Charles Sun
Principal at BDT & MSD Partners
Los Angeles Metropolitan Area United States
Summary
👤
Senior🎓
Top SchoolCharles Sun is a Principal at BDT & MSD Partners with nine years of investment experience focused on middle-market and healthcare/technology companies, having advanced through roles at Pritzker Private Capital and Alpine Investors. He blends a technical foundation—a B.S. in Computer Science from UC Berkeley—with private equity deal execution, bringing a rare combination of engineering fluency and investment judgment to sourcing and operating portfolio businesses. Based in Los Angeles, he has led transactions and portfolio work across manufactured products, services, and healthcare, emphasizing long-term, founder-friendly partnerships. Beyond finance, Charles contributes to open-source AI tooling—notably enhancements to Ray’s RLlib—demonstrating hands-on expertise in reinforcement learning algorithms and offline dataset tooling. That mix of practical ML engineering experience and investment leadership helps him evaluate tech-enabled business models with both product sensibility and operational rigor. Colleagues describe him as a pragmatic thinker who moves fluid technical concepts into investable, scalable strategies.
9 years of coding experience
7 years of employment as a software developer
Bachelor's Degree Electrical Engineering and Computer Science, Bachelor's Degree Electrical Engineering and Computer Science at University of California, Berkeley
Github Skills (13)
pytorch10
machine-learning10
ray10
deeplearning-ai10
deep-learning10
python10
reinforcement-learning10
algorithms8
data-structures8
algorithm8
data-structure8
data-science8
tensorflow6
Programming languages (3)
Github contributions (5)
ray-project/ray
Jun 2022 - Aug 2022
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
ML Engineer
ML EngineerContributions:60 reviews, 6 commits, 8 PRs in 1 month
Contributions summary:Charles contributed to the RLlib library within the Ray project, specifically focusing on algorithms and related components. Their commits included migrating DDPG (Deep Deterministic Policy Gradient) to PolicyV2, which involves updating the policy architecture. Furthermore, the user added a Segmentation Buffer for Decision Transformer (DT) and enhanced the codebase by integrating a DTTorchModel and a DTTorchPolicy, indicating a focus on advanced reinforcement learning methods. The user also refactored dataset readers, incorporating normalization, and added testing for offline datasets to the rollout worker.
pythonconsistsruntimetensorflowserving
charlesjsun/ray
Jun 2022 - Aug 2022
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
Contributions:70 pushes, 8 branches in 1 month
apirayscalabledistributed-applicationshyperparameter
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