Bart P. G. Van Parys

28 papers A* 1Journal 26Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Globalized Adversarial Regret Optimization: Robust Decisions with Uncalibrated Predictions. CoRR Jannis Kurtz, Bart P. G. Van Parys
2025	J	`jnl`	Mean robust optimization. Math. Program. Irina Wang, Cole Becker, Bart P. G. Van Parys, Bartolomeo Stellato
2025	J	`jnl`	Stochastic Optimization with Optimal Importance Sampling. CoRR Liviu Aolaritei, Bart P. G. Van Parys, Henry Lam, Michael I. Jordan
2024	J	`jnl`	A Pareto Dominance Principle for Data-Driven Optimization. Oper. Res. Tobias Sutter, Bart P. G. Van Parys, Daniel Kuhn
2024	J	`jnl`	Branch-and-bound performance estimation programming: a unified methodology for constructing optimal optimization methods. Math. Program. Shuvomoy Das Gupta, Bart P. G. Van Parys, Ernest K. Ryu
2024	J	`jnl`	Efficient data-driven optimization with noisy data. Oper. Res. Lett. Bart P. G. Van Parys
2024	J	`jnl`	Exterior-Point Optimization for Sparse and Low-Rank Optimization. J. Optim. Theory Appl. Shuvomoy Das Gupta, Bartolomeo Stellato, Bart P. G. Van Parys
2024	J	`jnl`	From Distributional Robustness to Robust Statistics: A Confidence Sets Perspective. CoRR Gabriel Chan, Bart P. G. Van Parys, M. Amine Bennouna
2024	J	`jnl`	Optimal Learning for Structured Bandits. Manag. Sci. Bart P. G. Van Parys, Negin Golrezaei
2024	J	`jnl`	Publisher Correction: Branch-and-bound performance estimation programming: a unified methodology for constructing optimal optimization methods. Math. Program. Shuvomoy Das Gupta, Bart P. G. Van Parys, Ernest K. Ryu
2023	A*	`conf`	Certified Robust Neural Networks: Generalization and Corruption Resistance. ICML M. Amine Bennouna, Ryan Lucas, Bart P. G. Van Parys
2023	J	`jnl`	Certified Robust Neural Networks: Generalization and Corruption Resistance. CoRR M. Amine Bennouna, Ryan Lucas, Bart P. G. Van Parys
2023	J	`jnl`	Smoothed f-Divergence Distributionally Robust Optimization: Exponential Rate Efficiency and Complexity-Free Calibration. CoRR Zhenyuan Liu, Bart P. G. Van Parys, Henry Lam
2022	J	`jnl`	Bootstrap robust prescriptive analytics. Math. Program. Dimitris Bertsimas, Bart P. G. Van Parys
2022	J	`jnl`	Holistic Robust Data-Driven Decisions. CoRR M. Amine Bennouna, Bart P. G. Van Parys
2021	J	`jnl`	From Data to Decisions: Distributionally Robust Optimization Is Optimal. Manag. Sci. Bart P. G. Van Parys, Peyman Mohajerin Esfahani, Daniel Kuhn
2021	J	`jnl`	Learning and Decision-Making with Data: Optimal Formulations and Phase Transitions. CoRR M. Amine Bennouna, Bart P. G. Van Parys
2021	J	`jnl`	Optimal Transport in the Face of Noisy Data. CoRR Bart P. G. Van Parys
2021	J	`jnl`	Sparse classification: a scalable discrete optimization perspective. Mach. Learn. Dimitris Bertsimas, Jean Pauphilet, Bart P. G. Van Parys
2020	J	`jnl`	Optimal Learning for Structured Bandits. CoRR Bart P. G. Van Parys, Negin Golrezaei
2020	J	`jnl`	Sparse hierarchical regression with polynomials. Mach. Learn. Dimitris Bertsimas, Bart P. G. Van Parys
2019	J	`jnl`	Distributionally robust expectation inequalities for structured distributions. Math. Program. Bart P. G. Van Parys, Paul J. Goulart, Manfred Morari
2017	J	`jnl`	From Data to Decisions: Distributionally Robust Optimization is Optimal. CoRR Bart P. G. Van Parys, Peyman Mohajerin Esfahani, Daniel Kuhn
2017	J	`jnl`	Sparse Hierarchical Regression with Polynomials. CoRR Dimitris Bertsimas, Bart P. G. Van Parys
2016	J	`jnl`	Distributionally Robust Control of Constrained Stochastic Systems. IEEE Trans. Autom. Control. Bart P. G. Van Parys, Daniel Kuhn, Paul J. Goulart, Manfred Morari
2016	J	`jnl`	Generalized Gauss inequalities via semidefinite programming. Math. Program. Bart P. G. Van Parys, Paul J. Goulart, Daniel Kuhn
2013	J	`jnl`	Infinite Horizon Performance Bounds for Uncertain Constrained Systems. IEEE Trans. Autom. Control. Bart P. G. Van Parys, Paul J. Goulart, Manfred Morari
2012	—	`conf`	Infinite-horizon performance bounds for constrained stochastic systems. CDC Bart P. G. Van Parys, Paul J. Goulart, Manfred Morari

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.