Vansh Ramani
Email: vanshramani27@gmail.com | cs5230804@iitd.ac.in
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More about me:
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Coursework |
Ongoing Projects
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I am an undergraduate in Computer Science Engineering at Indian Institute of Technology Delhi
with a deep interest in Artificial Intelligence and its applications. My work spans graph deep learning, spatio-temporal data analytics, data distillation,
and high-dimensional nearest neighbor search. Recently, I've been exploring LLMs and agentic frameworks.
I will be joining Technical University of Denmark as an exchange student for the Spring 2026 semester.
I got selected into Y Combinator's Winter 26 batch to build ramAIn and am on a break from IIT Delhi.
I recently completed a Research Internship at Carnegie Mellon University
working with Prof. Pradeep Ravikumar in the
Machine Learning Department. My research focused on causal representation learning,
neurosymbolic AI, and machine unlearning with provable guarantees.
Previously, I was a Research Intern at the University of Copenhagen
working with Dr. Panagiotis Karras in the
Department of Computer Science, DIKU on developing high dimensional nearest neighbor search algorithms.
I am also a Research Assistant at IIT Delhi with
Dr. Sayan Ranu in the
Data Science and Information Retrieval Lab (DSIRE),
working on graph neural networks and data distillation.
I have also worked with Dr. Tarak Karmakar in the
Computational Chemistry, Materials & Biology (CCMB) lab at IIT Delhi
on molecular solubility prediction using graph neural networks.
I love reading random facts, listening to jazz music, and right now, I'm just trying to work my way through quizzes and academics. :3
I am always open to collaborations and discussions. Please feel free to reach out!
Other:
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Fun/Favorites
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Panorama: Fast-Track Nearest Neighbors
Vansh Ramani, Alexis Schlomer, Akash Nayar, Panagiotis Karras, Sayan Ranu, Jignesh M. Patel
arXiv preprint arXiv:2510.00566 (2025) - Under Review
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Bonsai: Gradient-free Graph Distillation for Node Classification
Mridul Gupta, Samyak Jain, Vansh Ramani, Hariprasad Kodamana, Sayan Ranu
The Thirteenth International Conference on Learning Representations (ICLR) 2025
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Graph Neural Networks for Predicting Solubility in Diverse Solvents Using MolMerger Incorporating Solute–Solvent Interactions
Vansh Ramani, Tarak Karmakar
Journal of Chemical Theory and Computation 2024, 20 (15), 6549-6558
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Research Assistant
May 2024 - Aug 2024
Carnegie Mellon University
Pittsburgh, PA, USA
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Worked with Prof. Pradeep Ravikumar in the Statistical & Symbolic Learning Group, MLD.
Research focus on formalizing unlearning, designing mechanisms with provable removal guarantees for data and representations.
Developing NeuroSymbolic architecture for lifting neural models via symbolic modules for interpretability and convergence.
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Research Intern
May 2024 - June 2024
University of Copenhagen
Copenhagen, Denmark
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Worked with Dr. Panagiotis Karras in Software, Data, People & Society, DIKU.
Developed PANORAMA: a machine learning-driven approach that tackles the ANNS verification bottleneck through data-adaptive learned orthogonal transforms.
Achieved 2-30× end-to-end speedup with no recall loss across diverse datasets from CIFAR-10 to modern embedding spaces including OpenAI's Ada 2 and Large 3.
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Research Assistant
Feb 2024 - Present
IIT Delhi
Delhi, India
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Working with Dr. Sayan Ranu in Data Science and Information Retrieval Lab (DSIRE).
Developed Bonsai: linear-time graph distillation algorithm achieving 22x faster processing across 6 real-world datasets.
Achieved state-of-the-art accuracy in 14/18 test scenarios, outperforming other baselines by ≥5% in multiple cases.
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Research Intern
Dec 2023 - Jan 2024
IIT Delhi
Delhi, India
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Worked with Dr. Tarak Karmakar in Computational Chemistry, Materials & Biology (CCMB).
Developed state-of-the-art GNN framework for solubility prediction using novel Merged-Molecule approach.
Achieved R² of 0.767 and MAE of 0.78 on test set; average MAE of 0.79 across 65 solvents.
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