Venkata Krishna Bharadwaj Parasaram, Senior Project Manager and AI Researcher, Thermo Fisher Scientific, USA

Title of the Talk :
Trustworthy AI for Secure Networked Systems

Abstract of Talk:
Artificial Intelligence is rapidly becoming a core component of modern digital systems, enabling smarter automation, advanced analytics, and stronger cybersecurity capabilities. As organizations increasingly depend on interconnected platforms and large-scale data environments, ensuring security, trust, and system resilience has become more important than ever. This keynote explores how trustworthy AI can be integrated with cloud computing and cybersecurity frameworks to build secure and reliable networked systems. The discussion will highlight practical approaches for improving system reliability, protecting sensitive data, and strengthening security in distributed computing environments.

The session will also examine emerging strategies for designing resilient digital infrastructures that support critical sectors such as healthcare research, enterprise platforms, and large-scale data-driven systems. By combining AI innovation with strong security principles, organizations can build more secure, scalable, and trustworthy digital ecosystems for the future.

Bio : Venkata Krishna Bharadwaj Parasaram is a Senior Project Manager and AI Researcher at Thermo Fisher Scientific with over nine years of professional experience in Artificial Intelligence, cybersecurity, clinical research systems, and enterprise technology leadership. He leads large-scale NIH-supported clinical research initiatives across more than 400 sites, managing multimillion-dollar programs that ensure compliance with GxP and 21 CFR Part 11 regulatory standards while integrating advanced AI and cybersecurity frameworks for healthcare trials and enterprise digital transformation.

In his leadership role, he oversees cross-functional teams, strategic technology implementations, and complex program delivery for mission-critical healthcare and research platforms. His work focuses on aligning advanced technologies such as AI, machine learning, and cloud-native architectures with organizational objectives to improve operational efficiency, data security, and large-scale system reliability.

Alongside his industry leadership, he actively contributes to academic research and peer review. He has authored more than 20 peer-reviewed publications and serves as a reviewer for IEEE conferences and journals in areas including artificial intelligence, machine learning, cybersecurity, healthcare analytics, and enterprise systems. His peer review contributions follow IEEE evaluation standards with a focus on originality, technical rigor, scalability, and research impact.

His work bridges industry practice and academic research, contributing to advancements in secure digital platforms, AI-driven systems, and emerging technology applications.