I started as a computer science major. And I wanted to do more research on maybe getting to good academia. I started doing PhD in actually, it was, I was first working on networking, and I was really interested in machine learning. So I switched my area to machine learning and did a PhD in UC, Riverside. I worked on some applied projects related to health care during my PhD towards the last few years, and that got really got me interested in doing applied research on health care and I wanted to do more of that. And there was an interesting project at Amazon that was related to applying machine learning to health care. So I joined that project, and I've been working on that project in the last few years.

I'm working as an applied scientist at Amazon and the responsibilities are refiltering the latest machine learning papers on the research that's going on. And, my team is working on mainly on the nature of language processing. So we get together as this team of scientists and read papers and talk about different areas of research and what we can do for the product which is named into recognition and relation extraction service for healthcare-related documents. So we read papers and implements them in deep learning frameworks, and write scripts to test, evaluate work with engineers to productionize those research applications and integrate them into the product. Also, a lot of my time is spent on coding, implementing reading papers and also working with engineers. So I work mostly in the office and I go to conferences usually twice a year. And then it's working from home is that we have a lot of meetings, so we don't usually work from home a lot. But if we want to, we can always work from home. That's something okay to do but maybe not so frequently.

We use deep learning frameworks and python usually. And there isn't like a preferred framework. We use all TensorFlow PyTorch MexNet frameworks and we implement in Python and iSight, but, engineering work is done more in Java so we're familiar with java as well. We work on deep learning algorithms for natural language processing and language models.