Dr. Alex Liu | CEO, RMDS Lab, Data Science Thought Leader
- Dr. Liu is the CEO of RMDS Labs, which provides data and AI services.
- He is an advisor to the Harvard Data Science Review.
Podcast
Overview
Join us as we explore the remarkable journey of Dr. Alex Liu from a background in sociology to becoming a leading figure in data science and AI. Alex shares insights into the critical inflexion points in his career, his extensive experience working with prestigious institutions and corporations, and his commitment to fostering a global ecosystem of data science expertise. Tune in now to delve into the world of data science, innovation, and community-driven problem-solving with Alex, a visionary leader in the field.
[00:12] – About Dr. Alex Liu
- Dr. Liu is the CEO of RMDS Labs, which provides data and AI services.
- He is an advisor to the Harvard Data Science Review.
[13:36] – How did IBM influence your approach to data science and problem-solving approach?
- My tenure at IBM allowed me to work closely with C-level executives.
- It was an opportunity to understand the importance of communicating data-driven insights in business language.
- This experience led me to advocate for an ecosystem-based approach to data science projects.
[40:46] – Please explain the approaches you use for utilizing AI models like ChatGPT in projects.
- Fine-tuning involves customizing existing models to specific needs.
- Open-source development allows for more control and customisation, which is useful for research projects.
[47:47] – Why did you join Million Peacemakers, and how do you plan to contribute to their mission?
- I joined because of my interest in using AI for social good and peacemaking efforts.
- I hope to leverage my background and expertise in technology to support Million Peacemakers’ initiatives and contribute to their goals of using technology for peace.
RESOURCES:
You can connect with Dr. Alex Liu: LinkedIn
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Profile
- Dr. Liu is the CEO of RMDS Labs, which provides data and AI services.
- He is an advisor to the Harvard Data Science Review.
