Tan M. Nguyen - RankMe

55 papers A* 4C 1Misc 2Journal 48

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Concept Heterogeneity-aware Representation Steering. CoRR Laziz U. Abdullaev, Noelle Y. L. Wong, Ryan T. Z. Lee, Shiqi Jiang, Khoi N. M. Nguyen, Tan M. Nguyen
2025	J	`jnl`	Activation Steering with a Feedback Controller. CoRR Dung V. Nguyen, Hieu M. Vu, Nhi Y. Pham, Lei Zhang, Tan M. Nguyen
2025	J	`jnl`	Angular Steering: Behavior Control via Rotation in Activation Space. CoRR Hieu M. Vu, Tan M. Nguyen
2025	J	`jnl`	CAMEx: Curvature-aware Merging of Experts. CoRR Dung V. Nguyen, Minh H. Nguyen, Luc Q. Nguyen, Rachel S. Y. Teo, Tan M. Nguyen, Linh Duy Tran
2025	J	`jnl`	Distance-Based Tree-Sliced Wasserstein Distance. CoRR Hoang V. Tran, Khoi N. M. Nguyen, Trang Pham, Thanh T. Chu, Tam Le, Tan M. Nguyen
2025	J	`jnl`	Dynamical Properties of Tokens in Self-Attention and Effects of Positional Encoding. CoRR Duy-Tung Pham, An Nguyen The, Viet-Hoang Tran, Nhan-Phu Chung, Xin T. Tong, Tan M. Nguyen, Thieu N. Vo
2025	J	`jnl`	Expert Merging in Sparse Mixture of Experts with Nash Bargaining. CoRR Dung V. Nguyen, Anh T. Nguyen, Minh H. Nguyen, Luc Q. Nguyen, Shiqi Jiang, Ethan Fetaya, Linh Duy Tran, Gal Chechik, Tan M. Nguyen
2025	J	`jnl`	Learning and predicting dynamics of compositional multiphase mixtures using Graph Neural Networks. J. Comput. Phys. Duc Thach Son Vu, Tan M. Nguyen, Weiqing Ren
2025	J	`jnl`	MMA: A Momentum Mamba Architecture for Human Activity Recognition with Inertial Sensors. CoRR Thai-Khanh Nguyen, Uyen Vo, Tan M. Nguyen, Thieu N. Vo, Trung-Hieu Le, Cuong Pham
2025	J	`jnl`	MoLEx: Mixture of Layer Experts for Finetuning with Sparse Upcycling. CoRR Rachel S. Y. Teo, Tan M. Nguyen
2025	J	`jnl`	On Linear Mode Connectivity of Mixture-of-Experts Architectures. CoRR Viet-Hoang Tran, Van-Hoan Trinh, Khanh Vinh Bui, Tan M. Nguyen
2025	J	`jnl`	Revisiting Transformers with Insights from Image Filtering. CoRR Laziz U. Abdullaev, Maksim Tkachenko, Tan M. Nguyen
2025	J	`jnl`	Selective Sinkhorn Routing for Improved Sparse Mixture of Experts. CoRR Duc Anh Nguyen, Huu Binh Ta, Nhuan Le Duc, Tan M. Nguyen, Toan Tran
2025	J	`jnl`	Spherical Tree-Sliced Wasserstein Distance. CoRR Viet-Hoang Tran, Thanh T. Chu, Khoi N. M. Nguyen, Trang Pham, Tam Le, Tan M. Nguyen
2025	J	`jnl`	The Blessing and Curse of Dimensionality in Safety Alignment. CoRR Rachel S. Y. Teo, Laziz U. Abdullaev, Tan M. Nguyen
2025	J	`jnl`	Tight Clusters Make Specialized Experts. CoRR Stefan K. Nielsen, Rachel S. Y. Teo, Laziz U. Abdullaev, Tan M. Nguyen
2025	J	`jnl`	Transformer Meets Twicing: Harnessing Unattended Residual Information. CoRR Laziz U. Abdullaev, Tan M. Nguyen
2025	J	`jnl`	Tree-Sliced Wasserstein Distance with Nonlinear Projection. CoRR Thanh Tran, Viet-Hoang Tran, Thanh T. Chu, Trang Pham, Laurent El Ghaoui, Tam Le, Tan M. Nguyen
2024	J	`jnl`	A Primal-Dual Framework for Transformers and Neural Networks. CoRR Tan M. Nguyen, Tam Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher
2024	J	`jnl`	An Attention-based Framework for Fair Contrastive Learning. CoRR Stefan K. Nielsen, Tan M. Nguyen
2024	J	`jnl`	Elliptical Attention. CoRR Stefan K. Nielsen, Laziz U. Abdullaev, Rachel S. Y. Teo, Tan M. Nguyen
2024	J	`jnl`	MomentumSMoE: Integrating Momentum into Sparse Mixture of Experts. CoRR Rachel S. Y. Teo, Tan M. Nguyen
2024	J	`jnl`	PIDformer: Transformer Meets Control Theory. CoRR Tam Nguyen, César A. Uribe, Tan M. Nguyen, Richard G. Baraniuk
2024	J	`jnl`	Tree-Sliced Wasserstein Distance on a System of Lines. CoRR Viet-Hoang Tran, Trang Pham, Tho Tran, Tam Le, Tan M. Nguyen
2024	J	`jnl`	Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis. CoRR Rachel S. Y. Teo, Tan M. Nguyen
2023	Misc	`conf`	A Probabilistic Framework for Pruning Transformers Via a Finite Admixture of Keys. ICASSP Tan M. Nguyen, Tam Nguyen, Long Bui, Hai Do, Duy Khuong Nguyen, Dung D. Le, Hung Tran-The, Nhat Ho, Stanley J. Osher, Richard G. Baraniuk
2023	J	`jnl`	ARIST: An Effective API Argument Recommendation Approach. CoRR Son Nguyen, Cuong Tran Manh, Trung Kien Tran, Tan M. Nguyen, Thu-Trang Nguyen, Kien-Tuan Ngo, Hieu Dinh Vo
2023	J	`jnl`	ARist: An effective API argument recommendation approach. J. Syst. Softw. Son Nguyen, Cuong Tran Manh, Trung Kien Tran, Tan M. Nguyen, Thu-Trang Nguyen, Kien-Tuan Ngo, Hieu Dinh Vo
2023	Misc	`conf`	DeepGRAND: Deep Graph Neural Diffusion. ACSSC Khang Nguyen, Hieu Nong, Khuong Nguyen, Tan M. Nguyen, Vinh Nguyen
2023	J	`jnl`	From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach. CoRR Tuan Nguyen, Tan M. Nguyen, Hirotada Honda, Takashi Sano, Vinh Nguyen, Shugo Nakamura
2023	J	`jnl`	Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals. CoRR Tam Nguyen, Tan M. Nguyen, Richard G. Baraniuk
2023	J	`jnl`	Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data. CoRR Hien Dang, Tho Tran, Tan M. Nguyen, Stanley J. Osher, Hung Tran-The, Nhat Ho
2023	J	`jnl`	p-Laplacian Transformer. CoRR Tuan Nguyen, Tam Nguyen, Vinh Nguyen, Tan M. Nguyen
2022	J	`jnl`	Improving Generative Flow Networks with Path Regularization. CoRR Anh Do, Duy Dinh, Tan M. Nguyen, Khuong Nguyen, Stanley J. Osher, Nhat Ho
2022	J	`jnl`	Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization. CoRR Tan M. Nguyen, Richard G. Baraniuk, Robert M. Kirby, Stanley J. Osher, Bao Wang
2022	J	`jnl`	Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. SIAM J. Imaging Sci. Bao Wang, Tan M. Nguyen, Tao Sun, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher
2022	J	`jnl`	Transformer with Fourier Integral Attentions. CoRR Tan M. Nguyen, Minh Pham, Tam Nguyen, Khai Nguyen, Stanley J. Osher, Nhat Ho
2021	C	`conf`	API parameter recommendation based on language model and program analysis. APSEC Tran Manh Cuong, Trung Kien Tran, Tan M. Nguyen, Thu-Trang Nguyen, Son Nguyen, Hieu Dinh Vo
2021	A*	`conf`	FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention. NeurIPS Tan M. Nguyen, Vai Suliafu, Stanley J. Osher, Long Chen, Bao Wang
2021	J	`jnl`	FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention. CoRR Tan M. Nguyen, Vai Suliafu, Stanley J. Osher, Long Chen, Bao Wang
2021	A*	`conf`	Heavy Ball Neural Ordinary Differential Equations. NeurIPS Hedi Xia, Vai Suliafu, Hangjie Ji, Tan M. Nguyen, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang
2021	J	`jnl`	Heavy Ball Neural Ordinary Differential Equations. CoRR Hedi Xia, Vai Suliafu, Hangjie Ji, Tan M. Nguyen, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang
2021	J	`jnl`	How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies. CoRR Bao Wang, Hedi Xia, Tan M. Nguyen, Stanley J. Osher
2021	J	`jnl`	Transformer with a Mixture of Gaussian Keys. CoRR Tam Nguyen, Tan M. Nguyen, Dung Le, Khuong Nguyen, Anh Tran, Richard G. Baraniuk, Nhat Ho, Stanley J. Osher
2020	J	`jnl`	Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference. IEEE J. Sel. Top. Signal Process. Yue Wang, Jianghao Shen, Ting-Kuei Hu, Pengfei Xu, Tan M. Nguyen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin
2020	A*	`conf`	MomentumRNN: Integrating Momentum into Recurrent Neural Networks. NeurIPS Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang
2020	J	`jnl`	MomentumRNN: Integrating Momentum into Recurrent Neural Networks. CoRR Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang
2020	A*	`conf`	Neural Networks with Recurrent Generative Feedback. NeurIPS Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar
2020	J	`jnl`	Neural Networks with Recurrent Generative Feedback. CoRR Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar
2020	J	`jnl`	Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. CoRR Bao Wang, Tan M. Nguyen, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher
2019	J	`jnl`	Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference. CoRR Yue Wang, Jianghao Shen, Ting-Kuei Hu, Pengfei Xu, Tan M. Nguyen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin
2019	J	`jnl`	InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers. CoRR Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk, Anima Anandkumar
2019	J	`jnl`	Learning Near-optimal Convex Combinations of Basis Models with Generalization Guarantees. CoRR Tan M. Nguyen, Nan Ye, Peter L. Bartlett
2019	J	`jnl`	Out-of-Distribution Detection Using Neural Rendering Generative Models. CoRR Yujia Huang, Sihui Dai, Tan M. Nguyen, Richard G. Baraniuk, Anima Anandkumar
2018	J	`jnl`	Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning. CoRR Nhat Ho, Tan M. Nguyen, Ankit B. Patel, Anima Anandkumar, Michael I. Jordan, Richard G. Baraniuk

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.