Haonan Wang

Haonan Wang

Johns Hopkins University

  • Currently Now:
    I am currently a graduate research assistant volunteer at the Johns Hopkins University, working withProf. Ziang Xiao and Postdoc Fellow. Jie Gao and I have also actively engaged in NLP discussions with Prof. Jason Eisner(Faculty advisor), Prof. Anjalie Field(Course project advisor) at the Center for Language and Speech Processing (CLSP). At the same time, I received my Master’s degree in Computer Science Engineering at Johns Hopkins University (JHU) in the 2024 Fall.

  • In my Undergraduate:
    Before joining JHU, I received my B.S. degree in Information and Computer Science, and Mathematics from Liaoning Technology University. During my undergrad study, I spent time at the Institute of Mathematics and Systems Science and the Institute of Machine Learning and Data Mining, where I was advised by Prof. Wei Liu and Prof.Yu Zhang works in Multivariate Statistical Analysis, Machine Learning, Reinforcement Learning, Informatics Theory, Numerical Analysis, and Math Modeling and Optimization Algorithms.
  • Another activity:
    I am also a Co-founder of SpringTeng AI, a startup and community based in the China National University Science Park. This community focuses on developing AI data analysis systems, Software Patents Apps for industry technologies, and collaborating such as EACON Driverless, and the Chinese Ming Group.

🤔 Research interest

My research interest focuses on the Large Language Models(LLMs) for Training and Evaluation, Human-Centered Artificial Intelligence (HCI-AI), and Machine learning/Advanced Statistical Inference. My work is motivated by fundamental questions
RQ1: how to study human-AI Alignment from multi-perspective Data Science(Annotation/Collection/Statistical Inference) for LLMs Training
RQ2: how to build text-world modeling for LLMs/RL and reasoning and evaluation\ Specifically, including:

  • 1. Human-AI Alignment for Social NLP Subjective data: How to achieve the bidirectional Human-AI Alignment to bridge the socio-technical gap in building LLMs for an effective data annotation/collection pipeline and LLMs for mining/analysis more enriched information in NLP subjective data tasks.
  • 2. LLMs for evaluation and code generation: How to evaluate and improve the capabilities of LLMs and agents to understand and interact with humans in the interaction process, like simulating the text-based world.
  • 3. Machine learning and Statistical Inference: How to understand data statistics dynamics using math modeling and developing efficient machine learning algorithms for large-scale data analysis and intelligent pattern recognition.