Chenggang Wu
Co-Founder & CTO at RunLLM
Berkeley, California, United States
Summary
🤩
Rockstar🎓
Top SchoolChenggang Wu is a PhD-trained computer scientist and entrepreneur with 10 years of experience building resilient distributed systems and developer-first tooling. As Co-Founder and CTO of RunLLM in Berkeley, he leads the creation of AI SRE agents that automate on-call triage, technical Q&A, and predictive log analysis to remove engineering toil. His background as a UC Berkeley researcher and instructor in databases informs a strong systems and data-management approach—he has contributed backend fixes and lattice-based consistency work to data-driven compute platforms. Comfortable shipping production server-side code and integrating storage systems, he blends academic rigor with startup velocity to move research ideas into practical incident-resolution products.
10 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at University of California, BerkeleyBachelor of Science (B.S.), Computer Science, GPA: 4.00, Bachelor of Science (B.S.), Computer Science, GPA: 4.00 at Brown University
English, Chinese, Japanese
Github Skills (13)
deserialization10
serializable10
c-language10
serializer10
cprogramming-language10
back-end-development10
serialization10
algorithms9
data-structure9
datastructures-algorithms9
algorithm9
data-structures9
zeromq7
Programming languages (11)
Github contributions (5)
hydro-project/fluent
Sep 2018 - Jul 2019
A data-driven compute platform
Role in this project:
Back-end Developer
Back-end DeveloperContributions:20 commits, 45 PRs, 31 pushes in 10 months
Contributions summary:Chenggang primarily contributed to the back-end functionality of the data-driven compute platform. Their work included fixing bugs related to data repartitioning, implementing set lattices, and enabling support for causal consistency through the introduction of a causal lattice. The user also worked on serializers for various lattice types (LWW, Set, Causal) and the integration with EBS storage. Furthermore, the user made changes to the core server logic and request handling components.
data-drivencompute
hydro-project/anna
Aug 2019 - Oct 2021
A low-latency, cloud-native KVS
Contributions:11 PRs, 7 pushes, 2 branches in 2 years 1 month
golanglatencyinfrastructurekubernetesdistributed-systems
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