# Jun J.

> Software Engineer, Machine Learning Researcher, PhD in CS

Location: New York, New York, United States
Profile: https://flows.cv/junj

## Work Experience
### Software Engineer - Machine Learning @ Arena
Jan 2024 – Present | New York City Metropolitan Area

### Machine Learning Researcher @ Santa Clara University
Jan 2021 – Jan 2024
-- Design occupancy detection system for residential building based on machine learning models.
-- Implement and fine-tune CNNs (Resnet50, FasterRCNN, YOLOv3) based on TensorFlow to identify and classify the collected image objects.
-- Design and implement four machine learning models (F1-score: Random Forest (0.98), Decision tree (0.93), SVM (0.87), KNN (0.93)) based on Scikit-learn to predict the occupancy status of residential buildings.
-- Implemented and fine-tuned the Bidirectional Encoder Representations from Transformers (BERT) model for document classification, leveraging its bidirectional contextual understanding for improved feature representation, resulting in a 11% increase in classification accuracy and ensuring the model's effectiveness in the target context.
-- Integrated the Generative Pre-trained Transformer (GPT) model into the Q/A chatbot architecture, leveraging its pre-trained capabilities to generate contextually relevant and accurate responses (question understanding and context-aware responses).
-- Build and deploy machine learning models and deep learning models in AWS/GCP, as well as dedicate to code maintenance and updates in open-source repositories (GitHub).
-- Develop and release via CI/CD and agile methodologies.

Project: Deep reinforcement learning model for user trading in double auction market
-- Propose an advanced deep reinforcement learning (DRL) model based on Deep Deterministic Policy Gradient to help users optimize their behaviors and obtain economic benefits in the market transactions.
-- Design neural networks by PyTorch to handle large number of high dimensional and continuous input data.
-- Fine-tune deep neural network based multi-agent reinforcement learning model (MARL), achieving an economic reward improvement of 24% to 49% compared to existing models.
-- Evaluate the performance of models and analyze feature importance to identify top factors.

### Data Scientist @ MMS
Jan 2021 – Jan 2021
-- Contributed to the design and development of user/item collaborative filtering algorithms and content filtering algorithm (two tower model) for recommendation system.
-- Conduct A/B testing and analysis to evaluate the performance of the recommendation system.
-- Collaborate with backend developers to integrate the recommendation system into the production environment.
-- Design privacy preserving models and Quantitative risk assessment models to de-identify joined data (including k-anonymity, Hashing, AES) from a variety of nonstandard data sources and/or from a set of documents only.

### Research Assistant @ Santa Clara University
Jan 2018 – Jan 2021 | Santa Clara County, California, United States
-- Design and develop efficient machine learning and deep learning models for large-scale data processing, detection system, recommendation system and modeling of user behaviors.
-- Develop attack models (blackhole attack, selective forward attack) and defense models (trust based defense model) in wireless network. 
-- Conduct experiments to evaluate model performance, including designing experiments to evaluate the performance of models, and then analyze and interpret the results to improve model performance.
-- Investigate and debug issues in designed systems and provide timely resolutions.
-- Conduct codebase review, identifying areas for improvement and performing code refactoring to enhance cleanliness and maintainability.

Project: Representation learning for text-based documents (NLP)
-- Preprocess the dataset: clean the dataset and visualize the statistics of the dataset; build vocabulary based on TF-IDF.
-- Train two models (LDA model and Doc2Vec model by Gensim) upon the vocabulary; visualize topics, topic distribution (as features) for each document, learned word and document embedding space by t-SNE and LdaVis.
-- Conduct document clustering by K-means with Topics distribution and Doc2Vec representations; analyze their performance by Normalized Mutual Information (NMI) score.

Project: Network Security: Selective forwarding attacks and defense scheme in wireless network
-- Design an advanced selective forwarding attack model, which can flexibly select the targeted victim node and the percentage of forwarding packets with low detection rate (0.2-0.5).
-- Develop a lightweight trust-based defense solution to detect and eliminate malicious selective forwarding and blackhole nodes from the network, achieving detection rate (0.8-0.95).

### Teaching Assistant @ San Jose State University
Jan 2016 – Jan 2018
Explain the experimental operating rules, teach experimental courses, grade assignments and guide students to complete the final circuit and PCB design.


## Education
### Doctor of Philosophy - PhD in Computer Science & Engineering
Santa Clara University
Jan 2018 – Jan 2023

### Master's degree in Electrical Engineering
San José State University
Jan 2016 – Jan 2018


## Contact & Social
- LinkedIn: https://linkedin.com/in/jun-j-007512126

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Source: https://flows.cv/junj
JSON Resume: https://flows.cv/junj/resume.json
Last updated: 2026-03-20