Adarsh Pal Singh Email and Phone Number

Software Engineer - 2 (Backend) in the GoFood team.

Software Engineer - 1 (Backend) in the GoFood team.

Pursued MS by Research as part of my Dual Degree course under Dr. Sachin Chaudhari.

Pursued research under Dr. Sachin Chaudhari on the application of Machine Learning towards smarter Internet of Things. I worked on both the hardware aspect (embedded systems) as well as the data analytics part. Published two papers, one in ACM SAC and the other in IEEE Globecom.

Served as the head TA for the course Embedded Hardware Design, Monsoon 2017. My primary job was to design lab experiments centered around Arduino and Xilinx Zedboard and take lab sessions every week.

1. Worked in the AI-ML Practice team on the problem of clustering in high dimensional text-based embeddings as well as optimizing existing machine learning algorithms for scale. 2. Implemented a CI/CD pipeline from scratch in Azure DevOps for an end-to-end Docker-based deep learning application.

I was selected as an LFN intern in the OPNFV project for a period of 6 months. I remotely coordinated with a team from Huawei to implement a kubernetes-based small-footprint edge cluster supporting cloud-native framework and developed exemplar microservice-oriented applications for the edge as well as the edge-cloud paradigm of the future.(https://wiki.opnfv.org/display/DEV/Intern+Project%3A+Edge+cloud+native+cluster)1. In the first phase of the project, a versatile Ansible script… Show more I was selected as an LFN intern in the OPNFV project for a period of 6 months. I remotely coordinated with a team from Huawei to implement a kubernetes-based small-footprint edge cluster supporting cloud-native framework and developed exemplar microservice-oriented applications for the edge as well as the edge-cloud paradigm of the future.(https://wiki.opnfv.org/display/DEV/Intern+Project%3A+Edge+cloud+native+cluster)1. In the first phase of the project, a versatile Ansible script was developed to form a kubernetes cluster out of two or more Raspberry Pi 3 devices. Exemplar microservice applications, containerized using Docker, were developed and tested on this edge cluster. In particular, a low-latency real-time video streaming app employing UV4L was developed and tested.2. The next phase of the project focused on an edge-cloud paradigm for machine learning applications. A demo was created which involved containerizing a YOLO-based real-time object detector for this edge as well as GKE (with Nvidia P100 GPU) and highlighting the pros and cons of a collaborative machine learning paradigm.I received the Gambia Community Award in the intern category for my contributions to OPNFV. I also got two opportunities to present my work: one at the Open Networking Summit, Amsterdam and another at the OPNFV Plugfest, Paris and was the only undergraduate speaker in both the places. Show less

Worked under Prof. Stefan Werner and Dr. Frank Kraemer on the problem of Transfer Learning in IoT-based Machine Learning models. Also explored the need of a collaborative edge-cloud paradigm for robust machine learning-based IoT systems.