Institution profile

Technische Universität Berlin

Academic institutioneurope · de
Official website
Research library1,021linked papers
Opportunities0open roles
Selected work

Representative Papers

How Viable are Energy Savings in Smart Homes? A Call to Embrace Rebound Effects in Sustainable HCI

Aug 01, 2023ACM J. Comput. Sustain. Soc.

Energy savings from smart home technologies are often undermined by rebound effects—behavioral or systemic compensations triggered by increased efficiency—rendering sustainability gains transient. Method: Through a cross-disciplinary literature mapping analysis across Web of Science, Scopus, IEEE Xplore, Springer, and ACM SIGCHI proceedings, this study systematically identifies research gaps concerning rebound effects in computing, human-computer interaction (HCI), and smart home domains. Contribution/Results: We propose the first classification framework for rebound effects tailored to sustainable HCI, along with corresponding intervention pathways. Findings reveal that current energy-efficiency evaluations routinely neglect rebound mechanisms, while HCI is uniquely positioned to advance rebound identification, computational modeling, and behaviorally informed interventions. This work establishes a theoretical foundation and methodological toolkit for accurately assessing the real-world environmental impact of smart home systems.

12 citations1 influentialRead paper

Lazy Gatekeepers: A Large-Scale Study on SPF Configuration in the Wild

Oct 24, 2023ACM/SIGCOMM Internet Measurement Conference

SPF configurations exhibit widespread security flaws, significantly increasing email spoofing risk. Method: We conducted a large-scale empirical study across 12 million domains, developing the first formal framework to quantify the relationship between SPF policy laxity and spoofing vulnerability—integrating DNS crawling, SPF parsing, IP-space mapping, and policy effectiveness validation. Contribution/Results: We found that while 56.5% of domains deploy SPF, 2.9% contain syntactic or semantic errors, and 34.7% employ overly permissive policies (authorizing ≥100,000 IPs), severely undermining authentication. Based on our analysis, we established actionable configuration guidelines, notified and engaged operators of over one million problematic domains in a closed-loop remediation process. Our work identified millions of high-risk domains, advanced SPF best practices, and directly informed IETF’s Email Security Working Group, contributing to DMARC policy optimization.

6 citations1 influentialRead paper

Manipulating Feature Visualizations with Gradient Slingshots

Jan 11, 2024arXiv.org

This work exposes a critical credibility vulnerability in feature visualization (FV) for deep neural network interpretability: FV outputs are susceptible to stealthy manipulation, leading to erroneous attribution of neuron semantics. To address this, we propose the first model-architecture-agnostic targeted FV manipulation method. Our approach integrates gradient redirection (via Slingshot optimization), adversarial latent-space perturbations, and neuron-activation-constrained regularization to achieve “semantic masking”—i.e., seamless substitution of a target neuron’s original FV explanation with an arbitrary user-specified semantic concept. Experiments across CNNs and Vision Transformers demonstrate successful concealment of functionally critical neurons: model accuracy degrades by less than 0.3%, yet FV-based auditing yields a 92% false-negative rate in detecting manipulated neurons. These results underscore the fragility of prevailing FV techniques and establish a new paradigm for robust model auditing and interpretability governance.

6 citationsRead paper

Comparison of Generative Learning Methods for Turbulence Modeling

Nov 25, 2024arXiv.org

High-fidelity turbulent flow simulations—such as direct numerical simulation (DNS) and large-eddy simulation (LES)—remain computationally prohibitive for routine engineering applications. This work systematically compares three generative probabilistic models—variational autoencoders (VAEs), deep convolutional generative adversarial networks (DCGANs), and denoising diffusion probabilistic models (DDPMs)—for modeling two-dimensional Karman vortex streets, trained exclusively on LES data. Evaluation is conducted across three dimensions: statistical fidelity, spatial structure preservation, and multiscale dynamical consistency. Results demonstrate that DCGAN achieves the best overall performance in generation fidelity, inference speed, and sample efficiency—accurately reconstructing turbulent fields from limited LES data. DDPM attains higher accuracy but suffers from prohibitively slow inference; VAE trains rapidly yet yields significant structural distortions. This study establishes generative modeling as a novel, high-fidelity, low-cost surrogate paradigm for turbulence, providing a scalable, data-driven methodology for turbulent flow simulation.

5 citationsRead paper

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models

Jan 20, 2026

This work proposes a practical, three-stage “Locate–Guide–Improve” framework that transforms mechanistic interpretability from a post-hoc diagnostic tool into an engineering-driven optimization methodology for large language models. By systematically integrating techniques for identifying critical neurons and pathways with targeted interventions—such as activation manipulation and module editing—the framework establishes a standardized protocol for model refinement while clearly distinguishing between localization and guidance mechanisms. Empirical results demonstrate significant improvements in model alignment, task performance, and reasoning efficiency, thereby advancing mechanistic interpretability toward real-world applicability.

4 citationsRead paper
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