Yifan Hou

Wenhang Ge

Haoping Xu

Shuchen Xue

Json Zhou

Jihye Choi

Zuobai Zhang

Zuobai Zhang is a Research Scientist with NVIDIA Research’s Fundamental Generative AI Research Group. He received his Ph.D. in Computer Science from Mila – Québec AI Institute and Université de Montréal in 2026, and his B.S. in Computer Science from Fudan University in 2021. His research focuses on developing biological foundation models, especially for proteins, from the perspectives of both representation learning and generative modeling. He is interested in both fundamental generative model techniques and their applications in scientific domains.

Shaokun Zhang

Shaokun Zhang is a Research Scientist at NVIDIA Research, where he actively work on agent training. He received his PhD degree from Pennsylvania State University in 2026.

Sionna Research Kit: A GPU-Accelerated Research Platform for AI-RAN

We introduce the NVIDIA Sionna Research Kit, a GPU-accelerated research platform for developing and testing AI/ML algorithms in 5G NR cellular networks. Powered by the NVIDIA Jetson AGX Orin, the platform leverages accelerated computing to deliver high throughput and real-time signal processing, while offering the flexibility of a software-defined stack. Built on OpenAirInterface (OAI) [1], it unlocks a broad range of research opportunities.

SALAD: Self-Adaptive Link Adaptation

Adapting the modulation and coding scheme (MCS) to the wireless link quality is critical for maximizing spectral efficiency while ensuring reliability. We propose SALAD (selfadaptive link adaptation), an algorithm that exclusively leverages ACK/NACK feedback to reliably track the evolution of the signalto-interference-plus-noise ratio (SINR), achieving high spectral efficiency while keeping the long-term block error rate (BLER) near a desired target. SALAD infers the SINR by minimizing the cross-entropy loss between received ACK/NACKs and predicted BLER values.

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