Ms. Fnu Neha | Biomedical Imaging | Best Researcher Award

PhD Candidate | Kent State University | United States

Neha is a Ph.D. candidate in Computer Science at Kent State University, specializing in artificial intelligence, deep learning, biomedical image processing, and medical informatics, with her dissertation focused on developing advanced AI-driven frameworks for detecting and classifying renal tumors using CT scans integrated with radiographic and biopsy-based features. She has completed undergraduate and postgraduate degrees in computer applications with top academic distinction and further strengthened her expertise with certifications in AI, deep learning, and data analytics from globally recognized platforms. Her professional experience spans roles as Graduate Assistant and Research Assistant at Kent State University, Assistant Professor at leading institutions in India, software developer, and quality assurance trainee, contributing to both academic innovation and applied computing solutions. Her research has produced more than twenty scholarly publications across international journals and conferences, complemented by co-authoring a book on generative AI, and she has actively contributed as a reviewer and program committee member for international conferences and journals. Recognized with multiple awards including outstanding teaching and women in computing honors, as well as international travel grants, she has demonstrated excellence in both research and teaching while serving in leadership roles such as chairperson of ACM-W and the Graduate Student Association. With over 100 citations by 92 documents, 22 indexed publications, and an h-index of 5, her scholarly impact is evident, and she is strongly positioned to pursue an academic career that advances AI-driven healthcare innovation while fostering research, teaching, and community engagement.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

Bhati, D., Neha, F., Guercio, A., Amiruzzaman, M., & Kasturiarachi, A. (Eds.). (n.d.). A beginner’s guide to generative AI: An introductory path to diffusion models, ChatGPT, and LLMs.

[Author(s)]. (n.d.). Clarifying confounders in genotype-negative HCM: A call for nuanced research approaches.