Keynote Speakers

Dylan is the Founder, CEO, and Chief Analyst of SemiAnalysis – the preeminent authority on all things AI and semiconductors. Through Dylan’s unwavering commitment to excellence, he has built the firm from the ground up as the recognized authority on the semiconductor supply chain to the cloud ecosystem, machine learning models, and all things in between. Since 2020, SemiAnalysis has transformed its business from a solo venture into a cohesive and focused team to provide breaking news and in-depth analysis for the most strategic, complex, and escalating challenges in the semiconductor industry.

Percy Liang is an Associate Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011) and the director of the Center for Research on Foundation Models (CRFM). He is currently focused on making foundation models (in particular, language models) more accessible through open-source and understandable through rigorous benchmarking. In the past, he has worked on many topics centered on machine learning and natural language processing, including robustness, interpretability, human interaction, learning theory, grounding, semantics, and reasoning. He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets. His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), a Microsoft Research Faculty Fellowship (2014), and paper awards at ACL, EMNLP, ICML, COLT, ISMIR, CHI, UIST, and RSS.

Dr. Sharon Zhou is the VP of AI at AMD and was the founder and CEO of Lamini, an AI startup that won last year’s VentureBeat Gen AI Startup Award and has been recognized as a Forbes Cloud 100 Rising Star. As a former faculty member at Stanford, she led a 50+ person Generative AI research group and published award-winning papers in generative AI. Sharon teaches some of the most popular AI courses on Coursera, including Fine-tuning LLMs and Diffusion models, reaching millions of developers and executives. She earned her PhD in AI from Stanford, where she was advised by Dr. Andrew Ng. Before her PhD, she worked as an AI product manager at Google. She received her bachelor’s degree in computer science and Classics from Harvard. Additionally, Sharon has served as an AI advisor in Washington, D.C., and has been featured in MIT Technology Review’s 35 Under 35 list.

Professor Eric P. Xing is the President of the Mohamed bin Zayed University of Artificial Intelligence, Professor of Computer Science at Carnegie Mellon University, and Chief Scientist at GenBio AI. His main research interests are the development of machine learning and statistical methodology, and large-scale computational system and architectures, for solving problems involving automated learning, reasoning, and decision-making in artificial, biological, and social systems. Prof. Xing has served on the editorial boards of leading scientific journals including the Journal of the American Statistical Association, Annals of Applied Statistics, PLOS Journal of Computational Biology, IEEE Journal of Pattern Analysis and Machine Intelligence, Machine Learning Journal, and Journal of Machine Learning Research. He was elected Fellows AAAI, ACM, ASA, IEEE, and IMS.

Nathan is a machine learning researcher who works on building, understanding, and advocating for open language models and other responsible autonomous systems.

He is currently a post-training lead at the Allen Institute for AI.

I am an Associate professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. In my research, I focus on algorithms that can enable autonomous agents to acquire complex behaviors through learning, especially general-purpose methods that could enable any autonomous system to learn to solve any task. Applications of such methods include robotics, as well as a range of other domains that require autonomous decision making. To see a more formal biography, click here.

Robert Nishihara is one of the co-founders of Anyscale and one of the co-creators of Ray, the leading open source framework for scalable AI. He did his PhD in machine learning and distributed systems in the computer science department at UC Berkeley. Before that, he majored in math at Harvard.

Ion Stoica is a Professor in the EECS Department and holds the Xu Bao Chancellor Chair at the University of California at Berkeley, the Director of Sky Computing Lab, and the Executive Chairman of Databricks and Anyscale. He is currently doing research on AI systems and cloud computing, and his work includes numerous open-source projects such as SkyPilot, vLLM, ChatBot Arena , Ray and Apache Spark. He is a Member of National Academy of Engineering, an Honorary Member of the Romanian Academy and an ACM Fellow. He also co-founded three companies, Anyscale (2019), Databricks (2013) and Conviva (2006)

Professor Anandkumar’s research interests are in the areas of large-scale machine learning, non-convex optimization and high-dimensional statistics. In particular, she has been spearheading the development and analysis of tensor algorithms for machine learning. Tensor decomposition methods are embarrassingly parallel and scalable to enormous datasets. They are guaranteed to converge to the global optimum and yield consistent estimates for many probabilistic models such as topic models, community models, and hidden Markov models. More generally, Professor Anandkumar has been investigating efficient techniques to speed up non-convex optimization such as escaping saddle points efficiently.

