Intro
I am a Research Associate at the MIT Department of Brain and Cognitive Sciences. I recently received my master of engineering from MIT in artificial intelligence, at the Computer Science and Artificial Intelligence Laboratory. I also just received my bachelor of science from MIT, with a major in computer science, minor in linguistics, and minor in math.
I am interested in all sorts of artificial intelligence and machine learning systems, with particular interest in computational models of human intelligence involving computational psycholinguistics, computer vision, reinforcement learning, and robotics. You can look at some of my selected research publications and contributions below. For a broader view of my research experience (including additional research projects, skills, honors, prizes, and awards), click on my curriculum vitae. I am happy to provide consulting about any of these topics or related topics.
A fun fact about me is that I used to be part of a competitive sailing team, and I still enjoy sailing with friends and sailing hydrofoil boats when I can find time. Also, I once built a car-sized strandbeest.
I am a Research Associate at the MIT Department of Brain and Cognitive Sciences. I recently received my master of engineering from MIT in artificial intelligence, at the Computer Science and Artificial Intelligence Laboratory. I also just received my bachelor of science from MIT, with a major in computer science, minor in linguistics, and minor in math.
I am interested in all sorts of artificial intelligence and machine learning systems, with particular interest in computational models of human intelligence involving computational psycholinguistics, computer vision, reinforcement learning, and robotics. You can look at some of my selected research publications and contributions below. For a broader view of my research experience (including additional research projects, skills, honors, prizes, and awards), click on my curriculum vitae. I am happy to provide consulting about any of these topics or related topics.
A fun fact about me is that I used to be part of a competitive sailing team, and I still enjoy sailing with friends and sailing hydrofoil boats when I can find time. Also, I once built a car-sized strandbeest.
Working Papers
Thrush, T. (2019). Compositional neural machine translation by removing the lexicon from Syntax. Unpublished Manuscript, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, CA.
Detry, R., & Thrush, T. High-accuracy stereo pose estimation with robust visual odometry features and dense segmentation for autonomous recovery of sample tubes on mars. Unpublished Manuscript, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA.
(additional authors will be added and title will likely change; no link yet)
Master's Thesis
Thrush, T. (2019). SAL: a Self-Aware Learning system (Master’s thesis). Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.
Journal Publications
Thrush, T., & Winston, P. (2018). The partial mental state inducer: Learning intuition with few training examples and k-line theory. Advances in Cognitive Systems, 7, 97-116.
Conference Publications, Talks, and Contributions
Thrush, T. (2018, August). The partial mental state inducer: Learning intuition with few training examples and k-line theory. Paper presented at the 6th annual meeting of Advances in Cognitive Systems, Stanford, CA.
Detry, R., Papon, J., & Matthies, L. (2017). Task-oriented grasping with semantic and geometric scene understanding. Proceedings of the 2017 International Conference on Intelligent Robots and Systems. Vancouver, Canada: IEEE/RSJ.
(Best Paper Award in Cognitive Robotics)
(I was acknowledged in the paper for my technical contributions to the project, from full time research at the Caltech/NASA JPL RoboSimian Lab during the winter of 2016-2017)
Workshop Talks
Thrush, T. (2018, November). A neural model for learning a humanlike vowel feature space. Paper presented at the 12th annual meeting of Northeastern Computational Phonology, Cambridge, MA.
Selected Technical Reports and Internal Reports
Thrush, T. (2018). A computational model for unsupervised vowel acquisition. Department of Linguistics and Philosophy, Massachusetts Institute of Technology, Cambridge, MA.
Thrush, T. (2017). A self-aware and hypothetical question-answering BHPN with drake control. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.
Thrush, T. (2016). Probabilistic lattice learning and backward chaining. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.
Other Selected Research Reports
Thrush, T. (2018). Convolutions inspired by the human retina enable learning of more robust features. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA.
Buhai, R., & Thrush, T. (2017). Machine learning approaches to capture the reliability of news articles. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA.
Thrush, T. (2019). Compositional neural machine translation by removing the lexicon from Syntax. Unpublished Manuscript, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, CA.
Detry, R., & Thrush, T. High-accuracy stereo pose estimation with robust visual odometry features and dense segmentation for autonomous recovery of sample tubes on mars. Unpublished Manuscript, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA.
(additional authors will be added and title will likely change; no link yet)
Master's Thesis
Thrush, T. (2019). SAL: a Self-Aware Learning system (Master’s thesis). Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.
Journal Publications
Thrush, T., & Winston, P. (2018). The partial mental state inducer: Learning intuition with few training examples and k-line theory. Advances in Cognitive Systems, 7, 97-116.
Conference Publications, Talks, and Contributions
Thrush, T. (2018, August). The partial mental state inducer: Learning intuition with few training examples and k-line theory. Paper presented at the 6th annual meeting of Advances in Cognitive Systems, Stanford, CA.
Detry, R., Papon, J., & Matthies, L. (2017). Task-oriented grasping with semantic and geometric scene understanding. Proceedings of the 2017 International Conference on Intelligent Robots and Systems. Vancouver, Canada: IEEE/RSJ.
(Best Paper Award in Cognitive Robotics)
(I was acknowledged in the paper for my technical contributions to the project, from full time research at the Caltech/NASA JPL RoboSimian Lab during the winter of 2016-2017)
Workshop Talks
Thrush, T. (2018, November). A neural model for learning a humanlike vowel feature space. Paper presented at the 12th annual meeting of Northeastern Computational Phonology, Cambridge, MA.
Selected Technical Reports and Internal Reports
Thrush, T. (2018). A computational model for unsupervised vowel acquisition. Department of Linguistics and Philosophy, Massachusetts Institute of Technology, Cambridge, MA.
Thrush, T. (2017). A self-aware and hypothetical question-answering BHPN with drake control. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.
Thrush, T. (2016). Probabilistic lattice learning and backward chaining. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA.
Other Selected Research Reports
Thrush, T. (2018). Convolutions inspired by the human retina enable learning of more robust features. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA.
Buhai, R., & Thrush, T. (2017). Machine learning approaches to capture the reliability of news articles. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA.