Hire Shrenik J. - Machine Learning Engineer

Designed real-time post-processing enhancement algorithms, addressing blocking, blurring, and ringing artifacts, and enhancing temporal coherence in high-frame-rate gameplay, ensuring consistent visual quality across PlayStation consoles for 120M+ monthly active users. Implemented single-step diffusion models for accelerated inference, achieving 15% gains in PSNR/PSNR-B and VMAF scores, enabling high-fidelity visual effects on constrained hardware while significantly reducing computational overhead. Leveraged encoder-side statistics (e.g., QP values, CU sizes, motion vectors) to condition enhancement networks, enabling content-adaptive inference and more precise artifact suppression.

* Conducted research on DYffusion, a dynamics-informed diffusion model for spatiotemporal climate forecasting, focusing on improving uncertainty quantification and stochastic representation of geophysical processes. Implemented and evaluated an almost-fair CRPS loss function (adapted from recent literature) to address biases in standard CRPS variants, yielding a 10% gain in predictive accuracy while preserving calibrated uncertainty estimates. Ran large-scale experimentation on spatiotemporal datasets, analyzing the effect of loss function choice, sampling strategies, and noise schedules on forecast stability and calibration.