Publications

2026

1. Digital Discovery

   Benchmarking Deep Learning Models for Raman Spectroscopy Across Open-Source Datasets

   Sineesh, Adithya, and Kamsali, Akshita Ramya

   Digital Discovery 2026

2025

1. NeurIPS-Workshop

   From Sensing to Reasoning: Multi-Modal Large Language Models Guiding Robotic Intelligence in Autonomous Labs

   Kamsali, Akshita Ramya, Hallas, Kristen, and Gongora, Aldair E.

   In Women in Machine Learning Workshop @ NeurIPS 2025 2025
2. NeurIPS-Workshop

   Speak, Start, See, Sense: How NLP, Robotics, and Computer Vision can Improve Automated Experimentation in Self-driving Labs

   Hallas, Kristen, Allaparti, Tarun,  Kamsali, Akshita Ramya, and Gongora, Aldair E.

   In Women in Machine Learning Workshop @ NeurIPS 2025 2025

2024

1. NeurIPS-Workshop

   Graph-based Change Detection for Natural Language

   Kamsali, Akshita Ramya

   In Women in Machine Learning Workshop @ NeurIPS 2024 Dec 2024

   Abs HTML

   Computational models such as machine learning (ML) and deep learning (DL) excel at processing structured data, including numbers, images, audio signals, and spectral data. These types of data can be easily converted into numerical patterns, which models can analyze to extract meaningful insights. In contrast, processing text is significantly more complex. Language is inherently rich, intricate, and nuanced. Techniques like tokenization and word embeddings, which transform text into numerical representations, often struggle to fully capture the subtle context and deeper meaning within the text. Challenges such as context dependency, disambiguation, and diverse writing styles can lead to multiple interpretations of the same text, even for humans. This complexity is further magnified in specialized domains, such as legal or medical documents. In this work we present LLM-based solution to build consistent knowledge graphs for change detection.