Brandon Amos

# bda@meta.com •  bamos.github.io • ð bdamos •  brandondamos§ bamos • Last updated on October 16, 2024

Current Positions
Research Scientist, Meta, Fundamental AI Research (FAIR), New York City
Visiting Lecturer, Cornell Tech, New York City

2019 – Present
2024 – Present

Education
Ph.D. in Computer Science, Carnegie Mellon University (0.00/0.00)
Thesis: Differentiable Optimization-Based Modeling for Machine Learning
Advisor: J. Zico Kolter2014 – 2019

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

2011 – 2014

Previous Positions
Research Assistant, Carnegie Mellon University (with J. Zico Kolter on ML and optimization)
Research Intern, Intel Labs, Santa Clara (with Vladlen Koltun on computer vision)
Research Intern, Google DeepMind, London (with Nando de Freitas and Misha Denil on RL)
Research Assistant, Carnegie Mellon University (with Mahadev Satyanarayanan on mobile systems)
Research Intern, Adobe Research, San Jose (with David Tompkins on distributed systems)
Research Assistant, Virginia Tech (with Layne Watson and David Easterling on optimization)
Research Assistant, Virginia Tech (with Jules White and Hamilton Turner on mobile systems)
Research Assistant, Virginia Tech (with Binoy Ravindran and Alastair Murray on compilers)
Software Intern, Snowplow (Scala development)
Software Intern, Qualcomm, San Diego (Python and C++ development)
Software Intern, Phoenix Integration, Virginia (C++, C#, and Java development)
Network Administrator Intern, Sunapsys, Virginia2016 – 2019
2018
2017
2014 – 20162014
2013 – 20142012 – 20142012 – 20142013 – 2014
2013
2012
2011

Honors & Awards
NeurIPS Top Reviewer
ICML Outstanding Reviewer
ICLR Outstanding Reviewer
NSF Graduate Research Fellowship
Nine undergraduate scholarships

2022
2022
2019
2016 – 20192011 – 2014

Roanoke County Public Schools Engineering, Salem–Roanoke County Chamber of Commerce, Papa John’s, Scottish Rite of Freemasonry, VTIntelligence Community Conter for Academic Excellence, VT Pamplin Leader, VT Benjamin F. Bock, VT Gay B. Shober, VT I. Luck Gravett

Publications [Google Scholar: 8.9k+ citations and an h-index of 37]
Selected publications I am a primary author on are highlighted.

2024. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs [code]
Anselm Paulus*, Arman Zharmagambetov*, Chuan Guo, Brandon Amos: , and Yuandong Tian:arXiv 2024

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2. Neural Optimal Transport with Lagrangian Costs [code]
Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, and Brandon Amos
UAI 2024
3. Learning to Warm-Start Fixed-Point Optimization Algorithms [code]
Rajiv Sambharya, Georgina Hall, Brandon Amos, and Bartolomeo Stellato
JMLR 2024
4. Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models
Sanae Lotfi, Yilun Kuang, Marc Anton Finzi, Brandon Amos, Micah Goldblum, andAndrew Gordon Wilson
NeurIPS 2024
5. Stochastic Optimal Control Matching
Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, and Ricky T. Q. Chen
NeurIPS 2024
6. Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
Lazar Atanackovic, Xi Zhang, Brandon Amos, Mathieu Blanchette, Leo J Lee, Yoshua Bengio,
Alexander Tong, and Kirill Neklyudov
ICML GRaM Workshop 2024
7. Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning
Mattia Silvestri, Senne Berden, Jayanta Mandi, Ali İrfan Mahmutoğulları, Brandon Amos, Tias Guns,
and Michele Lombardi
arXiv 2024
8. Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles
Buu Phan, Brandon Amos, Itai Gat, Marton Havasi, Matthew Muckley, and Karen Ullrich
arXiv 2024

