Tensorial-Professor Anima on AI
Artificial intelligence, Machine learning, Research, Science & Technology, Women in STEM
Update: Added discussions in the end based on Twitter conversations. Yesterday, I was on the debate team at DALI conference in gorgeous George in South Africa. The topic was: “DALI believes it is justified for industry researchers not to release code for reproducibility because of proprietary code and dependencies.” I was opposing the motion, and this…
This post reviews my experiences in 2018. I welcomed the year in the gorgeous beaches of Goa and am now ending it in the wilderness of South Africa. My highlights of 2018 are the following: Joining NVIDIA: I joined NVIDIA in September and started a new research group on core AI/ML. I am hiring at…
I am very happy to share the news that I am joining NVIDIA as Director of Machine Learning Research. I will be based in the Santa Clara HQ and will be hiring ML researchers and engineers at all levels, along with graduate interns. I will be continuing my role as Bren professor at Caltech and…
AI4science is a new initiative launched at Caltech that aims to broaden the impact of AI and ML across all areas of sciences. The inaugural workshop was held on Aug. 1st. My student Jeremy Bernstein wrote a detailed article on the workshop. The slides of the talks are also available there. https://sites.google.com/view/ai-for-science-workshop/about-ai4science A short blurb…
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!
Reasons I decided to start a blog.