Lavender Yao Jiang

12 papers Misc 2Journal 9Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	BPQA Dataset: Evaluating How Well Language Models Leverage Blood Pressures to Answer Biomedical Questions. CoRR Chi Hang, Ruiqi Deng, Lavender Yao Jiang, Zihao Yang, Anton Alyakin, Daniel Alexander Alber, Eric Karl Oermann
2024	J	`jnl`	Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model. CoRR Salman Rahman, Lavender Yao Jiang, Saadia Gabriel, Yindalon Aphinyanaphongs, Eric Karl Oermann, Rumi Chunara
2024	J	`jnl`	MedG-KRP: Medical Graph Knowledge Representation Probing. CoRR Gabriel R. Rosenbaum, Lavender Yao Jiang, Ivaxi Sheth, Jaden Stryker, Anton Alyakin, Daniel Alexander Alber, Nicolas K. Goff, Young Joon Fred Kwon, John Markert, Mustafa Nasir-Moin, Jan Moritz Niehues, Karl L. Sangwon, Eunice Yang, Eric Karl Oermann
2024	J	`jnl`	Refining Packing and Shuffling Strategies for Enhanced Performance in Generative Language Models. CoRR Yanbing Chen, Ruilin Wang, Zihao Yang, Lavender Yao Jiang, Eric Karl Oermann
2023	J	`jnl`	Health system-scale language models are all-purpose prediction engines. Nat. Lavender Yao Jiang, Xujin Chris Liu, Nima Pour Nejatian, Mustafa Nasir-Moin, Duo Wang, Anas Z. Abidin, Kevin Eaton, Howard Antony Riina, Ilya Laufer, Paawan Punjabi, Madeline Miceli, Nora C. Kim, Cordelia Orillac, Zane Schnurman, Christopher Livia, Hannah Weiss, David Kurland, Sean Neifert, Yosef Dastagirzada, Douglas Kondziolka, Alexander T. M. Cheung, Grace Yang, Ming Cao, Mona Flores, Anthony B. Costa, Yindalon Aphinyanaphongs, Kyunghyun Cho, Eric Karl Oermann
2023	J	`jnl`	Making the Most Out of the Limited Context Length: Predictive Power Varies with Clinical Note Type and Note Section. CoRR Hongyi Zheng, Yixin Zhu, Lavender Yao Jiang, Kyunghyun Cho, Eric Karl Oermann
2021	—	`conf`	Automated, Scalable and Generalizable Deep Learning for Tracking Cortical Spreading Depression Using EEG. NER Alireza Chamanzar, Xujin Liu, Lavender Yao Jiang, Kimon-Aristotelis Vogt, José M. F. Moura, Pulkit Grover
2020	Misc	`conf`	Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification. ACSSC Lavender Yao Jiang, John Shi, Mark Cheung, Oren Wright, José M. F. Moura
2020	J	`jnl`	Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification. CoRR Lavender Yao Jiang, John Shi, Mark Cheung, Oren Wright, José M. F. Moura
2020	J	`jnl`	Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology. IEEE Signal Process. Mag. Mark Cheung, John Shi, Oren Wright, Lavender Yao Jiang, Xujin Liu, José M. F. Moura
2020	J	`jnl`	Pooling in Graph Convolutional Neural Networks. CoRR Mark Cheung, John Shi, Lavender Yao Jiang, Oren Wright, José M. F. Moura
2019	Misc	`conf`	Pooling in Graph Convolutional Neural Networks. ACSSC Mark Cheung, John Shi, Lavender Yao Jiang, Oren Wright, José M. F. Moura

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.