design and experiment ads ranking and allocation mechanisms for long-term customer success:
* long-term aware ads allocation
* long-exposure long-term impact holdout experiment design
* strategically-important segments contextual optimization
* opportunity cost predictions and applications
* scalable ads relevance management
* offline simulation (replay)
* causal impact of ads configuration
Weblab bar-raiser, who oversees and approves launches across the ads org.
4 peer-reviewed papers published at amazon machine learning conference over my 3 years.
Misc.
Instructor at Amazon Machine Learning University
Speaker at Auction Summit (Seattle, 2018), ML Workshop (Bangalore, 2018), AdTech Meetup (Palo Alto, 2019), Amazon Machine Learning Conference (Seattle, 2019 and 2020)
Reviewer for internal research proposals/papers in the tracks of "Advertising", "Personalization and Recommendations", "Search and Information Retrieval", and "Data Science".
120+ interviews on machine learning, and data structure / algorithms.
