I am from India; the central part of India, from a town called Nagpur. I have lived there for first 21 years of my life, did my engineering. Then I moved around in India a bit. In 1988 I came to the US to study at Purdue University. About hobbies and stuff: a lot of things that typically people do to relax like watching movies, cricket; recently I've taken up a lot of trekking, mountaineering, things like that, I enjoy cooking, music. Even research you can count as part of the hobby.

PhD time, I was interested more in the design side of IS, how do you build systems that provide better business value. So, I worked on problems to do with the economic design of expert systems. The basic idea was that, if you have a system which is making some decisions, supporting some decisions and there is a cost associated with collecting the information to make those decisions accurately then what is an optimal strategy to collect that information. So, we applied it to things like medical diagnosis, loan granting, etc. And then for a couple of years, I continued to work in that direction, that essentially look at various IT systems, IT products and asked the question, can it be designed from a different perspective? Since we in IS don't build these systems, we are more users of technology. So the objective of building these systems essentially comes from engineering and computer science perspective like you know, maximize the utilization of CPU or memory etc. And they are not so interested in looking at economic effects; as the last example, I gave the cost of information needed to make these decisions. Another example of research I did in the first few years of Post doctrine work was to build a web server that is conscious of who it gives its capacity to. Imagine that there is E-commerce site & various people are shopping & the web server has to give in sort of round-robin way, attention to each of these shoppers. So, the design of these web servers such that, it gives equal time slice to each of these shoppers. But then from the business perspective, you might want to give more attention to say somebody who is maybe buying a diamond ring than somebody who's buying a pair of shoes. Also, you might want to give more attention to somebody who is looking like that he may be getting frustrated with the speed of the website, so he's likely to renege from the shopping site. So, those are the things that you do as a human being like if you go to a store, the people who are running the store are aware of who's doing what? what is the value they are probably going to buy? who's looking like he is impatient? But these ideas are not properly transferred into the design of a lot of e-commerce software, typically the server, the infrastructure that is supporting the shopping. So, we build some economic models and several research projects on that but basic idea was to take a system and redesign it. I guess the next stream of research I followed and I am still following to some extent, although it maybe staling off now is, software development methodologies. Before coming to UTD I was at the University of Washington, which is sort of the backyard of Microsoft. So, we had to several friends and professional acquaintances at Microsoft, so we were interested in finding out their development approach and what we could do to model it and make it better. So, one of the things that we worked on there and several projects that emerge from that was, during the early 2000s they had this, what Microsoft called daily build and smoke test. So, when they were building versions of NT operating systems, every day thousands of lines of code would be integrated and the integration package would print out the errors and integration and then before the next line of new code was written, they would first correct these errors before they move forward. So, we kind of asked the question, is this too intense integration policy? Maybe you should let the people left undisturbed writing code, say for example a week. And then when the test results are such that it looks like the software is getting unstable, the build is getting unstable at that you call for the integration testing and correction. So, essentially software could be built by thinking about the development phase followed by an integration phase and then, this one cycle and then many repetitions of this cycle. So, in this basically, we had some stochastic models, economic optimization models to capture the different forces involved and then in some cases we also had some data to validate it. So, I would say software development was a big area of interest for me and to a little extended it still is. Probably the next phase I would say, that when I came to UTD, UTD had some very famous faculty who were working on problems to do with dynamic optimization, especially methodology called control theory, optimal control theory. I had never come across this methodology before but there's a professor here, professor Suresh Sethi, who is very famous in that field. So, in fact, I sat in for his PhD class and I learned those techniques from him and that had a lot of applications in IS that I could see. So, one of the applications, again going back to the old problem of development followed by integration, there in the past I had looked upon it as of more discreet phenomenon, but now things are happening where this whole process has become much more continuous, like for example, in agile methodology. So the whole processes have moved very fast. So, it's almost like a continuous process of doing development and testing at the same time. So, it was appealing to apply for this optimal control methodology and we had some success in doing that, some new insights came for example, in the past people used to talk about so-called the waterfall model; where everything would be built and then at the end there would big phase of integration. So, the entire methodology kind of contradicted that-they said, no you should not do that you should keep doing development and testing-but it turns out that there are some situations where that is the most cost-effective way to build the system. So, it all depends on the environment that you are dealing with it. So, our method was able to capture both these ends of the spectrum. Big Bang approach we used to call it because at the end there will be a lot of fixing to do. When is big bang optimal, when is agile optimal, things like that? In the IS field, as you know very well, there has been a lot of movement in empirical work and I was never doing sort of this traditional empirical work like regression etc. More recently I found, what I think is a nice connection between econometrics and optimization, so the control theory and the idea is something like this that, suppose you are