K. N. Tucker

San Francisco · Bay Area · k.nathaniel.tucker@gmail.com

I am the founder of StartPlaying.Games a site that helps you book amazing gaming experiences hosted by professional game masters.

I was a Computer Science Concentrator at Harvard University in the School of Engineering and Applied Sciences where I received my A.B. and S.M. in Computer Science. While there I ran the Harvard College's first club on quantitative finance and research. Professionally I was an AI Researcher at Vicarious and CTO of Nebula Genomics. Previously, I worked at Jane Street Capital, a hedge fund in New York. I also worked at Goldman Sachs , Facebook (where I developed a product that made the team 38 million dollars per annum), Microsoft, and Google. While in school, I worked part-time for a few startups (like Kensho) and have devoted some time to starting my own. You can read more about that in the projects section. My research in school focused primarily on theoretical computer science. However I have always had a great interest in biology and economics, and will soon have a paper published there as well. Please see my research for more details. I am an avid reader and learner. I teach part time at General Assembly and am developing open source teaching material for data science and machine learning . I currently spend most of my time working at Vicarious, creating educational content for my YouTube Channel and doing data science consulting.

I do data science consulting, education, and strategy work as well, so if you have an interesting project, don't hesitate to reach out. Or if you are just interested in collaborating, check out my github collaboration page to check out my active projects and reach out.


YouTube Channel

All sorts of goodies from machine learning to data science with a bit of a focus on deep learning, but with all the goodness in between. I strongly urge you to just check it out.


Bootstrapping (or perhaps better named: computational statistical inference in python). I am building out a bootstrapping library for data scientists. Very early stages. And actively looking for collaborators.

Deep learning paper list

A list of deep learning papers that I have read with notes.

See More

I just wanted to highlight the above. This contains the full list, along with my github collaboration page.


The relationship between oxidative stress, reproduction, and survival in a bdelloid rotifer

Leigh C. Latta IV, K. Nathaniel Tucker & Robert A. Haney February 2019.

Modifying SIPS For A Bounded Loss Prediction Market On The Real Line

Perry A. Green, K. Nathaniel Tucker. December 2012.

Efficiency In Two-Candidate Elections With Differential Privacy

Perry A. Green, K. Nathaniel Tucker, Matthew Warshauer. May 2013.

Cooperative Multi-agent Path Planning Under Strict Real-time Constraints

Svilen Kanev, K. Nathaniel Tucker. May 2013.

South Africa Energy Market

K. Nathaniel Tucker. September 2013.

A Quantitative Approach to Finance

Chris Dantzlerward, Sam Meyer, K. Nathaniel Tucker. December 2013.

Approximate Stochastic Constraint Solvers over Watts-Strogatz Network Topologies

K. Nathaniel Tucker. December 2013.

Empirical Analysis of Randomized Leader Elections

K. Nathaniel Tucker. May 2015.

Examination of Data Based Concurrency Bug Detection

Jefferson Lee, K. Nathaniel Tucker. May 2015.