Arvind Seshan

Cambridge, MA

aseshan [at] mit.edu

I am a senior at the Massachusetts Institute of Technology pursuing a Bachelor of Science degree in Computer Science and Engineering (Course 6-3) as well as a Masters in Engineering.

I am passionate about developing new cutting-edge tools and techniques for computer scientists. I am also excited to use computational approaches to solve complex problems in other domains. I am especially interested in the intersection of computer vision, computational photography, graphics, machine learning, and robotics.

I have spent over 14 years doing robotics, including competing in FIRST Robotics. I have also volunteered as judge, referee, and tournament director. I continue to mentor middle school robotics teams and run three major educational websites designed to teach others computational thinking and mechanical design skills through LEGO robotics.

In my free time, I enjoy helping build the Desmond community on campus and playing soccer on an MIT intramural team. I have collaborated with The LEGO Group on many projects and you can find many of my designs featured around the world.

Please contact me by email if you would like a copy of my CV.

latest projects

Energy-Aware Camera Scheduling for Opportunistic 3D Mapping: Wearable devices operate under stringent energy constraints and cannot continuously capture video. This raises a fundamental systems question — given a limited sensing budget, when should a mobile device collect visual data to maximize the coverage and freshness of a continuously evolving 3D map? We address this with IndoorMapper, a budget-aware sensing framework for opportunistic 3D map maintenance that localizes users within an existing map, estimates the utility of potential observations, and dynamically allocates a user-specified sensing budget across a user’s trajectory.

BLINC: A Fault-Tolerant Neural Codec for Wireless Real-time Video Streaming: Wireless real-time video applications such as AR/VR, drones, cloud gaming, and broadcast streaming cannot always wait for retransmissions when packets are dropped or corrupted. In this project, we seek to understand whether a video codec preserve visual quality when wireless links deliver partially corrupted packets rather than only clean packets or full packet losses. We address this with BLINC, a bit-loss-insensitive neural codec that combines segmented entropy encoding, segment-wise loss-aware fine-tuning, and Wi-Fi link-layer integration to enable graceful degradation under bit-level corruption while reducing retransmissions and wireless airtime.