Data Science Day 2026
Date: May 6, 2026
Time: 1:00 PM - 7:30 PM
Location: Auditorium, Main Building, Friedrich Schiller University Jena, Fürstengraben 1, 07743 Jena
Registration: https://indico.rz.uni-jena.de/e/dsdj2026
Welcome to the Data Science Day Jena 2026!
Organized by the Interactive Inference Project at the Institute of Computer Science, this event brings together data science professionals from academia and industry for a day of exchange, inspiration, and collaboration.
The Data Science Day aims to create a space for sharing knowledge, presenting innovative approaches, and discussing current challenges and opportunities in data science.
What to Expect
- Expert Talks – Insights from leading voices in the field
- Company Exhibition – An overview of new developments and solutions from industry
- Networking – Opportunities to connect with professionals across disciplines
The event is designed for a broad audience, including practitioners, researchers, educators, and decision-makers from development, management, science, and education.
Thanks to the generous support of the Gerlind & Ernst Denert Foundation and the Carl Zeiss Foundation, Data Science Day Jena 2026 is free of charge for all participants, including companies and poster presenters.
Program
13:00 – 13:05
Welcome
Joachim Giesen
Friedrich Schiller University Jena
13:05 – 14:00
Keynote: The Modern Era of Deep Learning: From Foundations to the Present
Sören Laue
University of Hamburg
Abstract: Over the past fifteen years, deep learning has evolved from early image classifiers to powerful language models and agentic systems capable of multi-step reasoning and tool use. This talk revisits the key turning points along that trajectory: the rise of GPU-accelerated training, convolutional and residual networks, the transformer architecture, diffusion models, scaling laws, and scientific milestones such as AlphaFold. Rather than speculate about the future, the presentation analyzes how shifts in data, compute, and architectural design reshaped both capability and perspective. As scaling approaches practical and conceptual limits, a deeper understanding of learning dynamics and architectural behavior becomes increasingly essential.
14:00 – 14:45
Company Exhibition
Participating Companies
- Thüringer Kompetenznetzwerk für Forschungsdatenmanagement (TKFDM)
- Jena digital
- Accenture Technology Solutions GmbH
- INTERSHOP Communications AG
- TNG Technology Consulting
- d-fine GmbH
- Spleenlab GmbH
- dotSource SE
- Inverso GmbH
14:45 – 15:10
Diversifying the R Ecosystem – New Approaches for Package Binaries & Content Publishing
Patrick Schratz
devXY, Switzerland
Abstract: Patrick’s talk explores the motivation behind rpkgs.com, a new open-source approach for building architecture-agnostic R package binaries on Linux (including Alpine). He also demonstrates how these are integrated into ricochet (ricochet.rs), a new app for publishing static and dynamic content for R, Python, and Julia.
15:10 – 15:35
Data-driven Digital Evolution of Modern Laboratories
Marta Dembska
German Aerospace Center
Abstract: The digital evolution of modern laboratories requires moving beyond isolated data capture toward machine-actionable representations of entire experimental workflows. This means formally modelling not only measurement data, but also process plans, execution details, and provenance. This talk presents a data-driven approach to laboratory digitalisation based on semantic technologies, in particular ontologies and knowledge graphs.
Ontologies provide a structured, interoperable representation of laboratory processes that captures both intended procedures and their actual execution. Such explicit provenance modelling is essential for reproducibility, systematic error analysis, outlier detection, and predictive modelling. By integrating process descriptions with resulting data and performance metrics, semantic models create a coherent knowledge layer that supports traceability, comparative analysis, and data-driven decision-making in diverse laboratory settings.
Addressing practical challenges of scalability, interoperability, and usability, knowledge graphs serve as flexible and scalable data infrastructures, while alignment with established domain ontologies ensures seamless integration across systems. A reusable and extensible reference model builds upon and integrates existing standards rather than replacing them, providing adaptable modelling patterns for diverse laboratory contexts. This framework establishes a pragmatic foundation for FAIR laboratory ecosystems and supports the transition toward data-driven research and innovation.
15:35 – 16:00
Applied Data Science in Software Consulting: Trench Detection and Location Clustering
Sebastian Wuttke & Paul Kahlmeyer
TNG Technology Consulting
Abstract: Software consulting presents unique data science challenges where problems often lack the precise formulations common in academic settings. This talk presents two case studies from TNG that demonstrate how practical solutions emerge through domain-driven problem structuring.
The first case addresses trench path extraction from large-scale LIDAR point cloud data for an infrastructure project. Without an explicit mathematical model for trench detection, the approach relies on geometric assumptions such as elevation contrast, local density, linearity, and global tree topology. These are translated into a robust processing pipeline that operates within strict runtime constraints.
The second case tackles venue optimization for a distributed workforce, examining Affinity Propagation, the metric k-Median problem, and strategic search space reduction. Together, these examples highlight a key insight from applied data science: effective solutions often emerge not from optimizing predefined objectives, but from carefully introducing domain-specific structure to complex, ambiguous problems.
16:00 – 16:45
Company Exhibition
16:45 – 17:30
Capstone: Translating Machine Learning and Neuroimaging into Advances in Mental and Neurological Health
Thomas Wolfers
Laboratory for Mental Health Mapping / Carl Zeiss Professor for AI in Neural Systems Imaging
Abstract: Advances in large-scale neuroimaging and machine learning are beginning to transform how we understand individual differences in brain organization and their relevance for mental and neurological health. In this talk, I will discuss how population-scale brain imaging data can be leveraged to move from traditional group-level analyses toward individualized inference and clinically meaningful models of brain variation. After a brief high-level introduction to machine learning approaches for modeling brain variability, I will present in detail three recent studies from our group (submitted in 2026) that illustrate this translational pathway. These works focus on normative modeling and large-scale analyses of brain network dynamics across the lifespan, demonstrating how machine learning can establish reference models of brain organization, quantify individual deviations associated with mental and neurological disorders, and enable longitudinal tracking of brain dynamics at the level of single individuals. Together, these studies illustrate how combining big data, machine learning, and neuroimaging can help bridge the gap between neuroscientific discovery and clinical practice, providing a framework for translating brain-based insights into improved understanding, monitoring, and ultimately treatment of mental and neurological conditions.
17:30 – 19:30
Get-together
Snacks and drinks
