Open Agent Summit Speakers

Matt White is the CTO of the PyTorch Foundation and AgenticAI at the Linux Foundation. Matt is a distinguished expert in artificial intelligence and business, renowned for successfully deploying large-scale AI platforms across the telecom, gaming, media, and entertainment industries. With over two decades of experience, Matt has consistently demonstrated his ability to stay ahead of the curve in technological innovation, spearheading advancements in AI applications in diverse domains.

Dawn Song is a Professor in Computer Science at UC Berkeley and Co-Director of Berkeley Center for Responsible Decentralized Intelligence. Her research interest lies in AI safety and security, Agentic AI, deep learning, security and privacy, and decentralization technology. She is the recipient of numerous awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, ACM SIGSAC Outstanding Innovation Award, and more than 10 Test-of-Time Awards and Best Paper Awards from top conferences in Computer Security and Deep Learning. She has been recognized as Most Influential Scholar (AMiner Award), for being the most cited scholar in computer security. She is an ACM Fellow and an IEEE Fellow, and an Elected Member of American Academy of Arts and Sciences. She obtained her Ph.D. degree from UC Berkeley. She is also a serial entrepreneur and has been named on the Female Founder 100 List by Inc. and Wired25 List of Innovators.

Samuel Colvin is a Python and Rust developer and Founder of Pydantic Inc., backed by Sequoia to build Pydantic Logfire — developer first observability. The Pydantic library, which he created is downloaded over 300M/month and is a dependency of virtually every GenAI Python libraries including the OpenAI SDK, the Anthropic SDK, the Google Gen AI SDK, Langchain and LlamaIndex.

Chi Wang is founder of AutoGen (now AG2), one of the most widely used open-source frameworks for agentic AI, and MassGen, an open-source multi-agent scaling system. He is a Senior Staff Research Scientist at Google DeepMind with 15+ years of research experience across Microsoft Research, Meta, UIUC, and Tsinghua University. He is also the creator of FLAML, a fast library for AutoML and hyperparameter tuning. His work has earned multiple awards, including Best Paper at ICLR’24 LLM Agents Workshop, SIGKDD PhD Dissertation Award, Open100, and TheSequence’s pick of 5 favorite AI papers in 2023. AutoGen (AG2) is used by leading organizations including Nvidia, Google, Microsoft, JPMorgan, Walmart, AT&T, and top research institutions including Harvard, MIT, Cambridge, Oxford, Princeton, and Cornell, with over 50,000 GitHub stars. It has achieved top performance in SWE-bench, OSWorld, and WebArena benchmarks for software engineering, computer use, and web automation, and has been applied to scientific discovery across physics, life sciences, chemistry, material science, planetary science, and chip design. Dr. Wang teaches at Stanford, Berkeley, Coursera, and DeepLearning.AI, leads an open-source community with over 21,000 members, and serves as advisor and investor to agentic AI startups.

Yutaro Yamada is a research scientist at Sakana AI, where he co-led the development of the AI Scientist-v2. His research focuses on LLM agents, AI-driven automated scientific discovery, and self-improving AI. He completed his PhD in Statistics and Data Science from Yale University.

Sandi Besen is an AI Research Engineer and Ecosystem Lead at IBM Research, where she drives initiatives in research-to-product translation and ecosystem development. Her work focuses on turning cutting-edge research into scalable solutions, often in collaboration with the open-source community to accelerate innovation. Previously, as an Applied AI Researcher, she designed multi-agent AI systems and authored widely cited research papers and meta-analyses. She is also an active AI advisor to startups, guiding them from concept to deployment and aligning technical capabilities with strategic goals.

Chitra Venkatesh is a Product Manager at Google, where she is shaping the future of AI agents. Her areas of interest are Agent Identity, Trust, and Quality, which are foundational to building a reliable and collaborative agent ecosystem.

Mitesh is a Senior Manager at NVIDIA, leading the Developer Advocate team. His team is responsible for creating workflows that demonstrate how developers can utilize GPU acceleration for various applications, including Agentic AI, RAG, and deploying GenAI models using popular frameworks within the developer community. Prior to joining NVIDIA, Mitesh was a Senior Research Scientist at FX Palo Alto Laboratory, Inc. (FXPAL), where he focused on research problems in robotics, medical robotics, and user behavior modeling.

