Keynote Speakers

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

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.

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.

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.

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.

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.

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.