Publications

Y. B. Uslu, N. NaderiAlizadeh, M. Eisen, and A. Ribeiro, Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression, IEEE Transactions on Signal Processing, (submitted) June 2025

B. Salehi,  D. Roy,  M. Eisen,  A. Baxi,  D. Cavalcanti, and  K. Chowdhury, DARWIN: Digital Twin Assisted Robot Navigation and WIreless Network Management, IEEE Transactions on Mobile Computing, pg. 1-15, August 2025.

P. Behmandpoor, M. Eisen, P. Patrinos, and M. Moonen, A Deep Learning Based Resource Allocator for Communication Systems with Dynamic User Utility Demands, IEEE Transactions on Wireless Communications, August 2025

S. Mohanti, D. Roy, M. Eisen, D. Cavalcanti, and K. Chowdhury, L-NORM: Learning and Network Orchestration at the Edge for Robot Connectivity and Mobility in Factory Floor Environments, IEEE Transactions on Mobile Computing, pg. 1-16, April 2023.

N. NaderiAlizadeh, M. Eisen, and A. Ribeiro, Learning Resilient Radio Resource Management Policies with Graph Neural Networks, IEEE Trans. Signal Process., vol. 71, pg. 995-1009, March 2023.

N. NaderiAlizadeh, M. Eisen, and A. Ribeiro, State-Augmented Learnable Algorithms for Resource Management in Wireless Networks, IEEE Trans. Signal Process., vol. 70, pg. 5898-5912, December 2022.

M. Eisen, S. Sudhakaran, V. Mageshkumar, A. Baxi, and D. Cavalcanti, Joint Resource Scheduling for AMR Navigation over Wireless Edge Networks, IEEE Open Journal on Vehicular Technology, vol.4, pg. 36-47, October 2022.

A. Baxi, M. Eisen, S. Sudhakaran, F. Oboril, G. Murthy, V. Mageshkumar, M. Paulitsch, and M. Huang, Towards Factory-Scale Edge Robotic Systems: Challenges and Research Directions, IEEE Internet of Things Magazine, vol. 5, Issue 3, Sept. 2022, pg. 26-31.

V. Lima, M. Eisen, K. Gatsis, and A. Ribeiro, Large-Scale Graph Reinforcement Learning in Wireless Control Systems, IEEE Transactions on Control of Networked Systems (TCNS), (submitted) April 2022.

Z. Wang, M. Eisen, and A. Ribeiro, Learning Decentralized Wireless Resource Allocations with Graph Neural Networks , IEEE Trans. Signal Process., vol. 70, March 2022, pg. 1850-1863. 

V. Lima, M. Eisen, K. Gatsis, and A. Ribeiro, Model-Free Design of Control Systems over Wireless Fading Channels, Signal Processing, vol. 197, 108540, Feb 2022.

Z. Gao, M. Eisen,  and A. Ribeiro, Resource Allocation via Model-Free Deep Learning in Free Space Optical Networks, in IEEE Trans. on Communications, vol. 70, Issue 2, Feb 2022. pg. 920-934.

D. S. Kalogerias, M. Eisen, G. J. Pappas, and A. Ribeiro, Model-Free Learning of Optimal Ergodic Policies in Wireless Systems, IEEE Trans. Signal Process., vol. 68, Issue 23, Oct, 2020, pg. 6272-6286. 

M. Eisen and A. Ribeiro, Optimal Wireless Resource Allocation  with Random Edge Graph Neural Networks, IEEE Trans. Signal Process., vol. 68, April 2020, pg. 2977 - 2991 . 

M. Eisen, A. Mokhtari, and A. Ribeiro, A Primal-Dual Quasi-Newton Method for Exact Consensus Optimization, IEEE Trans. Signal Process., vol. 67, Issue 23, Dec, 2019, pg. 5983-5997. 

M. Eisen, M. Rashid, K. Gatsis, D. Cavalcanti, N. Himayat, A. Ribeiro, Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems, IEEE Internet of Things Journal, vol. 6, Issue 5, October,2019, pg. 7878-7890.

M. Eisen, C. Zhang, L.F.O. Chamon, D.D. Lee, and A. Ribeiro, Learning Optimal Resource Allocations in Wireless Systems, IEEE Trans. Signal Process., vol. 67, Issue 10, May, 2019, pg. 2775-2790.  [ Top 50 accessed articles in IEEE TSP,  May, July, Sept, Oct 2019 ] 

M. Eisen, K. Gatsis, G.J. Pappas, and A. Ribeiro, Learning in Wireless Control Systems over Non-Stationary Channels, IEEE Trans. Signal Process., vol. 67, Issue 5, January, 2019, pg. 1123-1137. 

A. Mokhtari, M. Eisen, and A. Ribeiro, IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate, SIAM Journal on Optimization, Vol. 28, No. 2, pp. 1670–1698, 2018. 

M. Eisen, A. Ribeiro, S. Segarra, and G. Egan, Stylometric Analysis of Early Modern period English Plays, Digital Scholarship in the Humanities, Vol. 33, Issue 3, September 2018, pp. 500-528.

