I hold a Ph.D. in Computer Science and Engineering with a specialization in Artificial Intelligence from Lovely Professional University, Punjab. With 24 years of experience in education and 1.5 years in the industry at various reputed institutes, I am passionate about teaching and mentoring students.
Over the years, I have taught multidisciplinary subjects, including Data Structures, OOAD using C, Python, Java, Machine Learning, Deep Learning, and Data Science. My focus is to help students effectively apply complex concepts to real-world applications through creative lesson planning, interactive discussions, and assessments aligned with NEP 2020.
I have also served in administrative roles such as University Exam Coordinator, Training and Placement Coordinator, IQAC Coordinator, NSS Program Officer, and Admission Counselor. In addition to teaching, I have published research papers, book chapters, and conference articles, and I am currently guiding four Ph.D. students at Lovely Professional University.
As a reviewer for international conferences and journals, I contribute to academic excellence and have participated in syllabus development for Mumbai University under NEP 2020. Passionate about teaching and research, I strive to inspire students to excel in their academic and professional journeys.
READ MY WORK 🧑💻
1. Machine learning approach towards mammographic breast density measurement for breast cancer risk prediction: an overview
Mammographic breast density” is a significant biomarker which has strong association with the occurrence of breast cancer. Generally, in dense breast tissues, the sensitivity of mammograms reduces significantly. It also increases the risk of false positive diagnosis in computer detection systems. Development in the cutting-edge technology has the ability to improve the breast density measurement standards, and lead to early prediction of breast cancer by overcoming the restriction of subjective analysis of breast density measurement. In spite of development of different techniques for automatic breast density measurement, very few tools are practically available for breast cancer risk prediction.
2. Investigating purification and activity analysis of urease enzyme extracted from jack bean source: A green chemistry approach
Urease is an enzyme of historical importance in the field of biochemistry, generally microbial and plant urease is the primary sources of urease. The significant applications of urease enzyme are found to be foremost in food industry, medical equipment’s and biosensors. In this work, urease has been extracted from Jack bean meal using ammonium sulphate and acetone precipitation. A significant amount of urease was precipitated and concentrated at 60% saturated solution of ammonium sulphate. The obtained precipitates were dissolved in 50 mM phosphate buffer (pH 8) after centrifugation, and subjected to sodium dodecyl-sulphate polyacrylamide gel electrophoresis (SDS-PAGE) to determine the molecular weight of urease.
3. Investigating Milk Contaminants on Health and the Need for Advanced Detection Methods
Food diseases and allergies are one of the important causes having major impact on health of the world population. Being one of the basic needs of nutrition, milk and milk products are important types of packed foods which are consumed by the modern world population. Milk sources get contaminated due to different sources like pesticide residues, heavy metals, and aflatoxin during the process of cattle feeding, pre-processing, and improper handling during post processing period. Therefore, there is a need of the society to have a more accurate, fast in response, durable and stable analytical device to detect different milk contaminants in packed milk and milk products.
4. Investigating Blockchain-Based Privacy Preservation Framework for Scalability in Smart Healthcare Big Data Management
Blockchain, a distributed ledger technology utilizing cryptographic methods, offers promising solutions for enhancing security and privacy in smart healthcare big data (HBD) management systems. However, scalability remains a significant challenge, as the decentralized nature of blockchain networks often leads to performance bottlenecks and increased transaction costs, especially when managing large volumes of healthcare data. This framework presents a Blockchain-Based Privacy Preservation Framework (PPF) designed to mitigate cyber threats in smart HBD management systems.
