Purdue University Krannert School of Management, Associate Professor
Purdue University Krannert School of Management Doctor of Philosophy (Ph.D.), Management Information Systems
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Where are you originally from? Have you lived in other places? What kind of things do you enjoy (eg sports, dance, music, food, art, movies, reading etc)?

Summarized By: Jeff Musk on Thu Sep 12 2019
First of all, let me thank you for giving me this opportunity. This is really a great opportunity to be able to speak to future scholars and I would like to thank you for doing this such a great initiative. Originally, I was born in Bangladesh, Dhaka. I moved to the US for my college after finishing high school in Bangladesh and then once I did my undergrad, my MBA, my PhD. I actually was at the University of Calgary, first 6 years of my career as an assistant professor there, then I became an associate there. So, I spent some time, 6 years in Canada and then I'm back in the US. In terms of substantially living in different places, these are the few places where I lived, substantial other time. I mean I guess in terms of enjoyment, it's funny, I guess your enjoyment change maybe with at least for some people with PhD and with the faculty life but I still enjoy music a lot. I'm a foodie. I love to eat and try all kinds of cuisine. If I get time, I actually like to do some gardening but that seems to be a rare commodity these days. I have a small son now, he's about four years old. A big chunk of my time these days in terms of enjoyment is just spending time with him, playing with him and doing things that he wants to do. Just as simple as catching the baseball or like to just following him and running around, whatever he wants. So that seems to be the life.

What do students learn in your bachelor program, and jobs students get afterwards? Please also discuss about your graduate program(s), if you offer any.

Summarized By: Jeff Musk on Thu Sep 12 2019
Great! So, we do have programs starting from bachelor’s to all the way to PhDs. So. let me start with Bachelor then I'll talk about the Graduate programs we have different versions and then the PhD program. In the bachelor program, we have few majors like management, we have finance, marketing. I’m in the area of Information Systems which is under the broad umbrella of Supply Chain and Information Analytics. Krannert and Purdue being more of a quant schools so there is a lot more focus on the quant side in terms of training our students. So, we try to make sure we are creating analytical leaders that are ready to sort of be leading in the business world where analytical skills are actually becoming more and more important and also how they can manage innovations because we, I guess, live in a world right now where innovations are coming up all the time, perhaps at a faster rate than other times. So, the goal in our program, in the undergrad program, is to prepare students where they have the foundation and generally it's more quantitative-oriented foundation. MIS or Information Systems or Supply Chain Analytics and Information Systems or data analytics kind of focus then they have all the skills in managing data. How do you leverage large data, big data to make decisions so that's the sort of the focus of our graduates. If you look at our finance program, still there is lot more quant to that. Marketing’s the same way, we have people/students who are taking classes of marketing analytics. So that is our strength and that's where we play a lot in the program. Personally, I teach a class in the undergraduate program, it’s called Data-Driven Decisions and Digital Markets, those all courses are designed around data-thinking. This resonates with our shift in terms of generating more and more data-savvy students. We have always been a quant school, but we are really trying to make sure all our students are also data-savvy and I think Purdue right there now use initiative to make sure this is the case for most majors, most students at Purdue. In my class, the goal is how do you do data-thinking. The idea is, if you have a business problem, how do you convert that business-problem into a data-problem and then what kind of data do you look for, how do you find the kind of right set of data? Can you always find the right set of data? Should we always look for data? There are even decision making that is great but there are scenarios where experience is important. Experience trumps what data is saying or data can possibly say or maybe data is not even there. We talk about those kinds of things but then once you convert a problem into a data-problem then do that analysis and then once we get things like how do you convert that into a business action? So that's sort of the goal we have for our Bachelor’s.At the graduate level, we have different programs. Of course, we have our MBA program, two years full-time MBA program and we also have some weekend MBA programs. But the full time MBA is basically again for people who have had some experience in the job now, they’re trying to climb up the ladder so we bring them back and we give them managerial skills so they're ready to basically lead the business. And we also have now a Master’s in Business Analytics and Information Management and a few other master’s like Master’s in Finance Marketing. Now, the master’s is designed for students who just finished bachelor’s, so who just have one or two years’ experience. Not quite there in terms of taking the managerial innovation leading kind of roles but still they want to get a graduate education to be more sort of deep in some sort of field. So, for example it could be Analytics, Business Analytics which is now very popular. So, these students we basically give them with all kinds of tools so we are getting a lot of students from diverse backgrounds like they come from Statistics, they come from Engineering but then the goal is again we give them tools on R, Python, so these are the basic sort of technical tools but then you also think about how we apply them so application tools. So for example I teach a class on Web Data Analytics. In this class, we talk about how do we use python to actually scrape data available on the web? It could be for companies that you work on that are trying to understand what’s the social media is talking about for the company. It could be if you're in finance, you're trying to actually get all the SEC filing. If you're working in an investment company or if you’re working some domain in financing and you want to know the SEC filing of all these mutual funds or all these 10k’s from publicly trading companies, you need these tools to be able to scrape and manipulate the data. And then we talk about how do you connect to APIs to actually get all the let's see if you want to know what Twitter is doing about your company so how do you get that stuff? How do you analyze? But then the data collection part and the analytics part is okay, how do you take the data and model it to come up with insights? In the business school, the goal is always how do we take something and convert it into a business action? So, that's what this one is due, and this is the program we have and there we have many different classes like that too. So, we have again a Marketing Analytics class. We have a Predictive Analytics class, we have such classes. We are actually thinking of introducing some of the more hard edge, cutting edge classes like Algorithmic Business thinking sort of classes in the future. So, that's our graduate level. And then for the PhD, of course, we have a PhD program and we have produced many good scholars in the past. The PhD program is really designed to train students on advanced skills. Primarily students who are interested in going into academia, but we are seeing some students who are also interested in going to industries/companies like Google or Amazon or as such. But here again the goal is, of course, training them rigorously in quantitative methods that’s really our goal. So, we are very big on Econometrics, Econ kind of training. And then also Computer Science kind of training. Purdue is a very transdisciplinary place. Students can go take classes from Computer Science. When I was a student here, I am taking classes from Computer Science. Right now, my key student, I asked him to go to Computer Science, take classes on Machine Learning, because we're combining Machine Learning and Econometrics to really answer questions. How do we establish causality and things like that? So, we have that. And in that program, that’s about four to five years. We take students who are even just as recently as a bachelor graduates but more often will also get students who are probably with some sort of graduate degree. So that’s sort of the spec we have in credit at Purdue. 

