Feiyang Kang - RankMe

25 papers A* 6A 1Journal 16Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Sustainable AI Economy Needs Data Deals That Work for Generators. CoRR Ruoxi Jia, Luis Oala, Wenjie Xiong, Suqin Ge, Jiachen T. Wang, Feiyang Kang, Dawn Song
2026	J	`jnl`	RepMSNet: multi-scale convolutional neural network combined with structural re-parameterization for the classification of agricultural pests. Signal Image Video Process. Junyan Wang, Haoran Yu, Feiyang Kang
2025	J	`jnl`	AdaDeDup: Adaptive Hybrid Data Pruning for Efficient Large-Scale Object Detection Training. CoRR Feiyang Kang, Nadine Chang, Maying Shen, Marc T. Law, Rafid Mahmood, Ruoxi Jia, José M. Álvarez
2025	J	`jnl`	Data Acquisition: A New Frontier in Data-centric AI. J. Data-centric Mach. Learn. Res. Lingjiao Chen, Bilge Acun, Newsha Ardalani, Yifan Sun, Feiyang Kang, Hanrui Lyu, Yongchan Kwon, Ruoxi Jia, Carole-Jean Wu, Matei Zaharia, James Zou
2025	A*	`conf`	Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls. EMNLP Feiyang Kang, Newsha Ardalani, Michael Kuchnik, Youssef Emad, Mostafa Elhoushi, Shubhabrata Sengupta, Shang-wen Li, Ramya Raghavendra, Ruoxi Jia, Carole-Jean Wu
2025	J	`jnl`	Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls. CoRR Feiyang Kang, Newsha Ardalani, Michael Kuchnik, Youssef Emad, Mostafa Elhoushi, Shubhabrata Sengupta, Shang-wen Li, Ramya Raghavendra, Ruoxi Jia, Carole-Jean Wu
2025	J	`jnl`	Quagmires in SFT-RL Post-Training: When High SFT Scores Mislead and What to Use Instead. CoRR Feiyang Kang, Michael Kuchnik, Karthik Padthe, Marin Vlastelica, Ruoxi Jia, Carole-Jean Wu, Newsha Ardalani
2024	J	`jnl`	AutoScale: Automatic Prediction of Compute-optimal Data Composition for Training LLMs. CoRR Feiyang Kang, Yifan Sun, Bingbing Wen, Si Chen, Dawn Song, Rafid Mahmood, Ruoxi Jia
2024	J	`jnl`	Data-Centric Defense: Shaping Loss Landscape with Augmentations to Counter Model Inversion. Trans. Mach. Learn. Res. Si Chen, Feiyang Kang, Nikhil Abhyankar, Ming Jin, Ruoxi Jia
2024	J	`jnl`	FASTTRACK: Fast and Accurate Fact Tracing for LLMs. CoRR Si Chen, Feiyang Kang, Ning Yu, Ruoxi Jia
2024	—	`conf`	FASTTRACK: Reliable Fact Tracing via Clustering and LLM-Powered Evidence Validation. EMNLP (Findings) Si Chen, Feiyang Kang, Ning Yu, Ruoxi Jia
2024	A*	`conf`	Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs. ICLR Feiyang Kang, Hoang Anh Just, Yifan Sun, Himanshu Jahagirdar, Yuanzhi Zhang, Rongxing Du, Anit Kumar Sahu, Ruoxi Jia
2024	J	`jnl`	Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs. CoRR Feiyang Kang, Hoang Anh Just, Yifan Sun, Himanshu Jahagirdar, Yuanzhi Zhang, Rongxing Du, Anit Kumar Sahu, Ruoxi Jia
2024	A*	`conf`	The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes. CVPR Myeongseob Ko, Feiyang Kang, Weiyan Shi, Ming Jin, Zhou Yu, Ruoxi Jia
2024	J	`jnl`	The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes. CoRR Myeongseob Ko, Feiyang Kang, Weiyan Shi, Ming Jin, Zhou Yu, Ruoxi Jia
2023	J	`jnl`	Data Acquisition: A New Frontier in Data-centric AI. CoRR Lingjiao Chen, Bilge Acun, Newsha Ardalani, Yifan Sun, Feiyang Kang, Hanrui Lyu, Yongchan Kwon, Ruoxi Jia, Carole-Jean Wu, Matei Zaharia, James Zou
2023	A*	`conf`	LAVA: Data Valuation without Pre-Specified Learning Algorithms. ICLR Hoang Anh Just, Feiyang Kang, Tianhao Wang, Yi Zeng, Myeongseob Ko, Ming Jin, Ruoxi Jia
2023	J	`jnl`	LAVA: Data Valuation without Pre-Specified Learning Algorithms. CoRR Hoang Anh Just, Feiyang Kang, Jiachen T. Wang, Yi Zeng, Myeongseob Ko, Ming Jin, Ruoxi Jia
2023	A*	`conf`	Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources. NeurIPS Feiyang Kang, Hoang Anh Just, Anit Kumar Sahu, Ruoxi Jia
2023	J	`jnl`	Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources. CoRR Feiyang Kang, Hoang Anh Just, Anit Kumar Sahu, Ruoxi Jia
2023	—	`conf`	RepCFT: A Fusion Model of Reparameterized Convolutional Neural Network and Visual Transformer for Wood Species Recognition. ICAICE Junyan Wang, Ziyan Lian, Feiyang Kang, Jinkai Chen
2023	A*	`conf`	Towards Robustness Certification Against Universal Perturbations. ICLR Yi Zeng, Zhouxing Shi, Ming Jin, Feiyang Kang, Lingjuan Lyu, Cho-Jui Hsieh, Ruoxi Jia
2021	J	`jnl`	Visual Navigation with a 2-pixel Camera - Possibilities and Limitations. CoRR John Baillieul, Feiyang Kang
2018	J	`jnl`	Prediction-Based Fast Thermoelectric Generator Reconfiguration for Energy Harvesting from Vehicle Radiators. CoRR Hanchen Yang, Feiyang Kang, Caiwen Ding, Ji Li, Jaemin Kim, Donkyu Baek, Shahin Nazarian, Xue Lin, Paul Bogdan, Naehyuck Chang
2018	A	`conf`	Prediction-based fast thermoelectric generator reconfiguration for energy harvesting from vehicle radiators. DATE Hanchen Yang, Feiyang Kang, Caiwen Ding, Ji Li, Jaemin Kim, Donkyu Baek, Shahin Nazarian, Xue Lin, Paul Bogdan, Naehyuck Chang

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.