Aayush Gauba
Aayush Gauba is a researcher and developer working at the intersection of machine learning, quantum-inspired models, and AI security. He has created open-source projects such as AIWAF, an adaptive web application firewall, and has published research on quantum-inspired neural architectures and robust learning methods. His work focuses on building practical tools that are both scientifically innovative and accessible to the wider Python community. Outside of research, Aayush is passionate about sharing knowledge through talks, tutorials, and collaborations that bridge theory with real world application.
Session
Machine learning often assumes clean, high-quality data. Yet the real world is noisy, incomplete, and messy, and models trained only on sanitized datasets become brittle. This talk explores the counterintuitive idea that deliberately corrupting data during training can make models more robust. By adding structured noise, masking inputs, or flipping labels, we can prevent overfitting, improve generalization, and build systems that survive real world conditions. Attendees will leave with a clear understanding of why “bad data” can sometimes lead to better models.