I am a third-year PhD student in Computer Science at Stanford University,
co-advised by Prof. Jure Leskovec
and Prof. Carlos Guestrin.
I am grateful to be supported by the Amazon Core AI Fellowship (2025-27),
and earlier by the School of Engineering Fellowship (2023-24).
Before this,
I spent a lovely year learning under
Prof. Zachary Lipton
as a visiting researcher at Carnegie Mellon University (CMU).
I did my undergrad in CS at IIT Delhi,
where I was the President's Gold Medalist for the class of 2022.
I am passionate about deep learning research.
These days I develop foundation models for relational domains,
such as SQL databases, spreadsheets, tables and time series,
to bring modern AI to enterprise data
(e.g., see this).
In my free time,
I like to swim, watch movies, and
read.
pre-prints
Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data Rishabh Ranjan, Valter Hudovernik, Mark Znidar, Charilaos Kanatsoulis, Roshan Upendra, Mahmoud Mohammadi, Joe Meyer, Tom Palczewski, Carlos Guestrin, Jure Leskovec Under review, 2025 paper
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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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