Hire Ran X. - Fine-Tuning Engineer

Full-stack participation in a computer vision (CV) project for law enforcement, including research initiatives, data acquisition, model improvement, auxiliary pipeline development, and product deployment and maintenance at the client site. Supported internal and client reporting with customized tabular and graphic formats at each stage. Led the development and deployment of a video activity detection pipeline, integrating image and video components into the company's media processing product for client-site deployment. Co-led enhancement experiments on an image transformer model. Drove significant improvements and exceeded the desired performance level. Co-led a client-site annotation project, customizing and deploying annotation tools, training government agencies, and ensuring quality assurance. Contributed to research and presentations on alternative approaches to age estimation and activity detection. Assisted in the development of a novel annotation schema for law enforcement use cases, contributing to training material across various formats. Acted as an on-site representative, facilitating communication between government clients and development teams to drive project improvements.

* Participated in researching the usage of LIME and SHAP in Explainable Natural Language Processing: * Fine-tuned various transformer models from Hugging Face and other Machine Learning models for sentiment analysis and text summarization. * Evaluated in detail how LIME and SHAP are used in classification model interpretation at the local and global levels. Particularly on how LIME explains the impact of various feature engineering processes and how SHAP explains transformer models in sentiment analysis and text summarization. * Solved various incompatibility and technical issues with LIME and SHAP by implementing multiple wrapper classes. * Proposed recommendations and use case analysis to company VPs.

* Led the design and launch of an experiment analyzing technical requirements from multi-industrial job post dataset: * Prepared the dataset with UBIAI annotation tool, and experimented with various feature engineering and vectorization methods using Regex, NLTK, SpaCy, and Gensim. * Developed a model that classifies short phrases by training and evaluating the Recurrent Neural Network (RNN) models with Word2Vec embedding, Long-Short-Term-Memory (LSTM), and Conditional Random Field (CRF) layers using TensorFlow and Keras. Also fine-tuned a SpaCy NER Transformer model for performance evaluations. * Experimented with the effect of using Variational Autoencoder (VAE) generated instances in model training by injecting them into the original dataset.