Jia Gu
Web Project Coordinator & Global Communication Specialist
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolJia Gu is a bilingual Web Project Coordinator and Global Communication Specialist with 8 years of cross-functional experience in marketing, digital strategy, and project management, currently supporting Schneider Electric’s first B2C web experience. A first-generation Latina who moved from Guatemala to Boston to study, she blends resilience and cultural fluency with hands-on skills in content localization, Builder.io operations, and creation of governance playbooks that scale across markets. Her background spans leadership roles in university marketing organizations, UX-focused graphic design, and technical work in ML—contributing TensorFlow-based EEG deep learning models—demonstrating an unusual mix of creative communication and data-driven engineering. Known for proactive onboarding resources and streamlined deployments, she pairs storytelling and analytics to drive measurable engagement while mentoring peers across global teams.
8 years of coding experience
3 years of employment as a software developer
Bachelor of Science - BS, Business Administration and Management, General, Bachelor of Science - BS, Business Administration and Management, General at Boston University
Spanish, English, Chinese
Stackoverflow
11reputation
224reached
1answer
1question
Github Skills (17)
transformers10
python10
machine-learning10
data-preprocessing10
mask-rcnn10
deep-learning10
tensorflow10
faster-rcnn10
data-science9
scikit9
scikit-learn9
rnn-model8
n8
lstm7
huggingface-transformers6
Programming languages (13)
Github contributions (5)
SuperBruceJia/EEG-DL
Jul 2020 - Jan 2023
A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
Role in this project:
ML Engineer / Data Scientist
ML Engineer / Data ScientistContributions:107 commits, 296 pushes, 1 branch in 2 years 6 months
Contributions summary:Jia primarily focused on developing and refining machine learning models for EEG signal classification. Their contributions included data preprocessing, model architecture design (CNN, Transformer), and hyperparameter tuning. They implemented data loading and preparation pipelines, demonstrating expertise in handling EEG data and building models using TensorFlow. The user's work directly aligns with the repository's purpose of EEG signal processing and classification using deep learning.
tensorflowclassificationrnntransformersone-shot-learning
SuperBruceJia/pytorch-flask-deploy-webapp
Sep 2020 - Jan 2023
Contributions:45 commits, 44 pushes, 1 branch in 2 years 5 months
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