Baihong Jin - RankMe

30 papers C 4Journal 17Unranked 8

Year	Rank	Type	Title / Venue / Authors
2022	—	`conf`	Class-wise Thresholding for Robust Out-of-Distribution Detection. CVPR Workshops Matteo Guarrera, Baihong Jin, Tung-Wei Lin, Maria A. Zuluaga, Yuxin Chen, Alberto L. Sangiovanni-Vincentelli
2022	J	`jnl`	Predicting Electricity Infrastructure Induced Wildfire Risk in California. CoRR Mengqi Yao, Meghana Bharadwaj, Zheng Zhang, Baihong Jin, Duncan S. Callaway
2021	J	`jnl`	Class-wise Thresholding for Detecting Out-of-Distribution Data. CoRR Matteo Guarrera, Baihong Jin, Tung-Wei Lin, Maria A. Zuluaga, Yuxin Chen, Alberto L. Sangiovanni-Vincentelli
2021	J	`jnl`	Explainable AI for Chiller Fault-Detection Systems: Gaining Human Trust. Computer Seshadhri Srinivasan, Pandarasamy Arjunan, Baihong Jin, Alberto L. Sangiovanni-Vincentelli, Zuraimi Sultan, Kameshwar Poolla
2021	J	`jnl`	One-class graph neural networks for anomaly detection in attributed networks. Neural Comput. Appl. Xuhong Wang, Baihong Jin, Ying Du, Ping Cui, Yingshui Tan, Yupu Yang
2020	J	`jnl`	Are Ensemble Classifiers Powerful Enough for the Detection and Diagnosis of Intermediate-Severity Faults? CoRR Baihong Jin, Yingshui Tan, Yuxin Chen, Kameshwar Poolla, Alberto L. Sangiovanni-Vincentelli
2020	J	`jnl`	Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI. CoRR Yingshui Tan, Baihong Jin, Xiangyu Yue, Yuxin Chen, Alberto L. Sangiovanni-Vincentelli
2020	J	`jnl`	Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation. CoRR Yingshui Tan, Baihong Jin, Qiushi Cui, Xiangyu Yue, Alberto L. Sangiovanni-Vincentelli
2020	J	`jnl`	Gordian: Formal Reasoning-based Outlier Detection for Secure Localization. ACM Trans. Cyber Phys. Syst. Matthew Weber, Baihong Jin, Gil Lederman, Yasser Shoukry, Edward A. Lee, Sanjit A. Seshia, Alberto L. Sangiovanni-Vincentelli
2020	—	`—`	Incipient Anomaly Detection with Ensemble Learning. Baihong Jin
2020	—	`conf`	Super-Resolution Reconstruction of Interval Energy Data. BuildSys Jieyi Lu, Baihong Jin
2020	J	`jnl`	Super-Resolution Reconstruction of Interval Energy Data. CoRR Jieyi Lu, Baihong Jin
2020	J	`jnl`	Using Ensemble Classifiers to Detect Incipient Anomalies. CoRR Baihong Jin, Yingshui Tan, Albert Liu, Xiangyu Yue, Yuxin Chen, Alberto L. Sangiovanni-Vincentelli
2019	—	`conf`	A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection. ICPHM Baihong Jin, Yuxin Chen, Dan Li, Kameshwar Poolla, Alberto L. Sangiovanni-Vincentelli
2019	J	`jnl`	A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection. CoRR Baihong Jin, Yuxin Chen, Dan Li, Kameshwar Poolla, Alberto L. Sangiovanni-Vincentelli
2019	C	`conf`	A tractable ellipsoidal approximation for voltage regulation problems. ACC Pan Li, Baihong Jin, Ruoxuan Xiong, Dai Wang, Alberto L. Sangiovanni-Vincentelli, Baosen Zhang
2019	J	`jnl`	A tractable ellipsoidal approximation for voltage regulation problems. CoRR Pan Li, Baihong Jin, Ruoxuan Xiong, Dai Wang, Alberto L. Sangiovanni-Vincentelli, Baosen Zhang
2019	C	`conf`	An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing. ICMLA Yingshui Tan, Baihong Jin, Alexander J. Nettekoven, Yuxin Chen, Yisong Yue, Ufuk Topcu, Alberto L. Sangiovanni-Vincentelli
2019	J	`jnl`	An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing. CoRR Baihong Jin, Yingshui Tan, Alexander J. Nettekoven, Yuxin Chen, Ufuk Topcu, Yisong Yue, Alberto L. Sangiovanni-Vincentelli
2019	J	`jnl`	Augmenting Monte Carlo Dropout Classification Models with Unsupervised Learning Tasks for Detecting and Diagnosing Out-of-Distribution Faults. CoRR Baihong Jin, Yingshui Tan, Yuxin Chen, Alberto L. Sangiovanni-Vincentelli
2019	—	`conf`	Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks. ICPHM Baihong Jin, Dan Li, Seshadhri Srinivasan, See-Kiong Ng, Kameshwar Poolla, Alberto L. Sangiovanni-Vincentelli
2019	J	`jnl`	Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks. CoRR Baihong Jin, Dan Li, Seshadhri Srinivasan, See-Kiong Ng, Kameshwar Poolla, Alberto L. Sangiovanni-Vincentelli
2019	—	`conf`	MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. ICANN (4) Dan Li, Dacheng Chen, Baihong Jin, Lei Shi, Jonathan Goh, See-Kiong Ng
2019	J	`jnl`	MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks. CoRR Dan Li, Dacheng Chen, Lei Shi, Baihong Jin, Jonathan Goh, See-Kiong Ng
2018	J	`jnl`	Design Automation for Smart Building Systems. Proc. IEEE Ruoxi Jia, Baihong Jin, Ming Jin, Yuxun Zhou, Ioannis C. Konstantakopoulos, Han Zou, Joyce Kim, Dan Li, Weixi Gu, Reza Arghandeh, Pierluigi Nuzzo, Stefano Schiavon, Alberto L. Sangiovanni-Vincentelli, Costas J. Spanos
2017	—	`conf`	Online computation of polytopic flexibility models for demand shifting applications. CASE Baihong Jin, Mehdi Maasoumy, Pierluigi Nuzzo, Alberto L. Sangiovanni-Vincentelli
2016	C	`conf`	A fast and accurate approach for common path pessimism removal in static timing analysis. ISCAS Baihong Jin, Guojie Luo, Wentai Zhang
2015	—	`conf`	A Contract-based Framework for Integrated Demand Response Management in Smart Grids. BuildSys Baihong Jin, Pierluigi Nuzzo, Mehdi Maasoumy, Yuxun Zhou, Alberto L. Sangiovanni-Vincentelli
2015	—	`conf`	A Fast and Simple Block-Based Approach for Common Path Pessimism Removal in Static Timing Analysis. CAD/Graphics Guojie Luo, Baihong Jin, Wentai Zhang
2015	C	`conf`	Learning Convex Piecewise Linear Machine for Data-Driven Optimal Control. ICMLA Yuxun Zhou, Baihong Jin, Costas J. Spanos

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.