Paul J. Werbos

39 papers B 8C 1Journal 25Unranked 5

Year	Rank	Type	Title / Venue / Authors
2023	J	`jnl`	Guest Editorial: Cyber-Physical-Social Intelligence: Toward Metaverse-Based Smart Societies of 6I and 6S. IEEE Trans. Syst. Man Cybern. Syst. Fei-Yue Wang, Ying Tang, Paul J. Werbos
2018	B	`conf`	AI Intelligence for the Grid 16 Years Later: Progress, Challenges and Lessons for Other Sectors. IJCNN Paul J. Werbos
2016	J	`jnl`	Analog quantum computing (AQC) and the need for time-symmetric physics. Quantum Inf. Process. Paul J. Werbos, Ludmila Dolmatova
2015	J	`jnl`	Complete stability analysis of a heuristic approximate dynamic programming control design. Autom. Yury Sokolov, Robert Kozma, Ludmilla D. Werbos, Paul J. Werbos
2015	J	`jnl`	Stochastic Path Model of Polaroid Polarizer for Bell's Theorem and Triphoton Experiments. Int. J. Bifurc. Chaos Paul J. Werbos
2015	J	`jnl`	Time-Symmetric Physics: A Radical Approach to the Decoherence Problem. CoRR Paul J. Werbos
2014	J	`jnl`	From ADP to the Brain: Foundations, Roadmap, Challenges and Research Priorities. CoRR Paul J. Werbos
2014	B	`conf`	From ADP to the brain: Foundations, roadmap, challenges and research priorities. IJCNN Paul J. Werbos
2013	J	`jnl`	Complete stability analysis of a heuristic ADP control design. CoRR Yury Sokolov, Robert Kozma, Ludmilla Werbos, Paul J. Werbos
2013	—	`conf`	Fuzziness, Probability, Uncertainty and the Foundations of Knowledge. On Fuzziness (2) Paul J. Werbos
2012	J	`jnl`	Metamodeling and the Critic-based approach to multi-level optimization. Neural Networks Ludmilla Werbos, Robert Kozma, Rodrigo Silva-Lugo, Giovanni Egidio Pazienza, Paul J. Werbos
2012	J	`jnl`	Neural networks and the experience and cultivation of mind. Neural Networks Paul J. Werbos
2011	J	`jnl`	Computational Intelligence for the Smart Grid - History, Challenges, and Opportunities. IEEE Comput. Intell. Mag. Paul J. Werbos
2011	B	`conf`	Metamodeling for large-scale optimization tasks based on object networks. IJCNN Ludmilla Werbos, Robert Kozma, Rodrigo Silva-Lugo, Giovanni Egidio Pazienza, Paul J. Werbos
2011	B	`conf`	Neural networks as a path to self-awareness. IJCNN Paul J. Werbos
2010	J	`jnl`	Brain-Like Stochastic Search: A Research Challenge and Funding Opportunity CoRR Paul J. Werbos
2009	J	`jnl`	Intelligence in the brain: A theory of how it works and how to build it. Neural Networks Paul J. Werbos
2009	B	`conf`	Putting more brain-like intelligence into the electric power grid: What we need and how to do it. IJCNN Paul J. Werbos
2008	J	`jnl`	Beyond Feedforward Models Trained by Backpropagation: A Practical Training Tool for a More Efficient Universal Approximator. IEEE Trans. Neural Networks Roman Ilin, Robert Kozma, Paul J. Werbos
2008	J	`jnl`	Foreword: ADP - The Key Direction for Future Research in Intelligent Control and Understanding Brain Intelligence. IEEE Trans. Syst. Man Cybern. Part B Paul J. Werbos
2007	J	`jnl`	Beyond Feedforward Models Trained by Backpropagation: a Practical Training Tool for a More Efficient Universal Approximator CoRR Roman Ilin, Robert Kozma, Paul J. Werbos
2006	B	`conf`	Cellular SRN Trained by Extended Kalman Filter Shows Promise for ADP. IJCNN Roman Ilin, Robert Kozma, Paul J. Werbos
2002	J	`jnl`	Classical ODE and PDE which Obey Quantum Dynamics. Int. J. Bifurc. Chaos Paul J. Werbos
2001	C	`conf`	Reconfigurable flight control via neurodynamic programming and universally stable adaptive control. ACC Paul J. Werbos
2000	—	`conf`	Extending Chaos and Complexity Theory to Address Life, Brain and Quantum Foundations. IJCNN (4) Paul J. Werbos
2000	—	`conf`	New Directions in ACDs: Keys to Intelligent Control and Understanding the Brain. IJCNN (3) Paul J. Werbos
1999	B	`conf`	New designs for universal stability in classical adaptive control and reinforcement learning. IJCNN Paul J. Werbos
1998	B	`conf`	Brain-like intelligent control: from neural nets to larger-scale systems. ESANN Paul J. Werbos
1996	—	`conf`	Finding time series among the chaos: stochastics, deseasonalization, and texture-detection using neural nets. CIFEr Paul J. Werbos
1993	J	`jnl`	Elastic Fuzzy Logic: A Better Fit to Neurocontrol and True Intelligence. J. Intell. Fuzzy Syst. Paul J. Werbos
1993	J	`jnl`	Neurocontrol and elastic fuzzy logic: capabilities, concepts, and applications. IEEE Trans. Ind. Electron. Paul J. Werbos
1992	J	`jnl`	Neurocontrol and fuzzy logic: Connections and designs. Int. J. Approx. Reason. Paul J. Werbos
1990	J	`jnl`	Backpropagation through time: what it does and how to do it. Proc. IEEE Paul J. Werbos
1990	J	`jnl`	Consistency of HDP applied to a simple reinforcement learning problem. Neural Networks Paul J. Werbos
1989	J	`jnl`	Maximizing long-term gas industry profits in two minutes in Lotus using neural network methods. IEEE Trans. Syst. Man Cybern. Paul J. Werbos
1988	—	`conf`	Backpropagation: past and future. ICNN Paul J. Werbos
1988	J	`jnl`	Generalization of backpropagation with application to a recurrent gas market model. Neural Networks Paul J. Werbos
1987	J	`jnl`	Building and Understanding Adaptive Systems: A Statistical/Numerical Approach to Factory Automation and Brain Research. IEEE Trans. Syst. Man Cybern. Paul J. Werbos
1978	J	`jnl`	An Empirical Test of New Forecasting Methods Derived from a Theory of Intelligence: The Prediction of Conflict in Latin America. IEEE Trans. Syst. Man Cybern. Paul J. Werbos, Jim Titus

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.