• Product purchase increased by 12% on ”High Confidence” UI for Echo devices with display by implementing product videos using Java & Alexa Presentation Language. Cleaned legacy code to increase efficiency and re-usability of code.
• Increased product purchase by 19% on ”Product Details Page” UI for Echo devices with display by implementing product videos on the UI using JSLT and Alexa Presentation Language within 1 work week as an additional project.

• Working on National Science Foundation (NSF) funded project: Leveraging Crowd-sourced Data to Assess Spatiotemporal Patterns of Resilience in Diverse Gulf Coast Communities Impacted by Natural Hazards. A Thesis and Publication of paper(s) in reputable journals will be the outcome of this fully funded research.

• Reduced downtime for Quarterly Full Stack DB patching by 60% by developing a python script to automate the execution of cmds, analyse the logs/errors to take action on executing the appropriate commands and generate a report.
• Enhanced the security by coding a python module which replaced the existing RSA key based SSH used by automations to an alternate secure password and API based SSH method and migrated all automations to use the new method.
• Improved the execution time of Fynderr (An in-house developed log parser and error identifier tool) by 75% using time complexity efficient data structures and algorithms and added support for Oracle EBS and Oracle SOA.
• Developed a machine learning algorithm as a side project to demonstrate the advantages of proactive over reactive approaches to handle peak server resource utilisation failures by training the algorithm to with existing resource utilisation and other essential parameters to identify resource utilization patterns leading to failure conditions.
• Expedited the daily server checks by 75% by designing a py script to perform the checks & generate web-page reports.
• Reduced the time taken to bounce multiple servers by 83% by developing a python script to parallelize the server bounce.

• Worked on ML projects such as stock market prediction, Chatbots and IOT home automation projects.
• Developed a miniature autonomous vehicle using Computer Vision and ML which could detect lanes, road signs and traffic signals. The vehicle could react to the road signs and traffic signals and move accordingly by following the lanes.

• A device developed using computer vision, deep learning and IOT which assisted a visually impaired to carry out his daily tasks with ease. It accepted voice commands and inputs from remote control.
• The device described the user his surroundings, recognized known faces around the user, alerted the user of potential threat, read text (book/newspaper), identified currency, weather and news updates etc.

• A gamified tool for speech-impaired patients prompts them to repeat syllables and words. It uses machine learning to analyze and compare the audio with stored data, progressing if there’s an 80% match. A final score reflects improvement.

CGPA: 4.0/4.0

Aug 2022 - May 2024

Arlington, TX

CGPA: 8.98/10.0

Aug 2016 - May 2020

Nitte, India

Courses

• AI, Machine Learning & Neural Networks
• Advanced Data Structures & Algorithms
• Advanced Operating Systems & Computer Networks
• Advanced Java & Python Programming
• Cryptography & Network Security
• Cloud Computing & Big Data

Languages

• Python, Java, C, C++, JavaScript, SQL, JSLT, APL

Technology/Frameworks

• MySQL, MongoDB, Oracle DB, Docker, Kubernetes, SQLite, Git, OCI, AWS, Django, Node.js, React.js