Mitesh earned his Ph.D. in Robotics from the University of Technology Sydney, Australia.

Brad is Tech Lead for Data and Machine Learning at Block. He built and maintains Block’s open source AI agent framework that automates repetitive tasks for more than 1,000 engineers in the company. He also leads company-wide initiatives to optimize data and ML workflows and evolve platform architecture. Brad has a long history in python, distributed compute systems, and applied machine learning. Brad previously earned his physics PhD from UC Berkeley and worked on processing and analyzing data at the Large Hadron Collider at CERN in Geneva.

Adam Jones is working to make the transition to advanced AI systems go well. He’s currently a member of technical staff at Anthropic, helping develop the Model Context Protocol.

Previously, he led AI safety talent programs at BlueDot Impact, inculding the large-scale AI Safety Fundamentals courses. He also advised the UK Government’s Department for Science, Innovation and Technology (DSIT) on AI safety policy.

Outside of work, he enjoys making things! This includes writing blog articles (mainly about AI safety) and building popular open-source tools: his AWS Email Simulator has over 1 million downloads, his YouTube thumbnail-hiding browser extension serves 30,000+ weekly users, and his Airtable MCP server has spurred 60 forks. Finally, he also contributes media to Wikimedia Commons, with his work appearing in textbooks, academic papers, and YouTube videos with millions of views.

When not making new things, he enjoys co-operative board games, video games, sunny weather, and playing capture the flag on Hampstead Heath.

Michael Goin is a lead maintainer of vLLM, the high-performance open-source engine for LLM inference. His contributions span core areas including kernel optimization, system scheduling, new model architectures, and hardware efficiency across GPUs and emerging accelerators. Michael led performance engineering at Neural Magic, which was acquired by Red Hat, and brings experience in inference systems to the open source AI community.

John Parello is a Principal Engineer driving AI strategy and evangelism at Cisco Outshift, where he leads open source efforts for AGNTCY, the Internet-of-Agents initiative. Previously, he built and led engineering for Cisco’s global Innovation Labs, is a Cisco Hall of Fame speaker, and author of IP Enabled Energy Management. He pioneered Cisco’s first sustainability products (EnergyWise, Digital Ceiling) and has led work in networking, observability, telephony, and wireless. Earlier, he headed engineering at a network correlation startup and worked in high-volume systems at Morgan Stanley. He holds degrees from NYU and Stevens Institute of Technology and was an Adjunct Professor of computer science at Pace University.

I am a third year PhD student in Computer Science in the UC Berkeley Sky Computing Lab advised by Ion Stoica and Matei Zaharia, and supported by the Amazon AI PhD Fellowship. Previously, I worked in Network Infrastructure at Google Cloud. Before that, I graduated from Harvard with a BA in Computer Science advised by James Mickens.

My research interests are in designing and building flexible and efficient systems for model training and inference, and leveraging these systems to develop models useful in real-world tasks. My current research focuses on systems for post-training, especially reinforcement learning. Along with several wonderful collaborators, I am a co-lead of the NovaSky team and building SkyRL. Our work has been featured in The New York Times, The Wall Street Journal, and The Information.

I am a CS PhD student at UCLA advised by Prof. Quanquan Gu. I am also a member of lmsys.org and working closely with Prof. Ying Sheng and Prof. Lianmin Zheng.

I feel honored to be part of SGLang team and lead our RL Group to facilitate RLHF systems.

I obtained my Bachelor of Engineer Degree at THU, Department of Computer Science and Technology, where I worked on LLM Alignment and Data Synthesis.

During my undergraduate years, I was fortunately supervised by Prof. Graham Neubig and Prof. Sherry Tongshuang Wu and mentored by Vijay Viswanathan at CMU. Their research guidance and tastes are invaluable to me.

I like building products that use AI.

A decade of building AI applications, from early CNNs to today’s LLMs.

Raji Rajagopalan is a Vice President in the CoreAI group at Microsoft. She and her team are responsible for building technology to enable efficient execution of AI across cloud and edge. In the last two decades in the tech industry, Raji has worked across Engineering and Product disciplines, building startups and running global teams. She has helped over fifty entrepreneurs start their own social enterprises that make a positive impact in their communities. She is the author of a book on leadership and a speaker at the UN. You can learn more about her on her website, www.rajiraj.com