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Adieu to AWS

I recently exited out of my role as principal scientist at Amazon Web Services (AWS). In this blog post, I want to recollect the rich learning experiences I had and the amazing things we accomplished over the last two years.
We launched a vast array of AI services at all levels of the stack. I was most closely involved in the design, development and launch of SageMaker. Its broad adoption led to AWS increasing its ML user base by more than 250 percent over the last year. It was personally fulfilling to build topic modeling on SageMaker (and AWS comprehend) based on my academic research, which uses tensor decompositions.
It was exciting to grow applied research at AWS. I looked for problems that posed the biggest obstacles in the real world. The “data problem” is the proverbial “elephant in the room”. We developed and tested efficient deep active learning, crowdsourcing and semi-supervised learning methods in a number of domains. All these projects were executed with an excellent cohort of interns and AWS scientists.
Being at AWS gave me a platform for community outreach to democratize AI. The Caltech-Amazon partnership funded graduate fellowships and cloud credits which is is transforming fundamental scientific research at Caltech.
I had the privilege to work with and learn from so many amazing individuals. It was enlightening to hear about the early days of AWS from veteran AWS engineer and team VP Swami Sivasubramanian. I learnt good management principles and business practices at Amazon. Leadership principles is a succinct list of desirable leadership qualities. I also found that working backwards from customer needs and having “two pizza” teams resulted in focused discussions with great outcomes.
To summarize, I am very thankful for the learning experience I had at AWS. In the next post, I will talk about my upcoming plans. Stay tuned!