How would you encourage students to apply to your programs? Would you like to clear any misconceptions that discourage certain students from applying to your programs?

Summarized By: Jeff Musk on Thu Sep 12 2019
Yeah, so first of all, I think it's always important for students to know that we are very welcoming to students from all over the world, specially all over the states. We have students from many different states. A lot of students just think like you got to go to school within your state, that’s not necessarily true, right? So, we are a very welcoming place. I guess one of the things I would say is if you're looking for a safe and a very collegial learning environment, Purdue is a good fit. It's a great place for students to be getting a lot of opportunities. There are a lot of things happening so right now like for example I'm running a data dive which is basically with a large company, Cisco. Cisco is giving their real business problem they’re facing right now. And they're giving data, real data, that's all Cisco data that only their employees have access to and they're working on, actually running an internal competition on the same problem. So, we have these now inter-Purdue, across the campus, all undergraduate and master's students, graduate students are welcome to compete in this competition. Get a great experience and they'll present in front of some of the top executives. Like last year and I guess this year I'm also gonna have some senior leaders from Cisco. Actually, this year I’m probably gonna have two, some of the CIOs and the higher-level executives from other companies. And then you present in front of them and then you get direct feedback. I do not think you can get that sort of opportunity in many different places. In fact, I think, the data dive idea is from what we believe in law as we are one of the first ones to do this. I came up with this idea in 2015 and then we did it with Walmart in 2016. There are a lot of opportunities like that, so it's not just we provide the educational opportunities but there are other extracurricular activities. Other activities that are here where you can experience and learn, and I think that’s becoming very important before you go to work. Right now, in a lot of industries actually expect that the students have some internship experience. They are actually involved with case competitions and things like that. So, we do have all those kinds of things. We have a lot of other things like clubs, a lot of musical programs and cultural programs, things like that. I guess in terms of misconception, some people think Purdue is probably very expensive which is not true. We have had a tuition freeze for about now 7/8 years? So, it's probably I think it’s starting out to be more economical for many students even out of states than they’re in state schools. And I think one thing I would say in terms of business versus other fields. So, I know a lot of students, they are very interested in Math or Engineering, so they're very good at Math and then next road they think they need to go to Math or Engineering programs. I would highly encourage them to think of a business program like ours. It’s a very quantitative business program, I know a lot of students later on they want to move to the business school. I would say to look into thinking about the business school for the beginning. If you have a very strong quant background, do that. It might be a good time to start from the beginning because you're not gonna get any less Math than many other programs that you're doing outside of the business school. And then you're working on many interesting business problems. I would say that's an important thing for students to think about.

