Neural Superintelligence Lab

Previously Open Neural Network Research Lab (01.2025-12.2025)

Supported by MODULABS and Brian Impact Foundation

Neural Superintelligence Lab is an open research center where deep learning researchers from various companies and labs gather to research next-generation neural networks.

Current Lab director is Vincent-Daniel Yun from USC. If you have any question, please contact [juyoung dot yun @ usc dot com].

Our current research interests are:

  • Low-Cost LLM Training & Inference
  • Neural Network Optimization and Generalization Improvement
  • Novel Architectures of Neural Network
  • Analysis of Legal Issue for Deep Learning Applications

Researchers from various institutions collaborated on artificial intelligence research, successfully completing the inaugural program.


[Important] We do not engage in any commercial activities, nor do we maintain any financial, contractual, or other conflicts of interest with any company. This lab serves as a collaborative research space where researchers, united by a shared mission, pursue artificial intelligence research aimed at advancing the public good and contributing to a better world.

Recent news

Nov, 2025
[Research Grant / $6,000]
Our four papers have been supported by the Brian Impact Foundation and MODULABS from Korea, with a total research grant of $6,000.
Sep, 2025
[CIKM 2025 HCAI]
Congratulations! Our paper entitled "Fast Fourier Transform-Based Spectral and Temporal Gradient Filtering for Differential Privacy" is accepted at CIKM 2025 Human-Centric AI Workshop.
[Paper] [Workshop]
Sep, 2025
Our paper entitled "MedCLM: Learning to Localize and Reason via a CoT-Curriculum in Medical Vision-Language Models" is submitted to the NLP Conference.
[Paper]
Sep, 2025
[NeurIPS 2025 OPT]
Congratulations! Our paper entitled "Why Does Stochastic Gradient Descent Slow Down in Low-Precision Training?" is accepted at Conference on Neural Information Processing Systems (NeurIPS) 2025 OPT Workshop.
[Paper] [Workshop]
Sep, 2025
[NeurIPS 2025 OPT]
Congratulations! Our paper entitled "Sharpness-Aware Minimization with Z-Score Gradient Filtering" is accepted at Conference on Neural Information Processing Systems (NeurIPS) 2025 OPT Workshop.
[Paper] [Workshop]
Sep, 2025
[NeurIPS 2025 OPT]
Congratulations! Our paper entitled "Hyperparameter-Free Auto-Scaled Gradient Normalization via Global Standard Deviation Dynamics" is accepted at Conference on Neural Information Processing Systems (NeurIPS) 2025 OPT Workshop.
[Paper] [Workshop]


Lab Alumni

02.2025
Suin Cho currently working at Boston University as a Researcher
02.2025
Gyuho Shim currently a Korea University student