2023. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9. Tutorial on amortized optimization [code]
Brandon Amos
Foundations and Trends in Machine Learning 2023
10. On amortizing convex conjugates for optimal transport [code]
Brandon Amos
ICLR 2023
11. End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization [code]
Rajiv Sambharya, Georgina Hall, Brandon Amos, and Bartolomeo Stellato
L4DC 2023
12. Meta Optimal Transport [code]
Brandon Amos, Samuel Cohen, Giulia Luise, and Ievgen Redko
ICML 2023
13. Multisample Flow Matching: Straightening Flows with Minibatch Couplings
Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman,
and Ricky T. Q. Chen
ICML 2023

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14. Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
Qinqing Zheng, Mikael Henaff, Brandon Amos, and Aditya Grover
ICML 2023
15. TaskMet: Task-Driven Metric Learning for Model Learning
Dishank Bansal, Ricky T. Q. Chen, Mustafa Mukadam, and Brandon Amos
NeurIPS 2023
16. Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information
Arman Zharmagambetov, Brandon Amos, Aaron Ferber, Taoan Huang, Bistra Dilkina, andYuandong Tian
NeurIPS 2023
17. Koopman Constrained Policy Optimization: A Koopman operator theoretic method for differentiable
optimal control in robotics
Matthew Retchin, Brandon Amos, Steven Brunton, and Shuran SongICML Differentiable Almost Everything Workshop 2023

2022. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18. Cross-Domain Imitation Learning via Optimal Transport [code]
Arnaud Fickinger, Samuel Cohen, Stuart Russell, and Brandon Amos
ICLR 2022
19. Matching Normalizing Flows and Probability Paths on Manifolds
Heli Ben-Hamu*, Samuel Cohen*, Joey Bose, Brandon Amos, Aditya Grover, Maximilian Nickel,
Ricky T. Q. Chen, and Yaron Lipman
ICML 2022
20. Semi-Discrete Normalizing Flows through Differentiable Tessellation
Ricky T. Q. Chen, Brandon Amos, and Maximilian Nickel
NeurIPS 2022
21. Theseus: A Library for Differentiable Nonlinear Optimization [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, andMustafa Mukadam
NeurIPS 2022
22. Nocturne: a driving benchmark for multi-agent learning [code]
Eugene Vinitsky, Nathan Lichtlé, Xiaomeng Yang, Brandon Amos, and Jakob Foerster
NeurIPS Datasets and Benchmarks Track 2022

2021. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
23. On the model-based stochastic value gradient for continuous reinforcement learning [code] [slides]
Brandon Amos, Samuel Stanton, Denis Yarats, and Andrew Gordon Wilson
L4DC 2021 (Oral)
24. Riemannian Convex Potential Maps [code] [slides]
Samuel Cohen*, Brandon Amos*, and Yaron Lipman
ICML 2021
25. CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints [code]
Anselm Paulus, Michal Rolı́nek, Vı́t Musil, Brandon Amos, and Georg Martius
ICML 2021

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26. Scalable Online Planning via Reinforcement Learning Fine-Tuning
Arnaud Fickinger, Hengyuan Hu, Brandon Amos, Stuart Russell, and Noam Brown
NeurIPS 2021
27. Aligning Time Series on Incomparable Spaces [code] [slides]
Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, and Marc Peter Deisenroth
AISTATS 2021
28. Learning Neural Event Functions for Ordinary Differential Equations [code]
Ricky T. Q. Chen, Brandon Amos, and Maximilian Nickel
ICLR 2021
29. Neural Spatio-Temporal Point Processes [code]
Ricky T. Q. Chen, Brandon Amos, and Maximilian Nickel
ICLR 2021
30. Improving Sample Efficiency in Model-Free Reinforcement Learning from Images [code]
Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, and Rob Fergus
AAAI 2021
31. Neural Fixed-Point Acceleration for Convex Optimization [code]
Shobha Venkataraman* and Brandon Amos*
ICML AutoML Workshop 2021
32. Sliced Multi-Marginal Optimal Transport
Samuel Cohen, Alexander Terenin, Yannik Pitcan, Brandon Amos, Marc Peter Deisenroth, andK S Sesh Kumar
NeurIPS OTML Workshop 2021
33. Input Convex Gradient Networks
Jack Richter-Powell, Jonathan Lorraine, and Brandon Amos
NeurIPS OTML Workshop 2021
34. Imitation Learning from Pixel Observations for Continuous Control
Samuel Cohen, Brandon Amos, Marc Peter Deisenroth, Mikael Henaff, Eugene Vinitsky, andDenis Yarats
NeurIPS DeepRL Workshop 2021
35. MBRL-Lib: A Modular Library for Model-based Reinforcement Learning [code]
Luis Pineda, Brandon Amos, Amy Zhang, Nathan Lambert, and Roberto Calandra
arXiv 2021

