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.
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]
A Deep Belief Network Approach to Learning Depth from Optical Flow (May 2015)
Reuben Feinman
Sc.B. Honors thesis, Brown University
Providing Adversarial Perturbations to Media (Feb 2020)
Saurabh Shintre, Reuben Feinman
US No. 10,542,034
Systems and Methods for Trichotomous Malware Classification (Jul 2019)
Reuben Feinman, Javier Echauz, Andrew B. Gardner
US No. 10,366,233
Systems and Methods for Detecting Malware Based on Event Dependencies (May 2019)
Jugal Parikh, Reuben Feinman
US No. 10,282,546
Systems and Methods for Detecting Malware (Nov 2018)
Reuben Feinman, Jugal Parikh
US No. 10,133,865
Developing the next generation of 3D creation engines with AI.
Completed doctoral dissertation at NYU's Center for Neural Science, advised by Brenden Lake.
Worked with chief AI scientist Yann LeCun investigating self-supervised learning algorithms for computer vision applications.
Worked as a full-time research engineer at Symantec's Center for Advanced Machine Learning, a newly chartered R&D lab with a focus on state-of-the-art pattern recognition technologies.
Conducted undergraduate research in the Serre Lab at Brown under supervision of Thomas Serre and Stuart Geman.
Completed dissertation titled "Generative neuro-symbolic models of concept learning," advised by Brenden Lake.
Graduated with Honors. GPA: 3.9/4.0.
NYU Neuroscience department presentation
[Slides]
Oct 20, 2020
NYU Computational Cognitive Science group meeting.
[Slides]
Feb 27, 2019
Cambridge, MA
reuben@csm.ai
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