Yucheng Yin
Senior Software Engineer at Conviva
Pittsburgh, Pennsylvania, United States
Summary
👤
Senior🎓
Top SchoolYucheng Yin is a Senior Software Engineer and PhD candidate at Carnegie Mellon University with nine years of experience building systems at the intersection of networking, security, and machine learning. His research applies deep generative models to practical problems like synthetic network trace generation, yielding publications at SIGCOMM, USENIX Security, and NDSS. He pairs academic rigor with industry practice—now at Conviva after research stints at Bosch Research and the University of Michigan—shipping production-grade software informed by cutting-edge research. Yucheng is adept at turning complex network and security data into usable tools and datasets, and he brings a track record of reproducible, publication-quality engineering to product teams. Based in Pittsburgh, he is actively exploring industry roles that bridge ML-driven systems research and scalable software engineering.
9 years of coding experience
1 year of employment as a software developer
Computer Science, Computer Science at University of MichiganDoctor of Philosophy - PhD Electrical and Computer Engineering, Doctor of Philosophy - PhD Electrical and Computer Engineering at Carnegie Mellon UniversityBachelor's degree Electrical and Computer Engineering, Bachelor's degree Electrical and Computer Engineering at Shanghai Jiao Tong University
Github Skills (53)
transformers10
pytorch10
language-model10
python10
machine-learning10
synthetic10
generative-adversarial-network10
seq2seq10
flax10
netflow10
deep-learning10
differential-privacy10
tensorflow10
natural-language-processing10
bert10
Programming languages (4)
Github contributions (5)
netsharecmu/NetShare
Jun 2022 - Jan 2023
(SIGCOMM '22) Practical GAN-based Synthetic IP Header Trace Generation using NetShare
Contributions:17 reviews, 95 commits, 24 PRs in 7 months
autoencoderspracticalsyntheticsynthetic-dataset-generationtensorflow
netsharecmu/SDMetrics_timeseries
Jan 2023 - Jul 2023
Metrics to evaluate quality and efficacy of synthetic datasets.
Contributions:4 PRs, 79 pushes, 1 branch in 6 months
evaluateefficacysyntheticdatasetsmetrics
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