I am originally from Ukraine, I got my bachelor's in applied mathematics and a Masters in applied mathematics in the Ukraine University of Kyiv. Over there my major was applied mathematics and I was doing graph theory. Then I applied to the US and got into the Ph.D. program at the University of California at Irvine. I said the chance to work on applying graph theory and statistics and problems in bioinformatics. When I was doing my Ph.D. coursework and research, I got really interested in machine learning. After finishing my Ph.D I got a job at Microsoft as an applied researcher. I spent three and a half years at Microsoft working on some projects inside the search engine. Then I switched to Apple where I worked as an applied researcher on the condition problems for Apple stores and Itunes. Now I am working at NVIDIA and I've been here for three and a half years. Regarding experiences, obviously educational experience is very important. My original education was more in pure applied maths but during my student days at the University of Kyiv I got the chance to participate in programming competitions organized by Microsoft Gold Imagine Cup. Those experiences convinced me to move to mass from computer science.

At NVIDIA I'm applied research scientist and I am part of a product group. We are not part of media research but we also do applied science. We work on the problems related to conversational AI. It involves special cognition, natural language processing and speech generation. We write a lot of open-source quotes which we share with our partners It's open-source so it is basically shared with everybody. We give the code for free to grow GPU usage for deep learning. I make it easier for everybody to use it's a major goal of our work. We also do research in the sense that we develop new models that run faster or maybe use less memory or maybe achieve better accuracy. When we get something interesting we publish it online or it goes to the conference. In terms of weekly hours as applied research scientist my work typically cycles between development and doing research. Once you have signed my idea you need to implement it. Typically it involves a lot of engineering especially if your idea involves something like bringing a lot of GPU nodes. There is some software engineering aspect you need to handle first. Once you're done with that you go to the experimentation stage where you can run experiments, do model considerations and so on it cycles between those two modes. In terms of hours, I think it's true for not only NVIDIA but for all other companies at Silicon Valley that we don't have any hard requirements that we must come in at like nine or leave at five or six. Most meetings happen in the middle of the day. It's totally fine to work from home when you have to and you can come late and then leave late or vice versa it is pretty flexible.

In my current role and in terms of languages we mostly use Python. It is the most popular language for deploying right now for deploying you would use some framework. In my day to day work, we're using PyTorch. We were using TensorFlow before but I would also say that this is very flexible. It depends on a particular researcher what he or she is willing to use TensorFlow or PyTorch. it. Actually a few years ago, I can define this policy for myself that I just try new projects with a new framework but now it is mostly PyTorch. If you want to do something with codes rather than the framework. Then you are actually out of the Python territory and into c++ territory. The knowledge of lower-level languages such as C++ helps.