Brain Sequin (now GDM Alchemy) team.

• Focusing on protein understanding and optimization under uncertainty.
• Multi-stage peptide library design, co-optimizing cell permeability and protein binding.
• LLM models for protein function annotation and target-conditional optimization.

Co-TLM of the TF-Agents team (2018 - 2021).

TLM of the Brain Learned Systems Team (2017 - 2022). Clients include Spanner, Compiler, and Cloud infrastructure teams.

• Built smarter query optimizers, cache eviction algorithms, inlining and register allocation passes.
• Grew the Learned Systems team from 1 to 7 researchers and engineers.
• Aligned engagements between Brain, Technical Infrastructure, and Cloud orgs.
• Set research direction for systems and ML engineers.

SWE on Brain Applied Machine Intelligence team.

• Core TensorFlow maintainer.
• Developed interfaces and support for sparse and sequential input, debugged graph control flow, implemented CPU and GPU kernels; whatever needed doing.
• Founding SWE / API designer of TF Distributions (now Tensorflow Probability).

I worked on two teams:

• Computational Climatology: Statistical weather forecasting in the short-to-medium-term scale (2 weeks-2 years) using a combination of techniques from climatology, machine learning/statistics, and spatiotemporal signal processing.
• Computational Agronomy: Analyzed, assimilated, and reconciled remotely sensed weather and agricultural data. Built growth forecasts for corn, sorghum, soy, and winter wheat.

Focused on applications of Compressive Sensing to inverse problems in medical imaging.

• Developed CS-based estimator for Computational Tomography with Sinogram Occlusion.
• Developed a novel CS-based reconstruction technique for Ultrasound tomography.