Lee Tanenbaum
Machine Learning Scientist by career and by passion,
Delighted to Educate.
I have been building Machine Learning systems since 2011. Now that Data Science has become such a hot term, I find myself teaching individuals and groups in different settings and from different backgrounds.
I've built a blog(medium.com/@leetandata) and this website to teach ML and describe what Machine Learning does for companies. Different posts are curated for a non-technical business user trying to understand ML, a mathematician trying to become a data scientist, and a software engineer trying to learn ML.
About Me
SHort BIo
Lee grew up in California, studied EECS at Berkeley, and worked as a software engineer building first-gen ML systems for medical devices to start his career. As the FDA cracked down and development slowed, Lee got a graduate certificate from Stanford part time and built ML systems for startups.
Moving to NYC and moving back to academia, Lee got a Masters from NYU Center for Data Science while leading ML teams in Manhattan. This gave Lee leadership experience, opportunities to teach
Please reach out for any of the following:
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You represent a conference and would like to invite me to speak.
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You're an academic and you'd like me to collaborate with you on research.
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You're an aspiring data scientist and would like guidance.
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You're a data scientist, software engineer, or data engineer, and would like to apply to work on my team.
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You need help on a machine learning innovation project and would like to hire me for some part time/contract work.
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You need a member for a pickup soccer game in Manhattan
For the Full CV/Resume, email me!
current Activities
Learning
I devote a lot of time to personal learning. I take a class at a time to stay involved in academia. Reading and implementing new research ideas is important to follow modern ML.
Academic Teaching
I TA for Deep Learning and Intro to Data Science at NYU. I also work with professors on research projects in NLP, math, and economics.
Consulting
I organize a Machine Learning/Software engineering group where we pick new research topics to discuss and implement using good software engineering principals to ensure we thoroughly understand modern ML research.
We went through UCB cs294 Deep RL, built a DNC, experimented with RL in pysc2, and build all you need is attention-style transformers
Reach out if you'd like to join!