Invited Speaker 2026
Invited Speaker Ⅰ
Assoc. Prof. Sunil Kumar
GLA University, Greater Noida, India
Brief Introduction: Dr. Sunil Kumar holds a Ph.D. in Computer Science & Engineering and brings over 20 years of academic, research, and administrative leadership experience. He currently serves as Associate Head and Associate Professor at GLA University, Greater Noida, India. His core research expertise spans Artificial Intelligence, Deep Learning, Semantic Information Extraction, Cloud Computing Optimization, and Smart IoT Systems.
Key Highlights
1. Publications: Over 30 SCI/SCIE-indexed journal papers in high-impact platforms, including Nature Scientific Reports, IEEE Internet of Things Journal, and Sustainable Cities and Society.
2. Research Impact: H-index of 25 and i10-index of 35.
3. Books & Editorial Work: Author and editor of multiple scholarly books focusing on Artificial Intelligence, Machine Learning, and Industrial IoT (IIoT).
4. Academic Leadership: Extensive experience serving as Keynote Speaker, Session Chair, reviewer, and advisory board member for international academic conferences
Speech Title: Autonomous Cloud Optimization for Generative AI & Big Data Analytics: Balancing Performance, Cost, and Scalability
Abstract: The integration of Generative AI and big data analytics offers unprecedented analytical power, yet running foundation models alongside massive data pipelines creates immense compute, memory, and cost challenges. Traditional auto-scaling and static provisioning often fail under these heavy workloads, leaving organizations trapped between exorbitant cloud bills and degraded query performance. This keynote presents autonomous, AI-driven cloud optimization frameworks designed to resolve this tradeoff dynamically. By leveraging predictive resource scheduling, smart query execution, and decoupled cloud architectures, organizations can scale high-throughput analytics and GenAI inference efficiently without compromising system responsiveness or exceeding budget constraints.
1. AI-Driven Cloud FinOps: Automating spot-instance allocation, predictive auto-scaling, and execution planning using machine learning.
2. Decoupled Architecture Efficiency: Optimizing cloud lakehouses (such as Delta Lake and Apache Iceberg) to drastically reduce data scan volumes and egress fees.
3. Cost-Performance Balancing: Maintaining low-latency real-time inference while systematically driving down operational infrastructure overhead.