Randolf Scholz

16 papers A* 3Journal 12Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time Series Forecasting Based on Biological ODEs. CoRR Christian Klötergens, Vijaya Krishna Yalavarthi, Randolf Scholz, Maximilian Stubbemann, Stefan Born, Lars Schmidt-Thieme
2025	A*	`conf`	Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs. ICLR Christian Klötergens, Vijaya Krishna Yalavarthi, Randolf Scholz, Maximilian Stubbemann, Stefan Born, Lars Schmidt-Thieme
2025	A*	`conf`	Probabilistic Forecasting of Irregularly Sampled Time Series with Missing Values via Conditional Normalizing Flows. AAAI Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born, Lars Schmidt-Thieme
2024	J	`jnl`	A workflow management system for reproducible and interoperable high-throughput self-driving experiments. Comput. Chem. Eng. Federico Mione, Lucas Kaspersetz, Martin F. Luna, Judit Aizpuru, Randolf Scholz, Maxim Borisyak, Annina Karolin Kemmer, Marie-Therese Schermeyer, Ernesto C. Martínez, Peter Neubauer, Mariano Nicolás Cruz Bournazou
2024	A*	`conf`	GraFITi: Graphs for Forecasting Irregularly Sampled Time Series. AAAI Vijaya Krishna Yalavarthi, Kiran Madhusudhanan, Randolf Scholz, Nourhan Ahmed, Johannes Burchert, Shayan Jawed, Stefan Born, Lars Schmidt-Thieme
2024	—	`conf`	Learning Set Embeddings for Fashion Compatibility Recommendation. SURE@RecSys Indra Firmansyah, Randolf Scholz, Adrian Nahmendorff, Ngoc Son Le, Shereen Elsayed, Lars Schmidt-Thieme
2024	J	`jnl`	Marginalization Consistent Mixture of Separable Flows for Probabilistic Irregular Time Series Forecasting. CoRR Vijaya Krishna Yalavarthi, Randolf Scholz, Kiran Madhusudhanan, Stefan Born, Lars Schmidt-Thieme
2024	J	`jnl`	Probabilistic Forecasting of Irregular Time Series via Conditional Flows. CoRR Vijaya Krishna Yalavarthi, Randolf Scholz, Stefan Born, Lars Schmidt-Thieme
2023	J	`jnl`	Forecasting Irregularly Sampled Time Series using Graphs. CoRR Vijaya Krishna Yalavarthi, Kiran Madusudanan, Randolf Scholz, Nourhan Ahmed, Johannes Burchert, Shayan Jawed, Stefan Born, Lars Schmidt-Thieme
2022	J	`jnl`	Put Attention to Temporal Saliency Patterns of Multi-Horizon Time Series. CoRR Nghia Duong-Trung, Stefan Born, Kiran Madhusudhanan, Randolf Scholz, Johannes Burchert, Danh Le Phuoc, Lars Schmidt-Thieme
2022	J	`jnl`	When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development. CoRR Nghia Duong-Trung, Stefan Born, Jong Woo Kim, Marie-Therese Schermeyer, Katharina Paulick, Maxim Borisyak, Ernesto C. Martínez, Mariano Nicolás Cruz Bournazou, Thorben Werner, Randolf Scholz, Lars Schmidt-Thieme, Peter Neubauer
2021	J	`jnl`	Deep Metric Learning for Ground Images. CoRR Raaghav Radhakrishnan, Jan Fabian Schmid, Randolf Scholz, Lars Schmidt-Thieme
2021	J	`jnl`	Multi-script Handwritten Digit Recognition Using Multi-task Learning. CoRR Mesay Samuel Gondere, Lars Schmidt-Thieme, Durga Prasad Sharma, Randolf Scholz
2020	J	`jnl`	Improving Sample Efficiency with Normalized RBF Kernels. CoRR Sebastian Pineda-Arango, David Obando-Paniagua, Alperen Dedeoglu, Philip Kurzendörfer, Friedemann Schestag, Randolf Scholz
2019	J	`jnl`	Chameleon: Learning Model Initializations Across Tasks With Different Schemas. CoRR Lukas Brinkmeyer, Rafael Rêgo Drumond, Randolf Scholz, Josif Grabocka, Lars Schmidt-Thieme
2019	J	`jnl`	Learning Surrogate Losses. CoRR Josif Grabocka, Randolf Scholz, Lars Schmidt-Thieme

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.