My name is Reuben. I am currently working at Common Sense Machines, a Cambridge-based AI company focused on 3D content creation. Previously, I completed my Ph.D. in the Human & Machine Learning Lab at NYU, funded by a Google Ph.D. Fellowship in Computational Neuroscience. With my thesis advisor, Brenden Lake, I developed a new computational framework to model the structure of human conceptual knowledge, emphasizing everyday concepts like animals, vehicles, and handwritten characters. By reconciling key modeling ingredients from deep neural networks and structured Bayesian models, my thesis helps provide a comprehensive account for the flexibility of human concepts. While at NYU, I completed an internship at Facebook AI Research (FAIR) where I worked directly under chief AI scientist Yann LeCun developing self-supervised learning algorithms.

Before NYU, I earned my Sc.B. in Applied Mathematics at Brown University, where I completed my thesis in computational vision with Thomas Serre and Stuart Geman. Following undergrad, I worked at Symantec’s Center for Advanced Machine Learning in Mountain View, CA as a full-time research engineer for two years. There, I helped found a new paradigm for the company’s threat detection technologies based on artificial intelligence, filing 8 U.S. patents and improving the detection of malicious software on over 100 million customer machines worldwide.

For more information about me, including my education and work experience, see my background page.

Publications

• Compositional Diversity in Visual Concept Learning (Jan 2024)
  Yanli Zhou, Reuben Feinman, Brenden M. Lake
  Cognition, 244, 105711

• Generative Neuro-Symbolic Models of Concept Learning (Sep 2023)
  Reuben Feinman
  Ph.D. thesis, New York University

• Learning Task-General Representations with Generative Neuro-Symbolic Modeling (Jun 2020)
  Reuben Feinman, Brenden M. Lake
  International Conference on Learning Representations (ICLR 2021)
  Code

• Generating New Concepts with Hybrid Neuro-Symbolic Models (Mar 2020)
  Reuben Feinman, Brenden M. Lake
  In Proceedings of the 42nd Annual Conference of the Cognitive Science Society

• A Linear Systems Theory of Normalizing Flows (Feb 2020)
  Reuben Feinman, Nikhil Parthasarathy
  arXiv preprint arXiv:1907.06496
  Code

• Learning a Smooth Kernel Regularizer for Convolutional Neural Networks (Mar 2019)
  Reuben Feinman, Brenden M. Lake
  In Proceedings of the 41st Annual Conference of the Cognitive Science Society
  Code

• Learning Inductive Biases with Simple Neural Networks (Feb 2018)
  Reuben Feinman, Brenden M. Lake
  In Proceedings of the 40th Annual Conference of the Cognitive Science Society
  Code Poster

• Detecting Adversarial Samples from Artifacts (Mar 2017)
  Reuben Feinman, Ryan R. Curtin, Saurabh Shintre, Andrew B. Gardner
  arXiv preprint arXiv:1703.00410
  Code

• Cleverhans v1.0.0: An Adversarial Machine Learning Library (Dec 2016)
  Nicolas Papernot, Ian Goodfellow, Ryan Sheatsley, Reuben Feinman, Patrick McDaniel
  arXiv preprint arXiv:1610.00768
  Code