researchers

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diff --git a/README.md b/README.md
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     - [Benchmark Datasets](https://yueyuel.github.io/ReliableLM4Code/docs/LM4Code/benchmark/)
 - [Relevant Surveys and Tutorial](https://yueyuel.github.io/ReliableLM4Code/docs/relevant_surveys/)
 - [Explanable LM4Code](https://yueyuel.github.io/ReliableLM4Code/docs/xai_lm4code/)
+- [Top Researchers in LM4Code](https://yueyuel.github.io/ReliableLM4Code/docs/researchers/)
 - [Relevant Venus](https://yueyuel.github.io/ReliableLM4Code/docs/venus/)
 
 

diff --git a/docs/relevant_surveys.md b/docs/relevant_surveys.md
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 ## Relevant Surveys on LM4Code
-- Large Language Models for Software Engineering: Survey and Open Problems, 2023, [paper](https://arxiv.org/pdf/2310.03533)
-- Large Language Models for Software Engineering: A Systematic Literature Review, 2023, [paper](https://arxiv.org/abs/2308.10620)
-- A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends, 2023, [paper](https://arxiv.org/pdf/2311.10372.pdf)
-- Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code, 2023, [paper](https://arxiv.org/abs/2311.07989)
-- Software testing with large language model: Survey, landscape, and vision, 2023, [paper](https://arxiv.org/pdf/2307.07221)
-- Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey, 2023, [paper](https://arxiv.org/pdf/2310.17903)
-- Generative Artificial Intelligence for Software Engineering--A Research Agenda, 2023, [paper](https://arxiv.org/pdf/2310.18648)
--  A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly, 2023, [paper](https://arxiv.org/abs/2312.02003)
-- Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps, 2023, [paper](https://arxiv.org/pdf/2309.04142)
-- Large language models meet NL2Code: A survey, 2023, [paper](https://aclanthology.org/2023.acl-long.411.pdf)
-- A Survey on Pretrained Language Models for Neural Code Intelligence, 2022, [paper](https://arxiv.org/abs/2212.10079)
+- **Large Language Models for Software Engineering: Survey and Open Problems**, 2023, [paper](https://arxiv.org/pdf/2310.03533)
+- **Large Language Models for Software Engineering: A Systematic Literature Review**, 2023, [paper](https://arxiv.org/abs/2308.10620)
+- **A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends**, 2023, [paper](https://arxiv.org/pdf/2311.10372.pdf)
+- **Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code**, 2023, [paper](https://arxiv.org/abs/2311.07989)
+- **Software testing with large language model: Survey, landscape, and vision**, 2023, [paper](https://arxiv.org/pdf/2307.07221)
+- **Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey**, 2023, [paper](https://arxiv.org/pdf/2310.17903)
+- **Generative Artificial Intelligence for Software Engineering--A Research Agenda**, 2023, [paper](https://arxiv.org/pdf/2310.18648)
+- **A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly**, 2023, [paper](https://arxiv.org/abs/2312.02003)
+- **Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps**, 2023, [paper](https://arxiv.org/pdf/2309.04142)
+- **Large language models meet NL2Code: A survey, 2023**, [paper](https://aclanthology.org/2023.acl-long.411.pdf)
+- **A Survey on Pretrained Language Models for Neural Code Intelligence**, 2022, [paper](https://arxiv.org/abs/2212.10079)
 
