Fan Bai - RankMe

23 papers A* 2A 2Journal 13Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	FIRE-Bench: Evaluating Agents on the Rediscovery of Scientific Insights. CoRR Zhen Wang, Fan Bai, Zhongyan Luo, Jinyan Su, Kaiser Sun, Xinle Yu, Jieyuan Liu, Kun Zhou, Claire Cardie, Mark Dredze, Eric P. Xing, Zhiting Hu
2026	J	`jnl`	Reading, Not Thinking: Understanding and Bridging the Modality Gap When Text Becomes Pixels in Multimodal LLMs. CoRR Kaiser Sun, Xiaochuang Yuan, Hongjun Liu, Chen Zhao, Cheng Zhang, Mark Dredze, Fan Bai
2025	A*	`conf`	LLMs are Better Than You Think: Label-Guided In-Context Learning for Named Entity Recognition. EMNLP Fan Bai, Hamid Hassanzadeh, Ardavan Saeedi, Mark Dredze
2025	J	`jnl`	Label-Guided In-Context Learning for Named Entity Recognition. CoRR Fan Bai, Hamid Hassanzadeh, Ardavan Saeedi, Mark Dredze
2025	J	`jnl`	What Is Seen Cannot Be Unseen: The Disruptive Effect of Knowledge Conflict on Large Language Models. CoRR Kaiser Sun, Fan Bai, Mark Dredze
2024	J	`jnl`	Give me Some Hard Questions: Synthetic Data Generation for Clinical QA. CoRR Fan Bai, Keith Harrigian, Joel Stremmel, Hamid Hassanzadeh, Ardavan Saeedi, Mark Dredze
2024	—	`—`	Information Extraction on Scientific Literature under Limited Supervision. Fan Bai
2024	A*	`conf`	PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization. ICLR Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P. Xing, Zhiting Hu
2024	—	`conf`	Schema-Driven Information Extraction from Heterogeneous Tables. EMNLP (Findings) Fan Bai, Junmo Kang, Gabriel Stanovsky, Dayne Freitag, Mark Dredze, Alan Ritter
2023	A	`conf`	Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding. EACL Janvijay Singh, Fan Bai, Zhen Wang
2023	J	`jnl`	PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization. CoRR Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P. Xing, Zhiting Hu
2023	J	`jnl`	Schema-Driven Information Extraction from Heterogeneous Tables. CoRR Fan Bai, Junmo Kang, Gabriel Stanovsky, Dayne Freitag, Alan Ritter
2022	—	`conf`	Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts. EMNLP (Findings) Nghia T. Le, Fan Bai, Alan Ritter
2022	J	`jnl`	Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts. CoRR Nghia T. Le, Fan Bai, Alan Ritter
2022	J	`jnl`	Frustratingly Simple Entity Tracking with Effective Use of Multi-Task Learning Models. CoRR Janvijay Singh, Fan Bai, Zhen Wang
2022	—	`conf`	SynKB: Semantic Search for Synthetic Procedures. EMNLP (Demos) Fan Bai, Alan Ritter, Peter B. Madrid, Dayne Freitag, John Niekrasz
2022	J	`jnl`	SynKB: Semantic Search for Synthetic Procedures. CoRR Fan Bai, Alan Ritter, Peter B. Madrid, Dayne Freitag, John Niekrasz
2021	—	`conf`	Pre-train or Annotate? Domain Adaptation with a Constrained Budget. EMNLP (1) Fan Bai, Alan Ritter, Wei Xu
2021	J	`jnl`	Pre-train or Annotate? Domain Adaptation with a Constrained Budget. CoRR Fan Bai, Alan Ritter, Wei Xu
2021	A	`conf`	Process-Level Representation of Scientific Protocols with Interactive Annotation. EACL Ronen Tamari, Fan Bai, Alan Ritter, Gabriel Stanovsky
2021	J	`jnl`	Process-Level Representation of Scientific Protocols with Interactive Annotation. CoRR Ronen Tamari, Fan Bai, Alan Ritter, Gabriel Stanovsky
2019	—	`conf`	Structured Minimally Supervised Learning for Neural Relation Extraction. NAACL-HLT (1) Fan Bai, Alan Ritter
2019	J	`jnl`	Structured Minimally Supervised Learning for Neural Relation Extraction. CoRR Fan Bai, Alan Ritter

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.