Geon Park - RankMe

15 papers A* 4A 1B 1Journal 8Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Breaking the Pre-Sampling Barrier: Activation-Informed Difficulty-Aware Self-Consistency. CoRR Taewoong Yoon, Geunyeong Jeong, Geon Park, Sihyeong Yeom, Harksoo Kim
2026	J	`jnl`	DQE-CIR: Distinctive Query Embeddings through Learnable Attribute Weights and Target Relative Negative Sampling in Composed Image Retrieval. CoRR Geon Park, Ji-Hoon Park, Seong-Whan Lee
2025	A*	`conf`	A Training-Free Sub-quadratic Cost Transformer Model Serving Framework with Hierarchically Pruned Attention. ICLR Heejun Lee, Geon Park, Youngwan Lee, Jaduk Suh, Jina Kim, Wonyong Jeong, Bumsik Kim, Hyemin Lee, Myeongjae Jeon, Sung Ju Hwang
2025	J	`jnl`	InfiniteHiP: Extending Language Model Context Up to 3 Million Tokens on a Single GPU. CoRR Heejun Lee, Geon Park, Jaduk Suh, Sung Ju Hwang
2025	A	`conf`	Mitigating Knowledge Degradation Caused by Knowledge Editing on Identical Subjects through Two-Step Editing. CIKM Seonghee Lee, Geon Park, Geunyeong Jeong, Juoh Sun, Harksoo Kim
2025	B	`conf`	RaDL: Relation-aware Disentangled Learning for Multi-Instance Text-to-Image Generation. SMC Geon Park, Seon Bin Kim, Gunho Jung, Seong-Whan Lee
2025	J	`jnl`	RaDL: Relation-aware Disentangled Learning for Multi-Instance Text-to-Image Generation. CoRR Geon Park, Seon Bin Kim, Gunho Jung, Seong-Whan Lee
2025	A*	`conf`	VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding. CVPR Kangsan Kim, Geon Park, Youngwan Lee, Woongyeong Yeo, Sung Ju Hwang
2024	J	`jnl`	HiP Attention: Sparse Sub-Quadratic Attention with Hierarchical Attention Pruning. CoRR Heejun Lee, Geon Park, Youngwan Lee, Jina Kim, Wonyoung Jeong, Myeongjae Jeon, Sung Ju Hwang
2024	J	`jnl`	VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding. CoRR Kangsan Kim, Geon Park, Youngwan Lee, Woongyeong Yeo, Sung Ju Hwang
2022	—	`conf`	BiTAT: Neural Network Binarization with Task-Dependent Aggregated Transformation. ECCV Workshops (7) Geon Park, Jaehong Yoon, Haiyang Zhang, Xing Zhang, Sung Ju Hwang, Yonina C. Eldar
2022	J	`jnl`	BiTAT: Neural Network Binarization with Task-dependent Aggregated Transformation. CoRR Geon Park, Jaehong Yoon, Haiyang Zhang, Xing Zhang, Sung Ju Hwang, Yonina C. Eldar
2022	A*	`conf`	Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization. ICML Jaehong Yoon, Geon Park, Wonyong Jeong, Sung Ju Hwang
2022	J	`jnl`	Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization. CoRR Jaehong Yoon, Geon Park, Wonyong Jeong, Sung Ju Hwang
2021	A*	`conf`	Task-Adaptive Neural Network Search with Meta-Contrastive Learning. NeurIPS Wonyong Jeong, Hayeon Lee, Geon Park, Eunyoung Hyung, Jinheon Baek, Sung Ju Hwang

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.