Ms. Soma Samanta

Assistant Professor

Soma Samanta is currently serving as an Assistant Professor in the Department of Artifi-cial Intelligence & Machine Learning at Dayananda Sagar University, Bengaluru, India. She obtained her Master of Technology (M.Tech.) in Mathematics & Computing from the Indian Institute of Technology (IIT) Patna. Prior to this, she completed her Master of Sci-ence (M.Sc.) in Applied Mathematics and Bachelor of Science (Honours) in Mathematics from Vidyasagar University. She has a strong academic background in mathematics, arti-ficial intelligence, machine learning, and computational sciences.

Her research interests include Artificial Intelligence, Machine Learning, Deep Learning, Medical Image Analysis, Computer Vision, Diffusion Magnetic Resonance Imaging (dMRI), Physics-Informed Deep Learning, Vision Transformers, Generative Artificial Intelligence, Large Language Models (LLMs), and Scientific Computing. Her current re-search focuses on developing physics-informed deep learning frameworks for estimating brain microstructural biomarkers from single-shell diffusion MRI for clinically relevant neuroimaging applications.

During her postgraduate research at IIT Patna, she developed novel deep learning meth-odologies for Free-Water Elimination (FWE) and Intracellular Volume Fraction (ICVF) estimation from diffusion MRI. Her research integrates mathematical modelling with modern deep learning techniques and has been validated using publicly available neu-roimaging datasets. She has authored research publications in the field of medical image analysis, including a paper accepted at the International Conference on Pattern Recogni-tion (ICPR 2026), and continues to pursue research in artificial intelligence and medical imaging.

In addition to her research in medical image analysis, she has worked on projects involv-ing Natural Language Processing and intelligent recommendation systems. She is profi-cient in Python, PyTorch, MATLAB, R, SQL, FastAPI, LangChain, Scikit-learn, and sci-entific computing tools. Her teaching interests include Artificial Intelligence, Machine Learning, Deep Learning, Generative Artificial Intelligence, Large Language Models, Computer Vision, Medical Image Analysis, Data Science, and Applied Mathematics. She is committed to promoting excellence in teaching, interdisciplinary research, and mentor-ing students to address real-world challenges through artificial intelligence and data-driven technologies.