Jul 2022 – Jul 2025 · 3 yrs

• 1. Designed and delivered scalable platforms for notification systems, email encryption, spam mitigation, message tracking, AI summarized notifications and disaster recovery with a focus on high reliability and performance.
• 2. Improved tenant-level configurations, enabling custom domain support for enterprise users and enhancing email deliverability across diverse ecosystems.
• 3. Optimized CI/CD pipelines, reducing deployment times and increasing build reliability to streamline global rollouts.
• 4. Led impactful hackathon projects, including a code-sharing tool (PasteBin) awarded Best Hackathon Project and an AI-powered notification digest recognized as a runner-up in innovation.
• 2 patents under review

Jan 2021 – Jul 2022 · 1 yr 6 mos

• 1. Enhanced deployment verification processes, significantly reducing post-deployment errors and improving system resiliency across environments.
• 2. Supported region-specific deployments, enabling high availability and seamless operations in global and sovereign clouds.
• 3. Improved localization workflows and automated critical build processes, enhancing efficiency and accessibility.
• 4. Enabled enterprise collaboration through the design and rollout of team notification systems, driving higher engagement and reliability.

Jul 2020 – Jan 2021 · 6 mos

• I was working with Prof. Joyojeet Pal at Microsoft Research (MSR) India on analyzing the Twitter feeds of politicians in India and in the US. My recent research projects have been centred around the use of social media platforms in mainstream politics, specifically around the political brand building in India and in the US.

May 2019 – Jul 2019 · 2 mos · Bengaluru Area, India

• Worked on Microsoft's Outlook contextual add-ins recommendation system for real time add-ins recommendations while composing/reading email with direct installation and launch integrated platform.

Jul 2019 – Jul 2019 · 0 mo

• Selected for an all-expenses paid research summer camp by HKUST Seng School of Enginnering - Elite Student 2019.

Dec 2018 – Aug 2019 · 8 mos · Cambridge, Massachusetts · On-site

• For exploring research in deep learning, I went for a semester-long research visit at CSAIL (MIT), where I worked on through-wall Identity Recognition under the supervision of Prof. Dina Katabi. I trained a cross-modal teacher-student network for pose-estimation that transfers the visual knowledge of human pose using synchronized images and radio signals as a bridge. The student network generates confidence maps for all key-points of all people in the scene. Many motion-relevant GAIT features were extracted for each skeleton to uniquely model the identity of the individual. I used aggregation strategies on these extracted features for learning unsupervised representations robust to occlusions and environments. It was tested in through-wall identification scenarios for personalized and real-time sensing in smart homes as well. The stimulating environment in the lab and my interactions with fellow graduate students helped me realize the opportunities within the pursuit of further education. The opportunity to lead weekly meetings was also a learning experience, enabling me to develop soft skills to articulate my points well.

Dec 2018 – Feb 2019 · 2 mos · Cambridge, Massachusetts · On-site

• Represented as one of the 300 delegates from all over the world for the HPAIR Harvard College Conference held at Harvard University, USA in Feb 2019

May 2018 – Jul 2018 · 2 mos · Bengaluru Area, India · On-site

• During my Software Engineering Internship at Microsoft in 2018, I developed an interactive real-time monitoring and visualization tool using the Bokeh framework in Python. The tool analyzed performance metrics for big data platforms like Azure Data Lake Store (ADLS) and Cosmos, enabling efficient comparison of CRUD operation latencies and automated detection of low-performing benchmarks. This project enhanced system reliability and operational efficiency while providing actionable insights for performance optimization.
• Received Pre-Placement Offer from Microsoft after the successful completion of summer internship.

May 2017 – Jul 2017 · 2 mos · Urbana-Champaign, Illinois Area

• My introduction to the potential of AI was in a summer internship at the University of Illinois at Urbana-Champaign (UIUC) in the summer of 2017. I was supervised by Prof. Marianne Winslett on designing a novel attack framework to successfully reverse engineer the blueprints of an object from the factory floor using magnetometer side-channel data. Exploiting the design shortcomings of a 3D printer, I was able to experimentally show that the placement of motors on such a device can render it insecure. Integrating the entire reconstruction pipeline was a significant challenge since classical methods of segmentation on magnetometer spectrograms failed to generalize to new settings and environments. To build more adaptable systems, I started exploring the field of Machine Learning, which became a pivotal point in my career.

Jan 2017 – Apr 2017 · 3 mos · Assistech Labs, IIT Delhi

• The project was done under the course COP 315 – An Embedded Design project, in IIT Delhi, under Prof. M. Balakrishnan in 2017. We designed a platform comprising of a Desktop IDE, and an android app. The purpose of the project is to assist blind people in reading data from the tactile sheets using an android app which plays the audio of the part corresponding to a click-gesture using real-time finger scanning. To find more about the project, follow the link.

May 2016 – Jul 2016 · 2 mos · IIT Delhi

• Designed a Waste Management Android Application that stores type, and quantity of waste among different blocks of the village, and synchronises this data on the MySQL database online, aiding in quick-data analytics.

Jan 2016 – Jan 2017 · 1 yr · New Delhi Area, India

• Use PickMe to order a rickshaw at your footstep in this scorching heat. Just pick your phone up, tap a button to get the number and make the call, the rickshaw will come to you. PickMe makes use of GPS to connect you with the nearest three available rickshaws in the IIT Delhi campus. Then you are just one call away. It is fast, simple and works on just one click. The charges are the same as normal rickshaw rates, that is, there is no extra cost for using the app.** Exclusively functional for IIT Delhi
• Now you don’t need to walk back from institute area to your hostel, just order a rickshaw. The application is designed to save the hassle of all IITD residents. There is NO EXTRA CHARGE for using the app to call the rickshaw. PickMe is operational at every point inside the campus.