Mr. Abhilash Reddy Pabbath Reddy | Computer Science | Best Researcher Award
Software Engineer at Axle/National Institute of health, United States
Abhilash Reddy Pabbath Reddy is a seasoned DevSecOps engineer and researcher with extensive experience in artificial intelligence, cybersecurity, and cloud computing. With a passion for leveraging cutting-edge technology to drive innovation, he has contributed significantly to both industry and academia. Currently, he works with the National Institute of Health, where his expertise helps enhance security and efficiency in software systems.
Profile
Education 🎓
Abhilash earned a Master of Science degree in Electrical Engineering from Texas Tech University, Lubbock, TX, in December 2015. During his academic journey, he received the prestigious TTU Seacat EE Scholarship for his outstanding achievements.
Experience 💼
Abhilash has accumulated over a decade of professional experience, working with renowned clients such as IBM, Mercedes, T-Mobile, and the National Institute of Health. His expertise spans software engineering, DevSecOps, cloud security, and artificial intelligence. In his roles, he has successfully implemented advanced technologies to address complex challenges, ensuring secure and scalable software solutions.
Research Interests 🔬
Abhilash’s research focuses on artificial intelligence, machine learning, cybersecurity, AIOps, MLOps, healthcare, and cloud computing. His innovative work explores AI-driven solutions for cloud security, predictive analytics, and proactive threat detection.
Honors and Awards 🏆
Abhilash was awarded the TTU Seacat EE Scholarship by Texas Tech University in recognition of his academic excellence and contributions to electrical engineering. He is also a proud member of IEEE, staying at the forefront of technological advancements.
Publications 📚
Abhilash has authored several impactful articles and patents that bridge the gap between AI and cybersecurity. Notable works include:
The Role of Artificial Intelligence in Proactive Cyber Threat Detection In Cloud Environments
- Year: 2021
- Cited by: 18
Automating Incident Response: AI-Driven Approaches To Cloud Security Incident Management
- Year: 2020
- Cited by: 13
Machine Learning Models for Anomaly Detection in Cloud Infrastructure Security
- Year: 2021
- Cited by: 12
Securing Multi-Cloud Environments with AI And Machine Learning Techniques
- Year: 2021
- Cited by: 11
Navigating the Cloud’s Security Maze: AI and ML as Guides
- Year: 2023
- Cited by: 9
The Future Of Cloud Security: AI-Powered Threat Intelligence And Response
- Year: 2022
- Cited by: 9
Harnessing the Power of AI and ML Transforming Cybersecurity in the Cloud Era
- Year: 2022
- Cited by: 2
Defending the Cloud: How AI and ML Are Revolutionizing Cybersecurity
- Year: 2019
- Cited by: 2