What are your research interests? Can you discuss major research projects you have worked on?

Summarized By: Jeff Musk on Thu Sep 12 2019
Absolutely, I guess that's the fun part, right? In terms of my research interests, so my research is actually broadly in the domains of digital transformation and sort of digital crisis. This relates to digital transformations. I have worked on things like how market structures, so local market structures matter in terms of competing with online transformations that are happening whether it’s retail. That’s what I've worked on before. For now, we are seeing a lot of transformations which are green tech. And we’re also seeing a lot of transformations with the sharing economy like for example like the hospitality sector is changing because of Airbnb or if you think about Uber, things like that. How do they sort of compete? How to they interact and how does the sort of impact of these transformations vary across geographic area? It has been one of my major passions. And then I also worked a lot on digital traces in the sense that because of the digital transformation we know a lot about people who worked on the internet. We can see a lot more in terms of what they do, their activities. If he’s just thing called server logs like if you’re gonna work outside, we’re gonna see every click you make and the decisions you're making. So, I have worked a lot on that in terms of combining the server logs from different retailers and then seeing how consumers make decisions on those things. And I’m still continuing on that, so right now in terms of the project that I'm working on right now. I'm working on one project, so let me start with the digital transformation first, sort of stream that I talked about. My student and I are working on. Well, what’s the impact of Airbnb? So, if you think about sharing economy. What’s the impact of sharing economy (Airbnb) in terms of spill work to local economic activities? Can it be a local economic engine? So, there has been a lot of discussions in terms of policy revelations and in academia about how Airbnb is competing with the substitutions (direct substitutions) like hotels or like Uber is competing with taxis? But then again, the perspective we're bringing in is like, well how about some sort of complimentary activities so like is there employment growth because of Airbnb in your local area? If you think about Airbnb in local areas, they could be in more distributed areas than any city where you just have the hotels. Hotels they have to follow zoning and other things, therefore located in central areas but then when you think about Airbnb they’re probably spread all across the city, when you look at New York that's very visible. But now when people are staying in this Airbnb places, of course, they’re directly benefiting the host who is getting money but then two things can happen. The visitors who are coming to these areas, they may just stay in that location, in that house, or apartment and just go to the main central hubs (visitor hubs) and do everything there and therefore it will not benefit the local area at all, they’ll just use it as a lodging spot. The other could be that while they use it as a logging spot, yet they go to central hubs but then they’ll also frequent and support the local businesses like restaurants. But then all of a sudden, you'll see spillover effects that is going-on on the restaurant business and the employment there. And that's what we're working on and we do find that Airbnb activities for example doubles on an average in New York City, we see about almost two percent job growth in local areas. So, that's pretty big if you think about the impact. And so that's one project we're working on. Similarly, another project I'm working on with the sharing economy peer to peer lending there has been a lot of people who can get money from these lending platforms like Prosper or for Lending Club, no matter where you live because that's the biggest difference. If you think about financial market, it’s quite interesting that it is probably one of the most right places for first degree price discrimination or individual discrimination because a loan is priced at you, is very much individualized to you and that’s how banks operate. But when you think about this lending platforms, there is no such discrimination. They put you in a bucket. It doesn't matter if you live in Utah, if you live in New York, if you live in wherever you get access to the same platform, same sort of rate. Now the question though is, you have the local market structure there the local banks that are there. We live in West Lafayette, we have much fewer options in terms of banks than someone who lives in Salt Lake City or in New York. Now if these banks are strategically reacting to this competition from the platform then the benefits again change. Although you’re thinking we all have equal access to the loans from Lending Club and Prosper, that's true, but then once you get the loan, if your local banks start to sort of compete against Prosper and Lending Club and compete against each other. The more banks you have, you're probably better off than fewer banks you have. So, again the geography is gonna matter where you live would matter and that's what we find. We find that if you live in a bigger place, in a bigger city where you have more banks, you're more likely to prefer an online peer to peer loan and less likely to default from such a loan. And if you look up, think about the mechanism what it is-is that when you have more banks around you, you have a lot more competition, they're willing to give you a lower rate, they fight to give you a lower rate and you get a lower rate than the Prosper Loan or the Lending Club loan you have and then just replace that with the lower rate you have from locally. But when you live in the rural place or places where you don't have enough banks, you don't have that opportunity, so then you just keep paying the higher rate. Interestingly, we think that the internet and specially this peer to peer lending platforms are working as a great equalizer but that's not really true. So that’s the kind of work I'm doing in terms of transformations. In terms of digital traces, quickly, so I'm working on a project actually that Adobe recently also gave us some money for these projects, gave us a grant. We're trying to understand. If you think about all the sites, many are (not all) many actually sites where we go, they're supported by advertising. I noticed you don't have advertising on your website but then that seems to be the revenue source for many different sites. Now the question then becomes, many of these publishers, they are trying to get revenue from these advertising and I think there has been some discussions about like the type of Ad and how do you sort of target people based on who you are and what kind of Ad we show. But then there isn't a lot of understanding of the number of ads shown to you. How does that interfere your engagement with the site? So, if you come to a site and if you’re shown no Ads versus if you’re shown five Ads, does that matter? The number of ads in terms of how much you're going to be based with the site and I guess the other part of this is, does it matter which source you came from? Because that could tell us the type of interest you have in the sites. If you're coming from a social media post, maybe your engagement and sort of intentions and types are different than if you are actually, organically looking for some terms and then Google brought you to the site or directly came to the site. Maybe these are different ways that you're coming and signaling your intention to interact with the site. And we're trying to estimate, if you come from different traffic sources, does it matter, the number of ads that are shown, does that matter? So, does this sort of affect your engagement with the site ‘coz ultimately, in terms of revenue maximization it's important that you are engaged with the site. If I just show you like tons of ads then you just leave there's nothing to gain. We're really trying to understand that problem and it's all from digital traces because we really have to look at all the sources that people are coming from and what they're doing on the site and things like that. 

