I got my bachelor's in computer science in India, and I worked in a software services company for about three years building software for various insurance and banking clients. And, along the way, it's gonna figure out what I lack in the job is, building strong algorithmic solutions. And often that was because these were large banking systems for which all we had to do, was sort of maintain legacy code. And that wasn't very challenging for me. One of the things that I always loved doing was algorithms and I've taken a couple of basic machine learning classes when I was in undergrad, and that was one of the things that I thought I must in the things that I was doing. So I applied to grad school ten-eleven years ago And, when I did apply to grad school, I applied for a master's program because largely because I wanted a lay in the runoff from the theory and machine learning. I wasn't sure what exact problems I would be facing and Master's was a way for me to get my feet right. Trying to figure out what are the different opportunities that are available in graduate school. Eventually, I found myself at the University of Utah, who I worked with for a number of years, and I converted to a PhD program because I quickly figured out the list of problems that excited me and I started working on a number of unsupervised learning problems and defining the scope of Meta-Learning. One thing that helped me grow the graduate school part was to go for internships. Try and figure out what the problem is that the industry is excited about and to see if they can bring the Alogirthimic challenges back from the industry and trying to solve it, and that helps. That helped with my dissertation. I eventually ended up in a full-time job at Yahoo labs. I worked on different advertising scientists' problem for a while, moved on to a startup for a couple of years and now I'm at Expedia, where I manage medium-sized group of scientists, we work on a number of different problems from marketing dynamic pricing, the other some other natural language problems on and some vision problems.

When I started at Expedia, it was as an individual contributor. So in general, when you are in an industry, you have two major lines of a path as someone in the software industry. You can either be an individual contributor where you're working together with your team, you're collaborating, but you're still responsible for sort of building code, building algorithms, and building software. The other part is the managerial path where you can not only be involved in the technical design of algorithms, but you are also in charge of managing a team taking care of people's aspect as well. So, while I started as being an individual contributor or an IC, I recently moved on to managerial responsibility. And one of the things that I enjoy doing personally, it's to be able to get a small team together, go to find a problem, figure out the right approaches to the problem. Both help design and build solutions and then actively test the solutions to these problems. This path gave me an opportunity to sort of define everything from how do we define the problem to all the way to how do we test these algorithms in the right way. It's put me in a slightly different path than what I would have gone into a traditional IC. Something that I enjoy a lot. Working hours? Between 4 to 5 hours looking at collaborating with the scientists on the team, trying to define the problem, define approaches. About an hour or two is for meeting status updates and all that. the more history. And work from home? It's not something that people do every day of the week because of the commute and other personal reasons.

I'm a firm believer of having the right hammer for the nail. Often the simplest solution might just be enough to solve some problems. Not every business problem requires a mission learning solution. So when we start looking at the business problems that are sort of given to us and then you try to extract them out in clear problem of the machine, for which then try to find what the right algorithm is. Often they end up not being a Machine Learning solutions. Maybe it's a simple heuristic. Maybe it's a creative solution. Maybe the known off the shelf Machine learning algorithm. Occasionally it's a custom defining a new machine learning algorithm from the scratch. And so, from a frameworks software program's perspective, I believe whatever is the right tool to for the problem, what we have ended up using is anywhere from my perspective Pythons, Scala, from the framework perspective, anywhere from Spark and TensorFlow, also PyTorch occasionally. A lot of stuff that we do, the data and computation of the cloud, like that most internet services related companies. So having tools that could integrate with these in whether it be AWS or the Azure, the tools that are easily integrated with these cloud platforms we try and they can choose those. It depends on what we mean by tools If we talk about tools like IDE, I like Python a lot. I use PyCharm because it provides a lot of flexibility. There were days when people used to write codes in them. It was great and I do love them. There's a lot of advantages of using a modern IDE. If we're talking about IDE, PyCharm is one of my favorites.