2025 Asia Pacific Workshop on Data Science and Information Theory
Invited Talks
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Invited Talks


Topic 1: Advanced Coding Technology

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Wai Ho MOW

Associate Dean of Engineering (Undergraduat Studies), Professor

The Hong Kong University of Science and Technology

Personal Webpage

Title: To be announced.

Abstract: To be announced.

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Xiaohu Tang

Professor

Southwest Jiaotong University

Title: Research on Hardware-Friendly LDPC Code

Abstract: To be announced.

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Qin Huang

Associate Professor

Beihang University

Personal Webpage

Title: Decomposition and Combination: Way to Near-Optimal Decoding

Abstract: To be announced.

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Ling Liu

Associate Professor

Xidian University (Guangzhou)

Personal Webpage

Title: Improving the performance of polar codes using feedback

Abstract: To be announced.


Topic 2: Semantic Communication

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Jun Chen

Professor

McMaster University

Personal Webpage

Title: On the Fundamental Limits of Generative Communication

Abstract: Motivated by the emerging paradigm of generative communication, this talk explores the problem of channel-aware optimal transport, where a block of i.i.d. random variables is transmitted through a memoryless channel to generate another block of i.i.d. random variables with a prescribed marginal distribution such that the end-to-end distortion is minimized. With unlimited common randomness available to the encoder and decoder, the source-channel separation architecture is shown to be asymptotically optimal as the blocklength approaches infinity. On the other hand, in the absence of common randomness, the source-channel separation architecture is generally suboptimal. For this scenario, a hybrid coding scheme is proposed, which partially retains the generative capabilities of the given channel while enabling reliable transmission of digital information. It is demonstrated that the proposed hybrid coding scheme can outperform both separation-based and uncoded schemes.

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Kai Niu

Professor

Beijing University of Posts and Telecommunications

Personal Webpage

Title: Semantic Information Theory and Method

Abstract: The convergence of communication and artificial intelligence represents a pivotal trend in future information processing, with semantic information emerging as a new medium for information interaction. This talk begins by introducing the fundamental characteristics of semantic information, thati is synonymity, followed by a concise overview of the basic framework of semantic information theory, including its measurement system and the performance limits of semantic communication. Finally, it presents the system framework based on synonymous mapping and typical results of semantic encoding and transmission. It is foreseeable that semantic communication will become a new technological paradigm in future communications, offering promising application prospects

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Yongjune Kim

Associate Professor

Pohang University of Science and Technology

Personal Webpage

Title: CrossMPT: Cross-attention message-passing transformer for error correcting codes

Abstract: Error correcting codes (ECCs) are indispensable for reliable transmission in communication systems. Recent advancements in deep learning have catalyzed the exploration of ECC decoders based on neural networks. Among these, transformer-based neural decoders have achieved state-of-the-art decoding performance. We propose a novel Cross-Attention Message-Passing Transformer (CrossMPT), which shares key operational principles with conventional message-passing decoders. While conventional transformer-based decoders employ a self-attention mechanism without distinguishing between magnitude and syndrome embeddings, CrossMPT updates these two types of embeddings separately and iteratively via two masked cross-attention blocks. The mask matrices are determined by the code's parity-check matrix, which explicitly captures and removes irrelevant relationships between the magnitude and syndrome embeddings. Our experimental results show that CrossMPT significantly outperforms existing neural network-based decoders for various code classes. Notably, CrossMPT achieves this decoding performance improvement while significantly reducing memory usage, computational complexity, inference time, and training time.

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Bo Bai

Director of Theory Lab, Chief Scientist of Information Theory

Huawei Technologies Co., Ltd.

Title: Forget BIT, It' s All about TOKEN!-Towards the Mathematical Theory of Semantic

Abstract: To be announced.

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Dong Liu

Professor

The University of Science and Technology of China

Personal Webpage

Title: A Deep Learning Approach to the Rate-Distortion Bounds of Image Compression

Abstract: To be announced.

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Tao Guo

Associate Professor

SouthEast University

Personal Webpage

Title: Rate-distortion theory for multi-user semantic compression

Abstract: To be announced.


Topic 3: Crytography and Information Theory

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Amin Gohari

Vice-Chancellor Associate Professor

The Chinese Uinversity of Hong Kong

Personal Webpage

Title: On the Source Model Key Agreement Problem

Abstract: To be announced.

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Mitsugu Iwamoto

Professor

University of Electro-Communications

Personal Webpage

Title: Information-theoretic security, revisited

Abstract: To be announced.

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Vinod Prabhakaran

Associate Professor

Tata Institute of Fundamental Research, India

Personal Webpage

Title: Byzantine Distributed Function Computation

Abstract: To be announced.

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Guodong Shi

Associate Professor

The university of Sydney

Personal Webpage

Title: Differential Privacy over Affine Manifolds

Abstract: To be announced.