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Researcher profile

Felix Biessmann

Felix Biessmann contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine LearningArtificial Intelligencecs.CYeess.SPHuman-Computer Interaction

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Published work

7 published item(s)

preprint2026arXiv

A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge

Aging combined sewer systems in many historical cities are increasingly stressed by extreme rainfall events, which can trigger combined sewer overflows (CSO) with significant environmental and public health impacts. Forecasting the filling dynamics of overflow basins is critical for anticipating capacity exceedance and enabling timely preventive actions for CSO. We present a web-based demonstrator (https://riwwer.demo.calgo-lab.de) that integrates Deep Learning forecasting methods in both cloud and edge settings into an interactive monitoring dashboard for overflow monitoring, resilient to network outages. A video showcase is available online (https://cloud.bht-berlin.de/index.php/s/b9xt4T3SdiLBiFZ).

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy

Machine Learning (ML) has transformed many scientific fields, yet key applications still lack standardized benchmarks. Raman spectroscopy, a widely used technique for non-invasive molecular analysis, is one such field where progress is limited by fragmented datasets, inconsistent evaluation, and models that fail to capture the structure of spectral data. We introduce RamanBench, the first large-scale, fully reproducible benchmark for ML on Raman spectroscopy, consisting of streamlined data access, evaluation protocols and code, as well as a live leaderboard. It unifies 74 datasets (including 16 first released with this benchmark) across four domains, comprising 325,668 spectra and spanning classification and regression tasks under diverse experimental conditions. We benchmark 28 models under a standardized protocol, including classical methods (e.g., PLS), Raman-specific (e.g., RamanNet), Tabular Foundation Model (TFM) (e.g., TabPFN), and time-series approaches (e.g., ROCKET). TFM consistently outperform domain-specific and gradient boosting baselines, while time-series models remain competitive. However, no method generalizes across datasets, revealing a fundamental gap. Therefore, we invite the community to contribute new approaches to our living benchmark, with the potential to accelerate advances in critical applications such as medical diagnostics, biological research, and materials science.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows

Climate change poses increasingly complex challenges to our society. Extreme weather events such as floods, wild fires or droughts are becoming more frequent, spontaneous and difficult to foresee or counteract. In this work we specifically address the problem of sewage water polluting surface water bodies after spilling over from rain tanks as a consequence of heavy rain events. We investigate to what extent state-of-the-art interpretable time series models can help predict such critical water level points, so that the excess can promptly be redistributed across the sewage network. Our results indicate that modern time series models can contribute to better waste water management and prevention of environmental pollution from sewer systems. All the code and experiments can be found in our repository: https://github.com/TeodorChiaburu/RIWWER_TimeSeries.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

GreenDB -- A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods

The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Machine Learning (ML) can help to foster sustainable consumption patterns by accounting for sustainability aspects in product search or recommendations of modern retail platforms. However, the lack of large high quality publicly available product data with trustworthy sustainability information impedes the development of ML technology that can help to reach our sustainability goals. Here we present GreenDB, a database that collects products from European online shops on a weekly basis. As proxy for the products' sustainability, it relies on sustainability labels, which are evaluated by experts. The GreenDB schema extends the well-known schema.org Product definition and can be readily integrated into existing product catalogs. We present initial results demonstrating that ML models trained with our data can reliably (F1 score 96%) predict the sustainability label of products. These contributions can help to complement existing e-commerce experiences and ultimately encourage users to more sustainable consumption patterns.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

GreenDB: Toward a Product-by-Product Sustainability Database

The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Modern retail platforms rely heavily on Machine Learning (ML) for their search and recommender systems. Thus, ML can potentially support efforts towards more sustainable consumption patterns, for example, by accounting for sustainability aspects in product search or recommendations. However, leveraging ML potential for reaching sustainability goals requires data on sustainability. Unfortunately, no open and publicly available database integrates sustainability information on a product-by-product basis. In this work, we present the GreenDB, which fills this gap. Based on search logs of millions of users, we prioritize which products users care about most. The GreenDB schema extends the well-known schema.org Product definition and can be readily integrated into existing product catalogs to improve sustainability information available for search and recommendation experiences. We present our proof of concept implementation of a scraping system that creates the GreenDB dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards ML Methods for Biodiversity: A Novel Wild Bee Dataset and Evaluations of XAI Methods for ML-Assisted Rare Species Annotations

Insects are a crucial part of our ecosystem. Sadly, in the past few decades, their numbers have worryingly decreased. In an attempt to gain a better understanding of this process and monitor the insects populations, Deep Learning may offer viable solutions. However, given the breadth of their taxonomy and the typical hurdles of fine grained analysis, such as high intraclass variability compared to low interclass variability, insect classification remains a challenging task. There are few benchmark datasets, which impedes rapid development of better AI models. The annotation of rare species training data, however, requires expert knowledge. Explainable Artificial Intelligence (XAI) could assist biologists in these annotation tasks, but choosing the optimal XAI method is difficult. Our contribution to these research challenges is threefold: 1) a dataset of thoroughly annotated images of wild bees sampled from the iNaturalist database, 2) a ResNet model trained on the wild bee dataset achieving classification scores comparable to similar state-of-the-art models trained on other fine-grained datasets and 3) an investigation of XAI methods to support biologists in annotation tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Sensor Artificial Intelligence and its Application to Space Systems -- A White Paper

Information and communication technologies have accompanied our everyday life for years. A steadily increasing number of computers, cameras, mobile devices, etc. generate more and more data, but at the same time we realize that the data can only partially be analyzed with classical approaches. The research and development of methods based on artificial intelligence (AI) made enormous progress in the area of interpretability of data in recent years. With growing experience, both, the potential and limitations of these new technologies are increasingly better understood. Typically, AI approaches start with the data from which information and directions for action are derived. However, the circumstances under which such data are collected and how they change over time are rarely considered. A closer look at the sensors and their physical properties within AI approaches will lead to more robust and widely applicable algorithms. This holistic approach which considers entire signal chains from the origin to a data product, "Sensor AI", is a highly relevant topic with great potential. It will play a decisive role in autonomous driving as well as in areas of automated production, predictive maintenance or space research. The goal of this white paper is to establish "Sensor AI" as a dedicated research topic. We want to exchange knowledge on the current state-of-the-art on Sensor AI, to identify synergies among research groups and thus boost the collaboration in this key technology for science and industry.

0 citations0 reviewsNo code linkedOpen access