2020. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
36. The Differentiable Cross-Entropy Method [code] [slides]
Brandon Amos and Denis Yarats
ICML 2020
37. Objective Mismatch in Model-based Reinforcement Learning
Nathan Lambert, Brandon Amos, Omry Yadan, and Roberto Calandra
L4DC 2020
38. QNSTOP: Quasi-Newton Algorithm for Stochastic Optimization [code]
Brandon Amos, David Easterling, Layne T. Watson, William Thacker, Brent Castle, andMichael Trosset
ACM TOMS 2020

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39. Neural Potts Model
Tom Sercu, Robert Verkuil, Joshua Meier, Brandon Amos, Zeming Lin, Caroline Chen, Jason Liu,Yann LeCun, and Alexander Rives
MLCB 2020
40. Deep Riemannian Manifold Learning
Aaron Lou, Maximilian Nickel, and Brandon Amos
NeurIPS Geo4dl Workshop 20202019. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
41. Differentiable Optimization-Based Modeling for Machine Learning [code]
Brandon Amos
Ph.D. Thesis 201942. Differentiable Convex Optimization Layers [code]
Akshay Agrawal*, Brandon Amos*, Shane Barratt*, Stephen Boyd*, Steven Diamond*, andJ. Zico Kolter*
NeurIPS 201943. The Limited Multi-Label Projection Layer [code]
Brandon Amos, Vladlen Koltun, and J. Zico Kolter
arXiv 2019
44. Generalized Inner Loop Meta-Learning [code]
Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov,Franziska Meier, Douwe Kiela, Kyunghyun Cho, and Soumith Chintala
arXiv 20192018. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
45. Learning Awareness Models
Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo,
Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, and Misha Denil
ICLR 201846. Differentiable MPC for End-to-end Planning and Control [code]
Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots, and J. Zico Kolter
NeurIPS 201847. Depth-Limited Solving for Imperfect-Information Games
Noam Brown, Tuomas Sandholm, and Brandon Amos
NeurIPS 201848. Enabling Live Video Analytics with a Scalable and Privacy-Aware Framework
Junjue Wang, Brandon Amos, Anupam Das, Padmanabhan Pillai, Norman Sadeh, andMahadev Satyanarayanan
ACM TOMM 20182017. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49. OptNet: Differentiable Optimization as a Layer in Neural Networks [code] [slides]
Brandon Amos and J. Zico Kolter
ICML 2017

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50. Input Convex Neural Networks [code] [slides]
Brandon Amos, Lei Xu, and J. Zico Kolter
ICML 2017
51. Task-based End-to-end Model Learning [code]
Priya L. Donti, Brandon Amos, and J. Zico Kolter
NeurIPS 2017
52. Quasi-Newton Stochastic Optimization Algorithm for Parameter Estimation of a Stochastic Model ofthe Budding Yeast Cell Cycle
Minghan Chen, Brandon Amos, Layne T. Watson, John Tyson, Yang Cao, Cliff Shaffer,
Michael Trosset, Cihan Oguz, and Gisella Kakoti
IEEE/ACM TCBB 2017
53. You can teach elephants to dance: agile VM handoff for edge computing
Kiryong Ha, Yoshihisa Abe, Thomas Eiszler, Zhuo Chen, Wenlu Hu, Brandon Amos,
Rohit Upadhyaya, Padmanabhan Pillai, and Mahadev Satyanarayanan
SEC 2017
54. An Empirical Study of Latency in an Emerging Class of Edge Computing Applications for Wearable
Cognitive Assistance
Zhuo Chen, Wenlu Hu, Junjue Wang, Siyan Zhao, Brandon Amos, Guanhang Wu, Kiryong Ha,
Khalid Elgazzar, Padmanabhan Pillai, Roberta Klatzky, Daniel Siewiorek, andMahadev Satyanarayanan
SEC 2017
55. A Scalable and Privacy-Aware IoT Service for Live Video Analytics [code]
Junjue Wang, Brandon Amos, Anupam Das, Padmanabhan Pillai, Norman Sadeh, andMahadev Satyanarayanan
ACM MMSys 2017 (Best Paper)2016. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
56. OpenFace: A general-purpose face recognition library with mobile applications [code]
Brandon Amos, Bartosz Ludwiczuk, and Mahadev Satyanarayanan
CMU 2016
57. Collapsed Variational Inference for Sum-Product Networks
Han Zhao, Tameem Adel, Geoff Gordon, and Brandon Amos
ICML 2016
58. Quantifying the impact of edge computing on mobile applications
Wenlu Hu, Ying Gao, Kiryong Ha, Junjue Wang, Brandon Amos, Zhuo Chen, Padmanabhan Pillai,
and Mahadev Satyanarayanan
ACM SIGOPS 201659. Privacy mediators: helping IoT cross the chasm
Nigel Davies, Nina Taft, Mahadev Satyanarayanan, Sarah Clinch, and Brandon Amos
HotMobile 20162015 and earlier. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60. Edge Analytics in the Internet of Things
Mahadev Satyanarayanan, Pieter Simoens, Yu Xiao, Padmanabhan Pillai, Zhuo Chen, Kiryong Ha,
Wenlu Hu, and Brandon Amos
IEEE Pervasive Computing 2015

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61. Bad Parts: Are Our Manufacturing Systems at Risk of Silent Cyberattacks?
Hamilton Turner, Jules White, Jaime A. Camelio, Christopher Williams, Brandon Amos, andRobert Parker
IEEE Security & Privacy 201562. Early Implementation Experience with Wearable Cognitive Assistance Applications
Zhuo Chen, Lu Jiang, Wenlu Hu, Kiryong Ha, Brandon Amos, Padmanabhan Pillai,
Alex Hauptmann, and Mahadev Satyanarayanan
WearSys 2015
63. The Case for Offload Shaping
Wenlu Hu, Brandon Amos, Zhuo Chen, Kiryong Ha, Wolfgang Richter, Padmanabhan Pillai,
Benjamin Gilbert, Jan Harkes, and Mahadev Satyanarayanan
HotMobile 2015
64. Are Cloudlets Necessary?
Ying Gao, Wenlu Hu, Kiryong Ha, Brandon Amos, Padmanabhan Pillai, andMahadev Satyanarayanan
CMU 2015
65. Adaptive VM handoff across cloudlets
Kiryong Ha, Yoshihisa Abe, Zhuo Chen, Wenlu Hu, Brandon Amos, Padmanabhan Pillai, andMahadev Satyanarayanan
CMU 2015
66. Global Parameter Estimation for a Eukaryotic Cell Cycle Model in Systems Biology
Tricity Andrew, Brandon Amos, David Easterling, Cihan Oguz, William Baumann, John Tyson, andLayne T. Watson
SummerSim 2014
67. Applying machine learning classifiers to dynamic Android malware detection at scale [code]
Brandon Amos, Hamilton Turner, and Jules WhiteIWCMC 2013

