Farzad Yousefian

37 papers A* 1B 1C 9Misc 4Journal 18Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Incentive-Aware Federated Averaging with Performance Guarantees under Strategic Participation. CoRR Fateme Maleki, Krishnan Raghavan, Farzad Yousefian
2026	J	`jnl`	On Performance Guarantees for Federated Learning with Personalized Constraints. CoRR Mohammadjavad Ebrahimi, Daniel Alberto Burbano, Farzad Yousefian
2026	J	`jnl`	Zeroth-Order Gradient and Quasi-Newton Methods for Nonsmooth Nonconvex Stochastic Optimization. SIAM J. Optim. Luke Marrinan, Uday V. Shanbhag, Farzad Yousefian
2025	—	`conf`	A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning. CDC Yuyang Qiu, Kibaek Kim, Farzad Yousefian
2025	J	`jnl`	A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning. CoRR Yuyang Qiu, Kibaek Kim, Farzad Yousefian
2025	J	`jnl`	Improved Guarantees for Optimal Nash Equilibrium Seeking and Bilevel Variational Inequalities. SIAM J. Optim. Sepideh Samadi, Farzad Yousefian
2024	C	`conf`	Achieving Optimal Complexity Guarantees for a Class of Bilevel Convex Optimization Problems. ACC Sepideh Samadi, Daniel Alberto Burbano, Farzad Yousefian
2024	C	`conf`	Distributed Gradient Tracking Methods with Guarantees for Computing a Solution to Stochastic MPECs. ACC Mohammadjavad Ebrahimi, Uday V. Shanbhag, Farzad Yousefian
2024	B	`conf`	Privacy-Preserving Federated Learning for Science: Challenges and Research Directions. IEEE Big Data Kibaek Kim, Krishnan Raghavan, Olivera Kotevska, Matthieu Dorier, Ravi K. Madduri, Minseok Ryu, Todd S. Munson, Robert Ross, Thomas Flynn, Ai Kagawa, Byung-Jun Yoon, Christian Engelmann, Farzad Yousefian
2024	J	`jnl`	Randomized Lagrangian stochastic approximation for large-scale constrained stochastic Nash games. Optim. Lett. Zeinab Alizadeh, Afrooz Jalilzadeh, Farzad Yousefian
2024	J	`jnl`	Stochastic Approximation for Estimating the Price of Stability in Stochastic Nash Games. ACM Trans. Model. Comput. Simul. Afrooz Jalilzadeh, Farzad Yousefian, Mohammadjavad Ebrahimi
2023	J	`jnl`	A Fish Rheotaxis Mechanism as a Zero-Order Optimization Strategy. IEEE Access Daniel Alberto Burbano, Farzad Yousefian
2023	J	`jnl`	An Incremental Gradient Method for Optimization Problems With Variational Inequality Constraints. IEEE Trans. Autom. Control. Harshal D. Kaushik, Sepideh Samadi, Farzad Yousefian
2023	J	`jnl`	Complexity guarantees for an implicit smoothing-enabled method for stochastic MPECs. Math. Program. Shisheng Cui, Uday V. Shanbhag, Farzad Yousefian
2023	A*	`conf`	Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization. NeurIPS Yuyang Qiu, Uday V. Shanbhag, Farzad Yousefian
2022	J	`jnl`	A Variable Sample-Size Stochastic Quasi-Newton Method for Smooth and Nonsmooth Stochastic Convex Optimization. Math. Oper. Res. Afrooz Jalilzadeh, Angelia Nedic, Uday V. Shanbhag, Farzad Yousefian
2021	J	`jnl`	A Method with Convergence Rates for Optimization Problems with Variational Inequality Constraints. SIAM J. Optim. Harshal D. Kaushik, Farzad Yousefian
2021	C	`conf`	An Incremental Gradient Method for Large-scale Distributed Nonlinearly Constrained Optimization. ACC Harshal D. Kaushik, Farzad Yousefian
2021	C	`conf`	Bilevel Distributed Optimization in Directed Networks. ACC Farzad Yousefian
