K Sailaja Kumar

Associate Professor

ksailaja@rvu.edu.in

About
Publication & Works
Research Summary
Awards & Achievements

Dr. K. Sailaja Kumar holds a Ph.D. in Computer Applications from Visvesvaraya Technological University (VTU) and an M.Phil. from Madurai Kamaraj University. With over 24 years of academic experience and 2 years in industry, she is an Associate Professor at RV University, Bangalore. She specialises in Data Science, Machine Learning, Predictive Analytics, and Big Data.

Before RV University, Dr. Sailaja was a Senior Subject Matter Expert at UNext Learning Pvt. Ltd., providing corporate training and managing data science projects for clients like HPE, Deloitte, and Oracle. Her research, published in national and international journals, focuses on predictive analytics, recommender systems, and social network analysis.

In addition to teaching and research, she is involved in curriculum development as a Board of Studies member and has contributed to syllabus design, exam preparation, and student evaluations. She has also been actively involved in NBA, NAAC, IQAC, and NPTEL initiatives.

Dr. Sailaja is a life member of the Indian Society for Technical Education (ISTE), the Computer Society of India (CSI), and a member of the IEEE Computational Intelligence Society. She is dedicated to integrating technology into education and mentoring future data scientists.

सततं श्रमेण सिद्धिः। "Success is achieved through continuous effort"

Dr. K. Sailaja Kumar’s research spans across multiple domains, including data science, machine learning, predictive analytics, and social network analysis. Her work has been extensively published in reputable national and international journals. Below is a summary of her key research contributions:

Multi-Objective Optimization: One of her prominent works is "An Improved Multi-Objective Particle Swarm Optimization Routing on MANET" (2023), which addresses routing in Mobile Ad Hoc Networks (MANET) using optimization algorithms. This work has received significant attention for its advancements in network optimization.

Big Data Analytics: Dr. Sailaja has contributed significantly to the field of big data analytics, with publications such as "Performance Evaluation of Cloud Service with Hadoop for Twitter Data" (2019), and "Twitter Data Analysis Using Hadoop and 'R' and Emotional Analysis Using Optimized SVNN" (2023), where she explores cloud-based systems for processing large-scale social media data and sentiment analysis.

Social Network Analysis: Her research includes studying influential users in online social networks, as seen in "Identify the Influential User in Online Social Networks using R, Hadoop, and Python" (2016). Additionally, she has contributed to understanding event prediction through social media in "Prediction of Events in Educational Institutions Using Online Social Networks" (2014).

Predictive Analytics: Dr. Sailaja has worked on predictive analytics models for different applications, including aviation, education, and real estate. Her paper, "Predictive Analytics on Aviation Data" (2016), is a noteworthy contribution in applying machine learning models for safety and operational efficiency in the aviation sector.

Recommender Systems: She has also developed recommender systems for diverse fields, including crop production and information diffusion, as seen in "A Recommender System for Information Diffusion" (2022).

Heterogeneous Data Handling: In "A Methodology to Handle Heterogeneous Data Generated in Online Social Networks" (2020), she focuses on techniques to efficiently process and analyze complex datasets generated by social networks.

Optimization and Reliability Engineering: Her research extends to system reliability and cost optimization, exemplified in her work "Optimization of Life Cycle Cost with Reliability Parameters – Aeroengine" (2008).

Dr. Sailaja’s research is highly interdisciplinary, integrating advanced data science methods with applications in social networks, cloud computing, and real-world systems. Her work is particularly noted for its practical applications in predictive modeling, optimization, and network analysis.

Big Data Analytics: Focusing on the analysis and processing of large-scale data, particularly using technologies such as Hadoop and R for sentiment analysis and cloud-based performance evaluation.
Social Network Analysis: Investigating the behavior of online social networks, including user influence, information diffusion, and event prediction, with applications in disaster management and educational institutions.
Predictive Analytics: Applying machine learning techniques for predictive modeling in various sectors such as aviation, education, and real estate, focusing on improving decision-making and operational efficiency.
Optimization Algorithms: Researching multi-objective optimization techniques, particularly in Mobile Ad Hoc Networks (MANET) and life cycle cost optimization with reliability parameters.
Recommender Systems: Developing systems for personalized recommendations in areas such as agriculture, information diffusion, and resource optimization, utilizing data-driven approaches.
Heterogeneous Data Management: Creating methodologies for handling and analyzing heterogeneous data generated by online social networks, ensuring efficient processing of complex datasets.
Cloud Computing and Distributed Systems: Exploring the performance and scalability of cloud-based systems for data-intensive applications, with an emphasis on social media data analysis and real-time performance evaluation.
Research Interests:
Big Data Analytics
Social Network Analysis
Predictive Analytics
Optimization Algorithms
Recommender Systems
Heterogeneous Data Management
Cloud Computing and Distributed Systems

Awarded for research in predictive analytics for online social networks, analyzing data trends and event prediction models.
Recognized for expertise in database fundamentals with IBM DB2, showcasing advanced knowledge in database architecture.
Certified for developing and deploying machine learning models using Azure, showcasing skills in cloud-based AI solutions.
Certified for designing scalable cloud infrastructure using Google Cloud, focusing on reliability and efficiency.
Certified in Six Sigma methodology, emphasizing expertise in process improvement and data-driven quality management.
Recognized for expertise in big data technologies, specifically Hadoop and Spark, through IBM’s professional certification program.