Ivan Papusha is a computational scientist and engineer. He is
currently a senior analyst and subject matter expert in Washington,
D.C., with over a decade of research and development experience in
autonomous vehicles, air and space systems, and artificial intelligence.
His (continuing) mission: to seek computational
craftsmanship—the kind of insight and practical undertaking that
pleasingly melds the computational and human worlds.
He has a PhD from Caltech, and BS and MS degrees from Stanford
University. He is a recipient of the NDSEG Fellowship, a member of IEEE,
and the founder of Subgradient.
Y. Kouskoulas, T. J. Machado, D. Genin, A. Schmidt, I.
Papusha, and J. Brulé. “Envelopes and Waves: Safe Multivehicle
Collision Avoidance for Horizontal Non-deterministic Turns,” Journal
on Software Tools for Technology Transfer, 2022. [arXiv] [doi] [Coq
D. Genin, I. Papusha, J. Brulé, T. Young, G.
Mullins, Y. Kouskoulas, R. Wu, and A. Schmidt. “Formal Verification of
Neural Network Controllers for Collision-Free Flight,” Workshop on
Numerical Software Verification, part of International Conference on
Computer-Aided Verification (CAV), Los Angeles, CA, July 18–19,
Y. Kouskoulas, D. Genin, A. Schmidt, I. Papusha, R.
Wu, G. Mullins, T. Young, and J. Brulé. “Verification of Safety in
Artificial Intelligence and Reinforcement Learning Systems,” Johns
Hopkins APL Technical Digest, Volume 35, Number 4, 2021. [pdf]
I. Papusha, R. Wu, J. Brulé, Y. Kouskoulas, D.
Genin, and A. Schmidt. “Incorrect by Construction: Fine Tuning Neural
Networks for Guaranteed Performance on Finite Sets of Examples,”
Workshop on Formal Methods for ML-Enabled Autonomous Systems
(FoMLAS), part of International Conference on Computer-Aided
Verification (CAV), Los Angeles, CA, July 19, 2020. [arXiv] [lantern-smt
Air and Space Systems
M. A. Kelly, J. L. Carr, D. L. Wu, A. C. Goldberg, I.
Papusha, and R. T. Meinhold. “Compact Midwave Imaging System:
Results from an Airborne Demonstration,” Remote Sensing,
14(4):834, 2022. [doi]
M. A. Kelly, D. Wu, A. Goldberg, I. Papusha, J.
Wilson, J. Carr, J. Boldt, J. Greenberg, F. Morgan, S. Yee, A.
Heidinger, and L. Mehr. “Compact Midwave Imaging System (CMIS) for
Retrieval of Cloud Motion Vectors (CMVs) and Cloud Geometric Heights
(CGHs),” Proceedings of the SPIE, Volume 10776, Remote Sensing
of the Atmosphere, Clouds, and Precipitation VII, Honolulu, HI,
September 24–26, 2018. [doi] [AGU19]
M. Cubuktepe, N. Jansen, S. Junges, J.-P. Katoen, I.
Papusha, H. A. Poonawala, and U. Topcu. “Sequential Convex
Programming for the Efficient Verification of Parametric MDPs,”
International Conference on Tools and Algorithms for the
Construction and Analysis of Systems (TACAS), pp. 133–150, Uppsala,
Sweden, April 22–29, 2017. [arXiv] [doi]
S. S. Farahani, I. Papusha, C. McGhan, and R. M.
Murray. “Constrained Autonomous Satellite Docking via Differential
Flatness and Model Predictive Control,” IEEE Conference on Decision
and Control (CDC), pp. 3306–3311, Las Vegas, NV, December 12–14,
2016. [pdf] [doi]
Control Theory and
I. Papusha, U. Topcu, S. Carr, and N. Lauffer.
“Affine Multiplexing Networks: System Analysis, Learning, and
Computation,” arXiv:1805.00164 [math.OC], 2018. [arXiv] [slides] [amnet
I. Papusha, M. Wen, and U. Topcu. “Inverse Optimal
Control with Regular Language Specifications,” American Control
Conference (ACC), pp. 770–777, Milwaukee, WI, June 27–29, 2018. [pdf] [doi] [slides]
M. Wen, I. Papusha, and U. Topcu. “Learning from
Demonstrations with High-Level Side Information,” International
Joint Conference on Artificial Intelligence (IJCAI), pp. 3055–3061,
Melbourne, Australia, August 19–25, 2017. [pdf] [doi]
J. Fu, I. Papusha, and U. Topcu. “Sampling-based
Approximate Optimal Control Under Temporal Logic Constraints,” ACM
International Conference on Hybrid Systems: Computation and Control
(HSCC), pp. 227–235, Pittsburgh, PA, April 18–20, 2017. [pdf] [doi] [slides]
I. Papusha, J. Fu, U. Topcu, and R. M. Murray.
“Automata Theory Meets Approximate Dynamic Programming: Optimal Control
with Temporal Logic Constraints,” IEEE Conference on Decision and
Control (CDC), pp. 434–440, Las Vegas, NV, December 12–14, 2016.
[pdf] [doi] [slides] [sydar
I. Papusha. Robustness, Adaptation, and
Learning in Optimal Control. Ph.D. thesis, California Institute of
Technology, 2016. [pdf] [CaltechTHESIS]
I. Papusha and R. M. Murray. “Analysis of Control
Systems on Symmetric Cones,” IEEE Conference on Decision and Control
(CDC), pp. 3971–3976, Osaka, Japan, December 15–18, 2015. [pdf] [doi] [slides]
M. B. Horowitz, I. Papusha, and J. W. Burdick
“Domain Decomposition for Stochastic Optimal Control,” IEEE
Conference on Decision and Control (CDC), pp. 1866–1873, Los
Angeles, CA, December 15–17, 2014. [pdf] [arXiv] [doi]
I. Papusha, E. Lavretsky, and R. M. Murray.
“Collaborative System Identification via Parameter Consensus,”
American Control Conference (ACC), pp. 13–19, Portland, OR,
June 4–6, 2014. [pdf] [doi] [slides]
I. Papusha. “Fast Automatic Background Extraction
via Robust PCA,” 2011. [pdf] [pcp_admm.m code]
I. Papusha and M. Ho. “Hough Transform for
Directional Orientation,” 2010. [pdf]
Multiplexing Networks,” Applications of Formal Methods to
Control Theory and Dynamical Systems (FoMA Workshop), Carnegie
Mellon University, June 23, 2018. [pdf]