Hi! I’m Ivan Zhou

I’m a Senior Machine Learning Engineer at Uber’s Applied AI team. My primary focus is to build large scale foundation models to solve problems and create values. I’m most interested in applied AI research and engineering on multimodality. I have built AI systems that achieved extremely high performance, as well as distributed training/inference infrastructure from scratch.

I have been working at Stanford CRFM to train and evaluate large scale foundation models.

Previously, I have worked with Andrew Ng to initiate and develop the Data-Centric AI campaign and NeurIPS 2021 workshop. I built the state-of-the-art MLOps platform LandingLens from day-one.

Most Recent Work

[April 2023 - Present] Sr. Machine Learning Engineer, Uber

[Nov 2018 - April 2023] Sr. Machine Learning Engineer, Landing AI

Education

MS Computer Science, Stanford University

BS Engineering, University of Toronto

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Featured
My Ray Summit Talk - Building Multi-Modal Foundation Models for Document Automation
Machine Learning
My Ray Summit Talk - Building Multi-Modal Foundation Models for Document Automation
Machine Learning

I gave a presentation at the Ray Summit on my work building Multimodal Foundation Models for Document Automation at Uber. It is always a great pleasure to publicly share what I have been building over the past year!

Machine Learning
Release of Levanter 1.0
Machine Learning
Release of Levanter 1.0
Machine Learning

Today at Stanford, we released Levanter, a Jax-based framework for training foundation models. It is now open-source on Github under Apache 2.0.

Machine Learning
Things I Learned at Landing AI
Personal Growth
Things I Learned at Landing AI
Personal Growth

Over the past four and a half years at Landing AI, I have had the incredible opportunity to work with Andrew Ng, Dillon Laird and other amazing people to build AI applications across various industries. Each project has brought its unique challenges, pushing me to dive deeper into the ever-evolving world of AI. As I look back at this enriching journey, I am grateful and humble to share the lessons that I've learned in the hope of inspire others in the field.

Personal Growth
Featured
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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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