Prasanna Vijayanathan, a seasoned softwa re engineer and leader, has made significant impact in the tech industry over the course of a decade. With a passion for innovation, user obsession, and social empowerment, Prasanna has played integral roles at prominent companies such as Netflix, LinkedIn and Qualcomm. Using data and AI in his arsenal of expertise, he strives to build software applications that work for all users all over the world, making strides towards what he calls, equitable engineering.
Currently, Prasanna works as a software engineer at Netflix. He specializes in leveraging data analytics and machine learning to build high-performance applications and frameworks, to derive insights about the experience of their users on Netflix and to continuously enhance it. At Netflix, he has been researching the idea of equitable engineering - how to build software applications at scale that works equally well for all users, regardless of the environment they access it from. "How can Netflix provide the same delight to a user watching it on the latest high definition TV with a blazing fast internet as it does to a user watching on a slow mobile device with a bad wireless connection?"
Prior to Netflix, he held engineering and managerial roles at LinkedIn. It is here that Prasanna got the opportunity to work with large scale data and machine learning to understand users and app performance. During his time at LinkedIn, he built a team of engineers to personalize LinkedIn apps based on the network and device conditions of users in real time. He also worked extensively on building frameworks to understand and improve the performance of LinkedIn through data measurement, insights and optimizations.
His area of research has pushed the boundaries of traditional approaches to large-scale software development. Typically, software applications take a one-size-fits-all approach, especially when it comes to delivering features and performance tradeoffs. Prasanna believes, with data to back up, that every user's perception of experience is different. It is based on a number of factors, beyond what a single application could comprehend.
To gain this understanding, we'd need a tremendous amount of data processing and evaluation. Handling data at this scale in real time is a challenge that traditional applications haven't solved. But with deep learning, a branch of machine learning that works better with large scale data, Prasanna thinks it is possible to gauge this understanding - how delighted or frustrated a user is when using an application at the time. With this understanding of every user's experience in real time, it is possible to adapt the application in ways that would delight each user better. Prasanna has published about the idea and his work in realizing it at many international conferences.
Beyond his corporate pursuits, Prasanna is actively involved in community engagement and social empowerment. He has mentored many students and engineers through organizations such as Women In Tech (WIT), Women Entering and Staying in Tech (WEST) and Plato. He also serves as an Executive Board Member and Chair of the Technical Committee at Speakhire, a non-profit organization focused on improving the social capital of disadvantaged students to shape future leaders. He is also engaged with non-profits like the AI Innovation Consortium in advisory roles to build a better society through the use of technology and AI.
Prasanna's unwavering commitment to leveraging technology for the betterment of society serves as an inspiration to aspiring engineers and leaders around the world.