observing some... let's take an example, you look at the sales of a firm. Now, the sales of the firm are driven by certain controls the firm uses, say, for example, it uses price as a control or it uses advertising as a control etc. So, in some cases the controls are visible, like price is visible because we have declared a price but how much effort are they spending on advertising is not that visible but using control theory you can write down a theoretical model which will capture that, given this firms parameters what would be the optimal level of advertising they would use? And once you're able to solve that analytical problem, you can then substitute that control in the process and observe the process using data. It is a bit similar to structural estimation, except it is a single agent model of structural estimation. In many structural estimation problems in IS people have used the idea that, there is a firm which is dealing with many customers and they don't know the exact utility functions of these customers but they know the distribution about that and then they use data to try to estimate the parameters of that distribution. So there are many agents they are dealing with, mainly customers. Now, if you flip this around you think about the firm as the agent, firm is taking certain actions and your sitting outside and observing the firm's action. Then using these methodologies you can infer detailed parameter. which are hidden normally about the firm's behavior. And then for example, if you are another firm who is dealing with this frim, you can use this knowledge to align your actions in some sort of optimal way with the firm. So, for example, we are looking at data from Amazon workplace, where Amazon allows the booksellers to sell books on their storefront and these booksellers can periodically change the prices of those books. So, what Amazon does is that every day or maybe even multiple times a day, they generate this so-called featured list in each topic, say take a topic like business books, so business books will have an Amazon featured list one thousand books and Amazon sets the rank and the way they set the rank is using things like, the part, the price, the fields of the work in the pasts etc. but they don't reveal the exact ranking algorithm to the booksellers to avoid opportunism. But using our methodology we can make some structural assumptions about what Amazon is trying to do and uncover this hidden ranking algorithm and obviously the use of that is now that, each retailer can then optimize it's price anticipating what rank they would get. And therefore, they can maximize their horizon value, revenue, for example. So, those are some of the values of trying to discover, what an agent is doing, when you don't have full observation of it's internal action. Key insights? It's more like an opinion than a direct insight from a particular paper but I feel that more and more our field in IS is going into micro-level estimation, that we are reaching a point where we are making behavioral models for almost every customer. And it won't be very far away that they will have, almost like a virtual customer or say avatar of the customer at their site and then they will test the advertising programs or promotion programs on that virtual customer, which is built using a lot of data collected, as well as behavioral models AI models, etc. And then they will then decide what to do with the live customer. So, I think that seems to me there's a lot of work might be going in the future.

That is a good question and I don't know if I have a perfect answer for that question. I have always been motivated by following what is most interesting me. But then, when you're working with doctrine students-much of work is with doctrine students-you have to also look towards, how will students exceed at the job markets and what will be the impact on the long-term career of the student. But you know, you have to then hold back some of the very risky ideas, create an optimal point it cannot be something incremental, it cannot be too out there, somewhere in the Middle. Source of ideas, I think- this might be little spiritual but my firm belief is that the human brain doesn't really generate ideas, a human brain is a receptor of input from the world. In our the Indian text-the Rig Veda- we have very famous saying that, all the noble thoughts come from the universe. So, the universe is the source; what you have to do is to keep your mind open and more open your own mind is, more receptive it is to new ideas-then it catches it and then we can go forward with it. As an example, the same question I asked a very famous musician in India who plays the sitar, I said how do you develop new tune? how do you develop new music? is it that you are very good at playing the sitar or what is it? So, his answer was that, if I am not able to play sitar well then even if the idea comes I won't be able to execute it. So my fingers, my muscles, my instrument has to be perfectly tuned, I have to be highly skilled at converting the sensation in my brain to music. So, I think too that extent in our field, taking analogy- knowledge of methodologies is like having a very good instrument. If you don't have a good instrument, you may get a good idea but you may not be able to convince others, you may not be able to take it to its full fruition. But the real sourced of the idea, the creativity doesn't come from the instrument-in music as well as our research-the source of creativity, as I feel, comes really from your openness, how open you are to the world? do you box yourself into thinking that: oh those people they don't do good research, my field does better research than that field? which happens, often happens but I think we have to avoid that. And the other thing which I learned, very early in doctoral program I interacted with the many great scholars, I think in industrial engineering at Purdue. So, he used to tell me that never feel shy of discussing even the first version of your idea in the public, don't think that somebody will steal it and take it away, that is not a good equilibrium in academia. Sometimes it may happen that people get inspired by your idea and they do something but more likely than not, they will follow it in slightly different part and you will follow it in a slightly different way. So that is the other thing, when you have ideas don't be afraid of freely discussing the ideas. The fear often is that somebody will take it away. But I think you get much more from that small interaction than that small danger of somebody taking it away. I would say those are the two main things; feel free to discuss in open and train yourself in certain instruments, so that your ideas can come out and become good papers or whatever is your goal.