I am a research scientist in the Fundamental AI Research (FAIR) group at Meta AI in NYC and study foundational topics in machine learning and optimization, recently involving reinforcement learning, control, optimal transport, and geometry. My research is on learning systems that understand and interact with our world and focuses on integrating structural information and domain knowledge into these systems to represent nontrivial reasoning operations. A key theme of my work in this space involves the use of optimization as a differentiable building block in larger architectures that are endtoend learned. I believe that science should be open and reproducible and freely publish my research code to GitHub.
2014  2019
Ph.D. in Computer Science, Carnegie Mellon University
(0.00/0.00)

2011  2014
B.S. in Computer Science, Virginia Tech
(3.99/4.00)

2016  2019
Research Assistant, Carnegie Mellon University (with J. Zico Kolter on ML and optimization) 
2018
Research Intern, Intel Labs, Santa Clara (with Vladlen Koltun on computer vision) 
2017
Research Intern, Google DeepMind, London (with Nando de Freitas and Misha Denil on RL) 
2014  2016
Research Assistant, Carnegie Mellon University (with Mahadev Satyanarayanan on mobile systems) 
2014
Research Intern, Adobe Research, San Jose (with David Tompkins on distributed systems) 
2013  2014
Research Assistant, Virginia Tech (with Layne Watson and David Easterling on optimization) 
2012  2014
Research Assistant, Virginia Tech (with Jules White and Hamilton Turner on mobile systems) 
2012  2014
Research Assistant, Virginia Tech (with Binoy Ravindran and Alastair Murray on compilers) 
2013  2014
Software Intern, Snowplow (Scala development) 
2013
Software Intern, Qualcomm, San Diego (Python and C++ development) 
2012
Software Intern, Phoenix Integration, Virginia (C++, C#, and Java development) 
2011
Network Administrator Intern, Sunapsys, Virginia 
2022

2022

2019

2016  2019
NSF Graduate Research Fellowship

2011  2014
Nine undergraduate scholarships
Roanoke County Public Schools Engineering, SalemRoanoke County Chamber of Commerce, Papa John's, Scottish Rite of Freemasonry, VT Intelligence Community Conter for Academic Excellence, VT Pamplin Leader, VT Benjamin F. Bock, VT Gay B. Shober, VT I. Luck Gravett 
Selected publications are highlighted.
[Google Scholar: 6.8k+ citations and an hindex of 33.]
11. 
CrossDomain Imitation Learning via Optimal Transport
[abs] [code] Arnaud Fickinger, Samuel Cohen, Stuart Russell, and Brandon Amos ICLR 2022 
12. 
Matching Normalizing Flows and Probability Paths on Manifolds
[abs] Heli BenHamu*, Samuel Cohen*, Joey Bose, Brandon Amos, Aditya Grover, Maximilian Nickel, Ricky T. Q. Chen, and Yaron Lipman ICML 2022 
13. 
SemiDiscrete Normalizing Flows through Differentiable Tessellation
[abs] Ricky T. Q. Chen, Brandon Amos, and Maximilian Nickel NeurIPS 2022 
14. 
Theseus: A Library for Differentiable Nonlinear Optimization
[abs] [code] Luis Pineda, Taosha Fan, Maurizio Monge, Shobha Venkataraman, Paloma Sodhi, Ricky Chen, Joseph Ortiz, Daniel DeTone, Austin Wang, Stuart Anderson, Jing Dong, Brandon Amos, and Mustafa Mukadam NeurIPS 2022 
15. 
Nocturne: a driving benchmark for multiagent learning
[abs] [code] Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang, Brandon Amos, and Jakob Foerster NeurIPS Datasets and Benchmarks Track 2022 
29. 
The Differentiable CrossEntropy Method
[abs] [code] [slides] Brandon Amos and Denis Yarats ICML 2020 
30. 
Objective Mismatch in Modelbased Reinforcement Learning
[abs] Nathan Lambert, Brandon Amos, Omry Yadan, and Roberto Calandra L4DC 2020 
31. 
