• Tech lead (Founder) of App Ads CoreML engineering team (50+) at Meta Monetization org

• Driving state-of-the-art ranking model development and new ML foundation innovations on Ads products that generated >$1 billion incremental revenue

• Designed and implemented various machine learning models for ads targeting, e.g., predicting clicks/conversions/clicks conversions, preventing fraudulent clicks, and forecasting RTB spend

• Developed a privacy-centric abstract representation model to improve conversion performance under EU general data protection regulation (GDPR)

• Developed our new model training platform 'QCLearn' (data preparation in MapReduce + AWS model trainer in Python) and refactored all production models to run on QCLearn

• Redesign and developed our new model training pipeline all in Spark

• Mentored engineers, scientist and interns on various modeling and platform development projects

• Designed statistical inference algorithms for indoor navigation using WiFi and inertial sensors

• Designed robust optimization techniques to maximize the energy efficiency of location-aware networks

• Developed algorithms to optimize data throughput of broadband and multi-antenna systems

• Designed regression models to characterize bandwidth scaling behavior in wireless systems