Mahendra Nath Reddy E

An independent researcher with a Masters in Artificial Intelligence & Robotics, I have a passion for exploring how machine learning can address complex challenges in medicine.

My core research centers on medical image analysis, where I leverage deep learning models to analyze and extract insights from clinical data. This involves investigating novel methods that can aid and enhance the process of medical diagnosis.

I also explore AI's application in cybersecurity, focusing on techniques in authentication, privacy, and Over-the-Air (OTA) security to build a foundation of trust for next-generation technologies.

Research Interests: Medical Image Analysis | AI in Healthcare | AI for Diagnosis | AI in Cybersecurity (Authentication, Privacy, OTA)

Profile Picture

Selected Works

[PROJECT] An Empirical Investigation into Domain Generalization in Histopathology: Framing a deep learning model as a scientific instrument, this ongoing thesis investigates the critical challenge of domain generalization, aiming to quantify why and how pathology models fail when applied to unseen clinical data. [View Project Details]
[PROJECT] Unsupervised Clustering for Nuclei Segmentation: An award-winning evaluation of three distinct unsupervised methods for nuclei segmentation in H&E images. This work, which provides a scalable alternative to supervised techniques, was recognized with the 2nd Prize at the UH Data Science Project Club poster presentation. [View Project Details]
[PUBLICATION] M. N. Reddy E and L. B. Rananavare, "Deep Learning Approaches for Plant Disease Management: A State-of-the-Art Analysis." A peer-reviewed paper analyzing the state-of-the-art in deep learning for identifying and managing plant diseases, published in the proceedings of CSITSS 2023. 🔗 [Link]