QNSTOP: QuasiNewton Algorithm for Stochastic Optimization
[abs] [code] Brandon Amos, David Easterling, Layne T. Watson, William Thacker, Brent Castle, and Michael Trosset ACM TOMS 2020 
32. 
Neural Potts Model
[abs] Tom Sercu, Robert Verkuil, Joshua Meier, Brandon Amos, Zeming Lin, Caroline Chen, Jason Liu, Yann LeCun, and Alexander Rives MLCB 2020 
33. 
Deep Riemannian Manifold Learning
[abs] Aaron Lou, Maximilian Nickel, and Brandon Amos NeurIPS Geo4dl Workshop 2020 
34. 
Differentiable OptimizationBased Modeling for Machine Learning
[abs] [code] Brandon Amos Ph.D. Thesis 2019 
35. 
Differentiable Convex Optimization Layers
[abs] [code] Akshay Agrawal*, Brandon Amos*, Shane Barratt*, Stephen Boyd*, Steven Diamond*, and J. Zico Kolter* NeurIPS 2019 
36. 
The Limited MultiLabel Projection Layer
[abs] [code] Brandon Amos, Vladlen Koltun, and J. Zico Kolter arXiv 2019 
37. 
Generalized Inner Loop MetaLearning
[abs] [code] Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, and Soumith Chintala arXiv 2019 
38. 
Learning Awareness Models
[abs] Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, and Misha Denil ICLR 2018 
39. 
Differentiable MPC for Endtoend Planning and Control
[abs] [code] Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots, and J. Zico Kolter NeurIPS 2018 
40. 
DepthLimited Solving for ImperfectInformation Games
[abs] Noam Brown, Tuomas Sandholm, and Brandon Amos NeurIPS 2018 
41. 
Enabling Live Video Analytics with a Scalable and PrivacyAware Framework
[abs] Junjue Wang, Brandon Amos, Anupam Das, Padmanabhan Pillai, Norman Sadeh, and Mahadev Satyanarayanan ACM TOMM 2018 
49. 
OpenFace: A generalpurpose face recognition library with mobile applications
[abs] [code] Brandon Amos, Bartosz Ludwiczuk, and Mahadev Satyanarayanan CMU 2016 
50. 
Collapsed Variational Inference for SumProduct Networks
[abs] Han Zhao, Tameem Adel, Geoff Gordon, and Brandon Amos ICML 2016 
51. 
Quantifying the impact of edge computing on mobile applications
[abs] Wenlu Hu, Ying Gao, Kiryong Ha, Junjue Wang, Brandon Amos, Zhuo Chen, Padmanabhan Pillai, and Mahadev Satyanarayanan ACM SIGOPS 2016 
52. 
Privacy mediators: helping IoT cross the chasm
[abs] Nigel Davies, Nina Taft, Mahadev Satyanarayanan, Sarah Clinch, and Brandon Amos HotMobile 2016 
27.8k+ GitHub stars across all repositories.
1.  2023 facebookresearch/amortizedoptimizationtutorial  208  Tutorial on amortized optimization 
2.  2023 facebookresearch/w2ot  38  Wasserstein2 optimal transport in JAX 
3.  2022 facebookresearch/theseus  1.4k  Differentiable nonlinear optimization library 
4.  2022 facebookresearch/metaot  86  Meta Optimal Transport 
5.  2022 bamos/presentations  114  Source for my major presentations 
6.  2021 facebookresearch/rcpm  64  Riemannian Convex Potential Maps 
7.  2021 facebookresearch/svg  53  Modelbased stochastic value gradient 
8.  2021 facebookresearch/mbrllib  828  Modelbased reinforcement learning library 
9.  2020 facebookresearch/dcem  118  The Differentiable CrossEntropy Method 
10.  2019 facebookresearch/higher  1.5k  PyTorch higherorder gradient and optimization library 
11.  2019 bamos/thesis  304  Ph.D. Thesis LaTeX source code 
12.  2019 cvxgrp/cvxpylayers  1.6k  Differentiable Convex Optimization Layers 
13.  2019 locuslab/lml  57  The Limited MultiLabel Projection Layer 
14.  2018 locuslab/mpc.pytorch  714  Differentiable PyTorch Model Predictive Control library 
15.  2018 locuslab/differentiablempc  162  Differentiable MPC experiments 
16.  2017 locuslab/icnn  260  Input Convex Neural Network experiments 
17.  2017 locuslab/optnet  459  OptNet experiments 