How did you come across these ideas? How did you decide that these projects would be worth pursuing?

Summarized By: Jeff Musk on Thu Sep 12 2019
Well that that's a good question. I think, in general, for a lot of the research ideas that I work on, it’s based on the experiences that I'm having as a consumer or the business problems and the issues that I read about. I actually read a lot about the technological changes, the business that are happening around us and then try to think about what are the big picture questions that we don't have answers to. Now in terms of how do you decide on pursuing these questions, we will probably come up with many different ideas during the time. Your ability or your sort of perspective on which problems to pursue would change over time with experience, I think. When I was PhD student, of course, I probably had much less idea about what projects to pursue versus I guess now. I hope I have more sense on what we need to pursue. I mean I look at these problems and some of the problems, basically, are what kind of intuition and sort of insights am I expecting is a big part? If I expect that I'm going to be coming up with insights that would matter for practice, for policy and also for knowledge-building, those are the ideas that I pursue. Whereas you know, there are ideas that are interesting, I want to know the answer but then you know that this answer is not going to be such that is so generalizable, its knowledge-building that is going to contribute to future discussions and future work. Those are the ones you say cute and you probably pursue. 

What criteria do you use to evaluate papers while reviewing? What are common reasons for papers getting rejected? How can authors improve the chance of getting their papers accepted?