2020	J	`jnl`	On Stochastic and Deterministic Quasi-Newton Methods for Nonstrongly Convex Optimization: Asymptotic Convergence and Rate Analysis. SIAM J. Optim. Farzad Yousefian, Angelia Nedic, Uday V. Shanbhag
2019	C	`conf`	A First-order Method for Monotone Stochastic Variational Inequalities on Semidefinite Matrix Spaces. ACC Nahidsadat Majlesinasab, Farzad Yousefian, Mohammad Javad Feizollahi
2019	C	`conf`	A Randomized Block Coordinate Iterative Regularized Subgradient Method for High-dimensional Ill-posed Convex Optimization. ACC Harshal Kaushik, Farzad Yousefian
2019	C	`conf`	An Iterative Regularized Incremental Projected Subgradient Method for a Class of Bilevel Optimization Problems. ACC Mostafa Amini, Farzad Yousefian
2019	J	`jnl`	Self-Tuned Mirror Descent Schemes for Smooth and Nonsmooth High-Dimensional Stochastic Optimization. IEEE Trans. Autom. Control. Nahidsadat Majlesinasab, Farzad Yousefian, Arash Pourhabib
2018	—	`conf`	A Variable Sample-Size Stochastic Quasi-Newton Method for Smooth and Nonsmooth Stochastic Convex Optimization. CDC Afrooz Jalilzadeh, Angelia Nedic, Uday V. Shanbhag, Farzad Yousefian
2018	Misc	`conf`	Recent Trends in stochastic Gradient Descent for Machine Learning and Big Data. WSC David Newton, Raghu Pasupathy, Farzad Yousefian
2017	Misc	`conf`	A smoothing stochastic quasi-newton method for non-lipschitzian stochastic optimization problems. WSC Farzad Yousefian, Angelia Nedic, Uday V. Shanbhag
2017	J	`jnl`	On smoothing, regularization, and averaging in stochastic approximation methods for stochastic variational inequality problems. Math. Program. Farzad Yousefian, Angelia Nedic, Uday V. Shanbhag
2016	J	`jnl`	Self-Tuned Stochastic Approximation Schemes for Non-Lipschitzian Stochastic Multi-User Optimization and Nash Games. IEEE Trans. Autom. Control. Farzad Yousefian, Angelia Nedic, Uday V. Shanbhag
2016	—	`conf`	Stochastic quasi-Newton methods for non-strongly convex problems: Convergence and rate analysis. CDC Farzad Yousefian, Angelia Nedic, Uday V. Shanbhag
2014	—	`conf`	Optimal robust smoothing extragradient algorithms for stochastic variational inequality problems. CDC Farzad Yousefian, Angelia Nedic, Uday V. Shanbhag
2013	C	`conf`	A distributed adaptive steplength stochastic approximation method for monotone stochastic Nash Games. ACC Farzad Yousefian, Angelia Nedic, Uday V. Shanbhag
2013	Misc	`conf`	A regularized smoothing stochastic approximation (RSSA) algorithm for stochastic variational inequality problems. WSC Farzad Yousefian, Angelia Nedic, Uday V. Shanbhag
2012	J	`jnl`	On stochastic gradient and subgradient methods with adaptive steplength sequences. Autom. Farzad Yousefian, Angelia Nedic, Uday V. Shanbhag
2011	Misc	`conf`	A regularized adaptive steplength stochastic approximation scheme for monotone stochastic variational inequalities. WSC Farzad Yousefian, Angelia Nedic, Uday V. Shanbhag
2011	J	`jnl`	On Stochastic Gradient and Subgradient Methods with Adaptive Steplength Sequences CoRR Farzad Yousefian, Angelia Nedic, Uday V. Shanbhag
2010	C	`conf`	Convex nondifferentiable stochastic optimization: A local randomized smoothing technique. ACC Farzad Yousefian, Angelia Nedic, Uday V. Shanbhag

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.