Information Retrieval
We develop advanced algorithms and systems for efficient and effective information retrieval from large-scale data collections. Our work focuses on improving search accuracy, relevance, and user satisfaction.
Key Topics:
- Neural ranking models and learning to rank
- Query understanding and expansion
- Document representation and indexing
- Web search and enterprise search systems
- Evaluation metrics and test collections
Natural Language Processing
We develop NLP techniques that enable computers to understand, interpret, and generate human language in meaningful ways.
Key Topics:
- Text classification and sentiment analysis
- Named entity recognition and relation extraction
- Question answering systems
- Machine translation and cross-lingual NLP
- Text summarization and generation
Data Mining and Analytics
We extract valuable insights and patterns from large-scale datasets using advanced data mining and analytical techniques.
Key Topics:
- Pattern recognition and discovery
- Recommender systems
- Temporal and spatial data analysis
- Large-scale data processing
- Scholarly data mining
Social Media Analytics
We analyze social media data to understand user behavior, track trends, and extract actionable insights from user-generated content.
Key Topics:
- Sentiment analysis and opinion mining
- Community detection and influence analysis
- Event detection and trend analysis
- Misinformation and fake news detection
- Social network analysis
Bias Detection and Mitigation
We research methods to identify and address fairness, bias, and transparency issues in AI and information systems to ensure equitable outcomes.
Key Topics:
- Fairness-aware machine learning
- Bias in ranking and recommendation
- Algorithmic auditing and impact assessment
- Interpretable and explainable AI (XAI)
- Mitigation strategies in NLP and IR
Multimodal Bio-NLP
We focus on integrating textual, visual, and molecular data to solve complex biomedical problems, leveraging the synergy between different data modalities.
Key Topics:
- Medical Visual Question Answering (VQA)
- Biomedical image captioning and report generation
- Cross-modal retrieval in healthcare
- Multi-modal representation learning for diagnosis
- Integrating omics data with biomedical text
Computational Biology
We utilize algorithms and computational models to understand biological systems, focusing on the analysis of genomic and proteomic data.
Key Topics:
- Genomic sequence analysis
- Protein structure prediction and folding
- Drug discovery and repurposing
- Biological network analysis
- Phylogenetic inference