Applied AI Summit
Free online conference | October 14-16, 2025Synthetic Data and Scalable AI: Enhancing Model Performance Across Domains
Synthetic data is revolutionizing the way AI systems are trained and scaled across industries. In this session, we explore how synthetic datasets—generated using techniques like Generative Adversarial Networks (GANs) and advanced LLM-based tools—can address key challenges such as data scarcity, privacy constraints, and bias in training data. We’ll examine how integrating synthetic data into machine learning pipelines improves generalization, model robustness, and cross-domain adaptability. Drawing from real-world applications in cybersecurity, finance, and healthcare, this talk demonstrates how organizations are using synthetic data to enhance performance and reduce reliance on sensitive or hard-to-source data. Attendees will learn best practices for generating and deploying synthetic data at scale, understand its role in expanding the reach of AI into high-risk or low-data environments, and gain insight into future trends in this space. This session is ideal for AI professionals seeking scalable, ethical, and efficient ways to boost model effectiveness across varied domains.
About the speaker
Cibaca Khandelwal
Machine Learning Engineer at RaySecur Inc.
Cibaca Khandelwal is a Boston-based AI engineer and researcher with over seven years of experience in artificial intelligence, machine learning, and cloud infrastructure. As a Machine Learning Engineer at RaySecur Inc., she develops AI-driven solutions for threat detection and cybersecurity Cibaca’s expertise lies in designing scalable AI pipelines and pioneering synthetic data generation techniques to enhance model performance across diverse scenarios. Her work has notably improved detection precision and reduced response times in high-complexity domains. She holds a Bachelor of Engineering in Computer Engineering from the University of Mumbai, a Master of Science in Information Systems from Northeastern University, and completed a Post Graduate Program in Artificial Intelligence and Machine Learning from Great Learning. Beyond her professional endeavors, Cibaca actively contributes to the tech community through publications on Medium and open-source projects on GitHub, focusing on topics at the intersection of cloud computing, software development, and machine learning.