Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
Published:
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Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
publications
Feature Selection for Network Intrusion Detection
Published in KDD'25, 2009
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Partial Information Decomposition for Data Interpretability and Feature Selection
Published in AISTATS'25, 2010
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Mutual Information Preserving Neural Network Pruning
Published in , 2015
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Information-theoretic State Variable Selection for Reinforcement Learning
Published in , 2024
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talks
Partial Information Decomposition for Data Interpretability and Feature Selection
Published:
I will present my work on Partial Information Decomposition (PIDF) as part of the opening session of AiStats 2025. This will cover novel methods for quantifying redundant, unique, and synergistic information in high-dimensional datasets, with applications to interpretable machine learning and feature selection.
Feature Selection for Network Intrusion Detection: An Information-Theoretic Approach
Published:
I will present my research on information-theoretic feature selection for network intrusion detection systems. This work introduces novel methods to identify minimal yet maximally discriminative features in cybersecurity datasets, significantly reducing computational overhead while maintaining state-of-the-art detection accuracy. The talk will include: