Yale Chang - RankMe

22 papers A* 4A 4Journal 8Unranked 6

Year	Rank	Type	Title / Venue / Authors
2022	—	`conf`	Multi-Task Prediction of Murmur and Outcome from Heart Sound Recordings. CinC Yale Chang, Luoluo Liu, Corneliu Antonescu
2021	—	`conf`	Convolution-Free Waveform Transformers for Multi-Lead ECG Classification. CinC Annamalai Natarajan, Gregory Boverman, Yale Chang, Corneliu Antonescu, Jonathan Rubin
2021	J	`jnl`	Convolution-Free Waveform Transformers for Multi-Lead ECG Classification. CoRR Annamalai Natarajan, Gregory Boverman, Yale Chang, Corneliu Antonescu, Jonathan Rubin
2021	J	`jnl`	Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series. CoRR Asif Rahman, Yale Chang, Jonathan Rubin
2021	J	`jnl`	Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome. npj Digit. Medicine Emma H. Schwager, Katharina Jansson, Asif Rahman, S. Schiffer, Yale Chang, Gregory Boverman, Brian Gross, Minnan Xu-Wilson, P. Boehme, Hubert Truebel, Joseph J. Frassica
2020	—	`conf`	A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification. CinC Annamalai Natarajan, Yale Chang, Sara Mariani, Asif Rahman, Gregory Boverman, Shruti Vij, Jonathan Rubin
2020	—	`conf`	Phenotyping with Prior Knowledge using Patient Similarity. MLHC Asif Rahman, Yale Chang, Bryan Conroy, Minnan Xu-Wilson
2020	—	`conf`	Shapelet Discovery for Atrial Fibrillation Detection. CinC Saman Parvaneh, Yale Chang
2019	—	`conf`	A Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series. CinC Yale Chang, Jonathan Rubin, Gregory Boverman, Shruti Vij, Asif Rahman, Annamalai Natarajan, Saman Parvaneh
2019	J	`jnl`	Deep Kernel Learning for Clustering. CoRR Chieh Wu, Zulqarnain Khan, Yale Chang, Stratis Ioannidis, Jennifer G. Dy
2019	J	`jnl`	Solving Interpretable Kernel Dimension Reduction. CoRR Chieh Wu, Jared Miller, Yale Chang, Mario Sznaier, Jennifer G. Dy
2019	A*	`conf`	Solving Interpretable Kernel Dimensionality Reduction. NeurIPS Chieh Wu, Jared Miller, Yale Chang, Mario Sznaier, Jennifer G. Dy
2019	J	`jnl`	Spectral Non-Convex Optimization for Dimension Reduction with Hilbert-Schmidt Independence Criterion. CoRR Chieh Wu, Jared Miller, Yale Chang, Mario Sznaier, Jennifer G. Dy
2018	A	`conf`	Crowdclustering with Partition Labels. AISTATS Junxiang Chen, Yale Chang, Peter J. Castaldi, Michael H. Cho, Brian D. Hobbs, Jennifer G. Dy
2017	J	`jnl`	A Bayesian Nonparametric Model for Disease Subtyping: Application to Emphysema Phenotypes. IEEE Trans. Medical Imaging James C. Ross, Peter J. Castaldi, Michael H. Cho, Junxiang Chen, Yale Chang, Jennifer G. Dy, Edwin K. Silverman, George R. Washko, Raúl San José Estépar
2017	J	`jnl`	A Robust-Equitable Measure for Feature Ranking and Selection. J. Mach. Learn. Res. A. Adam Ding, Jennifer G. Dy, Yi Li, Yale Chang
2017	A	`conf`	Clustering from Multiple Uncertain Experts. AISTATS Yale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, Jennifer G. Dy
2017	A	`conf`	Clustering with Domain-Specific Usefulness Scores. SDM Yale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, Jennifer G. Dy
2017	A*	`conf`	Informative Subspace Learning for Counterfactual Inference. AAAI Yale Chang, Jennifer G. Dy
2017	A*	`conf`	Multiple Clustering Views from Multiple Uncertain Experts. ICML Yale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, Jennifer G. Dy
2016	A	`conf`	A Robust-Equitable Copula Dependence Measure for Feature Selection. AISTATS Yale Chang, Yi Li, A. Adam Ding, Jennifer G. Dy
2016	A*	`conf`	Interpretable Clustering via Discriminative Rectangle Mixture Model. ICDM Junxiang Chen, Yale Chang, Brian D. Hobbs, Peter J. Castaldi, Michael H. Cho, Edwin K. Silverman, Jennifer G. Dy

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.