Accelerating training, inference, and ML applications on NVIDIA GPUs

Maggie Zhang is a deep learning software engineer at NVIDIA, where she works on deep learning frameworks. She earned her PhD in computer science and engineering from the University of New South Wales in Australia. Her research background includes GPU and CPU heterogeneous computing, compiler optimization, computer architecture, and deep learning.

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Nathan Luehr is a senior developer technology engineer at NVIDIA, where he works to accelerate deep learning frameworks. His background is in theoretical chemistry. He holds a doctoral degree from Stanford University, where he worked to accelerate electronic structure calculations on GPUs.

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Josh Romero is a developer technology engineer at NVIDIA. He has extensive experience in GPU computing from porting and optimizing high-performance computing (HPC) applications to more recent work with deep learning. Josh earned his PhD from Stanford University, where his research focused on developing new computational fluid dynamics methods to better exploit GPU hardware.

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Pooya Davoodi is a senior software engineer at NVIDIA working on accelerating TensorFlow on NVIDIA GPUs. Previously, Pooya worked on Caffe2, Caffe, CUDNN, and other CUDA libraries.

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Davide Onofrio is a senior deep learning software technical marketing engineer at NVIDIA. He’s focused on development and presentation of deep learning technical developer-oriented content at NVIDIA. Davide has several years of experience working as a computer vision and machine learning engineer in biometrics, VR, and the automotive industry. He earned a PhD in signal processing at the Politecnico di Milano.