1) Certified Google Cloud Professional Security Engineer, AWS Solutions Architect (Associate) and AWS Developer (Associate). 2) Primary engineer responsible for maintaining the availability, scalability and security of three of the organization's projects on the Google Cloud Platform (GCP) – Electronic Health Records Analytics, Real-time Threat Detection Software, and the official website. 3) Automated the Software Development Life Cycle for these projects by creating a DevSecOps pipeline using Jenkins, Bitbucket, GCP resources and other open-source tools for security checks. 4) Actively resolved and coordinated the resolution of security flaws in the Google Cloud infrastructure across the organization's GCP-based projects. The GCP Cloud Security Command Center has been leveraged to resolve security issues and vulnerabilities detected across the organization's projects. 5) The following are the Machine Learning based projects that I have been handling so far: -> The Real-time Threat Detection Software uses predictive analytics on the security log data of an organization to detect what are the possible threats that it can face, thereby helping the organization to proactively take measures to prevent the expected likely threats. -> The Electronic Health Records Analytics software uses predictive analytics on a patient's health parameters to predict whether there is a high probability for them to get any of these ailments: Diabetes and Depression. By learning from previously recorded data, it can also predict how likely a patient might need to be admitted back into the hospital within 30 days of their release, and recommends possible treatment plans based on their personal data such as age, ailment, gender, ethnicity, etc.

The Digital and Data Sciences business group that I have been a part of in Virtue Group, has been merged with the parent company - Infolob Solutions. Therefore, I was moved to Infolob with the same role and team.

• Designed an internal, AWS-based web application using LAMP stack (Linux, Apache, mySQL, PHP) to create custom forms. The output of this application would be utilized for deliverables tracking, reporting and auditing. • Research and analysis of various Enterprise applications to determine compatibility between operating systems, enterprise software and end-user hardware which was used to automate the deployment of operating system upgrades & patches in the most efficient manner.

1) Light Swarm IoT System: ----------------------------- • Enforced broadcast communication between several ESP8266 devices by exchanging light sensor readings to choose a Master device and designed a NodeRED application on Raspberry Pi to transmit its readings to IBM Bluemix • Created an automated message sender using SMTP protocol which sends the reading to my email and Twitter account when the temperature crosses a threshold • Used a Python script to fetch all readings from the IBM Cloudant database for further data analysis 2) What you see is what you eat (Machine Learning): --------------------------------------------------------- • An image of food is captured, and a deep learning algorithm using Tensorflow, Keras API and VGG16 neural network is implemented to identify it • Its nutrition information is fetched from the US Department of Agriculture's API and is displayed on a website created using HTML, CSS and Flask 3) Specification and Modeling of a Canny Edge Detector algorithm for Drone Camera: ---------------------------------------------------------------------------------------------- • Optimized and recoded the Canny Edge Detector C program into System C by pipelining all modules and parallelizing the bottlenecks to improve its execution time by 83%, from 59 seconds to 10 seconds • Increased the final throughput of the program from 0.15 FPS to 3.37 FPS (22x times)

Implemented a model of a Smart Electric Car Charging Station in a team of 4 that replaces the traditional credit/debit card-based payments with automated, cryptocurrency-based payments between the car and the charging station, using MQTT and IOTA cryptocurrency (which is based on Tangle, an alternative architecture to Blockchain).

Created a prototype of an IoT system for a logistics and food transport company using Arduino and Amazon Web Services, which enables the company to track and analyze the temperature of food, as well as the location and path travelled by its delivery vehicles

Collaborated with Prof. V. K. Radhakrishnan in a Research project ‘Optimization of Kanchipuram Divyadesam Network’ and wrote a C program to implement a shortest path algorithm that finds the best path a traveler can take to cover multiple destinations.

Controlled electrical home appliances by sending SMS messages to a GSM module. Demonstrated successful models using a light bulb and a hand fan.

1) Certified Google Cloud Professional Security Engineer, AWS Solutions Architect (Associate) and AWS Developer (Associate). 2) Primary engineer responsible for maintaining the availability, scalability and security of three of the organization's projects on the Google Cloud Platform (GCP) – Electronic Health Records Analytics, Real-time Threat Detection Software, and the official website. 3) Automated the Software Development Life Cycle for these projects by creating a DevSecOps pipeline using Jenkins, Bitbucket, GCP resources and other open-source tools for security checks. 4) Actively resolved and coordinated the resolution of security flaws in the Google Cloud infrastructure across the organization's GCP-based projects. The GCP Cloud Security Command Center has been leveraged to resolve security issues and vulnerabilities detected across the organization's projects. 5) The following are the Machine Learning based projects that I have been handling so far: -> The Real-time Threat Detection Software uses predictive analytics on the security log data of an organization to detect what are the possible threats that it can face, thereby helping the organization to proactively take measures to prevent the expected likely threats. -> The Electronic Health Records Analytics software uses predictive analytics on a patient's health parameters to predict whether there is a high probability for them to get any of these ailments: Diabetes and Depression. By learning from previously recorded data, it can also predict how likely a patient might need to be admitted back into the hospital within 30 days of their release, and recommends possible treatment plans based on their personal data such as age, ailment, gender, ethnicity, etc.

The Digital and Data Sciences business group that I have been a part of in Virtue Group, has been merged with the parent company - Infolob Solutions. Therefore, I was moved to Infolob with the same role and team.

• Designed an internal, AWS-based web application using LAMP stack (Linux, Apache, mySQL, PHP) to create custom forms. The output of this application would be utilized for deliverables tracking, reporting and auditing. • Research and analysis of various Enterprise applications to determine compatibility between operating systems, enterprise software and end-user hardware which was used to automate the deployment of operating system upgrades & patches in the most efficient manner.

1) Light Swarm IoT System: ----------------------------- • Enforced broadcast communication between several ESP8266 devices by exchanging light sensor readings to choose a Master device and designed a NodeRED application on Raspberry Pi to transmit its readings to IBM Bluemix • Created an automated message sender using SMTP protocol which sends the reading to my email and Twitter account when the temperature crosses a threshold • Used a Python script to fetch all readings from the IBM Cloudant database for further data analysis 2) What you see is what you eat (Machine Learning): --------------------------------------------------------- • An image of food is captured, and a deep learning algorithm using Tensorflow, Keras API and VGG16 neural network is implemented to identify it • Its nutrition information is fetched from the US Department of Agriculture's API and is displayed on a website created using HTML, CSS and Flask 3) Specification and Modeling of a Canny Edge Detector algorithm for Drone Camera: ---------------------------------------------------------------------------------------------- • Optimized and recoded the Canny Edge Detector C program into System C by pipelining all modules and parallelizing the bottlenecks to improve its execution time by 83%, from 59 seconds to 10 seconds • Increased the final throughput of the program from 0.15 FPS to 3.37 FPS (22x times)