Cheng-en Wu
Research Scientist at Accenture
Madison, Wisconsin, United States
Summary
👤
Senior🎓
Top SchoolCheng-en Wu is a Research Scientist at Accenture’s Center for Advanced AI with eight years of experience focused on post-training for large language models and efficiency improvements in self-supervised learning. He completed a PhD at the University of Wisconsin–Madison studying training and inference efficiency for multimodal models and has interned at Microsoft, TikTok, and NEC Labs working on visual grounding, Code LLMs, prompt tuning, and video representation learning. At Accenture he developed an LLM post-training framework for the AI Refinery platform and also builds image and video generation models, bridging cutting-edge research with deployable tooling. Based in Madison, WI, he combines deep academic rigor with practical industry impact, often optimizing model pipelines in ways that materially reduce compute cost without sacrificing multimodal capabilities.
8 years of coding experience
Doctor of Philosophy - PhD Electrical and Computer Engineering, Doctor of Philosophy - PhD Electrical and Computer Engineering at University of Wisconsin-Madison
Github Skills (15)
classification-model10
multi-task-learning10
emotion-recognition10
face-recognition10
pytorch9
deep-neural-networks7
artificial-intelligence3
prompt-tuning3
computer-vision3
object-detection3
semantic-segmentation3
language-model2
tensorflow2
transformer2
inference1
Programming languages (1)
Github contributions (5)
ivclab/Merging-MobileNets-for-Multitask
Mar 2019 - Aug 2019
Contributions:31 commits, 32 pushes in 4 months
ivclab/CPG
Sep 2019 - Jan 2020
Steven C. Y. Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen, "Compacting, Picking and Growing for Unforgetting Continual Learning," Thirty-third Conference on Neural Information Processing Systems, NeurIPS 2019
Contributions:104 commits, 98 pushes, 1 comment in 3 months
deep-neural-networkscontinual-learningpytorchlifelong-learningclassification-model
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