Dr. Han Lu graduated from the Institute of Brain-Inspired Intelligence Science and Technology, Fudan University in September 2025. He joined the School of Artificial Intelligence, Shenzhen University in October of the same year as a master's supervisor. His research focuses on cutting-edge interdisciplinary research in artificial intelligence and brain science, primarily in computational modeling of emotion perception and the application of artificial intelligence in neural decoding of brain functions and the analysis of brain disease mechanisms. He also actively explores innovative applications of large-scale models in brain science research and investigates the construction of next-generation computational models that integrate domain knowledge. Several of his research findings have been published in high-level international journals and conferences such as *Nature Communications* and *AAAI*.
Education Background
2020.09-2025.09 Fudan University, PhD in Engineering
2016.09-2020.06 Shenzhen University, Bachelor of Engineering
Research Interests
Affective computing, multimodal large-scale models, computational neuroscience, smart healthcare
Recruitment Information
Two Master's students are recruited annually (slots still available for the 2026 cohort). There is no limit to the number of undergraduate students. Students majoring in Artificial Intelligence, Computer Science, Information Science, Biomedical Engineering, Neuroscience, and related fields are welcome to apply. Interested applicants should send their CVs to hanlu@szu.edu.cn
The research group has a relaxed and free atmosphere. Internships and exchange visits are supported, provided it does not affect graduation. Enjoy a happy learning and living environment.
Representative Research Achievements
（1）Lu H, Rolls E T, Liu H, et al., Luo Q. Genetic-Dependent Brain Markers of Resilience: Interactions among Childhood Abuse, Genetic Risks and Brain Function. Nature Communications, 2025. (Independent author，Chinese Academy of Sciences Zone 1，Nature IndexJournal，IF (2025) = 15.7）
（2）Lu H, Zhuang X, Luo Q. A brain-inspired way of reducing the network complexity via concept-regularized coding for emotion recognition, Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(1): 556-564. （Independent author，CCF-A）
（3）Lu H# , Xue G# , Li S, et al., Luo Q. An accurate prognostic prediction for aneurysmal subarachnoid hemorrhage dedicated to patients after endovascular treatment. Therapeutic advances in neurological disorders, 2022, 15: 17562864221099473. （There is one first each，IF (2022) = 6.5）
（4）Lu H, Becker B, Heinz A, et al., Luo Q. Deep Learning Reveals a Neurocomputational Mechanism Predicting Depression Risk in Adolescents. Science Advances (under review), 2025. (Independent author，IF (2025) = 11.7)
(5) Lu H, Xie P, Luo Q. Human-like Supramodal Concept Learning Boosts Unimodal Emotion Recognition. ICLR 2026 (under review). （Independent author，CCF-A）
(6) Li Q, Cao M, Stein D J, Lu H, et al., Luo Q. Cognitive predictors of mental health trajectories are mediated by inferior frontal and occipital development during adolescence. Molecular Psychiatry, 2025: 1-14. IF (2025) = 9.6
(7) Chen Changsheng, Lu Han, Huang Jiwu. Training Method for Deep Residual Networks for Demoiré De-QR Code: China, ZL 2020 1 0120759.9 [P]. Authorization Announcement Number: CN 111340729 B
Representative Research Projects
Shanghai Outstanding Academic Leader (Youth), 23XD1423400, Causal Modeling Theory and System Regulation Methods: A Case Study of Digital Research on Adolescent Mental Illness, 2023/06-2026/05, 400,000 RMB, Ongoing, Participant
