I am a research scientist in the Fundamental AI Research (FAIR) group at Meta in NYC and also teach machine learning at Cornell Tech. I 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.
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 
[Google Scholar: 8.8k+ citations and an hindex of 37]
Selected publications I am a primary author on are highlighted.
17. 
CrossDomain Imitation Learning via Optimal Transport
[abs] [code] Arnaud Fickinger, Samuel Cohen, Stuart Russell, and Brandon Amos ICLR 2022 
18. 
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 
19. 
SemiDiscrete Normalizing Flows through Differentiable Tessellation
[abs] Ricky T. Q. Chen, Brandon Amos, and Maximilian Nickel NeurIPS 2022 
20. 
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 
21. 
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 
35. 
The Differentiable CrossEntropy Method
[abs] [code] [slides] Brandon Amos and Denis Yarats ICML 2020 
36. 
Objective Mismatch in Modelbased Reinforcement Learning
[abs] Nathan Lambert, Brandon Amos, Omry Yadan, and Roberto Calandra L4DC 2020 
37. 
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 
38. 
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 
39. 
Deep Riemannian Manifold Learning
[abs] Aaron Lou, Maximilian Nickel, and Brandon Amos NeurIPS Geo4dl Workshop 2020 
40. 
Differentiable OptimizationBased Modeling for Machine Learning
[abs] [code] Brandon Amos Ph.D. Thesis 2019 
41. 
Differentiable Convex Optimization Layers
[abs] [code] Akshay Agrawal*, Brandon Amos*, Shane Barratt*, Stephen Boyd*, Steven Diamond*, and J. Zico Kolter* NeurIPS 2019 
42. 
The Limited MultiLabel Projection Layer
[abs] [code] Brandon Amos, Vladlen Koltun, and J. Zico Kolter arXiv 2019 
43. 
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 
44. 
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 
45. 
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 
46. 
DepthLimited Solving for ImperfectInformation Games
[abs] Noam Brown, Tuomas Sandholm, and Brandon Amos NeurIPS 2018 
47. 
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 
55. 
OpenFace: A generalpurpose face recognition library with mobile applications
[abs] [code] Brandon Amos, Bartosz Ludwiczuk, and Mahadev Satyanarayanan CMU 2016 
56. 
Collapsed Variational Inference for SumProduct Networks
[abs] Han Zhao, Tameem Adel, Geoff Gordon, and Brandon Amos ICML 2016 
57. 
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 
58. 
Privacy mediators: helping IoT cross the chasm
[abs] Nigel Davies, Nina Taft, Mahadev Satyanarayanan, Sarah Clinch, and Brandon Amos HotMobile 2016 
29.5k+ GitHub stars across all repositories.
1.  2024 facebookresearch/advprompter  104  Fast Adaptive Adversarial Prompting for LLMs 
2.  2024 facebookresearch/lagrangianot  37  Lagrangian OT 
3.  2023 facebookresearch/amortizedoptimizationtutorial  235  Tutorial on amortized optimization 
4.  2023 facebookresearch/taskmet  17  TaskMet: TaskDriven Metric Learning for Model Learning 
5.  2023 facebookresearch/w2ot  43  Wasserstein2 optimal transport in JAX 
6.  2022 facebookresearch/theseus  1.7k  Differentiable nonlinear optimization library 
7.  2022 facebookresearch/metaot  94  Meta Optimal Transport 
8.  2022 bamos/presentations  136  Source for my major presentations 
9.  2021 facebookresearch/rcpm  67  Riemannian Convex Potential Maps 
10.  2021 facebookresearch/svg  54  Modelbased stochastic value gradient 
11.  2021 facebookresearch/mbrllib  945  Modelbased reinforcement learning library 
12.  2020 facebookresearch/dcem  122  The Differentiable CrossEntropy Method 
13.  2019 facebookresearch/higher  1.6k  PyTorch higherorder gradient and optimization library 
14.  2019 bamos/thesis  316  Ph.D. Thesis LaTeX source code 
15.  2019 cvxgrp/cvxpylayers  1.8k  Differentiable Convex Optimization Layers 
16.  2019 locuslab/lml  58  The Limited MultiLabel Projection Layer 
17.  2018 locuslab/mpc.pytorch  852  Differentiable PyTorch Model Predictive Control library 
18.  2018 locuslab/differentiablempc  233  Differentiable MPC experiments 
19.  2017 locuslab/icnn  273  Input Convex Neural Network experiments 
20.  2017 locuslab/optnet  501  OptNet experiments 
21.  2017 locuslab/qpth  667  Differentiable PyTorch QP solver 
22.  2017 bamos/densenet.pytorch  822  PyTorch DenseNet implementation 
23.  2017 bamos/block  295  Intelligent block matrix constructions 
24.  2017 bamos/setGPU  106  Automatically use the leastloaded GPU 
25.  2016 bamos/dcgancompletion.tensorflow  1.3k  Image completion with GANs 
26.  2015 cmusatyalab/openface  15.1k  Face recognition with deep neural networks 
27.  2014 vtopt/qnstop  10  Fortran quasiNewton stochastic optimization library 
28.  2014 bamos/snowglobe  27  Haskelldriven, selfhosted web analytics with minimal configuration 
29.  2014 bamos/zshhistoryanalysis  224  Analyze and plot your zsh history 
30.  2014 bamos/beamersnippets  109  Beamer and TikZ snippets 
31.  2013 bamos/latextemplates  365  LaTeX templates 
32.  2013 cparse/cparse  333  C++ expression parser using Dijkstra's shuntingyard algorithm 
33.  2013 bamos/cv  396  Source for this CV: Creates LaTeX/Markdown from YAML/BibTeX 
34.  2013 bamos/pythonscripts  197  Short and fun Python scripts 
35.  2013 bamos/readinglist  185  YAML reading list and notes system 
36.  2012 bamos/dotfiles  238  Linux, xmonad, emacs, vim, zsh, tmux 
Slides for my major presentations are opensourced with a CCBY license at bamos/presentations.
