Information technology, which is sort of like information systems, basically at the underground level and I would say that our IT concentrators, it's become one of the largest concentration in the school because I think you'll hear me say throughout is that it's a great time to be in a technology field right now. So we have these different personas for our concentrators, I would say that the personas are first and foremost business analytics is very big and so the business analyst role is very important. But then we also have technology consultants and then folks doing digital innovation and even now we're increasingly getting into cybersecurity and health IT so what ends up happening is we have this choose your own adventure philosophy where we offer courses related to these personas but ultimately it's up to the students to decide and pick and choose which courses they want to take related to that and then they end up, of course, pursuing careers related to the personas that they're interested in. At the grad level, we have a lot of programs as well. We have an MS in the Management of IT. We also have MS Commerce, which is a fifth-year program, which has a Business Analytics Track that I oversee, and I'm currently working with Darden, which is our other business school. We have to launch a joint MS and Business Analytics degree program this coming fall and that program is for experienced professionals and also half of it is going to be online, so it's kind of like two schools, two formats.

So, first of all, I think it's a great time to be involved in technology as you heard me say earlier. I think technology has never been more central than it is now to industry to strategy so there are opportunities in the industry, but also, of course, in academia, we have a tremendous need for the next generation of educators and so a great time to come into this field. In terms of misconceptions, I think there is the biggest misconception that I see at least with our undergraduate, even grad students is regarding the prerequisite knowledge that they need. So some assume that to be a technologist, you have to already be a technologist before you enter a program, and that's not the case. I think the biggest thing we're looking for in a person is passion, we need people that are passionate about technology, love technology or love to be involved with data analytics or data science or whatever it is they want to do and then secondary to that is, of course, the skills, a lot of that we can teach. Now there is also another important misconception regarding diversity in technology fields. We're seeing all kinds of evidence around gender biases in tech companies and other things and those are obviously major concerns that we as a community, not just academia, but industry, the world really needs to look at. I would say that at least from what we've seen we have a very diverse student body at the University of Virginia and every year we actually give a technology achievement award, and I think, I might be off on this, but I believe the winner of that award for the last five years has been actually a female student. So I would argue and sorry fellas if there are men that are listening to this or watching this, I apologize for saying this ahead of time, but I can say that many of our best students are the most diverse from a gender and ethnicity perspective and yes, there are some concerns about the need for change in the industry but I think the best way to drive change is from the inside. So I would tell anyone who's interested in pursuing a career in these fields, whether it's academia, industry, that please don't shy away because of the negative stuff you're hearing. There's a lot of good that can be done in, and we need people like that in this field.

My work is for those of you who are not familiar with the information systems field, my work is really at the intersection of what's called Design Science and Data Science and what that means is Design Science is very much how to build IT artifacts. So we built a lot of systems, a lot of models, a lot of algorithms and Data Science is the type of work that I do, most of my work, my background is pretty much artificial intelligence so I do a lot of building machine learning methods. The nice thing is, a lot of my work has a lot of real-world applications to it. I tend to apply my methods to feels like health, cybersecurity and, of course, business being in a business school. I am most proud of the projects that get real-world attraction whether some broader impact or practical usage. I'm quite happy that I think at least five or six of my projects have resulted in large scale real-world usage that we've got a lot of systems being used by the government. We have systems being used by companies and in the health fields, security fields and that's really rewarding. In addition to the academic contribution, there's a strong practical contribution, and even when it's like 10 years later and you're still seeing that system being used and it's quite nice and gratifying.