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Posts by Collection
portfolio
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publications
On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction
Published in arXiv, 2021
We conducted experiments using graph neural network ensembles to predict molecular HOMO-LUMO gaps.
Fine-Tuning Large Language Models to Appropriately Abstain via Semantic Entropy
Published in NeurIPS Safe Generative AI Workshop 2024, 2024
We fine-tune LLMs using semantic entropy to make them abstain from answering uncertain questions.
Enhancing the Expressivity of Temporal Graph Networks via Source-Target Identification
Published in NeurIPS Symmetry and Geometry in Neural Representations Workshop 2024 (Oral), 2024
We show that adding source-target identification to Temporal Graph Networks (TGNs) strictly increases its expressivity and demonstrate that the resulting architecture outperforms all TG methods in dynamic node affinity prediction on all TGB datasets.
T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs
Published in KDD 2025 Machine Learning on Graphs Workshop (Oral) & KDD 2026 (Data & Benchmarks), 2025
We design a temporal ‘obstacle course’ to systematically benchmarks the performance of TGNNs at carefully designed synthetic temporal reasoning tasks.
Feedforward Mixing is as Sharp as it is Slow in Reverse
Published in ICML 2026 Workshop on Combining Theory and Benchmarks: Towards A Virtuous Cycle to Understand and Guarantee Foundation Model Performance (T&B), 2026
We analyze the feedforward mixing process in neural networks and establish backward mixing time as a key metric for evaluating information propagation.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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