Currently, I’m a PhD student at Berkeley Sky Computing Lab for machine learning system and cloud infrastructures. I am advised by Prof. Joseph Gonzalez and Prof. Ion Stoica. My latest focus is building an end to end stack for LLM inference on your own infrastructure:

• vLLM runs LLM inference efficiently.

Previous exploration includes:

• Conex: builds, push, and pull containers fast.
• SkyATC: orchestrate LLMs in multi-cloud and scaling them to zero.

I previously work on Model Serving System @anyscale.

• Ray takes your Python code and scale it to thousands of cores.
• Ray Serve empowers data scientists to own their end-to-end inference APIs.

Before Anyscale, I was a undergraduate researcher @ucbrise.

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.

Anush Elangovan is the Corporate Vice President of AI Software within the Artificial Intelligence Group (AIG) at AMD. Anush has 23 years of industry experience in AI, computer science, compilers, network security, operating systems, math, and its materialization on complex hardware systems. Anush co-founded and led Nod.ai until its acquisition by AMD. His current focus is on deploying AI solutions optimized for AMD products and integrating Nod.ai’s capabilities into AMD’s AI ecosystem.

Before starting Nod.ai, Anush was instrumental in the graphics stack on the first ARM Chromebook. And Chrome OS. He has also held key roles at companies like Cisco Systems and FireEye, contributing to advancements in virtual switching and security. Anush holds a master’s degree in computer science from Arizona State University and a bachelor’s degree from Mepco Schlenk Engineering College in India. He has earned 10 patents.

Joe Spisak is a Product Director in Meta’s Superintelligence Labs (MSL), where he leads product efforts across PyTorch and Meta’s Agentic platform. He has over a decade of experience building AI platforms, with leadership roles at Meta, Google, and Amazon. Joe helped make PyTorch the world’s leading open-source AI framework and guided its transition to the Linux Foundation in partnership with industry leaders. He spearheaded the open-source strategy for Llama, making cutting-edge models broadly accessible and enabling the community to scale AI in an open and collaborative way. Joe is also an active angel and advisor to next-generation AI startups such as Anthropic, General Reasoning, ReflectionAI and many others.

Olatunji (Tunji) Ruwase is a Principal Engineer at Snowflake, where he focuses on building high-performance AI infrastructure and systems for large-scale training and inference. Prior to joining Snowflake, he was a co-founder and lead of the DeepSpeed project at Microsoft, where his research and engineering contributions advanced the state of deep learning optimization across distributed training, inference, and hyperparameter tuning. His work has been deployed in key Microsoft products including Bing, Ads, HyperDrive, and Catapult. Tunji holds a PhD in Computer Science from Carnegie Mellon University and brings a deep background in compilers, operating systems, and hardware accelerators.

I’m a self motivated developer who loves diving into new technologies and takes an active interest in making things better.

Aparna Dhinakaran is the Co-Founder and Chief Product Officer at Arize AI, a leading AI and agent engineering platform. Prior to Arize, she was an ML engineer and leader at Uber, Apple, and TubeMogul (acquired by Adobe), where she built several core ML infrastructure platforms, including Michelangelo. Dhinakaran was a PhD researcher in the computer vision program at Cornell University before taking a leave of absence to start Arize. A frequent speaker at leading conferences and a recognized thought leader, she has been named to Forbes 30 Under 30, among other accolades.

Sarah Catanzaro is a General Partner at Amplify Partners, where she focuses on investing in and advising high-potential startups in machine intelligence, data management, and distributed systems. Prior to investing, Sarah led and worked on data science teams at startups and in the defense/intelligence sector for about a decade. At Amplify, Sarah works with several companies including RunwayML, Modal, LangChain, and Hex among others.