 ## General Surveys on AI4SE
-- A systematic literature review on the use of deep learning in software engineering research, TOSEM 2022, [paper](https://dl.acm.org/doi/pdf/10.1145/3485275)
-- A survey on deep learning for software engineering, CSUR 2022, [paper](https://dl.acm.org/doi/abs/10.1145/3505243)
-- Software engineering for AI-based systems: a survey, TOSEM 2021, [paper](https://dl.acm.org/doi/abs/10.1145/3487043)
-- Machine/deep learning for software engineering: A systematic literature review, TSE 2022, [paper](https://ieeexplore.ieee.org/abstract/document/9772253/)
-- Machine Learning Applied to Software Testing: A Systematic Mapping Study, 2019, [paper](https://ieeexplore.ieee.org/abstract/document/8638573/)
-- A survey of machine learning for big code and naturalness, CSUR 2018, [paper](https://dl.acm.org/doi/abs/10.1145/3212695)
+- **A systematic literature review on the use of deep learning in software engineering research**, TOSEM 2022, [paper](https://dl.acm.org/doi/pdf/10.1145/3485275)
+- **A survey on deep learning for software engineering**, CSUR 2022, [paper](https://dl.acm.org/doi/abs/10.1145/3505243)
+- **Software engineering for AI-based systems: a survey**, TOSEM 2021, [paper](https://dl.acm.org/doi/abs/10.1145/3487043)
+- **Machine/deep learning for software engineering: A systematic literature review**, TSE 2022, [paper](https://ieeexplore.ieee.org/abstract/document/9772253/)
+- **Machine Learning Applied to Software Testing: A Systematic Mapping Study**, 2019, [paper](https://ieeexplore.ieee.org/abstract/document/8638573/)
+- **A survey of machine learning for big code and naturalness**, CSUR 2018, [paper](https://dl.acm.org/doi/abs/10.1145/3212695)
 
 ## General Surveys on LLM
-- Large Language Models: A Comprehensive Survey of Applications, Challenges, Limitations, and Future Prospects, 2023, [paper](https://d197for5662m48.cloudfront.net/documents/publicationstatus/181139/preprint_pdf/edf41a1f2a93aadb235a3c3aff2dcf08.pdf)
-- A survey of large language models, 2023, [paper](https://arxiv.org/pdf/2303.18223.pdf?fbclid=IwAR3GYBQ2P9Cww2HVM3oUbML9i5i3DMDBVv5_FvYWfEi-vdZqZoSM78jE2-s)
-- A Survey on Evaluation of Large Language Models, 2023, [paper](https://arxiv.org/pdf/2307.03109.pdf)
-- Recent advances in natural language processing via large pre-trained language models: A survey, CSUR 2023, [paper](https://arxiv.org/pdf/2111.01243)
-- A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4, 2023, [paper](https://arxiv.org/pdf/2310.12321.pdf)
-- Challenges and Applications of Large Language Models: A Survey, 2023, [paper](https://arxiv.org/pdf/2307.10169.pdf)
-- Harnessing the power of llms in practice: A survey on chatgpt and beyond, 2023, [paper](https://arxiv.org/pdf/2304.13712.pdf)
-- A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT, 2023, [paper](https://arxiv.org/pdf/2303.04226.pdf)
+- **Large Language Models: A Comprehensive Survey of Applications, Challenges, Limitations, and Future Prospects**, 2023, [paper](https://d197for5662m48.cloudfront.net/documents/publicationstatus/181139/preprint_pdf/edf41a1f2a93aadb235a3c3aff2dcf08.pdf)
+- **A survey of large language models**, 2023, [paper](https://arxiv.org/pdf/2303.18223.pdf?fbclid=IwAR3GYBQ2P9Cww2HVM3oUbML9i5i3DMDBVv5_FvYWfEi-vdZqZoSM78jE2-s)
+- **A Survey on Evaluation of Large Language Models**, 2023, [paper](https://arxiv.org/pdf/2307.03109.pdf)
+- **Recent advances in natural language processing via large pre-trained language models: A survey**, CSUR 2023, [paper](https://arxiv.org/pdf/2111.01243)
+- **A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4**, 2023, [paper](https://arxiv.org/pdf/2310.12321.pdf)
+- **Challenges and Applications of Large Language Models: A Survey**, 2023, [paper](https://arxiv.org/pdf/2307.10169.pdf)
+- **Harnessing the power of llms in practice: A survey on chatgpt and beyond**, 2023, [paper](https://arxiv.org/pdf/2304.13712.pdf)
+- **A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT**, 2023, [paper](https://arxiv.org/pdf/2303.04226.pdf)
 
