Aaron Tuor - RankMe

36 papers A* 1C 3Journal 26Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Data Center Chiller Plant Optimization via Mixed-Integer Nonlinear Differentiable Predictive Control. CoRR Ján Boldocký, Cary Faulkner, Elad Michael, Martin Gulan, Aaron Tuor, Ján Drgona
2025	J	`jnl`	Homotopy-Guided Self-Supervised Learning of Parametric Solutions for AC Optimal Power Flow. CoRR Shimiao Li, Aaron Tuor, Draguna L. Vrabie, Larry T. Pileggi, Ján Drgona
2024	J	`jnl`	Learning Constrained Parametric Differentiable Predictive Control Policies With Guarantees. IEEE Trans. Syst. Man Cybern. Syst. Ján Drgona, Aaron Tuor, Draguna L. Vrabie
2022	—	`conf`	Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach. CDC Wenceslao Shaw-Cortez, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie
2022	J	`jnl`	Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach. CoRR Wenceslao Shaw-Cortez, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie
2022	J	`jnl`	Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles. CoRR Wenceslao Shaw-Cortez, Soumya Vasisht, Aaron Tuor, Ján Drgona, Draguna L. Vrabie
2022	C	`conf`	Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem. ACC Ethan King, Ján Drgona, Aaron Tuor, Shrirang Abhyankar, Craig Bakker, Arnab Bhattacharya, Draguna L. Vrabie
2022	J	`jnl`	Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problem. CoRR Ethan King, Ján Drgona, Aaron Tuor, Shrirang Abhyankar, Craig Bakker, Arnab Bhattacharya, Draguna L. Vrabie
2022	J	`jnl`	Learning Stochastic Parametric Differentiable Predictive Control Policies. CoRR Ján Drgona, Sayak Mukherjee, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie
2022	—	`conf`	Neural Lyapunov Differentiable Predictive Control. CDC Sayak Mukherjee, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie
2022	J	`jnl`	Neural Lyapunov Differentiable Predictive Control. CoRR Sayak Mukherjee, Ján Drgona, Aaron Tuor, Mahantesh Halappanavar, Draguna L. Vrabie
2022	C	`conf`	Neural Ordinary Differential Equations for Nonlinear System Identification. ACC Aowabin Rahman, Ján Drgona, Aaron Tuor, Jan Strube
2022	J	`jnl`	Neural Ordinary Differential Equations for Nonlinear System Identification. CoRR Aowabin Rahman, Ján Drgona, Aaron Tuor, Jan Strube
2022	J	`jnl`	Neuro-physical dynamic load modeling using differentiable parametric optimization. CoRR Shrirang Abhyankar, Ján Drgona, Andrew August, Elliott Skomski, Aaron Tuor
2022	J	`jnl`	Structural Inference of Networked Dynamical Systems with Universal Differential Equations. CoRR James Koch, Zhao Chen, Aaron Tuor, Ján Drgona, Draguna L. Vrabie
2021	—	`conf`	Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution. L4DC Elliott Skomski, Ján Drgona, Aaron Tuor
2021	C	`conf`	Constrained Block Nonlinear Neural Dynamical Models. ACC Elliott Skomski, Soumya Vasisht, Colby Wight, Aaron Tuor, Ján Drgona, Draguna L. Vrabie
2021	J	`jnl`	Constrained Block Nonlinear Neural Dynamical Models. CoRR Elliott Skomski, Soumya Vasisht, Colby Wight, Aaron Tuor, Ján Drgona, Draguna L. Vrabie
2021	J	`jnl`	Deep Learning Explicit Differentiable Predictive Control Laws for Buildings. CoRR Ján Drgona, Aaron Tuor, Soumya Vasisht, Elliott Skomski, Draguna L. Vrabie
2021	—	`conf`	Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning. MetaDL@AAAI Henry Kvinge, Zachary New, Nico Courts, Jung H. Lee, Lauren A. Phillips, Courtney D. Corley, Aaron Tuor, Andrew Avila, Nathan O. Hodas
2021	J	`jnl`	Prototypical Region Proposal Networks for Few-Shot Localization and Classification. CoRR Elliott Skomski, Aaron Tuor, Andrew Avila, Lauren A. Phillips, Zachary New, Henry Kvinge, Courtney D. Corley, Nathan O. Hodas
2020	J	`jnl`	Constrained Neural Ordinary Differential Equations with Stability Guarantees. CoRR Aaron Tuor, Ján Drgona, Draguna L. Vrabie
2020	J	`jnl`	Constrained Physics-Informed Deep Learning for Stable System Identification and Control of Unknown Linear Systems. CoRR Ján Drgona, Aaron Tuor, Draguna L. Vrabie
2020	J	`jnl`	Differentiable Predictive Control: An MPC Alternative for Unknown Nonlinear Systems using Constrained Deep Learning. CoRR Ján Drgona, Karol Kis, Aaron Tuor, Draguna L. Vrabie, Martin Klauco
2020	J	`jnl`	Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning. CoRR Henry Kvinge, Zachary New, Nico Courts, Jung H. Lee, Lauren A. Phillips, Courtney D. Corley, Aaron Tuor, Andrew Avila, Nathan O. Hodas
2020	J	`jnl`	Physics-Informed Neural State Space Models via Learning and Evolution. CoRR Elliott Skomski, Ján Drgona, Aaron Tuor
2020	J	`jnl`	Spectral Analysis and Stability of Deep Neural Dynamics. CoRR Ján Drgona, Elliott Skomski, Soumya Vasisht, Aaron Tuor, Draguna L. Vrabie
2020	—	`conf`	Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers. CVPR Workshops Loc Truong, Chace Jones, Brian Hutchinson, Andrew August, Brenda Praggastis, Robert Jasper, Nicole Nichols, Aaron Tuor
2020	J	`jnl`	Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers. CoRR Loc Truong, Chace Jones, Brian Hutchinson, Andrew August, Brenda Praggastis, Robert Jasper, Nicole Nichols, Aaron Tuor
2019	J	`jnl`	Multiple Document Representations from News Alerts for Automated Bio-surveillance Event Detection. CoRR Aaron Tuor, Fnu Anubhav, Lauren E. Charles
2018	J	`jnl`	A systematic exploration of ΔΔG cutoff ranges in machine learning models for protein mutation stability prediction. J. Bioinform. Comput. Biol. Richard Olney, Aaron Tuor, Filip Jagodzinski, Brian Hutchinson
2018	J	`jnl`	Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection. CoRR Andy Brown, Aaron Tuor, Brian Hutchinson, Nicole Nichols
2017	—	`conf`	Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams. AAAI Workshops Aaron Tuor, Samuel Kaplan, Brian Hutchinson, Nicole Nichols, Sean Robinson
2017	J	`jnl`	Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams. CoRR Aaron Tuor, Samuel Kaplan, Brian Hutchinson, Nicole Nichols, Sean Robinson
2017	A*	`conf`	Predicting User Roles from Computer Logs Using Recurrent Neural Networks. AAAI Aaron Tuor, Samuel Kaplan, Brian Hutchinson, Nicole Nichols, Sean Robinson
2017	J	`jnl`	Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection. CoRR Aaron Tuor, Ryan Baerwolf, Nicolas Knowles, Brian Hutchinson, Nicole Nichols, Robert Jasper

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.