18.  2017 locuslab/qpth  604  Differentiable PyTorch QP solver 
19.  2017 bamos/densenet.pytorch  796  PyTorch DenseNet implementation 
20.  2017 bamos/block  286  Intelligent block matrix constructions 
21.  2017 bamos/setGPU  105  Automatically use the leastloaded GPU 
22.  2016 bamos/dcgancompletion.tensorflow  1.3k  Image completion with GANs 
23.  2015 cmusatyalab/openface  14.8k  Face recognition with deep neural networks 
24.  2014 vtopt/qnstop  10  Fortran Quasinewton stochastic optimization library 
25.  2014 bamos/snowglobe  27  Haskelldriven, selfhosted web analytics with minimal configuration 
26.  2014 bamos/zshhistoryanalysis  202  Analyze and plot your zsh history 
27.  2014 bamos/beamersnippets  110  Beamer and TikZ snippets 
28.  2013 bamos/latextemplates  364  LaTeX templates 
29.  2013 cparse/cparse  319  C++ expression parser using Dijkstra's shuntingyard algorithm 
30.  2013 bamos/cv  389  Source for this CV: Creates LaTeX/Markdown from YAML/BibTeX 
31.  2013 bamos/pythonscripts  198  Short and fun Python scripts 
32.  2013 bamos/readinglist  188  YAML reading list and notes system 
33.  2012 bamos/dotfiles  235  Linux, xmonad, emacs, vim, zsh, tmux 
Slides for my major presentations are opensourced with a CCBY license at bamos/presentations.
1.  2023 On optimal control and machine learning, ICML Learning, Control, and Dynamical Systems Workshop 
2.  2023 Tutorial on amortized optimization, Brown University 
3.  2023 Learning with differentiable and amortized optimization, NYU AI Seminar 
4.  2023 Learning with differentiable and amortized optimization, Vanderbilt ML Seminar 
5.  2022 Learning with differentiable and amortized optimization, Microsoft Research 
6.  2022 Amortized optimization for computing optimal transport maps, Flatiron Workshop 
7.  2022 Learning with differentiable and amortized optimization, Cornell AI Seminar 
8.  2022 Learning with differentiable and amortized optimization, Cornell Tech Seminar 
9.  2022 Learning with differentiable and amortized optimization, Argonne National Laboratory 
10.  2022 Theseus: A library for differentiable nonlinear optimization, NYU 
11.  2022 Theseus: A library for differentiable nonlinear optimization, University of Zurich 
12.  2022 Differentiable optimizationbased modeling for machine learning, Colorado Mines AMS Colloquium 
13.  2022 Differentiable optimization, IJCAI Tutorial 
14.  2022 Differentiable optimization for control and RL, ICML Workshop on Decision Awareness in RL 
15.  2022 Differentiable optimizationbased modeling for machine learning, CPAIOR Master Class 
16.  2022 Tutorial on amortized optimization, ICCOPT 
17.  2022 Differentiable optimization for control and RL, Gridmatic 
18.  2021 Learning for control with differentiable optimization and ODEs, Columbia University 
19.  2021 Differentiable optimizationbased modeling for machine learning, IBM Research 
20.  2020 Differentiable optimization for control, Max Planck Institute (Tübingen) 
21.  2020 Differentiable optimizationbased modeling for machine learning, Mila Seminar 
22.  2020 Deep Declarative Networks, ECCV Tutorial 
23.  2020 On differentiable optimization for control and vision, CVPR Deep Declarative Networks Workshop 
24.  2020 Differentiable optimizationbased modeling for machine learning, Caltech CS 159 (Guest Lecture) 
25.  2020 Unrolled optimization for learning deep energy models, SIAM MDS Minisymposium 
26.  2019 Differentiable optimizationbased modeling for machine learning, NYU CILVR Seminar 
27.  2019 Differentiable optimizationbased modeling for machine learning, INFORMS 