 
 ## Repositories and Resources for LM4Code
-- LLM4SE: Large Language Models for Software Engineering
+- **LLM4SE: Large Language Models for Software Engineering**
     - [Repository](https://github.com/gai4se/LLM4SE)
     - This repository is associated with prominent software engineering conferences like ICSE, FSE, and ASE.
-- Awesome-Code-LLM
+- **Awesome-Code-LLM**
     - [Repository](https://github.com/codefuse-ai/Awesome-Code-LLM)
     - This is the repo for one survey - a comprehensive review of LLM researches for code. Works in each category are ordered chronologically. A curated list of language modeling researches for code and related datasets.
-- awesome-ai4code-papers
+- **awesome-ai4code-papers**
     - [Repository](https://github.com/bdqnghi/awesome-ai4code-papers)
     - A collection of recent papers, benchmarks and datasets of AI4Code domain.
-- ml4code
+- **ml4code**
     - [Repository](https://ml4code.github.io/)
     - Research on machine learning for source code.
-- awesome-machine-learning-on-source-code
+- **awesome-machine-learning-on-source-code**
     - [Repository](https://github.com/src-d/awesome-machine-learning-on-source-code)
     - Cool links & research papers related to Machine Learning applied to source code (MLonCode)
-- saltudelft/ml4se
+- **saltudelft/ml4se**
     - [Repository](https://github.com/saltudelft/ml4se)
     - A curated list of papers, theses, datasets, and tools related to the application of Machine Learning for Software Engineering
-- CUHK-ARISE/ml4code-dataset
+- **CUHK-ARISE/ml4code-dataset**
     - [Repository](https://github.com/CUHK-ARISE/ml4code-dataset)
     - A collection of datasets for machine learning for big code
 
 
 
 ## Repositories and Resources for LLM
-- Awesome-LLM4Tool: A Curated List of Resources for LLM Tools
+- **Awesome-LLM4Tool: A Curated List of Resources for LLM Tools**
     - [Repository](https://github.com/OpenGVLab/Awesome-LLM4Tool)
     - Offers a curated list of papers, repositories, tutorials, and resources related to large language models for tools.
-- LLMsPracticalGuide: A Curated List of Practical Resources
+- **LLMsPracticalGuide: A Curated List of Practical Resources**
     - [Repository](https://github.com/Mooler0410/LLMsPracticalGuide)
     - It includes an evolutionary tree of modern Large Language Models to trace the development over the years
-- Hannibal046/Awesome-LLM
+- **Hannibal046/Awesome-LLM**
     - [Repository](https://github.com/Hannibal046/Awesome-LLM)
     - Awesome-LLM: a curated list of Large Language Model
-- awesome-decentralized-llm
+- **awesome-decentralized-llm**
     - [Repository](https://github.com/imaurer/awesome-decentralized-llm)
     - Collection of LLM resources that can be used to build products you can "own" or to perform reproducible research.
-- RUCAIBox/LLMSurvey
+- **RUCAIBox/LLMSurvey**
     - [Repository](https://github.com/RUCAIBox/LLMSurvey)
     - The official GitHub page for the survey paper "A Survey of Large Language Models".
-- tensorchord/Awesome-LLMOps
+- **tensorchord/Awesome-LLMOps**
     - [Repository](https://github.com/tensorchord/Awesome-LLMOps)
     - An awesome & curated list of best LLMOps tools for developers
-- luban-agi/Awesome-Domain-LLM
+- **luban-agi/Awesome-Domain-LLM**
     - [Repository](https://github.com/luban-agi/Awesome-Domain-LLM)
     - A curated list of domain-specific large language models in Chinese
-- underlines/awesome-ml
+- **underlines/awesome-ml**
     - [Repository](https://github.com/underlines/awesome-ml)
     - Curated list of useful LLM / Analytics / Datascience resources