The Science Behind Rockfish

Rockfish builds on and extends 6+ years of foundational academic research on making Deep Generative Models practical for Enterprise-scale synthetic data generation.

Since 2016, at Carnegie Mellon, co-founders Giulia Fanti and Vyas Sekar have made significant advances in the theory and practice of generative models such as developing state-of-art GAN-based models for timeseries data, improving the stability and privacy of generative algorithms, practical approaches for rare sample generation, and domain-specific adaptations of deep generative models (e.g., telecommunications, IoT).

These peer-reviewed research papers have appeared at prestigious AI/ML venues​ such as NeuRIPS, ICML, AAAI, and  domain-specific venues such as IMC, SIGCOMM.