arXiv subjects

Vasudevan Venkateshwaran

Publications and source records attributed to Vasudevan Venkateshwaran.

3 recordsLinked to original sources

arXiv2026

Stan: An LLM-based thermodynamics course assistant

Discussions of AI in education focus predominantly on student-facing tools -- chatbots, tutors, and problem generators -- while the potential for the same infrastructure to support instructors remains largely unexplored. We describe Stan, a suite of tools for an undergraduate chemical engineering thermodynamics course built on a data pipeline that we develop and deploy in dual roles: serving students and supporting instructors from a shared foundation of lecture transcripts and a structured textbook index. On the student side, a retrieval-augmented generation (RAG) pipeline answers natural-language queries by extracting technical terms, matching them against the textbook index, and synthesizing grounded responses with specific chapter and page references. On the instructor side, the same transcript corpus is processed through structured analysis pipelines that produce per-lecture summaries, identify student questions and moments of confusion, and catalog the anecdotes and analogies used to motivate difficult material -- providing a searchable, semester-scale record of teaching that supports course reflection, reminders, and improvement. All components, including speech-to-text transcription, structured content extraction, and interactive query answering, run entirely on locally controlled hardware using open-weight models (Whisper large-v3, Llama~3.1 8B) with no dependence on cloud APIs, ensuring predictable costs, full data privacy, and reproducibility independent of third-party services. We describe the design, implementation, and practical failure modes encountered when deploying 7--8 billion parameter models for structured extraction over long lecture transcripts, including context truncation, bimodal output distributions, and schema drift, along with the mitigations that resolved them.

cs.CL↗

arXiv2025

Privacy-Preserving Generative Modeling and Clinical Validation of Longitudinal Health Records for Chronic Disease

Data privacy is a critical challenge in modern medical workflows as the adoption of electronic patient records has grown rapidly. Stringent data protection regulations limit access to clinical records for training and integrating machine learning models that have shown promise in improving diagnostic accuracy and personalized care outcomes. Synthetic data offers a promising alternative; however, current generative models either struggle with time-series data or lack formal privacy guaranties. In this paper, we enhance a state-of-the-art time-series generative model to better handle longitudinal clinical data while incorporating quantifiable privacy safeguards. Using real data from chronic kidney disease and ICU patients, we evaluate our method through statistical tests, a Train-on-Synthetic-Test-on-Real (TSTR) setup, and expert clinical review. Our non-private model (Augmented TimeGAN) outperforms transformer- and flow-based models on statistical metrics in several datasets, while our private model (DP-TimeGAN) maintains a mean authenticity of 0.778 on the CKD dataset, outperforming existing state-of-the-art models on the privacy-utility frontier. Both models achieve performance comparable to real data in clinician evaluations, providing robust input data necessary for developing models for complex chronic conditions without compromising data privacy.

cs.LG↗

arXiv2014

Connecting water correlations, fluctuations, and wetting phenomena at hydrophobic and hydrophilic surfaces

We use molecular simulations to demonstrate the connection between transverse water-water correlations and wetting phenomena for a range of hydrophobic to hydrophilic solid surfaces.Near superhydrophobic surfaces, the correlations are long ranged, system spanning, and are well described by the capillary wave theory. With increasing surface-water attractions, the correlations are quenched. At the critical attraction at which long range correlations disappear, the density profile normal to the surface changes from sigmoidal to layered, and the fluid begins to wet the surface. This behavior is displayed by both water and a Lennard-Jones fluid, highlighting the universality of the underlying physics.

cond-mat.soft↗