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Lawrence Wu

Principal Data Scientist @ UKG

Lawrence Wu is a Principal Data Scientist at UKG (Ultimate Kronos Group) with extensive experience in data science, machine learning, and business analytics.12

Professional Background

Lawrence leads a team of data scientists and engineers at UKG.2 Prior to his current role, he has held data science positions at several prominent companies:

  • Medidata
  • PIMCO
  • Payoff (now Happy Money)
  • Allianz36

Education and Expertise

Lawrence holds a degree from the Georgia Institute of Technology.2 He is known for his expertise in:

  • Data analysis and science
  • Machine learning
  • Business analytics
  • Large Language Models (LLMs)4

Online Presence

Lawrence maintains an active online presence:

  • LinkedIn: His profile can be found under the username lawrencewu9202
  • GitHub: He shares projects and code under the username lawwu5
  • Twitter: He tweets using the handle @law_wu6
  • Personal Website: Lawrence maintains a personal blog at lawwu.github.io3

Additional Information

Lawrence is AWS certified in LLMs and has been working in the data analysis field for over 17 years.7 He is also known to be involved in discussions and projects related to data analysis/science agents and code interpreters.4

Highlights

Jul 9 · linkedin

My heart goes out to all those that were affected by the #UKG layoffs last week. 14% of the company or 2,200 out of 15,000 was let go. Many of my teammates and friends were let go. I have been a part of companies that have had larger percentage layoffs (>30%). However, the sheer number of people that were affected is somewhat shocking. Considering the number of lives, families, and children that were disrupted is difficult to think about.

If you were affected, I would be happy to help: making intros to any of my connections on LinkedIn, resume review or even just to talk.

I also wrote a short post compiling a list of to-dos after getting laid off. Hopefully it's helpful:

Jun 24 · linkedin

Introducing TL;DL (Too Long Didn't Listen), a simple application that helps you engage with long-form podcasts more efficiently.

As data scientists, Bryan Travis Smith - Ph.D / MBA and I often struggle to find the time to listen to multiple hours-long podcasts each week. To address this challenge, we developed TL;DL to leverage LLM summarization and provide us with concise summaries of these podcasts.

The application automatically emails us summaries of new podcast episodes once their transcripts become available. This allows us to stay informed about the latest content without having to dedicate hours to listening to podcasts. And for those particularly lengthy episodes, like Lex Friedman's latest 4-hour podcast, TL;DL provides a convenient solution.

In addition to email summaries, TL;DL also features a simple RAG (Retrieval Augmented Generation) application. We have vectorized over 1,000 podcasts, enabling us to ask questions and receive relevant answers based on the podcasts' content. While the current version lacks time and entity filters, it still surfaces interesting and informative results.

For now, TL;DL offers two primary functions: subscription to email summaries of podcasts shortly after their release and RAG Q&A across podcasts.

We acknowledge that the UI may require some improvement, but we hope that you find TL;DL as useful as we do. Our goal is to enhance its functionality and expand the content library over time.

Give TL;DL a try at https://tldl.dev and let us know what you think!

Related Questions

What are some of Lawrence Wu's notable projects at UKG?
How did Lawrence Wu transition from Medidata to UKG?
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What courses or certifications has Lawrence Wu completed?
Lawrence Wu
Lawrence Wu, photo 1
Lawrence Wu, photo 2
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Location

Irvine, California, United States