Dr. Chenggang Chen is an Assistant Professor and Ph.D. Advisor in the School of Biomedical Engineering at Tsinghua University. His academic and research journey in Biomedical Engineering spans Jilin University (B.S., 2007–2011), Tsinghua University (Ph.D., direct doctoral track, 2011–2018), and Johns Hopkins University (2018–2026).
With an interdisciplinary background spanning neuroscience and artificial intelligence, Dr. Chen has authored 11 first-author publications in premier journals and conferences, including Nature Communications, ICML (Spotlight), and NeurIPS. Notably, over the past three years, he has published 5 papers as the sole first and sole corresponding author.
In July 2026, Dr. Chen joined the Tsinghua faculty full-time to establish the Computational Audition and NeuroAI Laboratory (CANAL). Leveraging the cutting-edge infrastructure of Tsinghua University’s Non-human Primate Research Center, Center for Biomedical Imaging, and the Tsinghua Laboratory of Brain and Intelligence (THBI), CANAL is dedicated to advancing empirical neuroscience, brain-inspired AI (NeuroAI), and brain-computer interface (BCI) technologies.
In open environments (such as adverse weather conditions like rain and snow, or complex "cocktail party" scenarios), the brain exhibits highly robust visual and auditory perception capabilities. In contrast, existing artificial intelligence (AI) frequently encounters bottlenecks, including poor noise tolerance and limited out-of-distribution (OOD) generalization. To overcome these limitations, drawing inspiration from the brain's audiovisual perceptual mechanisms has emerged as a crucial direction for advancing next-generation AI. Unlike most laboratory animals, the marmoset, a non-human primate, possesses auditory perceptual capacities similar to those of humans, along with rich vocal communication behaviors. This makes it an ideal biological model for investigating auditory perceptual mechanisms in complex acoustic scenes and for constructing brain-inspired AI.
Conducting research on auditory perception and brain-inspired AI not only aids in elucidating the operational mechanisms of the brain under both normal and pathological conditions—thereby laying a theoretical foundation for the real-world deployment of AI in open environments—but also serves as an indispensable step in translating auditory brain-computer interfaces (BCIs) into preclinical applications. Specifically, these endeavors involve: deciphering the neural encoding and decoding mechanisms of acoustic information within the primate auditory system, as well as validating various stimulus paradigms and observing the corresponding changes in neural activity across both non-human primates and brain-inspired AI models
1. Chenggang Chen, Mingxiu Cheng, Tetsufumi Ito# and Sen Song#. Neuronal organization in inferior colliculus revisited with cell-type-dependent monosynaptic tracing. The Journal of Neuroscience 38(13):3318-3332 (2018)
2. Chenggang Chen and Sen Song#. Differential cell-type dependent modulations of sensory representations in mouse inferior colliculus. Communications Biology 2(1):356 (2019)
3. Xindong Song, Yueqi Guo, Chenggang Chen and Xiaoqin Wang#. A silent two-photon imaging system for studying in vivo auditory neuronal functions. Light: Science & Applications 11(1):96 (2022)
4. Xindong Song, Yueqi Guo, Hongbo Li, Chenggang Chen, Zachary Schmidt and Xiaoqin Wang#. Mesoscopic landscape of cortical functions revealed by through-skull wide-field imaging in marmosets. Nature Communications 13:2238 (2022)
5. Chenggang Chen*, Evan Remington* and Xiaoqin Wang#. Sound localization acuity in the common marmoset. Hearing Research 430:108722 (2023) cover paper
6. Chenggang Chen#, Zhiyu Yang and Xiaoqin Wang. Neural Embeddings Rank: Aligning 3D latent dynamics with movements. NeurIPS 38:141461-141489 (2024) [Note: top 2%]
7. Xindong Song*, Yueqi Guo*, Chenggang Chen*, Jong Hoon Lee and Xiaoqin Wang#. Tonotopic organization of auditory cortex in marmosets revealed by widefield optical imaging. Current Research in Neurobiology 6:100132 (2024)
8. Chenggang Chen# and Sen Song. Distinct neuron types contribute to hybrid auditory spatial coding. The Journal of Neuroscience 44(43): e0159242024 (2024)
9. Chenggang Chen*, Sheng Xu*, Yunyan Wang and Xiaoqin Wang#. Location-specific neural facilitation in marmoset auditory cortex. Nature Communications 16:2773 (2025)
10. Chenggang Chen# and Zhiyu Yang. No free lunch from audio pretraining in bioacoustics: a benchmark study of embeddings. IJCNN Poster track #5A (2025)
11. Chenggang Chen#, Zhiyu Yang and Xiaoqin Wang. Deep neural network model of sound localization replicates “what” and “where” representations. NeurIPS 39: Workshop NeurReps (2025)
12. Chenggang Chen*, Evan Remington* and Xiaoqin Wang#. Dynamic representation of sound locations during task engagement in marmoset auditory cortex. PLoS Biology 24(3): e3003707 (2026)
13. Chenggang Chen#, Zhiyu Yang and Xiaoqin Wang. Real-world unsupervised models generalize to predict brain responses to out-of-distribution stimuli. ICML Spotlight (2026) [Note: top 2%].
2009 Samsung Scholarship
2008 National Scholarship
2009 China Undergraduate Mathematical Contest in Modeling (CUMCM) National Second Prize
2009 China Undergraduate Mathematical Contest in Modeling (CUMCM) Jilin Province First Prize
2010 Suzhou Industrial Park Scholarship
2010 National Scholarship
2010 Top Ten Students (Jilin University School of Electronics)
2011 Summa cum laude (Jilin University)
2018 Best Presentation Award, IDG/McGovern Institute for Brain Research at Tsinghua University
2022 ARO Member Spotlight
2022 NWB Hackathon Travel Award
2022 Neurodata Re-Analysis Travel Award
2022 Kavli Foundation Seed Grant
2022 MARC Travel Award
2023 BRAIN Initiative Annual Meeting Trainee Highlight Award
2023 ARO Annual Meeting Travel Award
2025 MARC Travel Award
