I am very happy to share the news that I am joining NVIDIA as Director of Machine Learning Research. I will be based in the Santa Clara HQ and will be hiring ML researchers and engineers at all levels, along with graduate interns.
I will be continuing my role as Bren professor at Caltech and will be dividing my time between northern and southern California. I look forward to building strong intellectual relationships between NVIDIA and Caltech. There are many synergies with initiatives at Caltech such as the Center for Autonomous Systems (CAST) and AI4science.
I found NVIDIA to be a natural fit and it stood out among other opportunities. I chose NVIDIA because of its track record, its pivotal role in the deep-learning revolution, and the people I have interacted with. I will be reporting to Bill Dally, the chief scientist of NVIDIA. In addition to Bill, there is a rich history of academic researchers at NVIDIA such as Jan Kautz, Steve Keckler, Joel Emer, and recent hires Dieter Fox and Sanja Fidler. They have created a nourishing environment that blends research with strong engineering. I am looking forward to working with CEO Jensen Huang, whose vision for research I find inspiring.
The deep-learning revolution would not have happened without NVIDIA’s GPUs. The latest Volta GPUs pack an impressive 125 teraFLOPS and have fueled developments in diverse areas. The recently released NVIDIA Tesla T4 GPU is the world’s most advanced inference accelerator and NVIDIA GeForce represents the biggest leap in performance for graphics rendering since it is the world’s first real-time ray tracing GPU.
As many of you know, NVIDIA is much more than a hardware company. The development of CUDA libraries at NVIDIA has been a critical component for scaling up deep learning. The CUDA primitives are also relevant to my research on tensors. I worked with NVIDIA researcher Cris Cecka to build extended BLAS kernels for tensor contraction operations a few years ago. I look forward to building more support for tensor algebraic operations in CUDA which can lead to more efficient tensorized neural network architectures.
I admire recent ML research that has come out of NVIDIA. This includes state-of-art generative models for images and video, image denoising etc. The convergence of ML research with state-of-art hardware is happening at rapid pace at NVIDIA. In addition, I am also thrilled about developments in design and visualization, self-driving, IoT/autonomous systems and data center solutions at NVIDIA.
I hope to continue building bridges between academia and industry, and between theory and practice in my new role.