Summarized By: Jeff Musk on Thu Sep 12 2019
 When we think about general publications, couple of things that come to my mind, basically, we have a clear sort of sense of what the problem is and why is this problem important? We need to have that understanding that this is why this problem is important, the motivation behind working on a problem. And the other thing I believe is important and I look for is the underlying mechanism. You're claiming some sort of insights or some sort of causality but then it’s important to show the underlying mechanisms or the building blocks. And the building blocks could be, a lot of times to be based on economic theories, so basically it could be Economics. For other scholars who are from Psychology or from Sociology, it could be that. So, people could be building their sort of building blocks from that. But it's important to make it very clear that this is how it's gonna work. If I must to give you an example, we talked about the Airbnb’s effect on employment. Well the idea is that, Airbnb is increasing the visitors in these areas. So, we have to clearly show that it is improving, it is increasing the number of visitors in these local areas and that is translating into local job growth and that's very important in terms of the underlying data generation process and the mechanism that is at play. And then of course when you're talking about top journals, we need rigor. We need to see that these results are not some sort of screwiest results and they actually withstand a set of rigorous standards that we look at, although alternatives are ruled out. And finally, I think, writing is very important. I see papers getting rejected just because of not conveying what they do. In terms of whether you're trying to express the initial ideas, or you are trying to explain why you are applying to Metallurgy to address this question. It's important to really make that very clear. And I think a lot of people miss that and don't maybe take it seriously. But I think it’s important to take that seriously. In terms of how can you improve the chances of getting your papers accepted? Of course, you need to start with the good, interesting idea. Something that you're passionate about, something that is interesting. But I think it's also important to think what are the mechanisms that are at play? I recommend to my students and junior colleagues, think about model-free evidence. Just try to look through the data (look through), visualize the data and see. Can you convince yourself without any complicated model that this is holding what you're trying to claim? And I think that needs to come out in the paper that we have this done. I always say, try to make sure writing is well done. If you believe that you would benefit from some sort of editorial services, it's good to get that done. Because a good idea could get rejected just because of bad writing. 

What are some major research gaps that you believe needed to be addressed?

Summarized By: Jeff Musk on Thu Sep 12 2019
That's a tough question. But I think we leave it in there and we are in a field where there are lots of interesting topics. It's tough because it's gonna vary from person to person. But I think we have a lot of things to work on. If you just think of the explosion of social media, social network. The way people are connected on Twitter, Facebook and other places. I think we need to understand a lot about how these are influencing people's behavior. I think we got to think about the next stages now. What kind of biases, what kind of malice is it spreading in terms of our decision making? Whether these are social decision-making, or it is a business decision-making. I think that's very important this time because as we are introducing more and more advanced algorithms like machine learning (is great) but machine learning is all based on past data. So, it learns from the previous data and that it applies that for whatever scenario that’s now facing. We need to understand where does it break? Where does it actually deal of biases? What kind of sort of social malice or ill decision-making situations are we creating through some of these medium so like for example, I’m right now thinking about, how could we think about. I think there is a recent study done by our student on a roll and his co-authors, that just came out in times last week but like in Twitter, false news spreads much faster than genuine news. That’s a scary thought, right? But it's scary also because we need to see what that news is doing in terms of decision-making. Spreading is already dangerous enough but then if it’s affecting our decisions in a significant way that's very dangerous. I think we need to sort of, we can work on. These are very important research problems that we don't have answers to. The other sort of problem that I'm very interested in these days is like how, with all the machine learning and A.I. we’re gonna achieve transformations like self-driving cars? Now how would that change our decision-making scenarios? I mean you're already seeing self-driving cars becoming a retail platform. GM has the marketplace (GM marketplace) but then what sort of decision-making that seems to be done? How would the companies perform or compete with/against each other? The car needs to really, will be making some of the things like lifting grocery orders for you and then figuring out where to pick it up. But then that changes the competitive dynamics of what choices are being made and how companies should compete with each other? And similarly there are challenges in terms of, well, how do we? It's one thing to sort of teach certain rules and narrowing the error which is what we could do with a lot of data and better predictions. But then what about our judgments? We make a lot of judgments on a daily basis and then how do you sort of interact that with this automated system we’ll be interacting with. Because they're gonna make decisions for us and how does that work with our judgments we have. What kind of interactions are we gonna have around that? I think these are important research problems to solve. And I guess the other sort of bigger picture problems, if you think about economic growth, right? So what sort of economic growth are we gonna get? Are we gonna displace a lot of workers with some of the technological advancements that we have done also in the past. This is exactly some sort of similar things happen for any culture transform into the current form. But then what sort of other economic activities? We’re in the field where we're talking a lot about the sharing economy (the Gig economy). But then are these jobs, sustainable jobs? Are these actually going to sustain the economic growth in the longer term? Because you know if you think about careers, are there progressions? There doesn't seem to be a lot of progressions in the jobs we are creating with Gig economy right now. So that but then that's not how you can build careers or are we then not worrying about carriers? I mean these are questions that all sorts of scholars can work on. I think these are important questions to work on. 