M. Eisen, A. Mokhtari, and A. Ribeiro, Decentralized Quasi-Newton Methods, IEEE Trans. Signal Process., vol. 65, Issue 10, May 2017, pg. 2613-2638. [ Top 50 accessed articles in IEEE TSP, March 2017 ] 

S. Segarra, M. Eisen, G. Egan and A. Ribeiro, Attributing the Authorship of the Henry VI Plays by Word Adjacency, Shakespeare Quarterly, Volume 67, Number 2, Summer 2016, pp. 232-256. 

S. Segarra, M. Eisen and A. Ribeiro, Authorship Attribution through Function Word Adjacency Networks, IEEE Trans. Signal Process., vol. 63, Issue 20, Oct 2015.

S. Fernández, R. G. Camargo, M. Eisen; A. Ribeiro, F. Larroca, On the Transferability of Graph Neural Networks for Resource Allocation in Wireless Networks, 2024 IEEE URUCON, Montevideo, UR, 2025

C. Tai, M. Eisen, D. Akhmetov, D. Das, D. Cavalcanti and R. Sivakumar, Model-Free Dynamic Traffic Steering for Multi-Link Operation in IEEE 802.11be, IEEE Int. Conference on Machine Learning for Comm. and Networking (ICMLCN), Stockholm, SE, 2024 (to appear)

A. Merwaday, R. Vannithamby, M. Eisen, S. Sudhakaran, D. Cavalcanti and V. Frascolla, Communication-Control Co-design for Robotic Manipulation in 5G Industrial IoT, IEEE Int. Conference on Industrial Informatics (INDIN), Lemgo, DE, 2023 (to appear).

S. Sudhakaran, I. Ali, M. Eisen, J. Perez-Ramirez, V. Frascolla, and D. Cavalcanti, "Zero-Delay Roaming for Mobile Robots enabled by Wireless TSN Redundancy", IEEE Int. Conference on Factory Communication Systems (WFCS), Pavia, IT, 2023 (to appear).

N. Naderializadeh, M. Eisen, and A. Ribeiro, "State-Augmented Algorithms for Wireless Resource Management with Graph Neural Networks", Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, (to appear) 2022. 

M. Eisen, S. Shukla, D. Cavalcanti, and A. S. Baxi, Communication-Control Co-design in Wireless Edge Industrial Systems, IEEE Int. Conference on Factory Communication Systems (WFCS), Pavia, IT, 2022.  [BEST PAPER AWARD]

N. Naderializadeh, M. Eisen, and A. Ribeiro, Adaptive Wireless Power Allocation with Graph Neural Networks, IEEE Int. Conf. Acoustics Speech Signal Process (ICASSP), Singapore, 2022.

Z. Wang, L. Ruiz, M. Eisen, and A. Ribeiro, Stable and Transferable Wireless Resource Allocation Policies via Manifold Neural Networks, IEEE Int. Conf. Acoustics Speech Signal Process (ICASSP), Singapore, 2022.

Z. Wang, M. Eisen, and A. Ribeiro, Unsupervised learning for asynchronous resource allocation in ad-hoc wireless networks, IEEE Int. Conf. Acoustics Speech Signal Process (ICASSP), Toronto, CA, 2021.

D. S. Kalogerias, M. Eisen, G. J. Pappas, and A. Ribeiro, “Almost-Zero Duality Gaps in Model-Free Resource Allocation for Wireless Systems,” 28th European Signal Processing Conference (EUSIPCO), pg. 1727-1731, Amsterdam, Netherlands, January 2021. 

V. Lima, M. Eisen, and A. Ribeiro, Learning Constrained Resource Allocation Policies in Wireless Control Systems, in IEEE Conference on Decision and Controls (CDC), pg. 2615-2621, Jeju Island, South Korea, December 2020.

Z. Gao, M. Eisen,  and A. Ribeiro, Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks, in IEEE Global Communications Conference (GLOBECOM), pg. 1-6, Taipei, Taiwan, December 2020. 

M. Eisen, A. Kg, A. S. Baxi, and D. Cavalcanti, Network Performance Adaptation in Wireless Control with Reinforcement Learning, Asilomar Conference on Signals, Systems and Computers, pg. 413-417, Pacific Grove, CA, November 2020. 

Z. Wang, M. Eisen, and A. Ribeiro, Decentralized Wireless Resource Allocation with Graph Neural Networks, Asilomar Conference on Signals, Systems and Computers, pg. 299-303, Pacific Grove, CA, November 2020. 

V. Lima, M. Eisen, K. Gatsis, and A. Ribeiro, Resource Allocation in Large-Scale Wireless Control Systems with Graph Neural Networks, IFAC-PapersOnLine 53 (2), 2634-2641.

N. Naderializadeh, M. Eisen, and A. Ribeiro, Wireless Power Control via Counterfactual Optimization of Graph Neural Networks, in International Workshop on Signal Processing Advances in Wireless Communications (SPAWC),  pg. 1-5, Atlanta, GA, 2020.

V. Lima, M. Eisen, K. Gatsis, and A. Ribeiro, Resource Allocation in Wireless Control Systems via Deep Policy Gradient, International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pg. 1-5, Atlanta, GA, 2020.

