Luntian Mou received the Ph.D. degree in computer science from the University of Chinese Academy of Sciences, China in 2012. He accomplished as a postdoctoral Researcher with the Institute of Digital Media, Peking University, China in 2014. From 2019 to 2020, he served as a visiting scholar at Donald Bren School of Information and Computer Sciences, University of California, Irvine, USA. He is currently an associate professor with the Beijing Institute of Artificial Intelligence, Beijing University of Technology, China. His research interests include artificial intelligence, machine learning, pattern recognition, affective computing, multimedia computing, and brain-like computing. He has published on renowned journals such as TAFFC, TMM, TOMM, and ESWA. He is the recipient of Beijing Municipal Science and Technology Advancement Award, IEEE Outstanding Contribution to Standardization Award, and AVS Outstanding Contribution on 15th Anniversary Award. He serves as a guest editor for Machine Intelligence Research, and a reviewer for many important international journals and conferences such as TIP, TAFFC, TCSVT, TITS, AAAI, etc. He is a senior member of IEEE and CCF, and a member of ACM. He is the chair of the organizing committee of the 2023 CSIG Conference on Emotional Intelligence (CEI). He is the founding chair of IEEE Workshop on Artificial Intelligence for Art Creation (AIART).

• 1. Explainable AI and Music, 2. AI-Enabled Robotic Theaters for Chinese Folk Art, 3. AIBO: Or How to Make a ‘Sicko’ Brainwave Opera, 4. Cross-Modal Generation of Visual and Auditory Content: A Survey, 5. Artistic Text Style Transfer, 6. Data-Driven Automatic Choreography, 7. Toward Human-AI Collaborative Sketching, 8. MemoMusic: A Personalized Music Recommendation and Generation Framework Based on Emotion and Memory, 9. Algorithmic Composition Techniques for Ancient Chinese Music Restoration and Reproduction: A Melody Generator Approach, 10. Understanding Music and Emotion from the Brain, 11. Music Question Answering: Cognize and Perceive Music, 12. Emotional Quality Evaluation for Generated Music, 13. A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction

Luntian Mou received the Ph.D. degree in computer science from the University of Chinese Academy of Sciences, China in 2012. He accomplished as a postdoctoral Researcher with the Institute of Digital Media, Peking University, China in 2014. From 2019 to 2020, he served as a visiting scholar at Donald Bren School of Information and Computer Sciences, University of California, Irvine, USA. He is currently an associate professor with the Beijing Institute of Artificial Intelligence, Beijing University of Technology, China. His research interests include artificial intelligence, machine learning, pattern recognition, affective computing, multimedia computing, and brain-like computing. He has published on renowned journals such as TAFFC, TMM, TOMM, and ESWA. He is the recipient of Beijing Municipal Science and Technology Advancement Award, IEEE Outstanding Contribution to Standardization Award, and AVS Outstanding Contribution on 15th Anniversary Award. He serves as a guest editor for Machine Intelligence Research, and a reviewer for many important international journals and conferences such as TIP, TAFFC, TCSVT, TITS, AAAI, etc. He is a senior member of IEEE and CCF, and a member of ACM. He is the chair of the organizing committee of the 2023 CSIG Conference on Emotional Intelligence (CEI). He is the founding chair of IEEE Workshop on Artificial Intelligence for Art Creation (AIART).

• 1. Explainable AI and Music, 2. AI-Enabled Robotic Theaters for Chinese Folk Art, 3. AIBO: Or How to Make a ‘Sicko’ Brainwave Opera, 4. Cross-Modal Generation of Visual and Auditory Content: A Survey, 5. Artistic Text Style Transfer, 6. Data-Driven Automatic Choreography, 7. Toward Human-AI Collaborative Sketching, 8. MemoMusic: A Personalized Music Recommendation and Generation Framework Based on Emotion and Memory, 9. Algorithmic Composition Techniques for Ancient Chinese Music Restoration and Reproduction: A Melody Generator Approach, 10. Understanding Music and Emotion from the Brain, 11. Music Question Answering: Cognize and Perceive Music, 12. Emotional Quality Evaluation for Generated Music, 13. A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction

Luntian Mou received the Ph.D. degree in computer science from the University of Chinese Academy of Sciences, China in 2012. He accomplished as a postdoctoral Researcher with the Institute of Digital Media, Peking University, China in 2014. From 2019 to 2020, he served as a visiting scholar at Donald Bren School of Information and Computer Sciences, University of California, Irvine, USA. He is currently an associate professor with the Beijing Institute of Artificial Intelligence, Beijing University of Technology, China. His research interests include artificial intelligence, machine learning, pattern recognition, affective computing, multimedia computing, and brain-like computing. He has published on renowned journals such as TAFFC, TMM, TOMM, and ESWA. He is the recipient of Beijing Municipal Science and Technology Advancement Award, IEEE Outstanding Contribution to Standardization Award, and AVS Outstanding Contribution on 15th Anniversary Award. He serves as a guest editor for Machine Intelligence Research, and a reviewer for many important international journals and conferences such as TIP, TAFFC, TCSVT, TITS, AAAI, etc. He is a senior member of IEEE and CCF, and a member of ACM. He is the chair of the organizing committee of the 2023 CSIG Conference on Emotional Intelligence (CEI). He is the founding chair of IEEE Workshop on Artificial Intelligence for Art Creation (AIART).

• 1. Explainable AI and Music, 2. AI-Enabled Robotic Theaters for Chinese Folk Art, 3. AIBO: Or How to Make a ‘Sicko’ Brainwave Opera, 4. Cross-Modal Generation of Visual and Auditory Content: A Survey, 5. Artistic Text Style Transfer, 6. Data-Driven Automatic Choreography, 7. Toward Human-AI Collaborative Sketching, 8. MemoMusic: A Personalized Music Recommendation and Generation Framework Based on Emotion and Memory, 9. Algorithmic Composition Techniques for Ancient Chinese Music Restoration and Reproduction: A Melody Generator Approach, 10. Understanding Music and Emotion from the Brain, 11. Music Question Answering: Cognize and Perceive Music, 12. Emotional Quality Evaluation for Generated Music, 13. A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction