I am a Machine Learning Research Engineer at Genesis Therapeutics, focusing on generative modeling for drug discovery. My role integrates active involvement in ML research with a strong foundation in software engineering. I hold a Master’s degree in Computer Science from Stanford and a Bachelor’s degree in Computer Science from UC Berkeley.

Publications
• Daniel Zeng, Tailin Wu, Jure Leskovec. ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy. ICML 2022 Beyond Bayes (Reasoning) Workshop. arXiv.
• Qian Huang, Hongyu Ren, Peng Chen, Gregor Kržmanc, Daniel Zeng, Percy Liang, Jure Leskovec. PRODIGY: Enabling In-context Learning Over Graphs. NeurIPS 2023 Spotlight. arXiv.

Industry Experiences
• Genesis Therapeutics | Machine Learning Research Engineer (July 2023 - Present)
• Google Research | Research Intern (June 2022 - September 2022)
• Stripe | Software Engineer Intern (May 2020 - August 2020)
• Microsoft | Software Engineer Intern (May 2019 - August 2019)
• NASA (Ames Research Center) | Software Engineer/Research Intern (May 2018 - August 2018)

Experiences at Stanford University
• SNAP Group | Graduate Researcher (September 2021 - May 2023)
• CS 330 | Graduate Teaching Assistant (October 2022 - December 2022)

Experiences at UC Berkeley
• Yu Lab | Undergraduate Researcher (September 2020 - August 2021)
• AutoLab (Berkeley AI Research) | Undergraduate Researcher (February 2019 - February 2020)
• Cal Launchpad | Project Leader/Machine Learning Developer (September 2017 - May 2019)
• Upsilon Pi Epsilon, Nu Chapter | Vice President (December 2018 - December 2019)

If you have any inquiries or questions, feel free to reach me at my LinkedIn (above).