Jerry is the co-founder/CEO of LlamaIndex, the data framework for building LLM applications. Before this, he has spent his career at the intersection of ML, research, and startups. He led the ML monitoring team at Robust Intelligence, did self-driving AI research at Uber ATG and worked on recommendation systems at Quora.

Peter Deng is a General Partner at Felicis. He invests in companies that help fulfill the full potential and promise of AI for consumers and businesses. His background is building products and was most recently a VP at OpenAI where he led the team working on ChatGPT. He was also Chief Product Officer at Airtable and he worked at Meta for a decade, working on core Facebook products, founding the Messenger team, building and leading the Instagram product team, and more. He graduated from Stanford University with a bachelor’s in Symbolic Systems and also got his master’s with a focus on human-computer interaction. He lives with his wife and children in the Bay Area. He loves all things food and is an avid sports fan.

A former product manager at Nest, Stephanie Zhan has sourced six investments since joining well-known venture capital firm Sequoia Capital in July 2015. Those deals include Graphcore, an AI processor designed for machine intelligence; Quidd, a marketplace to collect and trade digital goods; Tribe, a mobile live video gaming platform; and Against Gravity, a startup building RecRoom in virtual reality. The Stanford Engineering Honor Society member says she likes to play around with Snap’s latest AR filters in her spare time.

Peter Salanki is the Chief Technology Officer of CoreWeave, where he spearheads the development of innovative cloud solutions tailored for high-performance workloads. Originally from Sweden, Peter was recruited at just 18 to become Director of Engineering at Bahnhof AB before moving to the United States to lead Solutions Architecture for Procera Networks. Peter joined CoreWeave in 2019 and has played a pivotal role in scaling CoreWeave’s infrastructure to meet the demands of diverse clients, from AI startups to Fortune 500 companies, making it the world’s first AI-specific cloud service and reshaping the AI infrastructure landscape.

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.

I am a Senior Research Scientist at NVIDIA and Lead of AI Agents Initiative. My mission is to build generally capable agents across physical worlds (robotics) and virtual worlds (games, simulation). I share insights about AI research & industry extensively on Twitter/X and LinkedIn. Welcome to follow me!

My research explores the bleeding edge of multimodal foundation models, reinforcement learning, computer vision, and large-scale systems. I obtained my Ph.D. degree at Stanford Vision Lab, advised by Prof. Fei-Fei Li. Previously, I interned at OpenAI (w/ Ilya Sutskever and Andrej Karpathy), Baidu AI Labs (w/ Andrew Ng and Dario Amodei), and MILA (w/ Yoshua Bengio). I graduated as the Valedictorian of Class 2016 and received the Illig Medal at Columbia University.

I spearheaded Voyager (the first AI agent that plays Minecraft proficiently and bootstraps its capabilities continuously), MineDojo (open-ended agent learning by watching 100,000s of Minecraft YouTube videos), Eureka (a 5-finger robot hand doing extremely dexterous tasks like pen spinning), and VIMA (one of the earliest multimodal foundation models for robot manipulation). MineDojo won the Outstanding Paper Award at NeurIPS 2022. My works have been widely featured in news media, such as New York Times, Forbes, MIT Technology Review, TechCrunch, The WIRED, VentureBeat, etc.

Fun fact: I was OpenAI’s very first intern in 2016. During that summer, I worked on World of Bits, an agent that perceives the web browser in pixels and outputs keyboard/mouse control. It was way before LLM became a thing at OpenAI. Good old times!

Jeff Gehlhaar is Senior Vice President of Engineering at Qualcomm, leading Qualcomm’s AI software products. Jeff’s team focuses on the Qualcomm AI stack including all the quantization, optimization, runtime and accuracy debugging, performance analysis and AI visualization tools across all of Qualcomm’s Snapdragon portfolio of SOCs. This includes support for open source runtimes — LiteRT, Executorch and ONNX Runtime.

Previously, Jeff led the team responsible for research into hardware and software for spiking neural networking applications. During his 34 years at Qualcomm, Jeff has worked on a variety of projects encompassing software for embedded systems, hardware bring-up, and software engineering. Jeff led the software research team developing LTE and LTE-Advanced networking protocols. He is a holder of several patents, and a graduate of the Jacobs School of Engineering.