NICED : Scientists

Research Experience

Significant research experience was acquired in information hiding (steganography), image processing, and deep neural network-based face recognition systems through a variety of past projects.

Previous research focused on steganographic techniques, watermarking, and cryptographic algorithms to extract hidden information from multimedia covers. Various steganalysis methods were explored, leading to the creation of commercial software products for law enforcement agencies of India aimed at detecting and countering steganography.

In addition, a significant portion of the research was dedicated to deep neural network-based face recognition systems, where convolutional neural networks (CNNs) and other deep learning architectures were employed to improve accuracy in facial identification and verification. Efforts were concentrated on enhancing recognition performance under challenging conditions, including pose variation, occlusion, and illumination changes. Notably, the developed face recognition system is currently being utilized by the State Crime Records Bureau of Tamil Nadu to match history sheeters, demonstrating its practical application in law enforcement and security.

Industry exposure in Java development has reinforced expertise in scalable application development and object-oriented design principles, fostering a strong foundation in enterprise-level software solutions.

A deeper engagement with research has been undertaken in the areas of grid computing and load balancing, focusing on the challenges of distributed computing, resource optimization, and computational workload distribution.
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Research Interests

Significant research interest has emerged in AI-ML technologies, particularly in their applications to medical research, intelligent system design, data-driven decision-making, and predictive modeling. The transformative impact of AI-ML in areas such as disease diagnosis, medical imaging, drug discovery, and patient care optimization remains a central focus. Ongoing efforts are directed toward developing and implementing machine learning models for healthcare analytics, optimizing algorithms, and enhancing data-driven decision support systems.

Awards

Champion of "Agri AI Grand Challenge" contest of Telengana Government (under Telangana AI Mission [T-AIM]) by solving the use case �Real-time price discovery & volume management at e-marketplaces� by using machine learning techniques.