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Systematic Biomarker Discovery for Glioblastoma Subtyping Using Machine Learning

Published in 2025 IEEE 6th India Council International Subsections Conference (INDISCON), 2025

A systematic machine learning pipeline for discovering biomarkers that distinguish glioblastoma subtypes using transcriptomics data, feature selection, and gene co-expression network analysis.

Recommended citation: Sutradhar, A., Banerjee, A., Samanta, A., Hossain, S. F., Dey, A., & Biswas, S. (2025). "Systematic Biomarker Discovery for Glioblastoma Subtyping Using Machine Learning." IEEE INDISCON 2025. pp. 1–6.
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Learning Filter-Aware Distance Metrics for Nearest Neighbor Search with Multiple Filters

Published in arXiv preprint arXiv:2511.04073, 2025

We propose a method to learn filter-aware distance metrics that improve approximate nearest neighbor retrieval efficiency and accuracy under multiple conjunctive attribute filters.

Recommended citation: Sutradhar, A., Gupta, S., Krishnaswamy, R., Xu, H., Rastogi, A., & Srinivasa, G. (2025). "Learning Filter-Aware Distance Metrics for Nearest Neighbor Search with Multiple Filters." arXiv preprint arXiv:2511.04073.
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Side Effects of Character Training: Quantifying Cross-Constitution Drift in LLMs

Published in ICML 2026 Workshop on Pluralistic Alignment, 2026

We quantify how character training a language model toward one constitution induces unintended behavioral drift when the model is evaluated against other constitutions.

Recommended citation: Kumar, B., Sutradhar, A., Panigrahi, S., Chang, J., & Levine, L. (2026). "Side Effects of Character Training: Quantifying Cross-Constitution Drift in LLMs." ICML 2026 Workshop on Pluralistic Alignment.
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