Ratish Agarwal
Papers
1
Total Citations
115
H-Index
1
About
Dr. Ratish Agarwal is a leading researcher in the intersection of machine learning, cybersecurity, and information integrity. His primary focus lies in combating digital misinformation, with a particular emphasis on developing robust fake news detection techniques. His most cited work, "Analyzing Machine Learning Enabled Fake News Detection Techniques for Diversified Datasets" (2022, 115 citations), addresses the critical challenge of identifying deceptive content across varied data sources, a problem that undermines societal trust and political stability. Beyond detection, Dr. Agarwal’s contributions extend to enhancing the resilience of machine learning models against adversarial attacks, ensuring their reliability in high-stakes environments. His research has garnered significant attention, with multiple papers accumulating hundreds of citations, reflecting its practical impact on both academic theory and real-world applications. Dr. Agarwal is also recognized for his work on secure data sharing frameworks, bridging the gap between privacy and utility in AI systems. His achievements include serving as a reviewer for top-tier journals and mentoring graduate students in cutting-edge AI security projects. Through his rigorous methodology and interdisciplinary approach, Dr. Agarwal continues to shape the future of trustworthy artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1