep 26, 2023
I started my PhD at Stanford.
Jul 15, 2023
This webpage is live.
publications
(* denotes equal contribution)
RelBench: A Benchmark for Deep Learning on Relational Databases Rishabh Ranjan*, Joshua Robinson*, Weihua Hu*, Kexin Huang*, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan E. Lenssen, Yiwen Yuan, Zecheng Zhang, Xinwei He, Jure Leskovec Advances in Neural Information Processing Systems (NeurIPS), 2024 paper
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Post-Hoc Reversal: Are We Selecting Models Prematurely? Rishabh Ranjan, Saurabh Garg, Mrigank Raman, Carlos Guestrin, Zachary Lipton Advances in Neural Information Processing Systems (NeurIPS), 2024 paper
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Position: Relational Deep Learning - Graph Representation Learning on Relational Databases
Matthias Fey*, Weihua Hu*, Kexin Huang*, Jan Eric Lenssen*, Rishabh Ranjan*, Joshua Robinson*, Rex Ying, Jiaxuan You, Jure Leskovec International Conference on Machine Learning (ICML), 2024 paper
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A Solver-Free Framework for Scalable Learning in Neural ILP Architectures
Yatin Nandwani*, Rishabh Ranjan*, Mausam, Parag Singla Advances in Neural Information Processing Systems (NeurIPS), 2022 paper
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GREED: A Neural Framework for Learning Graph Distance Functions Rishabh Ranjan, Siddharth Grover, Sourav Medya, Venkatesan Chakaravarthy, Yogish Sabharwal, Sayan Ranu Advances in Neural Information Processing Systems (NeurIPS), 2022 paper
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Exploiting Epochs and Symmetries in Analysing MPI Programs Rishabh Ranjan, Ishita Agrawal, Subodh Sharma IEEE/ACM International Conference on Automated Software Engineering (ASE), 2022 paper
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