Hello! This is the website of Adam Lesnikowski. I am very interested in machine learning, statistical learning theory, convolutional neural networks and AI, especially applied to computer vision and other web scale data problems.
As of January 2017, I've joined Nvidia in Santa Clara, CA as a senior software perception engineer with the AI infrastructure group. I'm extremely excited to join the brilliant and ambitious group on the team!
Here's my resume.
Predicting Prices for House Shares using Deep Convolutional Neural Networks, with Rong Yuan, Genevieve Patterson, 2016.
How Much Did it Rain?: Predicting Real Rainfall Totals Based on Polarimetric Radar Data, paper based on a final project for UC Berkeley CS 289, April-May 2015, with Peter Bartlett and Alexei Efros, Machine Learning graduate course.
NP-Completeness Papers by Cook, Levin and Karp, P =?NP, and a Lost Letter, slides, with Justine Sherry, UC Berkeley CS 294, March-April 2014, article grad seminar.
A Geometric Interpretation of the Metaphysics of the Tractatus, paper, based on work done with Professor Paolo Mancosu, November-December 2014, in UC Berkeley Philosophy 290 Wittgenstein Seminar.
Inner Model Representations for Strong Cardinals: Set-Theoretic Universes Constructed Relative to Set-Sized Objects, paper draft, inner models and large cardinals, work done at UvA.
From 2012-16 I was working on a Ph.D. from UC Berkeley. I attended the Masters of Logic Program at the ILLC at the University of Amsterdam from 2009 to 2011, where I had a wonderful time studying logic and set theory. I also graduated with an honors AB in Philosophy and Mathematics from Harvard University in 2009, where I had the great fortune of working with and writing a senior thesis with Professor Warren Goldfarb, on formalizing the notion of a mathematical interpretation and on the logic of interpretability. When I can, I still very much enjoy reading papers in and thinking about computability theory, formal methods in philosophy, set theory and logic in general.