Author

Naren Ramakrishnan

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#artificial intelligence Preprint Oct 2026

DataWeave: Deploying Human-LLM Analytics for Exploratory Structured Data Analysis

Data journalism, the practice of using data analysis to surface newsworthy stories, depends increasingly on the ability of reporters and investigative journalists to uncover trends, disparities, and accountability narratives. In practice, exploring large structured datasets remains slow and brittle: journalists must na...

Raquib Bin Yousuf, H. Laxman, Vitaliy Shkremetko et al. · 0 citations

#human-computer interacti... Preprint Oct 2026

Understanding Student Use of Large Language Models Across Computer Science Subfields

This research full paper examines how undergraduate students use large language models (LLMs) across computer science subfields. As LLMs become increasingly integrated into computing education, understanding how their use varies across technical and pedagogical contexts is essential for designing effective, subfield-aw...

S. Nizamani, Yoonjeong Lee, Nikitha Donekal Chandrashekar et al. · 0 citations

Preprint Sep 2026

Skepticism vs. Convenience: Physics Students'Perceptions and Use of Large Language Models Before and After Instruction

The recent emergence of large language models (LLMs) has produced research focusing on the ability of these tools to solve physics problems, evaluate student work, or otherwise impact the problem-solving process of students. However, studies exploring how physics students perceive LLMs (in terms of capabilities, educat...

Jake O'Brien, Margaret Ellis, Alma Robinson et al. · 0 citations

Preprint Jul 2026

Adaptive Graph-of-Islands Evolution for Automatic Feature Engineering with LLMs

T TOPOFE is introduced, a framework that formulates AutoFE as graph-structured multi-island evolutionary program search that produces feature sets with lower redundancy and higher representational coverage, while the learned topology graph acquires meaningful task-specific transfer structure correlated with downstream...

Sha Li, Naren Ramakrishnan · 0 citations

#machine learning Preprint Jul 2026

TopoFE: topology-aware LLM-guided Automated Feature Engineering

Automatic feature engineering (AutoFE) for tabular learning can be naturally formulated as a program synthesis problem, where the objective is to discover predictive feature transformations from an exponentially large search space. Recent advances in large language models (LLMs) have expanded the expressiveness of Auto...

Sha Li, Naren Ramakrishnan · 0 citations