Research

Research Overview

Our laboratory focuses on applying advanced AI models across diverse domains including computational biology, smart manufacturing, chip design, and FinTech. We are committed to continuously updating our technologies, particularly in the application of Large Language Models (LLM) and Vision-Language Models (VLM), to address complex cross-domain challenges and drive innovative developments.

Research Areas

AI-driven Computational Biology

Applying advanced LLM and VLM models for biological data analysis, including RNA-Seq analysis, genome sequencing, and genomic data processing to understand complex biological systems.

Current Projects

  • LLM-enhanced RNA-Seq de novo assembly and analysis platform
  • AI-powered genomic variant detection and annotation
  • Deep learning for comparative genomics and evolutionary analysis

Key Techniques

Large Language Models (LLM)Vision-Language Models (VLM)RNA-Seq AnalysisGenome AssemblyAI-driven Variant Calling
Funding: Funded by NSTC and MOST - Multiple grants

Smart Manufacturing & Industry 4.0

Utilizing AI models and machine learning techniques to improve manufacturing processes, including quality control, predictive maintenance, and production optimization.

Current Projects

  • LLM-based manufacturing process optimization
  • Computer vision for automated optical inspection
  • AI-driven predictive maintenance systems

Key Techniques

Large Language Models (LLM)Computer VisionPredictive AnalyticsIndustrial IoTDeep Learning
Funding: Industry partnerships and government smart manufacturing grants

AI for Chip Design & EDA

Applying advanced AI technologies to semiconductor design automation, enhancing chip design efficiency and performance optimization.

Current Projects

  • LLM-assisted chip design automation
  • AI-powered electronic design automation (EDA) tools
  • Deep learning for circuit optimization

Key Techniques

Large Language Models (LLM)Machine Learning for EDACircuit Design OptimizationAI-driven Verification
Funding: Semiconductor industry collaborations and research grants

FinTech & Quantitative Trading

Combining LLM and deep learning technologies to develop intelligent trading systems and financial risk management solutions.

Current Projects

  • LLM agents for adaptive quantitative trading
  • AI-driven financial market analysis
  • Deep learning for risk assessment and management

Key Techniques

Large Language Models (LLM)Multi-Agent SystemsFinancial MLQuantitative AnalysisRisk Modeling
Funding: FinTech industry partnerships and financial research grants

Research Facilities

Laboratory Equipment

State-of-the-art instrumentation and equipment for conducting advanced AI and computational research.

Computational Resources

High-performance computing facilities for data analysis, machine learning model training, and simulation work.

Specialized Tools

Access to specialized software, databases, and analytical tools necessary for our research domains.

Collaborations

Our research benefits from active collaborations with leading researchers and institutions worldwide. These partnerships enable us to tackle complex challenges that require diverse expertise and resources.

Academic Partnerships

Collaborative projects with universities and research institutes globally, including active partnerships with Simon Fraser University (Canada) for bioinformatics and genomics research.

Industry Connections

Applied research partnerships with industry leaders to translate research into practice.

International Networks

Participation in international research consortiums and scientific networks.