John Dang

John Dang

AI Researcher

Cohere For AI

UCLA

I’m a Research Scholar at Cohere on the Cohere For AI team working on LLM post-training research (RLHF, Multilinguality, Multimodality) advised by Ahmet Üstün and Sara Hooker. I recently graduated with my MS and BS in computer science from UCLA where I was a machine learning researcher working on LLM alignment at the UCLA Machine Intelligence Group (MINT) advised by Professor Aditya Grover.

Previously, I’ve spent time at Motional (ML for autonomous driving), Skydio (ML for autonomous flight), and Amazon Web Services (Software Engineering for EC2). While I was an undergraduate student at UCLA, I worked on computer vision and VR for robotics research at the UCLA Center for Vision, Cognition, Learning, and Autonomy and ML for disease diagnosis at the Ozcan Research Group.

I care deeply about improving accessibility in STEM education. While I was a student at UCLA, I served president as of ACM AI at UCLA, the largest AI student organization at UCLA dedicated to developing a community of students interested in AI at UCLA and beyond through free workshops, events and other outreach initiatives. I currently serve as an advisor. I’ve also served as a TA for multiple UCLA undergraduate introductory CS courses (CS 31/33). I also love playing/writing/producing music (vocals, guitar, piano), working out, and trying new food!

Interests
  • Machine Learning
  • Foundation Models / LLMs
  • AI Alignment
Education
  • MS in Computer Science, Dec 2023

    University of California, Los Angeles (UCLA)

  • BS in Computer Science, Mar 2022

    University of California, Los Angeles (UCLA)

Papers

(2024). Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier. Technical Report 2024.

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(2024). RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs . EMNLP 2024 (Oral).

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(2024). Aya 23: Open Weight Releases to Further Multilingual Progress. Technical Report 2024.

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(2024). Group Preference Optimization: Few-Shot Alignment of Large Language Models. ICLR 2024.

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