I am a machine learning (ML) researcher and computer programmer. I am currently a Research Engineer at Facebook AI Research.
My career interests have been defined by two sets of work: machine learning platforms, research in machine learning
Machine Learning Platforms
I love open-source communities and advocate for open research. I think deeply about the cultural aspects and social dynamics of open-source software. I advocate for a simplistic style of programming, and I like fast prototyping of ideas, sometimes using unconventional engineering practices.
With this perspective, I’ve helped build the machine learning platform PyTorch and maintained Torch-7, EBLearn and several other open-source projects in the ML space.
My thoughts and experiences formed the basis for building vibrant and large communities of users and contributors around PyTorch and Torch. I am proud to have maintained Torch at it’s peak, when Deepmind, Twitter and Facebook were using it, and I am proud to co-maintain PyTorch with hundreds of companies, research labs and individuals using and maintaining it.
To deeply understand the products I build, I like helping users with their problems simple or complex, and have answered thousands of questions across the PyTorch and Torch forums, investing a significant portion of my professional career in this endeavor. These signals shape our own understanding of products deeply in important ways. I learnt more about building the right ML products from this exercise than from anything else.
I learned interesting things while maintaining Torch, which was written in Lua – a large project written in a niche language. If you ask me about it, I’ll tell you fun stories.
Research in Machine Learning
My current primary interest is to build a household robot that helps me with all kinds of chores. To help this robot reason well with little data, I want to build a world simulator (so that it can rollout scenarios in it’s head and pick the best ones). To build this world simulator, I’ve been interested in multi-modal models (that combine vision, speech, text, robots), generative models (for vision and speech) and efficient representations for encoding the human-centric world.
My secondary interests are in research that intersects between Computer Systems and Machine Learning, partly motivated by my work on Machine Learning Platforms.
I often try to avoid toy or hypothetical problems, even as proxies and ground my research towards applications with obvious benefits. I am not a theoretician for the lack of aptitude or focus, I respect those who make progress in theory.
I’ve made some progress on exploring my research interests.
In the past, I’ve worked on robotics, object and human detection, generative modeling of {images, videos}, AI for video games, ML systems research. Some of my most cited work is on Generative Adversarial networks (GANs), where I co-authored three well-cited papers: LAPGAN (demo), DCGAN (code/demo) and Wasserstein GAN.
You can see a full list of my peer-reviewed or pre-print manuscripts on my Google Scholar page.