Akshita Kamsali @NeurIPS'25

185, BHEE

465 Northwestern Ave

West Lafayette, IN

Actively seeking Internship opportunities for Summer/Fall 2026!

I am a PhD student in Electrical and Computer Engineering at Purdue University and a member of the Robot Vision Lab, advised by Dr. Avi Kak. My research is in deep learning, focusing on interpretabilty solutions for deep learning models. I have had the opportunity to work with various industry collaborators for real-life problem solving throughout my PhD. Proficient in Python, PyTorch, and Deep Learning, I also bring strong skills in scientific communication, public speaking, and presentations, enabling me to effectively bridge research and impactful real-world applications.

I have also interned at Lawrence Livermore National Lab, was a Top-presenter featured in LLNL newsletter and Purdue ECE top news.

For a detailed overview of my academic and professional journey, you can view my resume.

Let’s connect and explore how technology can transform the way we perceive and solve problems!

News

Oct 18, 2025	Attending NeurIPS 2025 and organizing Women in Machine Learning Workshop
Aug 18, 2025	GTA for ECE20875 Python for DataScience
May 20, 2025	Starting as a Research Intern at Data Science Institute @LLNL mentored by Aldair Gongora
Mar 11, 2025	Awarded Outstanding Service Award 2025 by College of Engineering for service to ECE
Mar 10, 2025	Featured on Purdue Graduate School Testimonials
Dec 10, 2024	NeurIPS 2024 - Received NSF travel grant to present poster on my change detection work.
Oct 10, 2024	Chair for ECE Graduate Symposium 2024. Moderated panel discussion on “ECE in the Age of AI”

Selected Publications

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
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
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.