28.  2019 Differentiable optimizationbased modeling for machine learning, Facebook AI Research 
29.  2018 Differentiable optimizationbased modeling for machine learning, ISMP 
30.  2018 Differentiable optimizationbased modeling for machine learning, Google Brain 
31.  2018 Differentiable optimizationbased modeling for machine learning, Bosch Center for AI 
32.  2018 Differentiable optimizationbased modeling for machine learning, Waymo Research 
33.  2018 Differentiable optimizationbased modeling for machine learning, Tesla AI 
34.  2018 Differentiable optimizationbased modeling for machine learning, NVIDIA Robotics 
35.  2018 Differentiable optimizationbased modeling for machine learning, Salesforce Research 
36.  2018 Differentiable optimizationbased modeling for machine learning, OpenAI 
37.  2018 Differentiable optimizationbased modeling for machine learning, NNAISENSE 
38.  2018 Differentiable optimization and control, UC Berkeley 
2023  present Anselm Paulus (visiting FAIR from Max Planck Institute, Tübingen) 
2022  present AramAlexandre Pooladian (visiting FAIR from NYU) 
2022  present Carles DomingoEnrich (visiting FAIR from NYU) 
2022  2023 Sanae Lotfi (visiting FAIR from NYU) 
2022  2023 Dishank Bansal (AI resident at FAIR) 
2021  2022 Arnaud Fickinger (visiting FAIR from Berkeley) 
2020  2022 Aaron Lou (visiting FAIR from Cornell and Stanford) 
2021  2022 Eugene Vinitsky (visiting FAIR from Berkeley, now incoming professor at NYU) 
2021  2022 Samuel Cohen (visiting FAIR from UCL, now CEO at FairGen) 
2020 Ricky Chen (visiting FAIR from Toronto, now scientist at FAIR) 
2020 Paul Liang (visiting FAIR from CMU) 
2018 Phillip Wang (at CMU, now CEO at Gather) 
2024 AAAI Senior Program Committee 
2023 NeurIPS Area Chair 
2023 NeurIPS Datasets and Benchmarks Area Chair 
2023 AAAI Senior Program Committee 
2020 NeurIPS Learning Meets Combinatorial Optimization Workshop Organizer 
2020 CVPR Deep Declarative Networks Workshop Organizer 
2020 ECCV Deep Declarative Networks Tutorial Organizer 
2014  2015 CMU CSD MS Admissions 
AAAI Conference on Artificial Intelligence 
American Controls Conference (ACC) 
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 
IEEE Conference on Decision and Control (CDC) 
IEEE Control Systems Letters (LCSS) 
IEEE International Conference on Computer Vision (ICCV) 
IEEE International Conference on Intelligent Robots and Systems (IROS) 
IEEE International Conference on Robotics and Automation (ICRA) 
International Conference on the Constraint Programming, AI, and Operations Research (CPAIOR) 
International Conference on Learning Representations (ICLR) 
International Conference on Machine Learning (ICML) 
International Conference on Machine Learning (ICML) SODS Workshop 
Journal of Machine Learning Research (JMLR) 
Learning for Dynamics and Control (L4DC) 
Mathematical Programming Computation (MPC) 
Neural Information Processing Systems (NeurIPS) 
Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track 
Neural Information Processing Systems (NeurIPS) OPT Workshop 
Neural Information Processing Systems (NeurIPS) DiffCVGP Workshop 
Neural Information Processing Systems (NeurIPS) Deep RL Workshop 
Optimization Letters 
Transactions on Machine Learning Research (TMLR) 
Graduate AI (CMU 15780), TA  S2017 
Distributed Systems (CMU 15440/640), TA  S2016 
Software Design and Data Structures (VT CS2114), TA  S2013 
Programming  C, C++, Fortran, Haskell, Java, Lua, Make, Mathematica, Python, R, Scala 
Frameworks  JAX, NumPy, Pandas, PyTorch, SciPy, TensorFlow, Torch7 
Toolbox  Linux, emacs, vim, evil, org, mu4e, xmonad, git, tmux, zsh 
Last updated on 20230921