Aditya Rana

Hi, I'm a machine learning engineer at Canva. I'm currently working on making it easier for people to create awesome designs with the power of generative language and vision models.

Prior to this, I worked on large-scale processing of remote-sensing and geospatial data, and building machine learning tooling around that. I've also worked in football analytics where I focused on applying graph neural networks to football tracking data, which later developed into my master's thesis. I completed my master's in computer vision at Universitat Autònoma de Barcelona in a joint program with 3 more universities and bachelor's in electrical and electronics engineering at BITS Pilani, India

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Research

Currently, my research is focused around transformers, generative language and vision models, and their applications in Design AI space.

graph_from_position Event Detection in Football using Graph Convolutional Networks
Aditya Rana, Dr. Antonio Rubio Romano, Dr. Francesc Moreno-Noguer
Master's Thesis, 2021
arxiv / The code was integrated into Kognia's platform

Football tracking data can be difficult to model because of lack of clarity on how to order players in a sequence and how to handle missing objects of interest. We show how to model the ball-player tracking data as graphs, and how to process them using graph convolutional networks. We focus on the task of event detection and present the results for different types of graph convolutional layers and losses that can be used to model the temporal context present around actions like goals, free-kicks, fouls etc.

importance_map_png Content Weighted Image Compression for Distributed Data Collection
Aditya Rana, Dr. Joan Serrat, Dr. Antonio Lopez
Bachelor's Thesis, 2020
pdf / code / bibtex

We show how to design lossy image compression models for optimizing data collection strategies from on road autonomous vehicles. The end goal is to gain advantage of the exposure of these vehicles to vast number of geographically diverse environments for training better detection and segmentation models.

Design courtesy of Jon Barron