Hoang Tran

Hoang Tran

Title: Senior Machine Learning Engineer
Company: Snorkel AI
Bio

Hoang Tran is a Senior Machine Learning Engineer at Snorkel AI, where he leverages his expertise to drive advancements in AI technologies. He also serves as a Lecturer at VietAI, sharing his knowledge and mentoring aspiring AI professionals. Previously, Hoang worked as an Artificial Intelligence Researcher at Fujitsu and co-founded Vizly, focusing on innovative AI solutions. He also contributed as a Machine Learning Engineer at Pictory.

Hoang holds a Bachelor’s degree in Computer Science from Minerva University, providing a solid foundation for his contributions to the field of artificial intelligence and machine learning.

Connect with Hoang to discuss AI research, machine learning projects, or opportunities in education and technology.

The latest from
Hoang Tran

Walking safely before building flying saucer seatbelts: introducing Enterprise Alignment

Snorkel takes a step on the path to enterprise superalignment with new data development workflows for enterprise alignment

How Snorkel topped the AlpacaEval leaderboard (and why we’re not there anymore)

Snorkel AI placed a model at the top of the AlpacaEval leaderboard. Here’s how we built it, and how it changed AlpacaEval’s metrics.

April 9, 2024

Scaling human preferences in AI: Snorkel’s programmatic approach

We’ve developed new approaches to scale human preferences and align LLM output to enterprise users’ expectations by magnifying SME impact.

January 31, 2024

How to fine-tune Llama 2 in Snorkel Flow

Data scientists can fine-tune Llama 2 to adapt it to specific tasks. The Snorkel Flow data development platform makes it easy to do so.

November 28, 2023

Enterprise LLM challenges and how to overcome them

Large language models open many new opportunities for data science teams, but enterprise LLM challenges persist—and customization is key.

November 16, 2023

How to fine-tune large language models for enterprise use cases

LLMs have a broad but shallow knowledge, but fall short on specialized tasks. For best performance, enterprises must fine tune their LLMs.

November 2, 2023
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