PH: Yes, that is actually an instance of very large dimensionality right?
SB: Right. As far as the drones are concerned, yes, we are looking at those things. Robotics is something we have started looking into. Currently, I am jointly looking working with Ashitava Ghosal, a mechanical engineer. He is a robotics man. His team has a four-legged robot and what we want to do is to learn the movements. We make sure that we apply reinforcement learning algorithms to make the robot learn to move and encounter obstacles and then work around those. Ultimately, the goal is to make move it up and down a staircase for example. If it is going down, say, we should ensure that it doesn’t fall and does not go too fast, rather its two back legs should come smoothly down; those are the challenges a robotic dog offers.
PH:Interesting. So, I think Google’s adoption of Bayesian filters which they started applying everywhere changed everything. Do you think that was a turning point?
SB: No, we don’t do the Bayesian learning. We don’t assume a model as such because we work by design with model free algorithms.
PH: So, there is no apriori.
SB:Nothing we just assume that there is nothing and we just encounter an event, take data in and the algorithm just answers itself. Its more learning by interaction with the environment.
PH: This may have a lot of industrial application like for example, controlling the HVAC systems. When people are moving in, it should be maintained at certain temperature and it should predict the required temperature over the next one hour or two hours so that the energy consumption is optimized.
SB: Correct. There are many interesting problems that arise. Take the micro-grid domain. If you have an excess of energy in a certain micro-grid can it transfer that at a certain price to anther micro-grid which is short of energy. This is an interesting optimization problem that arise which are model free. A lot of prior work assumes that they have information on the system model. Such algorithms, and frameworks are not reliable because the models cannot be 100% correct. There is a failure probability. Our algorithms, because they are model free, really don’t assume that we have a model of the system saying whether its sunny, its windy and so on. We really don’t care about that and as a result, we just look at data, whatever data is available. We just work with that and that’s nice about model free approach.
PH: Let us say, when you think of what is the probability that, another cyclone is going to hit Indian shores this week, a lot of things are dependent on various environmental incidents that are taking place, right. How do you approach this kind of a problem? Where do you start, if you don’t start with the models?
SB: If you are asking the “when” question: when is the next cyclone going to hit, I think you will need historical data to make that prediction. Suppose you ask the “what” question can apply generally to any situation because there is nothing much you can do once the cyclone hits except to make some precautionary arrangements like evacuate people. The problem to solve then would be: given the fact that cyclone may hit is a certain probability, should I make this decision, say, should I evacuate people on the coastal areas now or not. That is a control problem.
PH: So, you focus on the control problem and the trick is to ask the right question that brings to focus the control problem.
SB: Yes, we focus more on the control problem. I think the weather forecasters look at the problem of prediction but I think that if you do prediction then probably historical data and some trigger has to be there and you should be noticing some changes in the behavior of the seas and so on that essentially tells you that a cyclone is likely to make a land fall.
PH: Switching gears here, can you tell us what a young engineer or a researcher should do, what’s your advice. What are the challenging problems out there and what can they start off with?
SB: I think this is an important question. Some people make the mistake of going after areas that are extremely hyped up. Now AI is coming again, so everyone should work in AI or ML and that’s what we also see in our domain. The number of people interviewing for the ML area, for instance, in research interviews is so high, we often have a challenge scheduling those interviews. See, ultimately the goal is that people should be excited about the area itself. Whether it is hyped or not, research fields will come and go.
PH: What shortfalls do you see in our young pursuers, which skills can they hone to be better at research.
SB: I will give you a classic case which is quite common these days. Our department, for instance, is extremely strong in theory. There are theoretical computational complexities and all those algorithms on which people are working on. I think one of the key things we find particularly in our current BTech education country wide is the lack of mathematical background. People are really not focussed on acquiring mathematical skills and that becomes kind of a dampener when it comes to research. Let me illustrate this with an example. Say, you come up with an algorithm and normally what happens is that people will do some experiments using those algorithm, they will work on your device and then show that well the algorithm is doing better than some other algorithms but then the they will come back saying, “That’s okay on this setting, what about other settings,” and you end up trying 20 more settings and you have to keep on convincing. But if you also have [mathematical] analysis with that to go to say your device is doing optimal things then essentially there are not many complaints. So, it is much easier to publish papers if you have the mathematical analysis to back up your theory. I think mathematical rigour is required for doing good research.>