Li Chen

Major news for AI in Healthcare! We’re excited about our new collaboration with the UCSF UC Berkeley Joint Program in Computational Precision Health and UC Berkeley College of Computing, Data Science, and Society—a unique academic partnership that will host and manage the Nightingale Open Science Dataset. This is what meaningful change in health technology looks like, and we’re thrilled to be part of a team working towards a better future. The collaboration will expand access to a key resource that addresses some of the most persistent challenges in health AI research and deployment. Learn more about Nightingale Open Science: https://lnkd.in/eZwegzae

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Stanford Center for Research on Foundational Model's Marin project has released the first fully open model in JAX. It’s an 'open lab' sharing the entire research process - including code, data, and logs, to enable reproducibility and further innovation. Check out the project: https://goo.gle/44AMeLY

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UW ECE Affiliate Assistant Professor Saman Naderiparizi is co-founder of the new Seattle startup ElastixAI, which is developing an AI inference platform designed to optimize how large language models are run. Naderiparizi and his co-founders recently raised $16M to further ElastixAI's growth. Learn more in GeekWire: https://lnkd.in/gcZmmihF

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Longfeng Wu is an applied scientist intern at Amazon in Sunnyvale, CA, where she has been focusing on developing large-scale generative recommendation models based on semantic identifiers (IDs) aimed at fundamentally transforming how recommendations are generated and delivered. A Ph.D. student in computer science, Longfeng is advised by Dawei Zhou at the Sanghani Center for Artificial Intelligence and Data Analytics.

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AI Bots Accurately Analyze Big Data Set to Predict Preterm Birth | UC San Francisco: A team of UCSF researchers successfully tested several mainstream AI agents for the ability to analyze big data on women's reproductive health, ... #bigdata #cdo #cto

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Comparing models.. Finding that a small 1B #llm hallucinates less than a 4B for semantic to structural matching. Small llms for targeted tasks for the win. The new Meta llama3.2 1B is better than expected and cheap on token cost.

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Today’s guide by Reza Bahmanzadeh, AI Engineer on our Professional Services Team, teaches the essentials of serving large models with vLLM: https://lnkd.in/eHK9nwZm It builds from foundational neural network concepts, like transformers and attention, to introduce practical inference workflows, explore useful vLLM features and offer hands-on, operational guidance for efficient deployments. #LLMs #Serving #Inference #TechGuides

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UC Davis saw a 36% increase in records of invention (ROIs) in FY 2025, reversing a multiyear decline and reflecting a vibrant surge in research innovation, especially in health-focused fields like gene therapy, neuroscience and oncology. Over 70% of ROIs came from first-time inventors, suggesting that a strong, inclusive culture of commercialization is growing across the Davis and Sacramento campuses. From CRISPR-engineered cartilage to a faster, more cost-effective manufacturing process for gene therapy, UC Davis research continues to translate into breakthroughs that will improve people's lives. This momentum is more than a metric—it’s a movement. Read more: https://lnkd.in/gY8zUt3H #Innovation #TechTransfer #AcademicResearch #HealthInnovation #UCDavis #UCDavisHealth #Commercialization #AggieSquare

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📚The best data in the world is useless if no one can find it. Building accessible data portals that collect, organize, and share massive genomic datasets is more difficult than it sounds, but it's vital to empowering new discoveries about human health. A new paper from the UC Santa Cruz Genomics Institute shares lessons from building 10+ major portals, including ENCODE and the Human Cell Atlas. The authors highlight a persistent problem: the genomics community keeps building new portals from scratch instead of reusing existing software—creating inefficiency and headaches for researchers navigating multiple systems. They make recommendations for sharing data more sustainably. Key takeaways: 🔹 Without clear deadlines from funders, data often never gets shared 🔹 Human "data wranglers" ensure consistency that software alone can't achieve 🔹 Overly complex metadata schemas discourage submissions 🔹 Both wet-lab biologists and computational researchers need easy access 🔹 Most portals have no plan for when the grant runs out 📚 Speir et al., 2025, Scientific Data https://lnkd.in/eY3bdW-u

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Based on years of experience building LLM applications, Shuai Guo shares detailed insights for practitioners interested in optimizing their process before, during, and after their project launch.

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By building a taxonomy for compound AI systems, researchers are showing that when agents are optimized using structured evaluation frameworks, downstream task accuracy improves. Better evaluation → better agents. Featuring Melissa Pan and Mert Cemri of University of California, Berkeley / CSGE Berkeley Interviewed by Jessica Dai and SAIL Media Thank you to ‪Lambda‬ for making these interviews possible! 👉 Subscribe to our calendar for priority invites to upcoming events: lu.ma/twill #AI #ArtificialIntelligence #MultiAgentSystems #LLM #LLMAgents #CompoundAI #MachineLearning #AIResearch #BerkeleyAI #CSGEBerkeley #TechEvents #StartupCommunity #SiliconValley #FutureOfAI #AI #ArtificialIntelligence #MultiAgentSystems #LLM #LLMAgents #CompoundAI #MachineLearning #AIResearch #BerkeleyAI #CSGEBerkeley #TechEvents #StartupCommunity

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