Open Source Repositories
29.6k+ GitHub stars across all repositories.
1. facebookresearch/advprompter — ‹114 — Fast Adaptive Adversarial Prompting for LLMs
2. facebookresearch/lagrangian-ot — ‹42 — Lagrangian OT
3. facebookresearch/amortized-optimization-tutorial — ‹236 — Tutorial on amortized optimization
4. facebookresearch/taskmet — ‹18 — TaskMet: Task-Driven Metric Learning for Model Learning
5. facebookresearch/w2ot — ‹43 — Wasserstein-2 optimal transport in JAX
6. facebookresearch/theseus — ‹1.7k — Differentiable non-linear optimization library
7. facebookresearch/meta-ot — ‹95 — Meta Optimal Transport
8. bamos/presentations — ‹141 — Source for my major presentations
9. facebookresearch/rcpm — ‹68 — Riemannian Convex Potential Maps
10. facebookresearch/svg — ‹54 — Model-based stochastic value gradient
11. facebookresearch/mbrl-lib — ‹955 — Model-based reinforcement learning library
12. facebookresearch/dcem — ‹123 — The Differentiable Cross-Entropy Method
13. facebookresearch/higher — ‹1.6k — PyTorch higher-order gradient and optimization library
14. bamos/thesis — ‹318 — Ph.D. Thesis LaTeX source code
15. cvxgrp/cvxpylayers — ‹1.8k — Differentiable Convex Optimization Layers
16. locuslab/lml — ‹58 — The Limited Multi-Label Projection Layer
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17. locuslab/mpc.pytorch — ‹870 — Differentiable PyTorch Model Predictive Control library
18. locuslab/differentiable-mpc — ‹242 — Differentiable MPC experiments
19. locuslab/icnn — ‹275 — Input Convex Neural Network experiments
20. locuslab/optnet — ‹508 — OptNet experiments
21. locuslab/qpth — ‹674 — Differentiable PyTorch QP solver
22. bamos/densenet.pytorch — ‹823 — PyTorch DenseNet implementation
23. bamos/block — ‹297 — Intelligent block matrix constructions
24. bamos/setGPU — ‹106 — Automatically use the least-loaded GPU
25. bamos/dcgan-completion.tensorflow — ‹1.3k — Image completion with GANs
26. cmusatyalab/openface — ‹15.1k — Face recognition with deep neural networks
27. vtopt/qnstop — ‹10 — Fortran quasi-Newton stochastic optimization library
28. bamos/snowglobe — ‹27 — Haskell-driven, self-hosted web analytics with minimal configuration
29. bamos/zsh-history-analysis — ‹224 — Analyze and plot your zsh history
30. bamos/beamer-snippets — ‹109 — Beamer and TikZ snippets
31. bamos/latex-templates — ‹366 — LaTeX templates
32. cparse/cparse — ‹336 — C++ expression parser using Dijkstra’s shunting-yard algorithm
33. bamos/cv — ‹398 — Source for this CV: Creates LaTeX/Markdown from YAML/BibTeX
34. bamos/python-scripts — ‹197 — Short and fun Python scripts
35. bamos/reading-list — ‹185 — YAML reading list and notes system
36. bamos/dotfiles — ‹238 — ♥ Linux, xmonad, emacs, vim, zsh, tmux

