Qitao Shi - RankMe

16 papers A* 3A 2Journal 5Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	AntAkso: Claims Management System for Health Insurance in Alipay. KDD (1) Qitao Shi, Jun Zhou, Ya-Lin Zhang, Longfei Li, Chaoyi Ma, Yifan Wu, Xiaobo Qin
2024	J	`jnl`	A distribution-free method for probabilistic prediction. Expert Syst. Appl. Qitao Shi, Ya-Lin Zhang, Lu Yu, Feng Zhu, Longfei Li, Jun Zhou, Yanming Fang
2024	A*	`conf`	MoDE: A Mixture-of-Experts Model with Mutual Distillation among the Experts. AAAI Zhitian Xie, Yinger Zhang, Chenyi Zhuang, Qitao Shi, Zhining Liu, Jinjie Gu, Guannan Zhang
2024	J	`jnl`	MoDE: A Mixture-of-Experts Model with Mutual Distillation among the Experts. CoRR Zhitian Xie, Yinger Zhang, Chenyi Zhuang, Qitao Shi, Zhining Liu, Jinjie Gu, Guannan Zhang
2023	A*	`conf`	ALT: An Automatic System for Long Tail Scenario Modeling. ICDE Ya-Lin Zhang, Jun Zhou, Yankun Ren, Yue Zhang, Xinxing Yang, Meng Li, Qitao Shi, Longfei Li
2023	J	`jnl`	ALT: An Automatic System for Long Tail Scenario Modeling. CoRR Ya-Lin Zhang, Jun Zhou, Yankun Ren, Yue Zhang, Xinxing Yang, Meng Li, Qitao Shi, Longfei Li
2023	—	`conf`	AntTune: An Efficient Distributed Hyperparameter Optimization System for Large-Scale Data. DASFAA (4) Jun Zhou, Qitao Shi, Yi Ding, Lin Wang, Longfei Li, Feng Zhu
2023	A	`conf`	ElasticDL: A Kubernetes-native Deep Learning Framework with Fault-tolerance and Elastic Scheduling. WSDM Jun Zhou, Ke Zhang, Feng Zhu, Qitao Shi, Wenjing Fang, Lin Wang, Yi Wang
2023	J	`jnl`	Exploring the combination of self and mutual teaching for tabular-data-related semi-supervised regression. Expert Syst. Appl. Ya-Lin Zhang, Jun Zhou, Qitao Shi, Longfei Li
2022	—	`conf`	A Task-Aware Attention-Based Method for Improved Meta-Learning. APWeb/WAIM (2) Yue Zhang, Xinxing Yang, Feng Zhu, Yalin Zhang, Meng Li, Qitao Shi, Longfei Li, Jun Zhou
2022	A	`conf`	An Adaptive Framework for Confidence-constraint Rule Set Learning Algorithm in Large Dataset. CIKM Meng Li, Lu Yu, Ya-Lin Zhang, Xiaoguang Huang, Qitao Shi, Qing Cui, Xinxing Yang, Longfei Li, Wei Zhu, Yanming Fang, Jun Zhou
2021	—	`conf`	A Classification Based Ensemble Pruning Framework with Multi-metric Consideration. IntelliSys (1) Ya-Lin Zhang, Qitao Shi, Meng Li, Xinxing Yang, Longfei Li, Jun Zhou
2021	—	`conf`	Constraint-Adaptive Rule Mining in Large Databases. DASFAA (3) Meng Li, Ya-Lin Zhang, Qitao Shi, Xinxing Yang, Qing Cui, Longfei Li, Jun Zhou
2020	—	`conf`	AutoRec: A Comprehensive Platform for Building Effective and Explainable Recommender Models. ECML/PKDD (5) Qing Cui, Qitao Shi, Hao Qian, Caizhi Tang, Xixi Li, Yiming Zhao, Tao Jiang, Longfei Li, Jun Zhou
2020	A*	`conf`	SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks. ICDE Qitao Shi, Ya-Lin Zhang, Longfei Li, Xinxing Yang, Meng Li, Jun Zhou
2020	J	`jnl`	SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks. CoRR Qitao Shi, Ya-Lin Zhang, Longfei Li, Xinxing Yang, Meng Li, Jun Zhou

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.