LLMs and Agents

LLMs and Agents

In this research direction, we aim to develop algorithms and systems for Large Language Models and AI Agents.

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NLP4MatSci

NLP for Materials Science

In this research project, we aim to develop effective NLP techniques for materials discovery.

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Embodied Learning

Embodied learning

In this research direction, we aim to develop LLM-based sysmtes to solve multimodal and embodied learning tasks.

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DLG4NLP

Graph-based NLP

This project aims to develop efficient graph representation, learning, and reasoning techniques for NLP tasks.

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QG

Question Generation

This project aims to develop efficient algorithms and techniques for question generation and question answering.

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Causality and Analogy

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.

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KG

Concepts and Knowledge

This project focuses on Taxonomy/Ontology/Knowledge Graph construction and expansion.

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Doc AI

Document Intelligence

The general goal of this project is to develop document-grounded NLP systems to cope with real-world usage scenarios.

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Spatial Data Analysis

Spatial Data Analysis

This project is about spatial data analysis and recovery. We perform spatial recovery based on partial aggregated observations.

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Matrix

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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