baifeng shi

2026 BAIR Graduate Showcase

Congratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026! This year, BAIR celebrates another remarkable group of Ph.D. graduates whose curiosity, creativity, and perseverance have pushed the frontiers of artificial intelligence and machine learning. Their work spans the breadth of modern AI — robotics and embodied intelligence, large language models and reasoning, …

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Run NVIDIA Nemotron and OpenAI GPT OSS models on Amazon Bedrock in AWS GovCloud (US)

Government agencies running workloads in AWS GovCloud (US) need AI capabilities that keep pace with the commercial sector. At the same time, they can’t compromise the security and compliance controls their missions require. As open-weight foundation models (FMs) move from experimentation into mission systems, two requirements shape every model decision. First, the model must deliver …

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AlloyDB AI Functions – now with revolutionary performance boosts and cost savings

AlloyDB is an AI-native database—it isn’t just a passive data store, it intelligently understands and processes your data. With AlloyDB, you get industry-leading vector and hybrid search, near 100% accurate natural language-to-SQL capabilities to build conversational agents, tools to enable you to build with your agentic IDEs of choice, and the ability to bring the …

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Introducing Claude Sonnet 5 on AWS: Anthropic’s most capable Sonnet model

Today, we’re excited to announce the availability of Anthropic’s most advanced Sonnet model, Claude Sonnet 5, on Amazon Bedrock and Claude Platform on AWS. Claude Sonnet 5 is the first Sonnet model of Anthropic’s latest generation and represents a meaningful step forward. It delivers top-tier intelligence at Sonnet pricing for coding, agents, and everyday professional …

How Schrödinger sped up molecular discovery by 4x with Alphaevolve

Computational chemistry researchers have traditionally faced a frustrating trade-off when simulating molecular interactions: use fast classical force fields that sacrifice precision or rely on accurate quantum-mechanical methods that run too slowly on large jobs.  Machine-learned force fields (MLFFs) close that gap by training neural networks on high-fidelity quantum data. When it comes to modern drug …