Designed and developed landscape mode for Interpreter Mode, which included decisions on trade‐offs, design decisions for different surfaces, and flag guarding.

Led the launch and organized bug bashes for landscape mode.

Collaborated with team members to design possible feature ideas for Interpreter Mode using LLMs.

Designed, developed and successfully launched the Dual Screen Interpreter Mode for the Pixel Fold.

Led the design and launch of the inner screen for Dual Screen Interpreter Mode.

Collaborated with cross-functional teams across different time-zones.

Helped brainstorm and consult on the marketing demo. https://www.youtube.com/watch?v=veN7yMLhYVs&ab_channel=MadebyGoogle

Awarded a patent.

Led the design and development of a complex internal service end-to-end in order to decouple the handling of assistant query intents from language resolution.

Helped teammates ramp up on the new service through design docs and meetings.

Collaborated with different teams to sync development and launch timelines.

Investigated and analyzed large scale data to find the root causes of approximately 18% missing migration traffic.

Worked on feature end-to-end to reduce code technical debt and optimize HOS rulesets for the Drive app.

Improved various features for the Drive app to streamline the experience for thousands of clients and efficiently meet ELD compliance.

Identified high-priority issues for important clients and resolved them quickly with minimal supervision.

Mentored new software developers.

Developed and maintained large scale crawlers written in Python and PostgreSQL for mining software data.

Sampled, cleaned and analyzed software data using statistical, machine learning and data mining techniques.

Queried and visualized patterns of active modding communities with Python, R, and SQL.

Built predictive models (e.g., logistic regression and random forest) with Python and R.

Developed complex and robust modeling pipelines using various modeling strategies (e.g., feature selection) and statistical methods (e.g., ANOVA, cross-validation, and bootstrap sampling) to interpret models.

Implemented natural language processing (NLP) pipelines using stemming, lemmatization, word-embeddings, sentiment analysis, and latent Dirichlet allocation (LDA).

Built deep learning models (e.g., gated recurrent neural network) on Python using Keras and TensorFlow.