Hi! I’m Ivan Zhou

I am a research engineer at Databricks Mosaic. My primary focus is building high performing AI systems to solve real-world challenges and create values.

Previously, I have:

Most Recent Work

  • 2024-Present: Research Engineer, Databricks Mosaic

  • 2023-2024: Machine Learning Engineer, Uber

  • 2018-2023: Machine Learning Engineer, Landing AI

Education

  • MS Computer Science, Stanford University

  • BS Engineering, University of Toronto

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Featured
Shifting the Pareto Frontier of Enterprise AI with Automated Prompt Optimization
Machine Learning
Shifting the Pareto Frontier of Enterprise AI with Automated Prompt Optimization
Machine Learning

This week I published a research blog post with Databricks on how we shift the Pareto frontier of enterprise agents using automated prompt optimization.

Machine Learning
Joining Databricks Mosaic
Personal Growth
Joining Databricks Mosaic
Personal Growth

I am thrilled to be joining the Databricks Mosaic team to work on applied AI research. This is a fantastic opportunity for me to continue pushing the boundaries of AI advancement and contribute to building a successful AI platform for enterprises.

Personal Growth
From Vienna to Paris: A Collection of My Favorite Travel Photos in Europe
Journal
From Vienna to Paris: A Collection of My Favorite Travel Photos in Europe
Journal

My girlfriend and I recently traveled to Europe for a conference as well as a vacation. We had a great time walking and exploring each of the cities that we visited. In this post, I am sharing my favorite photos captured in each city.

Journal
Featured
Reading Note on gpt-oss - a new milestone for open-weight models

gpt-oss-120B marks a milestone for open-weight models. It delivers a frontier-level reasoning performance and is expected to receive mainstream adoption. Most likely, it will serve as the new baseline for benchmarking and the de facto production choice. I recommend it for workloads that demand top intelligence and high serving throughput.

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Reading Note on SimpleQA - Build High Quality Benchmark Dataset with LLM + Human

The OpenAI team built a new benchmark dataset called SimpleQA that evaluates large language models' (LLMs) ability to answer factual questions. A particularly intriguing aspect of this paper is, in this era of LLMs, how the team of researchers leverages LLMs in their own workflow to design, iterate, and analyze a new dataset.

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Reading Note on Thought Preference Optimization (TPO)

Thought Preference Optimization (TPO): Prompt the model to generate a thought process followed by the response. TPO demonstrates significant performance gains on non-reasoning categories, including translation, marketing, and health; reasoning categories like math and analysis also show improvements.

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Contact

You can contact me at mail@ivanzhou.me