• Led the Tabular Foundation Model (IngesTables) project and driving adoption in multiple product areas at Google.

• Designed, implemented, and optimized a few-shot object-detection pipeline, turning a tedious and error-prone week-long workflow into a one-step 7-hour workflow.

• Designed and implemented "partial run" capability in TFX, enabling teams to reuse artifacts and rapidly prototype their ML pipelines in a production environment, replacing the previous caching-based solution.

• Co-organized NeurIPS 2020 Competition Predicting Generalization in Deep Learning, a CodaLab competition that evaluates participants' code submissions. Minimized the operational toil of running the competition by autoscaling GPU nodes using Google Kubernetes Engine, Kubernetes, and Docker.

• Generalized the AdaNet algorithm by using DNN generalization gap predictors (besides Rademacher complexity) to drive neural architecture search.

• Derived a tighter upper-bound for the Rademacher complexity of CNNs with skip-connections.

• Designed and implemented features in AdaNet, a TensorFlow library for neural architecture search. Integrated AdaNet into Google products.

• Diagnosed partial failures in a multi-stage workflow with improved logging and automated bug filing, then designed a solution that was more robust and lightweight than the previously proposed solution.

• Acting Tech Lead for a team of 3 software engineers to upgrade a full-stack web app. Gathered requirements along with UX and product owners, designed the overall architecture, mentored junior developers, implemented a lightweight experimentation framework, and set up production monitoring, continuous integration and continuous deployment.

• Migrated an internal web application to Google Cloud Platform (CloudSQL, Cloud PubSub, and Cloud Monitoring).

Researched, implemented, and scaled a compression algorithm for Facebook’s operational time series data, resulting in a 96.9% reduction to the root-mean-squared error while maintaining the same compression ratio as the existing compression algorithm. Implemented in Java; scaled with Hadoop MapReduce on HBase data.

Built an internal tool to expedite Quality Assurance for the landing page generated by a pre­-existing algorithm. The internal tool consists of a pipeline that includes a Java program, a bash script, and a web interface that updates files on Amazon S3.

Increased visible traffic from one customer by 15% with a few bug fixes, which was projected to increase monthly revenue from that customer by 260%.

Developed a new product in Javascript that allows personalization of landing pages from AdSense and Facebook display advertisements.