1.  2024 Amortized optimization for optimal transport and LLM attacks, ISMP 
2.  2024 Differentiable optimization for robotics, RSS Optimization for Robotics Workshop 
3.  2024 Amortized optimizationbased reasoning for AI, University of Amsterdam 
4.  2024 Endtoend learning geometries for graphs, dynamical systems, and regression, LoG New York 
5.  2023 Amortized optimization for optimal transport, NeurIPS Optimal Transport and ML Workshop 
6.  2023 On optimal control and machine learning, ICML Learning, Control, and Dynamical Systems Workshop 
7.  2023 Tutorial on amortized optimization, Brown University 
8.  2023 Learning with differentiable and amortized optimization, NYU AI Seminar 
9.  2023 Learning with differentiable and amortized optimization, Vanderbilt ML Seminar 
10.  2022 Learning with differentiable and amortized optimization, Microsoft Research 
11.  2022 Amortized optimization for computing optimal transport maps, Flatiron Workshop 
12.  2022 Learning with differentiable and amortized optimization, Cornell AI Seminar 
13.  2022 Learning with differentiable and amortized optimization, Cornell Tech Seminar 
14.  2022 Learning with differentiable and amortized optimization, Argonne National Laboratory 
15.  2022 Theseus: A library for differentiable nonlinear optimization, NYU 
16.  2022 Theseus: A library for differentiable nonlinear optimization, University of Zurich 
17.  2022 Differentiable optimizationbased modeling for machine learning, Colorado Mines AMS Colloquium 
18.  2022 Differentiable optimization, IJCAI Tutorial 
19.  2022 Differentiable optimization for control and RL, ICML Workshop on Decision Awareness in RL 
20.  2022 Differentiable optimizationbased modeling for machine learning, CPAIOR Master Class 
21.  2022 Tutorial on amortized optimization, ICCOPT 
22.  2022 Differentiable optimization for control and RL, Gridmatic 
23.  2021 Learning for control with differentiable optimization and ODEs, Columbia University 
24.  2021 Differentiable optimizationbased modeling for machine learning, IBM Research 
25.  2020 Differentiable optimization for control, Max Planck Institute (Tübingen) 
26.  2020 Differentiable optimizationbased modeling for machine learning, Mila Seminar 
27.  2020 Deep Declarative Networks, ECCV Tutorial 
28.  2020 On differentiable optimization for control and vision, CVPR Deep Declarative Networks Workshop 
29.  2020 Differentiable optimizationbased modeling for machine learning, Caltech CS 159 (Guest Lecture) 
30.  2020 Unrolled optimization for learning deep energy models, SIAM MDS Minisymposium 
31.  2019 Differentiable optimizationbased modeling for machine learning, NYU CILVR Seminar 
32.  2019 Differentiable optimizationbased modeling for machine learning, INFORMS 
33.  2019 Differentiable optimizationbased modeling for machine learning, Facebook AI Research 
34.  2018 Differentiable optimizationbased modeling for machine learning, ISMP 
35.  2018 Differentiable optimizationbased modeling for machine learning, Google Brain 
36.  2018 Differentiable optimizationbased modeling for machine learning, Bosch Center for AI 
37.  2018 Differentiable optimizationbased modeling for machine learning, Waymo Research 
38.  2018 Differentiable optimizationbased modeling for machine learning, Tesla AI 
39.  2018 Differentiable optimizationbased modeling for machine learning, NVIDIA Robotics 
40.  2018 Differentiable optimizationbased modeling for machine learning, Salesforce Research 
41.  2018 Differentiable optimizationbased modeling for machine learning, OpenAI 
42.  2018 Differentiable optimizationbased modeling for machine learning, NNAISENSE 
43.  2018 Differentiable optimization and control, UC Berkeley 
2024  present Aaron Havens (visiting FAIR from UIUC) 
2022  2024 AramAlexandre Pooladian (visiting FAIR from NYU) 
2022  2024 Carles DomingoEnrich (visiting FAIR from NYU) 
2023  2024 Anselm Paulus (visiting FAIR from Max Planck Institute, Tübingen) 
2023 Matthew Retchin (Columbia MS thesis committee, now at Harvard) 
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, now scientist at OpenAI) 
2021  2022 Eugene Vinitsky (visiting FAIR from Berkeley, now 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, now professor at MIT) 
2018 Phillip Wang (at CMU, now CEO at Gather) 
2024 NeurIPS Area Chair 
2024 NeurIPS Datasets and Benchmarks Area Chair 
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) 
Applied Machine Learning (Cornell Tech CS5785), Coinstructor  F2024 
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 20240903