Benedikt Heidrich

26 papers Journal 13Unranked 12

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Decision-Focused Fine-Tuning of Time Series Foundation Models for Dispatchable Feeder Optimization. CoRR Maximilian Beichter, Nils Friederich, Janik Pinter, Dorina Werling, Kaleb Phipps, Sebastian Beichter, Oliver Neumann, Ralf Mikut, Veit Hagenmeyer, Benedikt Heidrich
2025	J	`jnl`	Optimizing Small Language Models for In-Vehicle Function-Calling. CoRR Yahya Sowti Khiabani, Farris Atif, Chieh Hsu, Sven Stahlmann, Tobias Michels, Sebastian Kramer, Benedikt Heidrich, M. Saquib Sarfraz, Julian Merten, Faezeh Tafazzoli
2024	—	`—`	Challenges in Deep Learning Based Forecasting of Time Series with Calendar-driven Periodicities Benedikt Heidrich
2024	—	`conf`	Decision-Focused Retraining of Forecast Models for Optimization Problems in Smart Energy Systems. e-Energy Maximilian Beichter, Dorina Werling, Benedikt Heidrich, Kaleb Phipps, Oliver Neumann, Nils Friederich, Ralf Mikut, Veit Hagenmeyer
2024	—	`conf`	Evaluating Probabilistic Forecasters with sktime and tsbootstrap - Easy-to-Use, Configurable Frameworks for Reproducible Science. SciPy Benedikt Heidrich, Sankalp Gilda, Franz J. Király
2024	J	`jnl`	Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators. CoRR Manuel Treutlein, Marc Schmidt, Roman Hahn, Matthias Hertel, Benedikt Heidrich, Ralf Mikut, Veit Hagenmeyer
2024	J	`jnl`	Generating probabilistic forecasts from arbitrary point forecasts using a conditional invertible neural network. Appl. Intell. Kaleb Phipps, Benedikt Heidrich, Marian Turowski, Moritz Wittig, Ralf Mikut, Veit Hagenmeyer
2024	—	`conf`	Intrinsic Explainable Artificial Intelligence Using Trainable Spatial Weights on Numerical Weather Predictions. e-Energy Oliver Neumann, Maximilian Beichter, Benedikt Heidrich, Nils Friederich, Veit Hagenmeyer, Ralf Mikut
2023	—	`conf`	AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models. e-Energy Stefan Meisenbacher, Benedikt Heidrich, Tim Martin, Ralf Mikut, Veit Hagenmeyer
2023	—	`conf`	Automating Value-Oriented Forecast Model Selection by Meta-learning: Application on a Dispatchable Feeder. EI.A Dorina Werling, Maximilian Beichter, Benedikt Heidrich, Kaleb Phipps, Ralf Mikut, Veit Hagenmeyer
2023	J	`jnl`	Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks. Appl. Intell. Benedikt Heidrich, Marian Turowski, Kaleb Phipps, Kai Schmieder, Wolfgang Süß, Ralf Mikut, Veit Hagenmeyer
2023	J	`jnl`	Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks. CoRR Kaleb Phipps, Benedikt Heidrich, Marian Turowski, Moritz Wittig, Ralf Mikut, Veit Hagenmeyer
2023	—	`conf`	Loss-Customised Probabilistic Energy Time Series Forecasts Using Automated Hyperparameter Optimisation. e-Energy Kaleb Phipps, Stefan Meisenbacher, Benedikt Heidrich, Marian Turowski, Ralf Mikut, Veit Hagenmeyer
2023	J	`jnl`	ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information. CoRR Benedikt Heidrich, Kaleb Phipps, Oliver Neumann, Marian Turowski, Ralf Mikut, Veit Hagenmeyer
2023	—	`conf`	The Impact of Forecast Characteristics on the Forecast Value for the Dispatchable Feeder. e-Energy (Companion) Dorina Werling, Maximilian Beichter, Benedikt Heidrich, Kaleb Phipps, Ralf Mikut, Veit Hagenmeyer
2023	J	`jnl`	Transformer Training Strategies for Forecasting Multiple Load Time Series. CoRR Matthias Hertel, Maximilian Beichter, Benedikt Heidrich, Oliver Neumann, Benjamin Schäfer, Ralf Mikut, Veit Hagenmeyer
2023	J	`jnl`	Transformer training strategies for forecasting multiple load time series. Energy Inform. Matthias Hertel, Maximilian Beichter, Benedikt Heidrich, Oliver Neumann, Benjamin Schäfer, Ralf Mikut, Veit Hagenmeyer
2022	—	`conf`	Adaptively coping with concept drifts in energy time series forecasting using profiles. e-Energy Benedikt Heidrich, Nicole Ludwig, Marian Turowski, Ralf Mikut, Veit Hagenmeyer
2022	J	`jnl`	AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models. CoRR Stefan Meisenbacher, Benedikt Heidrich, Tim Martin, Ralf Mikut, Veit Hagenmeyer
2022	J	`jnl`	Boost short-term load forecasts with synthetic data from transferred latent space information. Energy Inform. Benedikt Heidrich, Lisa Mannsperger, Marian Turowski, Kaleb Phipps, Benjamin Schäfer, Ralf Mikut, Veit Hagenmeyer
2022	—	`conf`	Enhancing anomaly detection methods for energy time series using latent space data representations. e-Energy Marian Turowski, Benedikt Heidrich, Kaleb Phipps, Kai Schmieder, Oliver Neumann, Ralf Mikut, Veit Hagenmeyer
2022	—	`conf`	Modeling and generating synthetic anomalies for energy and power time series. e-Energy Marian Turowski, Moritz Weber, Oliver Neumann, Benedikt Heidrich, Kaleb Phipps, Hüseyin Kemâl Çakmak, Ralf Mikut, Veit Hagenmeyer
2022	—	`conf`	Towards line-restricted dispatchable feeders using probabilistic forecasts for PV-dominated low-voltage distribution grids. e-Energy Dorina Werling, Benedikt Heidrich, Hüseyin Kemâl Çakmak, Veit Hagenmeyer
2021	J	`jnl`	Smart Data Representations: Impact on the Accuracy of Deep Neural Networks. CoRR Oliver Neumann, Nicole Ludwig, Marian Turowski, Benedikt Heidrich, Veit Hagenmeyer, Ralf Mikut
2021	J	`jnl`	pyWATTS: Python Workflow Automation Tool for Time Series. CoRR Benedikt Heidrich, Andreas Bartschat, Marian Turowski, Oliver Neumann, Kaleb Phipps, Stefan Meisenbacher, Kai Schmieder, Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer
2020	—	`conf`	Forecasting energy time series with profile neural networks. e-Energy Benedikt Heidrich, Marian Turowski, Nicole Ludwig, Ralf Mikut, Veit Hagenmeyer

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.