The Lab of Large Audio Model (LLAM) is committed to create innovative solutions that enhance privacy, security, and efficiency in decentralized and complex systems.
[21/08/2025]
[26/07/2025]
[26/06/2025]
[01/06/2025]
[16/05/2025]
Research on Federated Large Models focuses on advancing privacy-preserving distributed learning frameworks that enable collaborative training of large-scale AI models across decentralized data sources. This direction integrates cutting-edge techniques in federated learning, differential privacy, and model compression to address challenges in data silos, communication efficiency, and heterogeneous system environments. Key applications include cross-institutional medical analysis, secure financial risk prediction, and edge-device personalized AI services while ensuring strict compliance with data governance regulations.
Research on Trusted Computing aims to build secure and verifiable computing systems through hardware-rooted security mechanisms, enclave-based confidential computing, and decentralized trust verification protocols. We focus on designing architectures that guarantee data integrity, execution traceability, and resistance to adversarial attacks across cloud-edge environments. Our innovations are applied to blockchain consensus optimization, privacy-preserving biometric authentication, and AI model provenance tracking, establishing trust foundations for next-generation mission-critical systems.
Research on Graph Computing explores efficient algorithms and systems for analyzing complex relational data at web-scale. By developing novel graph neural network architectures, dynamic subgraph mining techniques, and heterogeneous graph embedding methods to address challenges in billion-edge network processing, real-time knowledge graph reasoning, and multimodal graph representation learning. Applications span social network fraud detection, drug discovery through molecular interaction networks, and smart city traffic optimization systems.
Research on Large Audio Models aims to advance the field of audio processing, generation, understanding, and multimodal processing. This research encompasses a wide range of applications, including speech recognition, virtual assistants, music composition, audio synthesis, and more. Within this broad scope, several key areas of focus include: Low resource TTS, Expressive TTS, Voice Conversion, Audio Caption, Speech Security, and Music AI.
The International Conference on Neural Information Processing (ICONIP) is an annual conference of the Asia Pacific Neural Network Society (APNNS). ICONIP brings together attendees from around the world, diverse disciplines and professions including researchers, academics, and industry experts, all working collaboratively to tackle real-world challenges and to contribute to the society.
The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025) is set to be a major event for researchers, practitioners, and enthusiasts in the field of natural language processing (NLP). Taking place from November 5th to 9th in Suzhou, China, this conference promises to showcase cutting-edge research, innovative applications, and thought-provoking discussions.
ICCV is hosted by the Institute of Electrical and Electronics Engineers (IEEE). It is the premier international computer vision event, and its proceedings represent the latest development trends and highest level in the field of computer vision. It is highly regarded in the industry and is the top - level conference with the lowest acceptance rate among the three major computer vision conferences.
The 21st International Conference on Information Security and Cryptology (INSCRYPT 2025) will be held in Xi’an from October 19th to October 21st, 2025, organized by the State Key Laboratry of Integrated Services Networks (ISN) of Xidian University and the State Key Laboratory of Cyberspace Security Defense (SKLCSD) of the Institute of Information Engineering of Chinese Academy of Science. Inscrypt 2025 seeks high-quality research contributions in the form of well developed papers. Topics of interest encompass research advances in ALL areas of information security, cryptology, and their applications. The conference proceedings will be published by Springer-Verlag in LNCS series.