Bangtian Liu - RankMe

13 papers A 1B 4C 1Misc 1Journal 4Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Memory-Efficient Training with In-Place FFT Implementation. CoRR Xinyu Ding, Bangtian Liu, Siyu Liao, Zhongfeng Wang
2024	—	`—`	Optimizing Irregular Computations on Parallel Processors Bangtian Liu
2024	J	`jnl`	SpEQ: Translation of Sparse Codes using Equivalences. Proc. ACM Program. Lang. Avery Laird, Bangtian Liu, Nikolaj S. Bjørner, Maryam Mehri Dehnavi
2022	B	`conf`	Combining Run-Time Checks and Compile-Time Analysis to Improve Control Flow Auto-Vectorization. PACT Bangtian Liu, Avery Laird, Wai Hung Tsang, Bardia Mahjour, Maryam Mehri Dehnavi
2022	A	`conf`	HDagg: Hybrid Aggregation of Loop-carried Dependence Iterations in Sparse Matrix Computations. IPDPS Behrooz Zarebavani, Kazem Cheshmi, Bangtian Liu, Michelle Mills Strout, Maryam Mehri Dehnavi
2020	B	`conf`	MatRox: modular approach for improving data locality in hierarchical (Mat)rix App(Rox)imation. PPoPP Bangtian Liu, Kazem Cheshmi, Saeed Soori, Michelle Mills Strout, Maryam Mehri Dehnavi
2018	B	`conf`	CSTF: Large-Scale Sparse Tensor Factorizations on Distributed Platforms. ICPP Zachary Blanco, Bangtian Liu, Maryam Mehri Dehnavi
2018	J	`jnl`	MatRox: A Model-Based Algorithm with an Efficient Storage Format for Parallel HSS-Structured Matrix Approximations. CoRR Bangtian Liu, Kazem Cheshmi, Saeed Soori, Maryam Mehri Dehnavi
2017	C	`conf`	A Unified Optimization Approach for Sparse Tensor Operations on GPUs. CLUSTER Bangtian Liu, Chengyao Wen, Anand D. Sarwate, Maryam Mehri Dehnavi
2017	J	`jnl`	A Unified Optimization Approach for Sparse Tensor Operations on GPUs. CoRR Bangtian Liu, Chengyao Wen, Anand D. Sarwate, Maryam Mehri Dehnavi
2017	B	`conf`	Multi-view Spectral Clustering via Tensor-SVD Decomposition. ICTAI Yan Zhang, Weiwei Yang, Bangtian Liu, Ge-Yang Ke, Yan Pan, Jian Yin
2016	—	`conf`	Optimizing Residue Number System on FPGA. iThings/GreenCom/CPSCom/SmartData Jiahe Liu, Bangtian Liu, Haohuan Fu
2015	Misc	`conf`	Optimizing Residue Number Reverse Converters through Bitwise Arithmetic on FPGAs. FCCM Bangtian Liu, Haohuan Fu, Lin Gan, Wenlai Zhao, Guangwen Yang

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.