Research & Innovation

Advancing the frontiers of cognitive computing and neural intelligence through rigorous research and collaboration

Our Research Focus Areas

🏗️

Neural Architecture Search

Automated discovery of optimal neural network architectures for specific tasks, reducing design time and improving performance.

🔍

Explainable AI (XAI)

Methods for making complex neural networks interpretable and transparent, building trust in AI decision-making.

📱

Edge AI

Efficient AI models for deployment on edge devices with minimal computational resources and latency.

🔐

Federated Learning

Distributed learning techniques preserving privacy while training models across multiple organizations.

⚖️

AI Ethics

Research into fairness, bias mitigation, and responsible AI development ensuring equitable outcomes.

🧠

Cognitive Science

Bio-inspired neural architectures and learning mechanisms based on cognitive science principles.

Recent Publications (2024-2025)

December 2024
Efficient Neural Architecture Search for Resource-Constrained Environments
By: Dr. Anh Nguyễn, Hương Trần, et al.
Novel approach to automated architecture search that balances model accuracy with computational efficiency for edge deployment.
Neural Architecture Edge AI Optimization
November 2024
Interpretability in Deep Transformers: A Comprehensive Framework
By: Tú Lê, AI Ethics Research Team
Framework for understanding and visualizing decision-making in transformer-based models, advancing explainable AI.
Explainable AI Transformers Interpretability
October 2024
Federated Learning for Privacy-Preserving Healthcare Analytics
By: Dr. Minh Hoàng, Data Science Team
Application of federated learning to healthcare domain enabling collaborative model training without sharing sensitive patient data.
Federated Learning Privacy Healthcare
September 2024
Fairness-Aware Representation Learning in Deep Neural Networks
By: Tú Lê, Hùng Võ
Novel techniques for detecting and mitigating bias in neural networks ensuring fair treatment across demographic groups.
Fairness Bias Mitigation Ethics
July 2024
Bio-Inspired Attention Mechanisms for Cognitive Computing
By: Dr. Anh Nguyễn, Research Team
Exploration of attention mechanisms inspired by cognitive neuroscience principles for improved model performance.
Attention Mechanisms Neuroscience Cognitive Computing

Research Partnerships

Vietnam National University

Collaborative research in neural networks, machine learning, and AI applications for Vietnamese market.

Hanoi University of Technology

Joint initiatives in AI ethics, responsible AI, and governance frameworks.

HCMC AI Research Institute

Partnership on advanced AI research, edge AI, and real-world AI applications.

International AI Consortium

Member of global AI research community collaborating on frontier research topics.

Innovation Lab

Our innovation lab explores experimental projects pushing the boundaries of AI research. Current projects include:

  • 🔬 Quantum-Inspired Neural Networks: Exploring quantum computing principles for neural network optimization.
  • 🧠 Neuromorphic Computing: Hardware-software co-design for brain-inspired computation.
  • 🌍 Few-Shot Learning: Models that learn from minimal data, enabling rapid adaptation.
  • 🔐 Secure AI: Adversarial robustness and security mechanisms for AI systems.

Open-Source Contributions

We actively contribute to open-source AI projects and maintain several popular libraries:

  • 📦 CognitiveFlow - Neural network framework optimized for enterprise use
  • 🔍 ExplainableAI-Toolkit - Tools for model interpretability and explanation
  • ⚡ EdgeML-Compiler - Compiler for deploying ML models on edge devices

Upcoming Research Directions

Looking ahead to 2025 and beyond, we're focusing on:

  • Multimodal Learning: Models that integrate text, images, audio, and video for richer understanding.
  • Continual Learning: AI systems that learn continuously from new data without forgetting old knowledge.
  • Trustworthy AI: Advances in robustness, safety, and verification of AI systems.
  • AI for Climate: Applying AI to environmental challenges and sustainability.