Home > Interview > An Interview with Prof. Shalabh Bhatnagar, Winner of ACCS-CDAC Foundation Award 2017

An Interview with Prof. Shalabh Bhatnagar, Winner of ACCS-CDAC Foundation Award 2017

PH: Let us take take traffic management as a case in point. Are not all queues by themselves optimized, and precisely why they organize themselves as a queue and not anything else?

SB: That’s exactly my point. We say that our objective function is to minimize queues. So that the level of congestion gets reduced. If the queue is constantly building up, then essentially you are experiencing longer and longer delays on the road. You don’t want that to happen. If the queues are not there then that’s perhaps the best situation. You really don’t have to worry too much about the amount of delay that you experience because as soon as the signal turns green, you are sure that you will cross the signal and go to the next one.

PH: Since we are now talking about systems and optimization, how do you see this impacting the area of machine learning?

SB: Now big data is something that everyone is talking of and working and trying, so that’s the real challenge now. Machine learning has been there for a while. There are challenges remaining in the machine learning area and people are trying to look at. One of the major challenges is of big data. The question is how to come up with efficient methods that tackle the high dimensionality. This enormous amount of data that is available and we work on schemes and techniques to come up with models which could be prediction of optimization [of anything. Essentially, the classical technique sort of fails when you have high dimensional problems — when the dimension and the amount of data is very large. The amount of computation that one usually encounters is so high that it becomes almost impossible to run through classical methods and so one needs to do some approximation.

So basically, the goal is to develop techniques that can handle big data and high dimensional data. Tackle high dimensional process, so traffic is one such example of a very high dimensional, very large data and effectively doing it in real time is a challenge. It is not easy to design something very good and that is fast as well.

PH: Do you see AI as just another fad that the computing fraternity has picked up again, after its so called classic failure from its mid-60s peak. What do you think of it?

SB: No AI is coming back. It is coming back in a very big way. There is lot of talk about the machines replacing man and so on and many people believe that is going to happen. We have been looking at drones, flying objects and how to control their movement. One instance is if there are multiple drones that are travelling, how do they coordinate their movement among themselves by just talking to one another, not that you are controlling them. If you want to do some surveillance of some area using drones, and you have sent the drone off to zones that is not very friendly, they should be able to talk to one another and figure out that I will cover this and you will cover that and stuff like that.

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