Qingfeng Lan - RankMe

23 papers A* 2A 1Journal 18Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Dynamic Speculative Agent Planning. CoRR Yilin Guan, Qingfeng Lan, Sun Fei, Dujian Ding, Devang Acharya, Chi Wang, William Yang Wang, Wenyue Hua
2025	J	`jnl`	Efficient Reinforcement Learning by Reducing Forgetting with Elephant Activation Functions. CoRR Qingfeng Lan, Gautham Vasan, A. Rupam Mahmood
2024	J	`jnl`	Learning to Optimize for Reinforcement Learning. RLJ Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan, Zhongwen Xu
2024	J	`jnl`	Loss of plasticity in deep continual learning. Nat. Shibhansh Dohare, J. Fernando Hernandez-Garcia, Qingfeng Lan, Parash Rahman, A. Rupam Mahmood, Richard S. Sutton
2024	J	`jnl`	More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling. RLJ Haque Ishfaq, Yixin Tan, Yu Yang, Qingfeng Lan, Jianfeng Lu, A. Rupam Mahmood, Doina Precup, Pan Xu
2024	J	`jnl`	More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling. CoRR Haque Ishfaq, Yixin Tan, Yu Yang, Qingfeng Lan, Jianfeng Lu, A. Rupam Mahmood, Doina Precup, Pan Xu
2024	A*	`conf`	Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. ICLR Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli
2024	J	`jnl`	Weight Clipping for Deep Continual and Reinforcement Learning. RLJ Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood
2024	J	`jnl`	Weight Clipping for Deep Continual and Reinforcement Learning. CoRR Mohamed Elsayed, Qingfeng Lan, Clare Lyle, A. Rupam Mahmood
2023	J	`jnl`	Elephant Neural Networks: Born to Be a Continual Learner. CoRR Qingfeng Lan, A. Rupam Mahmood
2023	J	`jnl`	Learning to Optimize for Reinforcement Learning. CoRR Qingfeng Lan, A. Rupam Mahmood, Shuicheng Yan, Zhongwen Xu
2023	J	`jnl`	Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation. Trans. Mach. Learn. Res. Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood
2023	—	`conf`	Optimizing Mesh Simplification: A Newton-Based Approach to Quadratic Error Metrics. CISP-BMEI Qingfeng Lan, Fujun Yang, Lei Yang
2023	J	`jnl`	Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. CoRR Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli
2022	J	`jnl`	Memory-efficient Reinforcement Learning with Knowledge Consolidation. CoRR Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood
2022	A	`conf`	Model-free Policy Learning with Reward Gradients. AISTATS Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi, Rupam Mahmood
2021	J	`jnl`	Model-free Policy Learning with Reward Gradients. CoRR Qingfeng Lan, A. Rupam Mahmood
2021	J	`jnl`	Predictive Representation Learning for Language Modeling. CoRR Qingfeng Lan, Luke Kumar, Martha White, Alona Fyshe
2021	J	`jnl`	Variational Quantum Soft Actor-Critic. CoRR Qingfeng Lan
2020	A*	`conf`	Maxmin Q-learning: Controlling the Estimation Bias of Q-learning. ICLR Qingfeng Lan, Yangchen Pan, Alona Fyshe, Martha White
2020	J	`jnl`	Maxmin Q-learning: Controlling the Estimation Bias of Q-learning. CoRR Qingfeng Lan, Yangchen Pan, Alona Fyshe, Martha White
2019	J	`jnl`	Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation. CoRR Zichen Zhang, Qingfeng Lan, Lei Ding, Yue Wang, Negar Hassanpour, Russell Greiner
2018	—	`conf`	A Deep Top-K Relevance Matching Model for Ad-hoc Retrieval. CCIR Zhou Yang, Qingfeng Lan, Jiafeng Guo, Yixing Fan, Xiaofei Zhu, Yanyan Lan, Yue Wang, Xueqi Cheng

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.