What approaches have you found to be effective in working with industry for funding, getting data, and picking consultancy projects?

Summarized By: Jeff Musk on Thu Sep 12 2019
So, I mean, I think one thing I would say is you need a lot of patience. I think it's important to make sure that you can make that complicated simple. So as an academic or as scholars I'm assuming this question is more for the younger scholars (the younger academics), we work on complex problems. We love to work on modes that are increasingly becoming complex. But then when we're trying to talk to industry folks in terms of getting data or like getting funding or consultancy projects, it is important to be able to communicate them key insights. And that basically means that you ought to know how to convert the complicated things in simple terms, but these simple insights should matter. They should be actionable. And I think it's important to also realize that you might have a lot of interactions with different folks and only if you will plan out. So, in other words that’s what I meant by patience. It's important to say, I'm okay. You have to be ready to know that not all paths will lead you to success when it comes to industry interactions. Because if you get very quickly frustrated though like I have two different discussions and then nothing happened, that's possible. Even what happens is you go down to a very deep path but then somebody changes on the industry end or like something, some company goes through a lot of things. You have to realize the ups and downs that they deal with. So, I think what’s effective is really being able to communicate the ideas very clearly. What you're gonna do? How do you’re gonna benefit them is not necessarily just being able to write end of the management Science paper or ace their i-star paper. It's really about what kind of actionable things you're gonna come up with because that's really what they care about. And then being clear about what you need because I think it's not, it’s the partnership doesn't go well when you have to change your specification data request all the time because they don't have a lot of time. They have to invest resources. I think what will happen, you have to realize is that you're probably gonna start, you’re gonna be successful when you're talking to somebody higher up, but this person will delegate this task to somebody down there who is not going to like the fact that you're basically adding more work to his or her plate. So, it's important to keep these things in mind, being able to have a communication that’s clear, transparent and sort of a planned. This is very important to be successful, I think, in this kind of relationships. 

What do you look for while accepting PhD students or postdocs? What kind of funding do they get and for how many years?

Summarized By: Jeff Musk on Thu Sep 12 2019
So, for PhD students, personally I think the most important thing to me is passion. We need to see that you're really passionate about. You’re putting in commitment for PhD program and we need to be convinced about your background to be successful in the program. Because the way I look at it is, it’s not only that we bring in a student if it's the wrong match, it's a miserable thing for the student as well. Because you spend some time you don't gain a whole lot then you have to leave. Or even if you graduate and get a good job that is not a good outcome in my mind. So, it's very important for us to see that you have sufficient quantitative background, so you can do the kind of courses we are interested in. So, like we look for Math background, successful Math background. We do not really care what sort of Major the students come from. We don't require any prior business background. We’re totally fine with people coming out of Engineering, coming out of Physics, coming out of Statistics, Math. Actually, they're very welcome to do that. I came from Computer Science background myself. But what matters is how deep you are in your quantitative skills and are you willing to take this and then apply into Applied Business problems that excites you. In terms of funding, we provide guaranteed funding for about four to five years. Our program used to be four years but, I think, increasingly all universities are seeing that students are staying for five years to be more sort of ready for the market. So, we provide funding for five years as long as there is satisfactory progression. At this moment, I don't think we have that many opportunities for postdocs but that changes with funding. So that is basically based on outside funding’s and postdocs are probably going to be about one to two years, generally. 

