Scientific Machine Learning Researcher

Neural operators that predict touch from vision: DeepONet and Fourier DeepONet models for visual-to-tactile acceleration.

• Built PyTorch multimodal CNN-MLP latent-fusion and DeepONet baselines for visual-to-tactile acceleration prediction; in preliminary runs, the time-domain DeepONet achieved 1.11 test RMSE, 35% below the CNN-MLP baseline's 1.72.
• Evaluated material generalization using exclusive texture splits; used Fourier encodings to encourage oscillatory predictions.
• Implemented a Fourier DeepONet on normalized log-FFT magnitudes to address spectral bias; achieved 0.92 preliminary test RMSE (17% below time-domain DeepONet; 46% below CNN-MLP) and explained over 80% of lower-frequency X-axis variance.