Jiaqi Zeng
Senior Applied Scientist at NVIDIA
California, United States
Summary
🤩
Rockstar🎓
Top SchoolJiaqi Zeng is a Senior Applied Scientist at NVIDIA with 7 years of experience building and productionizing efficient deep learning solutions for large language models and graph-based tasks. Trained at Carnegie Mellon, Jiaqi has practical expertise in parameter-efficient training, model alignment, and distributed training across hundreds of GPUs, having reduced storage costs by 99.9% for 3B-parameter models and productionized prompt- and p-tuning for thousands of conversational AI users. Her research background spans contrastive self-supervised learning, continual graph learning, and causal approaches for information extraction, with publications at AAAI and EMNLP and a hand in creating a 1.1M-conversation medical dialogue dataset. Based in California, she blends strong engineering rigor—CI/CD, Docker, Slurm, DDP, Apex—with academic depth to move cutting-edge ML methods into production-scale systems.
7 years of coding experience
2 years of employment as a software developer
Master's degree, Machine learning, Master's degree, Machine learning at Carnegie Mellon University School of Computer ScienceBachelor of Engineering - BE, Computer Science and Technology, Bachelor of Engineering - BE, Computer Science and Technology at Shanghai Jiao Tong University
Github Skills (30)
machine-translation10
hub10
rl-environment10
text-to-speech10
speech-synthesis10
multimodal10
large-language-models10
reinforcement-learning-environments10
automatic-speech-recognition10
speech10
generative-ai10
speaker-recognition10
asr10
ai10
api-client9
Programming languages (2)
Github contributions (5)
HeyyyyyyG/CFIE
Sep 2021 - Jan 2022
PyTorch implementation for our proposed CFIE in EMNLP 2021 paper "Uncovering Main Causalities for Long-tailed Information Extraction".
Contributions:23 commits, 21 pushes, 4 branches in 3 months
information-extractionpytorchnatural-language-processingmachine-learningcounterfactual
HeyyyyyyG/Multi-souce-Separation-and-Alignment-Network-for-EEG-Emotion-Classification
May 2020 - Nov 2020
Transfer learning for multi source EEG-emotion-classification
Contributions:9 commits, 8 pushes, 1 branch in 6 months
classificationtransfer-learningmulti-source-domain-adaptationeeg-classificationemotion-classification
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