I am a Robotics Ph.D Student at the Institute of Robotics and Intelligent Machines (IRIM) at Georgia-Tech. I work with Dr. Charles C. Kemp at the Healthcare Robotics Lab. My primary research interests are in the fields of Haptic Perception, Tactile Sensing, Machine Learning and Controls. As a part of my work, I also actively collaborate with Dr. James M. Rehg and Dr. Lena H. Ting.
During my Ph.D, I have also done multiple internships with Disney Research, Los Angeles where I worked with Dr. Günter Niemeyer on developing bio-inspired control strategies for effective human-robot interaction.
I did my B. Tech from National Institute of Technology-Calicut (NIT-Calicut). During my Junior and Senior years, I worked on the design and development of a Mobile Robot which avoids obstacles and the simulation of a cricket-playing robot using neural networks and fuzzy logic. After my B.Tech, I worked in TAL Manufacturing Solutions Ltd. (A TATA Motors subsidiary) on the design of an industrial pick-and-place robot and providing robotic and factory-automation solutions.
To visualize the progression of my research topics, I have created a word cloud of my papers before starting Ph.D and after starting Ph.D.
I used python, pdfminer to convert pdfs to texts, nltk and pyenchant for some post processing, and word_cloud to generate the word clouds.
Before Ph.D During Ph.D
Here's a link to my blog. I don't frequently write blog posts, which I guess defeats the purpose :( , but nevertheless ...
My Travel Map. I love travelling. And, I would like to travel a lot more ...
I like to read PHD Comics. Just for fun, I thought it would be cool to see if some of the trends in the comic strips hold true for me as well.
Here's one comic strip which shows the Grad Student Work Output.
And here's my Work Output ( I might be a little biased towards what counts as productivity :P ). I used RescueTime API and urllib to get the data. The graph should automatically update everyday.
Here's another fun example from PHD Comics. Its regarding average time spent per email from advisor to grad student and from grad student to advisor. Apparently, the no. of words per email is a clear indicator :)
Assuming time spent in writing a word is the same in both the cases (which, I agree, is a big assumption ;) ), here's a comparable graph of the average no. of words per email from my advisor to me and the other way round. I used Gmail Python API to get the data. Looks like, my advisor has been more kind to me :D