Sutanay Choudhury — accepted AI-conference papers

2024

CHEMREASONER: Heuristic Search over a Large Language Model’s Knowledge Space using Quantum-Chemical Feedback

Henry W. Sprueill, Carl Edwards, Khushbu Agarwal, Mariefel V Olarte, Udishnu Sanyal, Conrad Johnston +3

ICML 2024poster

The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic reasoning with quantum-chemistry based feedback from 3D atomistic re…

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2024

Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models

Andrew William Engel, Zhichao Wang, Natalie Frank, Ioana Dumitriu, Sutanay Choudhury, Anand Sarwate +1

ICLR 2024spotlight

A recent trend in explainable AI research has focused on surrogate modeling, where neural networks are approximated as simpler ML algorithms such as kernel machines. A second trend has been to utilize kernel functions in various explain-by-example or data attribution tasks. In this work, we combine…

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2023

A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks

Mehrdad Khatir, Nurendra Choudhary, Sutanay Choudhury, Khushbu Agarwal, Chandan K. Reddy

IJCAI 2023poster

Hyperbolic neural networks can effectively capture the inherent hierarchy of graph datasets, and consequently a powerful choice of GNNs. However, they entangle multiple incongruent (gyro-)vector spaces within a layer, which makes them limited in terms of generalization and scalability. In this w…

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2023

Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design

Henry W. Sprueill, Carl Edwards, Mariefel V Olarte, Udishnu Sanyal, Heng Ji, Sutanay Choudhury

EMNLP 2023short findings

Discovering novel catalysts requires complex reasoning involving multiple chemical properties and resultant trade-offs, leading to a combinatorial growth in the search space. While large language models (LLM) have demonstrated novel capabilities for chemistry through complex instruction following ca…

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2016

Multi-centrality graph spectral decompositions and their application to cyber intrusion detection

Pin-Yu Chen, Sutanay Choudhury, Alfred O. Hero III

ICASSP 2016accepted

Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful. Distinct from previous graph decomposition approaches based on subspace projection of a single topological feature, e.g., the Fiedler vector of centered…

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