How do you evaluate progress of PhD students or postdocs, and decide if they need to leave your program? What mistakes do you see them making in their initial years in the program?

Summarized By: Jeff Musk on Thu Sep 12 2019
So, Purdue PhD program is fairly well-structured. We actually provide handbook on our website, on our MIS PhD program. There’s a handbook. We have milestones. We expect the milestones to be met as the students’ progress. At the very minimal level, the first milestone is the grade that we look at after every semester in the first few years. So, they’re required to take some methodological process when they begin in Economics, mostly. And then we see how they're doing in those classes. These are your classes on Micro. This is your class for Mathematics, for Economics. These are your Econometrics classes. So, we need to see that there is a good progression in the classes. And then we require a first-year summer paper, sort of be working with the faculty member to work on some problem. And then second year there's a prelim exam but our prelim exam has actually two major parts. So, there are these exams where we test the students on the knowledge in terms of teaching MIS and also the seminars that takes of the research knowledge. But then the other part is writing and presenting a paper (a complete paper) as part of the prelim. Now of course, the students’ working with a faculty member, but they need to basically be in the driving seat to write this paper where they work through the whole thing and make a presentation and that’s when they pass. Now, if we don’t see students making progress in any other stages, we will give them a warning and if we still don't see those rectified and we asked them to leave. In terms of the mistakes that I see, I think one of the things I see is that a lot of the students are not serious about the courses they take in the initial years. So, in the initial is the master’s, actually. The first two years, primarily for course work, it’s important to really do well in those courses because they give you the tools to work on your research. So, I see students not taking that seriously, that’s one. The other thing I see is that a lot of students are transitions. So, some students will do very well in classes, but they will transition into a good researcher. And I think the challenge there is that, as you are taking classes, it is also important to think how I can work on different problems and be able to go deep into a problem? And I think for there, it’s important to really read on academic papers as well as read on news articles like different sort of industry articles so you understand the context very well. I always say this to all of my students at every level whether it's undergrad, masters, or PhD. But it’s very true for PhD students. You have to really understand the context for the problem that you're working on. Because if you don't understand the context, you're probably gonna make some assumptions or miss something that is basically going to give you a very naïve set of insights. And worse yet, if you think about it. If you're gonna go, make a presentation about a topic, you have to be the expert on this topic. If somebody else brings up something, it'd be much better if you know about it. If you can respond to that. If you don't know a lot about the context, it's probably gonna be difficult. I think that's where students don’t spend a lot of time understanding what's going on around them. Because if you understand that then you know a lot about your context and once you start working on the research problem, you actually can look at it from a holistic perspective, understand all the nuances and the moving parts. 

Do you have any parting advice for young educators? Is there anything you would have done differently?

Summarized By: Jeff Musk on Thu Sep 12 2019
I think for young educators, I mean I would say it's important to I believe in sort of mentoring others and also finding good mentors. So, it’s important to mentor others because this matures you. If a lot of young indicators feel like they don't want to get involved with too many other things, they just want to work on their research and get published. That’s absolutely important but I think it's also important to be involved in interacting with others to see how you can help them. So, like it could be just helping out the community in terms of reviewing, in terms of helping other colleagues by giving comments. It could be just like trying to interact with students. I think this is very important because this matures you. This also make sure that you're giving to the community. And the reason I say it’s important to have good mentors doesn't have, I mean much better if you can have more than one, because you will face difficulties. You will face different levels of professional difficulties. Be it with your reviewers, be it with your internal evaluations and so on and so forth. So, I think it's important to be able to talk to people who have experience. They can actually help you think about that. Would I do anything differently? Have done anything differently? I don't know. I mean I think I have tried to always be sort of very involved from very early on of my career in terms of the field, right. So, I have been very involved in terms of doing different kind of services, being interacting with other students and things like that. Maybe one thing I could think about that’s done differently is maybe like try to take up a little bit more classes. It’s interesting that I talk about it but I think I felt like I still want to go, learn about new things and it's harder to find that time now. When I was younger, I probably do research and I probably should be pushing myself a bit more to learn about the new methodologies and new tools that are coming up.