PyTorch Foundation Technical Advisory Council

Brian Granger, Amazon Web Services

Brian Granger is a Senior Principal Technologist at Amazon Web Services and a professor of physics and data science at Cal Poly State University in San Luis Obispo, CA. He works at the intersection of UX design and engineering on tools for scientific computing, data science, machine learning, and data visualization. Brian is a co-founder and leader of Project Jupyter, co-founder of the Altair project for statistical visualization, and creator of the PyZMQ project for ZMQ-based message passing in Python.

Jeff Daily, AMD

Jeff Daily is a Fellow at AMD and the chief architect of the Machine Learning Software Engineering group supporting ML frameworks such as PyTorch and onnxruntime on AMD GPUs. He enjoys delivering open source software to answer the challenges of the rapidly-changing ML landscape. For over five years, he has contributed to the PyTorch core as well as its extension libraries. His sustained contributions earned him the first ever Linux Foundation PyTorch Award “for excellence in long-term contributions across all PyTorch modalities.” Though he has not achieved the status of maintainer, Jeff is a trusted contributor. Jeff’s technical leadership is one of the reasons PyTorch runs out of the box without any code modifications on AMD GPUs. In addition to PyTorch technical contributions, Jeff is the AMD representative for the PyTorch Foundation’s Technical Advisory Committee.

Milos Puzovic, Arm

Milos Puzovic is Technical Director at Arm where he is working on accelerating machine learning frameworks such as PyTorch on AArch64. In the past, he worked on designing and developing infrastructure for rapid development and deployment to edge devices and cloud of novel neural models that were trained using semi-supervised approach. He also has interest in optimizing applications through hardware and software co-design by using machine learning, code generation, optimization and verification of high-level models for different types of architecture. Milos has PhD in Computer Science from University of Cambridge where his thesis was on hardware/software interface for dynamic multicore scheduling and BSc with First Class Honors in Joint Mathematics and Computer Science from Imperial College London.

Shauheen Zahirazami, Google

Shauheen has a PhD in control engineering and BSc in applied mathematics. He is currently leading Cloud TPU Machine Learning teams at Google who are responsible for ML Frameworks and 3P ecosystem including the PyTorch teams that develop PyTorch/XLA.

Yikun Jiang, Huawei

Mudhakar Srivatsa, IBM

Mudhakar Srivatsa is a distinguished engineer at IBM TJ Watson Research center responsible for Inference optimization of generative AI models across multiple AI accelerators. https://research.ibm.com/people/mudhakar-srivatsa

Jiong Gong, Intel (TAC Vice Chair)

Jiong Gong is a software architect and principal engineer at Intel, specializing in the optimization of Deep Learning Frameworks and their Intel-accelerated extensions. He has previously worked on Caffe and Caffe2 and is currently focused on PyTorch and the Intel Extension for PyTorch (IPEX). Jiong is responsible for architecture design across the software stack, including frontend APIs, operator optimization, graph compilers, and accelerator library design. As an active contributor to the PyTorch community and one of the maintainers of the PyTorch CPU module, he plays a key role in advancing deep learning technology. Jiong is also an expert in deep learning compression, contributing significantly to Intel’s low-precision deep learning technology, such as DL Boost.

Luca Antiga, Lightning AI (TAC Chair)

Luca, CTO at Lightning since 2022, was an early contributor to PyTorch core and co-authored “Deep Learning with PyTorch” (published by Manning). He started his journey as a researcher in bioengineering, and later co-founded Orobix, a company specializing in building and deploying AI in production settings.

Gregory Chanan, Meta

Soumith Chintala, Meta

Soumith Chintala is a Scientist-Engineer focused on AI and Robotics, leading influential AI work such as PyTorch, DCGAN and Torch-7; work which is used by several top institutions including NASA, Meta, Google, Tesla, Microsoft, Disney, Genentech, and numerous other Fortune-500 companies and in the curriculum of top-ranked universities such as Stanford, Harvard, Oxford and MIT. He currently leads PyTorch and other AI projects at Meta, is a Visiting Professor at New York University, and maintains advisory roles at various institutions.

Xavier Dupré, Microsoft

Graduated in 1999 from the ENSAE and doctor in 2004, Xavier Dupré began his career at A2iA, a company specialized in automatic reading of cheques and handwriting recognition. After a short passage in finance, he joined Yahoo in 2008 to work on search query rewriting problems by implementing statistical algorithms designed for all languages. In 2010, Xavier joined Microsoft to participate in the local search engine. He contributed to the partnership between PagesJaunes and Bing. Xavier worked on very large scale problems for Microsoft's search engine Bing. He is now working on Azure Machine Learning. Meanwhile, Xavier Dupré have been teaching programming since 2001 at the ENSAE. The courses have expanded in 2014 to machine learning and technologies associated to big data, including Azure through a partnership between Microsoft France and the ENSAE. More recently, Xavier has explored new ways to teach through hackathons (hackathon Microsoft- ENSAE-Croix-Rouge - November 2015 - video - article), collaborations between academic and non-profitable organization through students projects or coding snack.

Piotr Bialecki, NVIDIA

Piotr joined PyTorch team at NVIDIA in 2019 and currently manages the team. He drives NVIDIA's effort in maintaining and advancing PyTorch's CUDA backend and received the PyTorch SUPERHERO award in 2023 for his community contributions especially in the PyTorch discussion board. As a Core Maintainer, he is also focussed on PyTorch's long-term vision and development.