Daniel Preda is a software engineer and machine learning specialist with a PhD in Computer Science from UC Berkeley and over a decade of experience building and optimizing large-scale ML systems for advertising and real-time bidding. He has driven model training, feature engineering, optimization, and A/B testing at Twitter and led cross-functional ML efforts at startups and scale-ups like CrossInstall and Quantcast, where he advanced from individual contributor to team lead and filed a patent. Comfortable in production engineering, he has focused on high-throughput bidder systems and practical solutions that directly impact revenue and user experience. An active backend contributor to open-source projects such as the Red-DiscordBot, he blends rigorous academic training with hands-on coding, documentation, and i18n improvements. Based in the San Francisco Bay Area, Daniel is known for turning complex research into deployable systems and for thriving at the intersection of data science and software engineering.
7 years of coding experience
11 years of employment as a software developer
Bachelor of Science (B.Sc.), Physics, Bachelor of Science (B.Sc.), Physics at MIT
PhD, Computer Science, PhD, Computer Science at UC Berkeley
Contributions:26 reviews, 52 commits, 78 PRs in 3 years 11 months
Contributions summary:Daniel primarily contributed to the core functionality of the Red Discord Bot, making various improvements and fixing bugs related to command handling, audio features, and i18n support. They implemented changes to core commands, help formatting, and event handling, ensuring message deletion and cooldown messages were correctly managed. Furthermore, they focused on refining audio commands and incorporating enhancements to improve the bot's overall functionality and user experience. Additionally, the user updated documentation and fixed missing internationalization strings throughout the codebase.
Contributions:3 reviews, 4 PRs, 24 pushes in 1 year 9 months
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