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2012

Invited Talks
Slides for my major presentations are open-sourced with a CC-BY license at bamos/presentations.
1. Amortized optimization for optimal transport and LLM attacks — ISMP
2. Differentiable optimization for robotics — RSS Optimization for Robotics Workshop
3. Amortized optimization-based reasoning for AI — University of Amsterdam
4. End-to-end learning geometries for graphs, dynamical systems, and regression — LoG New York
5. Amortized optimization for optimal transport — NeurIPS Optimal Transport and ML Workshop
6. On optimal control and machine learning — ICML Control and Dynamical Systems Workshop
7. Tutorial on amortized optimization — Brown University
8. Learning with differentiable and amortized optimization — NYU AI Seminar
9. Learning with differentiable and amortized optimization — Vanderbilt ML Seminar
10. Learning with differentiable and amortized optimization — Microsoft Research
11. Amortized optimization for computing optimal transport maps — Flatiron Workshop
12. Learning with differentiable and amortized optimization — Cornell AI Seminar
13. Learning with differentiable and amortized optimization — Cornell Tech Seminar
14. Learning with differentiable and amortized optimization — Argonne National Laboratory
15. Theseus: A library for differentiable nonlinear optimization — NYU
16. Theseus: A library for differentiable nonlinear optimization — University of Zurich
17. Differentiable optimization-based modeling for machine learning — Colorado Mines AMS Colloquium
18. Differentiable optimization — IJCAI Tutorial
19. Differentiable optimization for control and RL — ICML Workshop on Decision Awareness in RL
20. Differentiable optimization-based modeling for machine learning — CPAIOR Master Class
21. Tutorial on amortized optimization — ICCOPT
22. Differentiable optimization for control and RL — Gridmatic
23. Learning for control with differentiable optimization and ODEs — Columbia University
24. Differentiable optimization-based modeling for machine learning — IBM Research
25. Differentiable optimization for control — Max Planck Institute (Tübingen)
26. Differentiable optimization-based modeling for machine learning — Mila Seminar
27. Deep Declarative Networks — ECCV Tutorial
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28. On differentiable optimization for control and vision — CVPR Deep Declarative Networks Workshop
29. Differentiable optimization-based modeling for machine learning — Caltech CS 159 (Guest Lecture)
30. Unrolled optimization for learning deep energy models — SIAM MDS Minisymposium
31. Differentiable optimization-based modeling for machine learning — NYU CILVR Seminar
32. Differentiable optimization-based modeling for machine learning — INFORMS
33. Differentiable optimization-based modeling for machine learning — Facebook AI Research
34. Differentiable optimization-based modeling for machine learning — ISMP
35. Differentiable optimization-based modeling for machine learning — Google Brain
36. Differentiable optimization-based modeling for machine learning — Bosch Center for AI
37. Differentiable optimization-based modeling for machine learning — Waymo Research
38. Differentiable optimization-based modeling for machine learning — Tesla AI
39. Differentiable optimization-based modeling for machine learning — NVIDIA Robotics
40. Differentiable optimization-based modeling for machine learning — Salesforce Research
41. Differentiable optimization-based modeling for machine learning — OpenAI
42. Differentiable optimization-based modeling for machine learning — NNAISENSE
43. Differentiable optimization and control — UC Berkeley

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Interns and Students
Aaron Havens (visiting FAIR from UIUC)
Aram-Alexandre Pooladian (visiting FAIR from NYU)
Carles Domingo-Enrich (visiting FAIR from NYU, now at MSR)
Anselm Paulus (visiting FAIR from Max Planck Institute, Tübingen)
Matthew Retchin (Columbia MS thesis committee, now at Harvard)
Sanae Lotfi (visiting FAIR from NYU)
Dishank Bansal (AI resident at FAIR)
Arnaud Fickinger (visiting FAIR from Berkeley)
Aaron Lou (visiting FAIR from Cornell and Stanford, now scientist at OpenAI)
Eugene Vinitsky (visiting FAIR from Berkeley, now professor at NYU)
Samuel Cohen (visiting FAIR from UCL, now CEO at FairGen)
Ricky Chen (visiting FAIR from Toronto, now scientist at FAIR)Paul Liang (visiting FAIR from CMU, now professor at MIT)
Phillip Wang (at CMU, now CEO at Gather)2024 – present
2022 – 2024
2022 – 2024
2023 – 2024
2023
2022 – 2023
2022 – 2023
2021 – 2022
2020 – 2022
2021 – 2022
2021 – 2022
2020
2020
2018

Professional Activities
AAAI Senior Program Committee
NeurIPS Area Chair
NeurIPS Datasets and Benchmarks Area Chair
AAAI Senior Program Committee
NeurIPS Area Chair
NeurIPS Datasets and Benchmarks Area Chair
AAAI Senior Program Committee
NeurIPS Learning Meets Combinatorial Optimization Workshop Organizer
CVPR Deep Declarative Networks Workshop Organizer
ECCV Deep Declarative Networks Tutorial Organizer
CMU CSD MS Admissions

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2014 – 2015

Reviewing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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 (L-CSS)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)

Teaching
Applied Machine Learning (Cornell Tech CS5785), Co-instructor
Graduate AI (CMU 15-780), TA
Distributed Systems (CMU 15-440/640), TA
Software Design and Data Structures (VT CS2114), TA

SkillsProgramming C, C++, Fortran, Haskell, Java, Lua, Make, Mathematica, Python, R, Scala
Frameworks
JAX, NumPy, Pandas, PyTorch, SciPy, TensorFlow, Torch7
ToolboxLinux, emacs, vim, evil, org, mu4e, xmonad, git, tmux, zsh

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F2024
S2017
S2016
S2013