Daniel Smolyak

15 papers Misc 1Journal 11Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	From "I have nothing to hide" to "It looks like stalking": Measuring Americans' Level of Comfort with Individual Mobility Features Extracted from Location Data. CoRR Naman Awasthi, Saad Mohammad Abrar, Daniel Smolyak, Vanessa Frías-Martínez
2025	—	`conf`	SynthTextEval: Synthetic Text Data Generation and Evaluation for High-Stakes Domains. EMNLP (System Demonstrations) Krithika Ramesh, Daniel Smolyak, Zihao Zhao, Nupoor Gandhi, Ritu Agarwal, Margrét V. Bjarnadóttir, Anjalie Field
2025	J	`jnl`	SynthTextEval: Synthetic Text Data Generation and Evaluation for High-Stakes Domains. CoRR Krithika Ramesh, Daniel Smolyak, Zihao Zhao, Nupoor Gandhi, Ritu Agarwal, Margrét V. Bjarnadóttir, Anjalie Field
2024	J	`jnl`	A data-driven optimization approach to baseball roster management. Ann. Oper. Res. Sean L. Barnes, Margrét V. Bjarnadóttir, Daniel Smolyak, Aurélie Thiele
2024	J	`jnl`	Assessing the Impact of Case Correction Methods on the Fairness of COVID-19 Predictive Models. CoRR Daniel Smolyak, Saad Mohammad Abrar, Naman Awasthi, Vanessa Frías-Martínez
2024	J	`jnl`	Auditing the Fairness of COVID-19 Forecast Hub Case Prediction Models. CoRR Saad Mohammad Abrar, Naman Awasthi, Daniel Smolyak, Vanessa Frías-Martínez
2024	J	`jnl`	DemOpts: Fairness corrections in COVID-19 case prediction models. CoRR Naman Awasthi, Saad Mohammad Abrar, Daniel Smolyak, Vanessa Frías-Martínez
2024	J	`jnl`	Improving Equity in Health Modeling with GPT4-Turbo Generated Synthetic Data: A Comparative Study. CoRR Daniel Smolyak, Arshana Welivita, Margrét V. Bjarnadóttir, Ritu Agarwal
2024	J	`jnl`	Systematic analysis of the effectiveness of adding human mobility data to covid-19 case prediction linear models. CoRR Saad Mohammad Abrar, Naman Awasthi, Daniel Smolyak, Vanessa Frías-Martínez
2023	J	`jnl`	Analysis of Performance Improvements and Bias Associated with the Use of Human Mobility Data in COVID-19 Case Prediction Models. ACM J. Comput. Sustain. Soc. Saad Mohammad Abrar, Naman Awasthi, Daniel Smolyak, Vanessa Frías-Martínez
2020	J	`jnl`	Coupled IGMM-GANs with Applications to Anomaly Detection in Human Mobility Data. ACM Trans. Spatial Algorithms Syst. Daniel Smolyak, Kathryn Gray, Sarkhan Badirli, George O. Mohler
2018	Misc	`conf`	A Muffin-Theorem Generator. FUN Guangqi Cui, John P. Dickerson, Naveen Durvasula, William Gasarch, Erik Metz, Jacob Prinz, Naveen Raman, Daniel Smolyak, Sung Hyun Yoo
2018	J	`jnl`	Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data. CoRR Kathryn Gray, Daniel Smolyak, Sarkhan Badirli, George O. Mohler
2018	—	`conf`	Coupled IGMM-GANs for improved generative adversarial anomaly detection. IEEE BigData Kathryn Gray, Daniel Smolyak, Sarkhan Badirli, George O. Mohler
2018	—	`conf`	TandemTrack: Promoting Consistent Exercise Leveraging Multimodal Training and Tracking. CHI Extended Abstracts Daniel Smolyak, Bongshin Lee, Eun Kyoung Choe

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.