Artificial intelligence, Machine learning, Research, Science Technology, Women in STEM
1. Neural Operators Accelerating Science: 2022 was an excellent year for neural operators, on the heels of being featured as a highlight in Math and Computer Science by Quanta Magazine in 2021. Neural operator learns mappings between function spaces, which makes them discretization-invariant, meaning they can work on any discretization of inputs and converge to a limit upon mesh Continue reading…
AI4Science in the limelight: We developed AI methods for simulating complex multi-scale phenomena such as weather, materials, etc. with orders of magnitude speed-ups. Our core technique, Fourier Neural Operator (FNO), was recently featured as a highlight of math and computer science advances in 2021 by Quanta Magazine. It was also featured in the GTC Fall Continue reading Top-10 AI Research…
2020 has been an exciting time for DL frameworks and the AI stacks. We have seen more consolidation of frameworks into platforms that are domain-specific such as NVIDIA Omniverse and NVIDIA Clara. We have seen better abstractions in the AI stack that helps democratize AI and enable rapid prototyping and testing such Pytorch Lightning. Below Continue reading 2020 AI Research Highlights: Learning…
Embodied AI is the union of “mind” (AI) and “body” (robotics). To achieve this, we need robust learning methods that can be embedded into control systems with safety and stability guarantees. Many of our recent works are advancing these goals on both theoretical and practical fronts. This is part of the blog series on 2020 Continue reading 2020 AI Research Highlights: Learning and Control (part 6)…
Generative models have greatly advanced over the last few years. We can now generate images and text that pass the Turing test: at the first glance, they look considerably realistic. A major unsolved challenge is the ability to control the generative process. We would like specify attributes or style codes for image generation; we would Continue reading 2020 AI Research Highlights: Controllable…
2020 has been a landmark year for AI4science. I have had the privilege to work with some of the world’s best experts in a number of challenging scientific domains. You can read previous posts for other research highlights: generalizable AI (part 1), handling distributional shifts (part 2), optimization for deep learning (part 3), controllable generation Continue reading 2020 AI Research…
In this post, I will focus on the new optimization methods we proposed in 2020. Simple gradient-based methods such as SGD and Adam remain the “workhorses” for training standard neural networks. However, we find many instances where more sophisticated and principled approaches beat these baselines and show promising results. You can read previous posts for Continue reading 2020 AI Research…
Distributional shifts are common in real-world problems, e.g. often simulation data is used to train in data-limited domains. Standard neural networks cannot handle such large domain shifts. They also lack uncertainty quantification: they tend to be overconfident when they make errors. You can read previous posts for other research highlights: generalizable AI (part 1), optimization Continue…
2020 has been an unprecedented year. There has been too much suffering around the world. I salute the brave frontline workers who have risked their lives to tackle this raging pandemic. Amidst all the negativity and toxicity in online social media, it is easy to miss many positive outcomes of 2020. Personally, 2020 has been Continue reading 2020 AI Research Highlights: Generalizable AI (part 1)…
I want to wholeheartedly apologize to everyone hurt by my words. I want to assure you that I bear no animosity. I want to be part of an inclusive community where all voices are heard. I am sorry if my actions/words have ever created a threatening environment. My intention was only to change hearts and Continue reading My heartfelt apology The post My heartfelt apology first appeared on Anima on AI…