1，Shaowen Wen is a dedicated AI professional with a portfolio of innovative projects. 

2，He has extensive hands-on experience in deep learning, data visualization, Python, and prompt engineering. 

3，His passion for AI has led to the completion of ambitious projects that leverage the potential of LLMs for a wide array of tasks. 

4，His knack for innovation and keen interest in AI continue to drive his work in the field.

1，NLP engineer(T8) in WeChat, with three years’ work experience. Focus on content understanding of WeChat search. 

2.Familiar with NLP and search, and adept at solving problems by data-driven approaches. 

3.Good at English, especially in speaking and reading.

1,5+ years experience specializing in machine learning, 3+ years experience in practical industrial AI. 

2,Hands‑on solid ability, a certain academic enthusiasm for artificial intelligence, good English reading and writing and literature research skills, and continuous tracking of cutting‑edge tech‑ nologies. 

3,Have Clear and rigorous thinking ability. Good listener and team player with a strong sense of responsibility.

1, Programming Languages: Python, Java, Node.js, C++, Julia, MATLAB 

2, Database: SQL: MySQL, MongoDB, Redis Others: Docker/Kubernetes, ZeroMQ 

1, Affective Computing Using Unsupervised Networks, Few-Shot Learning for Affective Computing, Social Network Clustering and Classification Based on Graph Theory

2, Research on Information Graph Theory Based on Natural Language Processing and High Performance Computing 

Machine Automatic Realization of Entity Clustering under Complex Information Network 

3, Design and Research of Large-scale Data Mutual Information Extraction System l Synonym Analysis and Disambiguation Design and Implementation of Automatic Machine Learning

Multimodal Joint Feature Learning

* Deep Learning and NLP

* Recommendation System

* Sentiment Analysis

* Confident in LSTM, BERT, CNN, ViT, CLIP, LDA, and Topic Models

Tool: PyTorch, TensorFlow

1, Programming languages: C, C++, Python, Matlab Platforms: Linux, windows, 2, ARM Subjects: Machine Learning, Computer Vision, Image Processing, Natural Language Processing, Electronic Design 

1. Proficient in natural language processing, including word segmentation, part-of-speech tagging, named entity recognition, syntactic parser, question and answer system, intent recognition, sentiment analysis, etc.; 2. Proficient in machine learning and deep learning, such as XGBoost, Hidden Markov Model (HMM), Conditional Random Field (CRF), CNN, RNN, LSTM, Transformer, BERT, GPT pre-training model, etc.; 3. Be familiar with various natural language open source frameworks, such as Sequence_tagging, Transformers, HanLP, etc., and be able to modify the source code based on needs; 4. Familiar with natural language annotation systems and corpora, such as: University of Pennsylvania Treebank, People's Daily Corpus, etc.; 5. Proficient in the architecture and development of similar recommendations and real-time personalized recommendation algorithms based on tags and user behavior;