Ph.D.: National Institute of Technology, warangal, Pursuing
PG: M.Tech (Computer Science and Engineering), Jawaharlal Nehru Technical University, Hyderabad, 2014
UG: B.Tech (Information Technology), Jawaharlal Nehru Technical University, Hyderabad, 2011
Teaching Experience: 7 Years 7 Months
Industry Experience: 1 year
Contact Number: +91-7416945839
BVRITN Employee ID: 642
JNTUH Registration ID: 69150404-154132
AICTE Registration ID: 2501285779
1.Data Science
2. Big Data Analytics
3. Software engineering
4. Social network analysis
5. Information Retrieval systems
6. Foundation of computer design
1. Completed Foundations In DATA SCIENCE course from One fourth Labs conducted by PadhAI.com duration of 22 weeks.
2. Completed “NASSCOM BIGDATA ANALYTICAL” Training program at VEDIC.
3. Successfully Completed the requirements to be recognized as a Microsoft Technology associate for “Introduction to Programming using python”
1. published a patent on Blockchain, Cloud, AI, ML based Criminal Digital Forensic Investigation Application.
2. published a paper on Predicting The Winter Olympic Medals: Logistic Regression, JASC Volume VII, Issue VI, June/2020, Page No:80
3. Published a textbook on blockchain Technology and decentralized apps in advanced application by independent publishers
1. Published a paper on Object Detection Using Single shot multibox detector in world Conference, WCSEM.
1. Three day national level workshop FDP for “MACHINE LEARNING AND PREDICTIVE ANALYTICS USING R-PROGRAMMING”.
2. 5day FDP for“HADOOP “
3. 5 day FDP for “Data Science and Big Data Analytics”
4. 2 day FDP for “Introduction on Python Programming”5. 6 day FDP for “Outcome Based Education & NBA Accreditation”.
6. 6 day FDP for “Artificial Intelligence”.
7. 6 day FDP for “Advanced Data Science and its Applications”.
8. One Week FDP for “Introduction to Machine Learning Developing Potentials-Emerging Technologies of computer science”.
9. 5 day FDP for “DATA SCIENCE BEHIND NATURAL LANGUAGE PROCESSING”.
10. 6 day FDP on “Artificial Intelligence Applications through Machine Learning”.
