Jan 2017 – May 2017 · 4 mos

Jul 2014 – Dec 2016 · 2 yrs 5 mos

• My Responsibilities include:
• 1. Take the scale of the services to another level.
• 2. Improve on the efficiency, availability and reliability of the services.
• 3. Inculcate multiple channels to the current services.
• 4. Active part of the team which works on tasks related to security.
• 5. Improving the monitoring and alerting systems of Vizury AdServing components.

Jan 2013 – Jun 2014 · 1 yr 5 mos

• I was part of AdServing team.

Jun 2012 – Dec 2012 · 6 mos · Noida, Uttar Pradesh, India

• I worked as an engineer here in the R & D (Android Phones) dept. of Samsung Engineering Labs in the wifi team. Major work was to write the wifi related features in the android devices. Some features were operator specific and some features were common for all devices.

Jan 2012 – Jun 2012 · 5 mos · Noida India

• I worked as software developer engineer intern at Samsung Engg. Labs.

May 2011 – Jun 2011 · 1 mo · Dispur, Assam, India

• I was a summer intern under Prof. Sushanta Karmakar, IIT Guwahati. I worked on a project A Robust approach to peer to peer computing and sampling.
• In this, a robust approach was devised to know the size of network i.e. counting the number of peers in the network. Our approach improves the previous work by providing a protocol that is accurate, i.e. the estimate will eventually converge to
• the true peer count in absence of churn. The protocol can handle extreme levels of churn, and automatically ensures that all participating nodes learn the outcome of the peer counting.

Jun 2010 – Jul 2010 · 1 mo · Greater Allahabad Area

• I worked as a intern at IIIT Allahabad on
• Texture segmentation using Gabor filter and face detection using back propagation network of ANN under the guidance of Dr. Tapobrata Lahiri, Associate Professor , IIIT Allahabad
• The Texture segmentation was done using Gabor filters making energy images using Gabor filters of varying orientation and frequency. The low energy images were discarded and others were clustered using k means clustering to have final
• clustering. The face detection was implemented using supervised back propagation network of Artificial Neural Networks taking face and non face points from training.