Scholar
Isaac Chung
Google Scholar ID: AINXOBMAAAAJ
Zendesk
Citations & Impact
Citations
70
H-index
2
i10-index
1
Publications
8
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Paper: 'Maintaining MTEB: Towards Long Term Usability and Reproducibility of Embedding Benchmarks', ICML 2025 Workshop
- Paper: 'MIEB: Massive Image Embedding Benchmark', arXiv:2504.10471, 2025
- Paper: 'MMTEB: Massive Multilingual Text Embedding Benchmark', ICLR 2025
- Paper: 'Efficient In-Domain Question Answering for Resource-Constrained Environments', arXiv:2409.17648, 2024
- Paper: 'Beyond Toxic: Toxicity Detection Datasets are Not Enough for Brand Safety', arXiv:2303.15110, 2023
- Frequent speaker at PyCon, PyData, Berlin Buzzwords, etc., on topics including LLM production, RAG, and reproducibility in embedding benchmarks
- Core maintainer of MTEB (Massive Text Embedding Benchmark)
- Open-source contributor to haystack, transformers, llama-index, auto-sklearn
Background
- Focuses on making AI systems scalable and maintainable
- Currently a Staff Machine Learning Scientist at Zendesk
- Tech stack includes Python, Docker, Kubernetes, PostgreSQL, and Go
- Previously at Clarifai, led custom enterprise solutions for visual search and text moderation, built multi-modal retrieval systems, and conducted applied research to improve question-answering systems
- Has spoken at various Python and ML conferences and meetups across Europe
Miscellany
- Works remotely from Tallinn, Estonia
- Contributes to open-source projects such as MTEB in spare time
- Enjoys traveling and outdoor activities; formerly an active triathlete
- Currently into cycling, running, and hiking
- Maintains a technical blog on Generative AI/ML, designed for 3–5 minute reads
- Has written technical blogs for Clarifai and Neptune
Co-authors