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Rongyao Huang

Senior Data Scientist - NLP at CB Insights

Rongyao Huang is a data scientist specializing in Natural Language Processing (NLP) at CB Insights.1 With a background in statistics and social science, Huang has extensive experience in the field of data science and machine learning.1

Professional Experience

Huang joined CB Insights as a Data Scientist - NLP in August 2018 and worked there until December 2019.1 Prior to this role, he held positions at various companies:

  • Senior Data Scientist at Verve (May 2018 - August 2018)
  • Data Scientist at Receptiv (April 2017 - May 2018)
  • Data Scientist at IgnitionOne (April 2015 - April 2017)

Education

Huang's educational background includes:

  • Master's Degree in Research Methodology and Quantitative Methods from Columbia University (2013-2014)
  • Bachelor's Degree in Economics & English Literature from Zhejiang University (2009-2013)
  • Academic Exchange Program in Statistics at Uppsala universitet (2011)

Skills and Expertise

Throughout his career, Huang has worked on various projects involving:

  • NLP systems for URL classification and user interest discovery
  • Machine learning pipelines for ad conversion prediction
  • Experimental design and analysis for ad product effectiveness
  • Big data processing using Spark and Scala

Current Work

As of 2023, Huang is a Lead Data Scientist at CB Insights, where he focuses on applying generative AI to build better data products.2 He has been working on leveraging large language models (LLMs) to process millions of signals monthly, aiming to provide better insights for tech decision-makers and researchers.2

Highlights

Mar 31 · linkedin

It was a captivating experience speaking at MLconf 2024 (https://mlconf.com/) this week. The best part for me is meeting curious minds across industries. Thank you, Richard Rivera, for spearheading this event since 2012!

Several speakers discussed LLM-related topics, some closing thoughts from me:

The past year witnessed the commercialization of large language models (LLMs) taking flight, ecosystems flourishing, and open source playing catch-up. The immediate next phase will see agentic AI products proliferate, from narrow domains to open-ended assistants — travel advisors, financial consultants, tutors, and personal aides.

However, without fundamental AI research breakthroughs, dividends will wane. I’ll be closely monitoring the following developments:

True Reasoning A breakthrough enabling true system 2 thinking in AI remains elusive—the biggest hurdle to artificial general intelligence (AGI). Reasoning, planning, and abstraction may not simply emerge from scale.

Efficiency: Energy is a zero-sum game, so will the future of intelligence be unless efficiency drastically improves.

As fellow speaker Andy Xu highlighted, current electricity and water consumption for supermodel inference approximates a mid-sized city—unsustainable. While nuclear fusion could quadruple energy supply, it does not replace the urgent need for AI efficiency.

Outpacing Moore's law for hardware is one challenge; developing better (perhaps entirely different) software is another. A single first-principles reasoning surpasses a million examples. While we continue to increase the compression ratio for knowledge representation, the ultimate efficiency might come from novel reasoning mechanisms.

Jun 22 · linkedin

A great course to catch up on the latest neural net advancements in NLP and be head/hands down in some math/pytorch fundamentals. A pat on the back for myself during this special time. Life long learning continues.

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Rongyao Huang
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Location

New York, New York, United States