Biography

Dr. Jibin Wu is an Assistant Professor in the Department of Computing, The Hong Kong Polytechnic University. Before joining PolyU, he was a research scientist at Sea AI Lab (SAIL) from 2021 to 2022. Dr. Wu received Bachelor and Ph.D. degrees from National University of Singapore (NUS) in 2016 and 2020, respectively.

Dr. Wu is currently affiliated with the MIND Lab@PolyU, which is dedicated to advancing the frontiers of nature-inspired artificial intelligence research. His research interests encompass a wide range of areas, focusing on brain-inspired artificial intelligence, neuromorphic computing, computational audition, speech processing, and machine learning. His primary dedication lies in unraveling the computational principles and architectures of biological brains while also striving to develop cutting-edge brain-inspired cognitive machines that possess exceptional intelligence, energy efficiency, robustness, adaptability, and explainability.

Dr. Wu has actively published in prestigious conferences and journals in artificial intelligence and speech processing, including NeurIPS, AAAI, TPAMI, TNNLS, TASLP, Neurocomputing, and IEEE JSTSP. He is currently serving as the Associate Editors for IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Cognitive and Developmental Systems.

Research Interests
  • Computational Neuroscience

    Learning Algorithms for Spiking Neural Networks, Neural Coding, Brain-inspired Neural Architecture Design

  • Computational Audition

    Human Auditory System Modelling, Auditory Attention Modelling, Cocktail Party Problem

  • Speech Processing

    Automatic Speech Recognition, Speech Separation, Speaker Extraction, Speaker Verification, etc.

  • Cognitive Robot

    Neuromorphic Sensing, Human-Robot Interaction, Embodied AI, Neural Cognitive Architecture

Education
  • Doctor of Philosophy, 2020

    National University of Singapore

  • BEng in Electrical Engineering, 2016

    National University of Singapore

Contact

Experience

 
 
 
 
 
Assistant Professor
2022 – Present Hong Kong
 
 
 
 
 
Research Scientist
2021 – 2022 Singapore

Editorial Service

Associate Editor
Associate Editor

Professional Service

Executive Committee Member
Organizer

Research Team

Postdoc Fellows

Xingyu WU

Research Interests

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Large Foundation Models

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Causality-based Machine Learning

Shenghao WU

Research Interests

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Evolutionary Transfer Optimization

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Transfer Learning

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Data-driven Optimization

PhD Students

Beichen HUANG

Research Interests

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High-performance Evolutionary Computation

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Neuro-evolution

Yinsong YAN

Research Interests

-  

Brain-inspired Computing

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Spiking Neural Networks

Yu ZHOU

Research Interests

-  

Federated Learning

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Evolutionary Computation

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Deep Reinforcement Learning

Haokai HONG

Research Interests

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Multi-objective Optimization

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Large-scale Optimization

Ziyuan YE

Research Interests

-  

NeuroAI

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AGI

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Deep Learning

Yajie ZHANG

Research Interests

-  

Multimodal Biomedical AI

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Causal Inference

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Domain Adaptation

Xiang HAO

Research Interests

-  

Audio/Speech Signal Processing

Xinyi CHEN

Research Interests

-  

Neuromorphic Computing

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Spiking Neural Networks

Chenxiang MA

Research Interests

-  

Spiking Neural Network

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Local Learning

Zeyang SONG

Research Interests

-  

Speech Processing

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Spiking Neural Network

Qu YANG

Research Interests

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Spiking Neural Network

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Neural Coding

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Neuromorphic Computing

Research Assistant

Mengqi XU

Research Interests

-  

Machine Learning

Shimin ZHANG

Research Interests

-  

Spiking Neural Networks

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Audio Signal Processing

Award

Track 1 (Algorithmic) Winner with Cash Prize of USD 15,000
Second Prize of Final Contest with Cash Prize of RMB 60,000
President’s Graduate Fellowship
First Prize of Final Contest with Cash Prize of RMB 100,000
Best Presentation Award
NUS Research Scholarship

Recent Publications

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(2024). Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation. in The 33rd International Joint Conference on Artificial Intelligence, Jeju, Korea. (Accepted).

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(2024). Scaling Supervised Local Learning with Augmented Auxiliary Networks. in International Conference on Learning Representations. (Accepted).

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(2024). EEG-based Auditory Attention Detection with Spiking Graph Convolutional Network. IEEE Transactions on Cognitive and Developmental Systems.

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(2024). Efficient Online Learning for Networks of Two-Compartment Spiking Neurons. in The International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan. (Accepted).

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(2024). Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks. in ICASSP-2024, Seoul, Korea. (Accepted).

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