AImplifier R&D
Amplifying AI & Its Impacts

About


AImplifier R&D is a multidisciplinary Research, Education, and Development Team committed to pushing the frontiers of artificial intelligence to drive positive change across humanity, education, and science. We strive to understand and amplify intelligence, both human and machine, and harness the power to shape a better, more equitable world.

Research

We bring together insights from artificial intelligence and brain-computer interface to deepen our understanding of intelligence, cognition, and their associations with psychiatric disorders. We aspire to uncover valuable insights that could contribute to personalized treatment of neuro-diseases (e.g., epilepsy), and advance the development of more neuro-inspired artificial intelligence systems (e.g., multi-modal AI).

Education

We organize Reading Group, AI talks, and AI competitions that empower university students to embrace problem-solving, strive for excellence, and ignite their passion for programming, AI, data science, and biomedical innovation. Through technology-driven AI talks and competitions, we connect bright minds and nurture the next generation of tech talent, shaping the future of innovation.

Development

We develop efficient time-series models and softwares for solar energy prediction, enhances seizure prediction and epileptogenic zone localization to support surgical planning, and refines diagnostic methodologies for sleep disorders by decoding brain signals. Furthermore, we develop AI models to formulate climate policies that optimize environmental, economic, and social trade-offs.

Projects

GENII: EEG & Source Imaging on Cloud with AI

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National AI, HCI, & BCI Competition (dPickleBall)

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Brain Computer Interface

TBA




Foundation Models & Model Merging

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Multi-Agent Reinforcement Learning & Climate AI

TBA

Publications since 2025

  1. [Preprint] Puah, J.H., Goh, S.K., Zhang, Z., Ye, Z., Chan, C.K., Lim, K.S., Fong, S.L. and Woon, K.S., 2025. EEGDM: EEG Representation Learning via Generative Diffusion Model. arXiv preprint arXiv:2508.14086.
  2. [Preprint] Chin, B.W.H., Yew, Y.T., Wu, H., Liang, L., Chan, C.K., Zain, N.M., Samdin, S.B. and Goh, S.K., 2025. SleepDIFFormer: Sleep Stage Classification via Multivariate Differential Transformer. arXiv preprint arXiv:2508.15215.
  3. [EMNLP, accepted] Fang, Z., Du, G., Yu, S., Guo, Y., Zhang, Y., Cao, Y., Li, J., Tang, H.K. and Goh, S.K., 2025. To See a World in a Spark of Neuron: Disentangling Multi-task Interference for Training-free Model Merging. arXiv preprint arXiv:2503.05320.
  4. [ACL] Du, G., Fang, Z., Li, J., Li, J., Jiang, R., Yu, S., Guo, Y., Chen, Y., Goh, S.K., Tang, H.K. and He, D., 2025. Neural parameter search for slimmer fine-tuned models and better transfer. arXiv preprint arXiv:2505.18713.
  5. [ACL] Li, J., Du, G., Li, J., Goh, S.K., Wang, W., Wang, Y., Liu, F., Tang, H.K., Alharbi, S., He, D. and Zhang, M., 2025. Multi-modality expansion and retention for llms through parameter merging and decoupling. arXiv preprint arXiv:2505.17110.

Team

Point of Contact

Sim Kuan GOH

Scientific Advisors

Kheng Seang LIM | Silei FONG | Chow Khuen CHAN | Tanka TANG | Vincent WOON

Postgraduate Researchers

Jia Hong PUAH | Yuin Torng YEW | Seohyun AHN | Yong Ler LAI

Undergraduate Researchers

TBA

Alumni

TBA

Collaborators

Please contact simkuangoh@gmail.com for collaboration.