Watch Advancing AI Event Developer Session Videos

Dr. Andrew Ng

Dr. Andrew Ng is a globally recognized leader in AI (Artificial Intelligence). He is Founder of DeepLearning.AI, Executive Chairman of LandingAI, General Partner at AI Fund, Chairman & Co-Founder of Coursera and an Adjunct Professor at Stanford University’s Computer Science Department.

In 2011, he led the development of Stanford University's main MOOC (Massive Open Online Courses) platform and taught an online Machine Learning course that was offered to over 100,000 students leading to the founding of Coursera where he is currently Chairman and Co-founder.

Previously, he was Chief Scientist at Baidu, where he led the company’s ~1300 person AI Group and was responsible for driving the company’s global AI strategy and infrastructure. He was also the founding lead of the Google Brain team.

As a pioneer in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, and has authored or co-authored over 200 research papers in machine learning, robotics and related fields. In 2023, he was named to the Time100 AI list of the most influential AI persons in the world. He holds degrees from Carnegie Mellon University, MIT and the University of California, Berkeley.

Dr. Ashish Vaswani

Dr. Ashish Vaswani is a Computer Scientist and Co-Founder and CEO of Essential.ai. In a scientific career spanning over a decade, he has made several important contributions to Deep Learning and AI. In the seminal paper "Attention is all you need," he co-invented the Transformer model, a vital component of modern AI systems. His work on Language Models in 2013 was among the first successful results of deep learning in language processing. His work, across fields from language to biology, has over 200,000 citations. He got his PhD in Computer Science at the University of Southern California. After 7 years in academia and Google Brain, he co-founded Adept AI Labs, the first company to democratize AI tool use. At Essential, Dr. Vaswani is pushing a new frontier for AI models with an Open Science philosophy.

The Future of Training and Reinforcement Learning

Explore the latest trends to accelerate training and their impact on kernels, reinforcement learning, and quantization. Additionally, discover how to prepare for a post-training future shaped by reinforcement learning. Discover the technical foundations behind GRPO, the algorithm behind DeepSeek-R1, and unlock the power of reward functions.

Daniel Han is the CEO of Unsloth AI, an open-source AI startup on a mission to make AI accessible and accurate for everyone. Unsloth has grown rapidly, earning over 38,000 GitHub stars and surpassing 10 million monthly downloads on Hugging Face. The team has contributed critical bug fixes to major models, including LLaMA, Phi, Gemma, and Mistral. Before founding Unsloth, Daniel worked at NVIDIA, achieving a 2000x speedup in t-SNE visualization.

Challenges and Opportunities in Large-Scale Training on AMD Instinct MI300X GPUs

Joe Chau
VP of Engineering, HPC & AI, Microsoft Azure

Peng Cheng
Senior Principal Research Manager, Microsoft Research Asia

Discuss the technical complexities of scaling GPU-based training for modern AI models, including resource optimization, distributed computing bottlenecks, and energy efficiency. The session highlights innovative solutions and advancements in Azure’s infrastructure and research, offering insights into the future of high-performance computing for AI development.

Joe Chau is Vice President of Engineering at Microsoft, where he leads the Azure High Performance Computing (HPC) and Artificial Intelligence (AI) infrastructure team. He is responsible for building and operating the large-scale systems that power Microsoft’s most advanced AI workloads. Joe’s work spans cloud infrastructure, systems engineering, and hardware-software co-design, enabling the deployment of cutting-edge GPU clusters and next-generation compute platforms. He has played a key role in scaling Microsoft’s AI capabilities through collaboration with internal teams and industry partners. Known for his technical depth and strategic clarity, Joe helps ensure Azure remains a global leader in AI infrastructure.

Peng Cheng is a Senior Principal Research Manager at Microsoft Research Asia (Vancouver), specializing in systems, networking, and artificial intelligence. His recent work focuses on pioneering advancements in AI infrastructure and AI-system co-evolution. Peng has authored over 30 papers in leading systems, networking, and AI conferences, and his research has played a key role in enhancing Microsoft products, including Azure, Microsoft Teams, and Bing.

Easy, Fast, and Cost-Effective LLM Serving for Everyone

Explore overcoming LLM inference hurdles with vLLM, which provided memory-efficient methods and enables fast, scalable AI deployment for real-time applications across industries.

Simon Mo is a PhD student at Berkeley Sky Computing Lab. He is a co-lead of vLLM project, which focuses on building an end-to-end stack for LLM inference on your own infrastructure. The project aims to improve the throughput of popular LLMs by 2-4x with the same level of latency compared to other systems.

High Performance AMD GPU Programming with Mojo

Explore using the Mojo programming and language and MAX framework to unlock the power of AMD GPUs for leading-edge Generation AI applications.

Chris Lattner is a co-founder and the CEO of Modular, which is building an innovative new developer platform for AI and heterogeneous compute. Modular develops MAX, an AI deployment system that accelerates GenAI inference, as well as the Mojo language - a Pythonic system for GPU programming and high performance. He co-founded the LLVM Compiler infrastructure project, the Clang C++ compiler, the Swift programming language, the MLIR compiler infrastructure, the CIRCT project, and has contributed to many other commercial and open source projects at Apple, Tesla, Google and SiFive.

Efficient LLM Inference with SGLang

Discover SGLang, an efficient serving engine for large language models and vision-language models. Lianmin covers core inference optimizations and recent advancements, such as prefill-decode disaggregation, large-scale expert parallelism, zero-overhead scheduler, and speculative decoding. SGLang is widely adopted across the industry to serve frontier models like Grok3 and DeepSeek V3/R1.

Lianmin Zheng is a Member of Technical Staff at xAI. Lianmin co-leads the open-source development of the SGLang project and inference optimizations at xAI.