M. Eisen, M. Rashid, D. Cavalcanti, and A. Ribeiro, Control-Aware Scheduling for Low Latency Wireless Systems with Deep Learning, in IEEE ICC Workshop on Machine Learning for Communications (ML4COM), pg. 1-7 Dublin, IE 2020. 

M. Eisen, M. Rashid, A. Ribeiro, and D. Cavalcanti, Scheduling Low Latency Traffic for Wireless Control Systems in 5G Networks, in IEEE International Conference on Communications (ICC), pg. 1-6, Dublin, IE 2020. 

D. S. Kalogerias, M. Eisen, G. J. Pappas, and A. Ribeiro, A Zeroth-order Learning Algorithm for Ergodic Optimization of Wireless Systems With No Models and No Gradients, in IEEE Int. Conf. Acoustics Speech Signal Process (ICASSP), pg. 5045-5049, Barcelona, Spain, 2020.

M. Eisen and A. Ribeiro, Transferable Policies for Large Scale Wireless Networks wit Graph Neural Networks, in IEEE Int. Conf. Acoustics Speech Signal Process (ICASSP), pg. 5040-5044, Barcelona, Spain, 2020.

Z. Gao, M. Eisen,  and A. Ribeiro, Optimal WDM Power Allocation via Deep Learning for Radio on Free Space Optics Systems, in IEEE Global Communications Conference (GLOBECOM), pg. 1-6, Hawaii, 2019. 

 M. Eisen and A. Ribeiro, Large Scale Wireless Power Allocation with Graph Neural Networks, in International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pg. 1-5, Cannes, France, July 2-5 2019. 

M. Eisen, M. Rashid, K. Gatsis, D. Cavalcanti, N. Himayat, and A. Ribeiro, Control Aware Communication Design for Time Sensitive Wireless Systems, in Proc. Int. Conf. Acoustics Speech Signal Process (ICASSP), pg. 4584-4588, Brighton UK May 12-17 2019. 

M. Eisen, C. Zhang, L.F.O. Chamon, D.D. Lee, and A. Ribeiro, Dual Domain Learning of Optimal Resource Allocations in Wireless Communication Systems, in Proc. Int. Conf. Acoustics Speech Signal Process (ICASSP), pg. 4729-4733, Brighton UK May 12-17 2019.

M. Eisen, C. Zhang, L.F.O. Chamon, D.D. Lee, and A. Ribeiro, Online Deep Learning in Wireless Communication System, Asilomar Conference on Signals, Systems and Computers pg.  1289-1293, Pacific Grove, CA, Oct 28-Oct 31 2018. 

M. Eisen, K. Gatsis, G. J. Pappas, and A. Ribeiro, Optimization of Switched Linear Systems over Non-Stationary Wireless Channels, International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) pg. 1-5 Kalamata, Greece, June 25-28, 2018. 

M. Eisen, K. Gatsis, G. J. Pappas, and A. Ribeiro, Learning in Non-Stationary Wireless Control Systems via Newton's Method, American Controls Conference (ACC) pg. 1410-1417, Milwaukee, WI, June 27-29, 2018. 

M. Eisen, K. Gatsis, G. J. Pappas, and A. Ribeiro, Learning Statistically Accurate Resource Allocations in Non-Stationary Wireless Systems, in Proc. Int. Conf. Acoustics Speech Signal Process. (ICASSP), pg. 3559 - 3563 , Calgary, AB April 15-20 2018. 

M. Eisen, A. Mokhtari, and A. Ribeiro, Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method, Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018, Lanzarote, Spain. PMLR: Volume 84 (supplementary material) 

M. Eisen, A. Mokhtari, and A. Ribeiro, "A Primal-Dual Quasi-Newton Method for Consensus Optimization", Asilomar Conference on Signals, Systems and Computers pg. 298-302, Pacific Grove, CA, Oct 29-Nov 1 2017. 

A. Mokhtari, M. Eisen, and A. Ribeiro, "An Incremental Quasi-Newton Method with a Local Superlinear Convergence Rate," in Proc. Int. Conf. Acoustics Speech Signal Process. (ICASSP), pg. 4039-4043, New Orleans, LA, March 5-9 2017. 

M. Eisen, A. Mokhtari, and A. Ribeiro, A Decentralized Quasi-Newton Method for Dual Formulations of Consensus Optimization, IEEE Conference on Decision and Control (CDC), pg. 1951 - 1958, Las Vegas, NV, Dec. 12-14, 2016 

M. Eisen, A. Mokhtari, and A. Ribeiro, An Asynchronous Quasi-Newton Method for Consensus Optimization, In Proc. IEEE Global Conference on Signal and Information Processing, pg. 570-574, Washington, DC, Dec. 7-9, 2016. 

S. Segarra, M. Eisen and A. Ribeiro, Authorship Attribution using Function Words Adjacency Networks Acoustics, Speech and Signal Processing (ICASSP), IEEE International Conference on , pp.5563-5567, Vancouver, Canada, May 26-31 2013.