![LLMs and Agents](./images/projects/agent/jubensha.png)
LLMs and Agents
In this research direction, we aim to develop algorithms and systems for Large Language Models and AI Agents.
read more![NLP4MatSci](./images/projects/ai4matsci/NLP4MatSci.png)
NLP for Materials Science
In this research project, we aim to develop effective NLP techniques for materials discovery.
read more![Embodied Learning](./images/projects/embodied/embodied.png)
Embodied learning
In this research direction, we aim to develop LLM-based sysmtes to solve multimodal and embodied learning tasks.
read more![DLG4NLP](./images/projects/dlg4nlp/dlg4nlp.png)
Graph-based NLP
This project aims to develop efficient graph representation, learning, and reasoning techniques for NLP tasks.
read more![QG](./images/projects/qg/qg.png)
Question Generation
This project aims to develop efficient algorithms and techniques for question generation and question answering.
read more![Causality and Analogy](./images/projects/causality-analogy/analogy.png)
Causal and Analogical AI
In this research direction, we want to explore the key roles that causality and analogy play in NLP and machine intelligence.
read more![KG](./images/projects/kg/kg.png)
Concepts and Knowledge
This project focuses on Taxonomy/Ontology/Knowledge Graph construction and expansion.
read more![Doc AI](./images/projects/doc-ai/doc-ai.png)
Document Intelligence
The general goal of this project is to develop document-grounded NLP systems to cope with real-world usage scenarios.
read more![Spatial Data Analysis](./images/projects/spatial/spatial.png)
Spatial Data Analysis
This project is about spatial data analysis and recovery. We perform spatial recovery based on partial aggregated observations.
read more![Matrix](./images/projects/network/RTTPredict.png)
Internet Latency Prediction
This project collected network latency data from the Seattle platform and developed new algorithms for network latency prediction.
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