Teebest
Oct 1, 2018
Graduate / Applying Umass, UCI, TAMU, Gatech - MS in computer science with Machine Learning as Specialization [3]
During my undergraduate days, i was sitting closely to my elder brother watching the Google I/O, when Sundar Pichai introduced various product's advancement and new features in Google home, photos and maps, using Artificial Intelligence and Machine Learning.
It was to my surprise when seeing the result of Deep Visual-Semantic Alignments for Generating Image Descriptions while working on Image captioning project which made me more curious for Computer Vision.
This made me understood more about supervised learning, unsupervised learning and reinforcement learning that made the problem-solving statements more interesting for me and deepened my interest in AI. All these exposure gave me the desire that lie within applicable ideas that have a strong theoretical foundation.
In order to learn the possibilities of ML and its practical utilities, I spent my summer holidays working as a Machine Learning Intern at Cuisinelinks, where I integrated Amazon Alexa with an external API facilitating online food ordering and reserving the table.
I prepare use cases for the clients of the company for using ML/AI in their business for forecasting their traffic, developing recommender system for the menu which has helped the company to increase in profit by 20%.
During this process, I observed an existing problem residing in the current system for navigation of vehicle in an unstructured and unmapped environment.
The solution proposed by Google, Waymo and Tesla to this problem is accurate - but incurs high cost and resources(like LiDARs).
This has imbue me in getting exposure to Computer Vision, Simultaneous Localization and Mapping, Artificial Intelligence which can also solve other perception problem in robotics.
While working, I faced challenges solving the path prediction problem on Indian Highways to avoid accidents.
As at that time, I was unable to do much work as I was diverted to another work but had developed curiosity and interest on how to work on such a challenging real world problem.
During my undergraduate days, i was sitting closely to my elder brother watching the Google I/O, when Sundar Pichai introduced various product's advancement and new features in Google home, photos and maps, using Artificial Intelligence and Machine Learning.
It was to my surprise when seeing the result of Deep Visual-Semantic Alignments for Generating Image Descriptions while working on Image captioning project which made me more curious for Computer Vision.
This made me understood more about supervised learning, unsupervised learning and reinforcement learning that made the problem-solving statements more interesting for me and deepened my interest in AI. All these exposure gave me the desire that lie within applicable ideas that have a strong theoretical foundation.
In order to learn the possibilities of ML and its practical utilities, I spent my summer holidays working as a Machine Learning Intern at Cuisinelinks, where I integrated Amazon Alexa with an external API facilitating online food ordering and reserving the table.
I prepare use cases for the clients of the company for using ML/AI in their business for forecasting their traffic, developing recommender system for the menu which has helped the company to increase in profit by 20%.
During this process, I observed an existing problem residing in the current system for navigation of vehicle in an unstructured and unmapped environment.
The solution proposed by Google, Waymo and Tesla to this problem is accurate - but incurs high cost and resources(like LiDARs).
This has imbue me in getting exposure to Computer Vision, Simultaneous Localization and Mapping, Artificial Intelligence which can also solve other perception problem in robotics.
While working, I faced challenges solving the path prediction problem on Indian Highways to avoid accidents.
As at that time, I was unable to do much work as I was diverted to another work but had developed curiosity and interest on how to work on such a challenging real world problem.