Mansi Agarwal

B.Tech Student

There is a saying, which says, “The only thing permanent in life is change”. This perfectly holds true to the world of Computer Science where novel ideas are not a novelty. It is this novelty that entices me to this field and gives me a deep sense of professional pride.

I am Mansi Agarwal,a Computer Science geek and a third year undergraduate student pursuing Computer Science and Engineering from Delhi Technological University(erstwhile Delhi College of Engineering).

The concepts of Machine Learning and Artificial Intelligence amaze me.A simple idea-“Can Machines see the world, like humans?”has intrigued the whole world. These basic ideas may look appealing to anyone, but to me, the beauty lies in the struggle and hard work that is put in by people who have converted these ideas into reality. Machine Learning and Artificial intelligence can be applied to so many situations and I believe that these fields when utilised to their true potential can save the world.

In my school days,I used to try my hands on every sport; every field be it drama, dance or debate.I strongly believe that a student must be an all-rounder to be successful in the true sense.Being a national level swimmer, I have learnt how to win and lose and realised that we human beings are much more capable of ourselves than we ever thought was imaginable and understood why the term “team” has been coined from the phrase “Together Everyone Achieves More”. Furthermore, participating in debates has taught me how to hold on to my decisions and how to accept others’ views as well.

I was in my high school when I ran my first code. The code was the utmost basic one but the joy, incomparable.It’s strange but the happiness and satisfaction I receive from a successful code(no matter how many hours and days I’ve spent it on)seem to never decease.It is this zeal that urges me to try more, explore more and be more.

“We can see only a short distance ahead, but we can see plenty that needs to be done.”–Alan Turing


Video Summarization using Global Attention with Memory Network and LSTM