Open Agent Summit Speakers

Matt White is the Executive Director of the PyTorch Foundation and GM of AI at the Linux Foundation. He is also the Director of the Generative AI Commons, an open community initiative focused on advancing responsible generative AI under the LF AI & Data Foundation. Matt has nearly 30 years of experience in applied research and standards in AI and data in telecom, media and gaming industries. He began his career programming expert systems in the telecom industry and since 2012 has been focused on machine learning and research at the intersection of AI, simulations and multi-sensory learning. Matt is the Co-founder and Chair of the Open Metaverse Foundation at the Linux Foundation. He is also a Chair at the Metaverse Standards Forum and runs the Silicon Valley Generative AI paper reading group and is a co-organizer at the GenAI Collective.

Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in deep learning and security. She has studied diverse security and privacy issues in computer systems and networks, including areas ranging from software security, networking security, database security, distributed systems security, applied cryptography, to the intersection of machine learning and security. She is the recipient of various 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, the George Tallman Ladd Research Award, the Okawa Foundation Research Award, the Li Ka Shing Foundation Women in Science Distinguished Lecture Series Award, the Faculty Research Award from IBM, Google and other major tech companies, and Best Paper Awards from top conferences. She obtained her Ph.D. degree from UC Berkeley. Prior to joining UC Berkeley as a faculty, she was an Assistant Professor at Carnegie Mellon University from 2002 to 2007.

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 the founder of AutoGen (AG2), the open-source AgentOS for agentic AI, and a Research Scientist at Google DeepMind. His work focuses on building agentic AI systems that bridge research and real-world applications, while fostering a global community advancing the future of agentic technology.

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.

I am an Associate Professor at the Computer Science Department, Columbia University. I am also a co-founder of Arklex.ai that centers its efforts on harnessing the power of AI Agents to empower and shape the future landscape of the workspace. Before that I was an Assistant Professor at UC Davis. I received my PhD at Language Technology Institute under School of Computer Science, Carnegie Mellon University 2017, working with Prof. Alan W Black and Prof. Alexander I. Rudnicky. 2015 summer and 2016 summer, I interned with Prof. David Suendermann-Oeft at ETS San Francisco Office on cloud based multimodal dialog systems. 2014 Fall, I interned with Dan Bohus and Eric Horvitz at Microsoft Research on situated multimodal dialogue systems. I was featured on Forbes 30 under 30 in Science in 2018. My team also won the 2018 Amazon Alexa Socialbot Competition. Our work on Persuasive Dialog Systems also won ACL 2019 best paper nomination.

Prior to CMU, I received a B.S. in Computer Science and a B.A. in Linguistics under English Language Major from Zhejiang University in 2011. I worked with Prof. Xiaofei He and Prof. Deng Cai on Machine Learning and Computer Vision. I also worked with Prof. Yunhua Qu on Machine Translation.

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.

Yina leads engineering for Microsoft Graph, the gateway to data and intelligence in Microsoft 365 and the largest enterprise API of the planet. During her career at Microsoft, she has been building the platform that enables developers to build applications that access data and relationships across Microsoft cloud services. She is from Bogota, Colombia and joined Microsoft in 2010 after graduating with a Master’s degree in Computer Engineering from the University of Virginia. She lives in Seattle with her husband and 4 energetic sons and actively participates in activities that grow, retain and empower women in technology. Find her on Twitter: @yina_arenas.

I like building products that use AI.

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