SELECT & COMPRESS
Learning from less
Coreset selection and dataset distillation: finding or creating compact datasets that preserve what matters for learning.
A CURIOUS MIND. A DATA-CENTRIC LENS.
PhD researcher at DFKI & RPTU
Kaiserslautern-Landau, Germany
I study how we can help models learn more from less data. My research brings together data selection, dataset distillation, and continual learning, with a growing focus on vision–language models.

01 / RESEARCH
What does a model really need to learn?
I explore the role of training data: what we keep, what we compress, and how we keep learning.
SELECT & COMPRESS
Coreset selection and dataset distillation: finding or creating compact datasets that preserve what matters for learning.
REMEMBER & ADAPT
Continual learning: exploring how data selection can support learning from new experiences while retaining previous knowledge.
CONNECT & UNDERSTAND
A growing focus on vision–language models, and how data-efficient learning can connect images and text.
02 / SELECTED WORK
DOCTORAL RESEARCH · ONGOING
Exploring data selection and distillation in continual and multimodal learning. How can a compact training set support effective learning when data is limited or constrained?
MASTER’S THESIS · RPTU
An audio-visual system for the humanoid robot EMAH, bringing together environmental sound, object detection, scene text, and human activity to better interpret its surroundings.
Robotics Research Lab ↗03 / THE PERSON BEHIND THE PAPERS
I’m Arundhati Shanbhag, a PhD researcher in artificial intelligence at DFKI and RPTU Kaiserslautern-Landau.
My path has taken me through recommendation systems, diffusion models, and multimodal robotics. Today, I’m interested in how the data we choose shapes what models learn.
Outside research, I enjoy cooking, reading, gardening, crocheting, and travelling to experience different cultures.
GOOD IDEAS START WITH A CONVERSATION
Questions about my research, shared interests, or a potential collaboration?
I’d love to hear from you.