Sharat Chandra Madanapalli

Sharat Chandra Madanapalli has a diverse work experience. Starting in 2015, they worked as a Joint Publicity Coordinator for the Association for Computing Machinery at BITS Pilani. In 2016, they were a Summer Intern at Tofler.in and at the Indian Institute of Remote Sensing, where they were given a letter of appreciation for their work. In 2017, they worked as a Research Assistant at UNSW, where they achieved a x100 scaling up of a video traffic monitoring solution to operate at 10 Gbps. In 2015, they worked as a Project Assistant at the Centre for Excellence in Software-Defined Networking (SDN Lab), where they set up a multi-vendor research testbed and built a monitoring framework. In 2018, they were a Research Collaborator at Cisco, where they built a system to model and monitor encrypted carrier wifi-calling sessions in enterprise networks. In 2019, they were a Research Collaborator at Telstra, where they analyzed network traffic datasets of Telstra 4G deployment. In 2018, they were a PhD Student at UNSW, where they measured, monitored and managed the internet traffic and the associated user experience. In 2020, they were an Academic Guest at ETH Zurich, where they investigated ways to use programmable packet scheduling to improve QoE of Internet applications. Since 2019, they have been working as Head of Data Science and Software Engineer ML/Data Science at CANOPUS Networks.

Sharat Chandra Madanapalli has a comprehensive education history. Sharat Chandra obtained their High School degree from FIITJEE in 2012-2014, and from Bharatiya Vidya Bhavan, Jubilee Hills in 2009-2012. Sharat Chandra then went on to obtain a Bachelor's Degree in Computer Science from Birla Institute of Technology and Science, Pilani in 2014-2018. Sharat Chandra is currently pursuing a Doctor of Philosophy - PhD in Computer Systems Networking and Telecommunications from UNSW since 2018. In addition to their formal education, Sharat Chandra Madanapalli has obtained several certifications from LinkedIn, including Deploying Scalable Machine Learning for Data Science, DevOps for Data Scientists, Docker: Continuous Delivery, Git for Teams, Kubernetes Essential Training: Application Development, Logic Pro X Essential Training (2020) and SQL Essential Training.