Cyber Security > Pulyala Radhika

Pulyala Radhika

Assistant Professor
JNTUH ID: 6252-230621-181330
At Sreenidhi Institute of Science and Technology we have always endeavoured for excellence, aiming to equip the engineers of tomorrow with the skills and exposure to be forefront of their field

About

As an assistant professor, I aspire to cultivate a dynamic and purpose-driven career within my specialized domain, leveraging my skills to foster student development while contributing to the organization growth. My goal is to advance both personally and professionally in tandem with the institution.

Educational Qualifications

M.Tech (PG)

B.Tech (UG)

Professional Experience

6.5 Years Experience

Area Of Interest

01

Machine Learning

02

Artificial Intelligence

03

Cyber Security

FDP's / Workshop Attended

01

Attended an Online Faculty Development Programme (FDP) on Metaheuristic Techniques For Engineering Applications organized by Kakatiya Institute of Technology & Science, Warangal in association with E&ICT Academy, NIT, Warangal

02

Attended 11 days FDP on Machine Learning for Data Analytics organized by E&ICT Academy, NIT Warangal and NIT Tiruchirappalli in collaboration with Ministry of Electronics and Information Technology (MeitY) GoI.

03

Attended 15 days FDP on Deep Learning for NLP and Computer Vision organized by Department of IT - CBIT in collaboration with ExcelR Solutions.

04

Attended 5 days FDP on Protection from Cyber Attacks organized by Department of CSE-Cyber Security, SNIST in collaboration with NITTTR,Chandigarh.

05

Attended 5 days FDP on NLP and ChatGPT Applications organized by SECAB Institute of Engineering & Technology in collaboration with ExcelR Solutions.

NPTEL Certifications

01

Got certified (ELITE) by NPTEL in Problem Solving through C Programming and The Joy of Computing with Python.

FDP's / Workshop Organized

01

Practical Hands-on Bootcamp on Cyber Security at Sreenidhi Institute of Science and Technology.

Publications

01

Presented a research paper entitled "Logistic Regression versus XGBoost: Machine Learning for Counterfeit News Detection," at 2021 Second International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), 2021, pp. 1-6, doi: 10.1109/ICSTCEE54422.2021.9708587 (Indexed in Scopus).