Are you interested in bringing artificial intelligence and data science to the forefront of health research? Three top-level institutions – the Karlsruhe Institute of Technology, the German Cancer Research Center and Heidelberg University – have joined forces to tackle this exciting area by providing a unique doctoral program. Send your application if you can see the potential in techniques such as deep learning in the health sector and if you are ready to shape the future of our well-being.

Mission Statement


The aim of the Helmholtz Information & Data Science School for Health (HIDSS4Health) is to attract, promote and train the best young talents at the interface between data science and health-related applications. HIDSS4Health offers a structured doctoral training program embedded in a highly interdisciplinary research environment, bringing together experts from the data and life sciences. The scientific curriculum is complemented by training measures that provide doctoral researchers with the key qualifications expected from future leaders in science and industry.

Research Areas


In Imaging & Diagnostics, we use machine or deep learning to exploit increasingly large and complex datasets generated by current high-throughput technologies in medicine, biology and health-related biotechnology. We need to deal with various challenges including real-time conditions, the quantification of uncertainty and ambiguity in imaging and omics data as well as the development of explicable decision-making.

In Surgery & Intervention 4.0, we focus on the role of data science in robot- and computer-assisted surgery and interventions. This includes the development and use of computational methods for planning and automation of examinations, surgery and interventions of different types and for intelligent assistive systems collaborating with the physicians, guiding them and supporting their learning process.

In Models for Personalized Medicine, we plan to integrate data-driven modeling, simulation, and visual exploration with first principles modeling. It includes models for real-time applications or patient models for interactive visualization. Data to be considered includes text data (such as intervention logs, admission notes), time series data, features extracted from images or omics data, as well as more traditional numerical data (e.g., lab results).

Projects


Projects 2022 (see also the proposals)

Imaging & Diagnostics

Using Anatomical Knowledge to Improve Medical Image Analysis
Using Anatomical Knowledge to Improve Medical Image Analysis

While there has been impressive progress in the field of biomedical image segmentation, current approaches hardly incorporate anatomical knowledge or common... (read more)

Analysis Pipelines and Data Fusion for Cerebral Organoids
Analysis Pipelines and Data Fusion for Cerebral Organoids

Organoids are self-assembled three-dimensional aggregates generated from human pluripotent stem cells (hPSC) with cell types and cytoarchitectures that resemble human organs... (read more)

Anomaly Detection in Sparse Image Time Series
Anomaly Detection in Sparse Image Time Series

Current image analysis of patient data only uses single images, with previous measurements not being incorporated into the model. Furthermore, image... (read more)

Interactive Annotation of Volumetric Imaging Data Incorporating Report Information
Interactive Annotation of Volumetric Imaging Data Incorporating Report Information

Annotated medical data is a prerequisite for successful and robust deep-learning models. However, the curation of labels for medical images is... (read more)

Surgery & Intervention 4.0

Model-based Artificial Intelligence in Surgical Data Science
Model-based Artificial Intelligence in Surgical Data Science

Death within 30 days after surgery has recently been found to be the third-leading cause of death worldwide [1], with research suggesting... (read more)

Models for Personalized Medicine

Pharmacogenomic Analyses in Next-Generation-Sequencing Data from Cancer Patients
Pharmacogenomic Analyses in Next-Generation-Sequencing Data from Cancer Patients

Pharmacogenomics (PGx) studies how variations in the genome affect drug response in patients. There are genomic variants in so-called pharmacogenes which... (read more)

Ensembling Experts for Improved Accuracy and Privacy in Predictive Models for Healthcare
Ensembling Experts for Improved Accuracy and Privacy in Predictive Models for Healthcare

In order to build machine learning models for predictive healthcare, the standard approach is to learn a unified prediction model on... (read more)

Projects 2021

Imaging & Diagnostics

Evaluation and combination of infectious disease forecasts
Evaluation and combination of infectious disease forecasts

During the ongoing COVID-19 pandemic, probabilistic forecasts of infectious disease spread have become a research priority, because they allow for a... (read more)

Data science approaches to evaluate and correct bias in prokaryotic single-cell transcriptomics
Data science approaches to evaluate and correct bias in prokaryotic single-cell transcriptomics

During the last decade, single-cell omics technologies such as single-cell transcriptomics (SCT) have rapidly emerged. As a complement to cultivation-based meta-transcriptomic... (read more)

Surgery & Intervention 4.0

Inverse Radiotherapy Treatment Planning using Machine Learning Outcome Prediction Models
Inverse Radiotherapy Treatment Planning using Machine Learning Outcome Prediction Models

Half of all cancer patients receive radiation therapy which delivers high-energetic, ionizing radiation to target cancerous tissue while sparing healthy tissue... (read more)

Data-driven Gamification to Improve Quality in Medical Image Annotation Tasks (GaMeIT)
Data-driven Gamification to Improve Quality in Medical Image Annotation Tasks (GaMeIT)

Machine Learning (ML) models are increasingly diffusing in healthcare. ML models can, for example, come into play in cognitive surgical robots... (read more)

Models for Personalized Medicine

Leveraging large-scale single-cell datasets for personalized cancer cell-of-origin inference
Leveraging large-scale single-cell datasets for personalized cancer cell-of-origin inference

The goal is to develop machine learning strategies for deciphering cell-of-origin related cancer heterogeneity. Building on variational autoencoders and related dimensionality... (read more)

Supervised Machine Learning to Predict Radical Transfer Mechanisms across Collagen Genetic Disorders (SMaRT)
Supervised Machine Learning to Predict Radical Transfer Mechanisms across Collagen Genetic Disorders (SMaRT)

Collagen is the most abundant protein in our body and performs a variety of functions, including strengthening and supporting skin, tendons... (read more)

Using invertible neural networks to predict molecular interactions
Using invertible neural networks to predict molecular interactions

Protein-biomolecule-interactions are ubiquitous in life, from DNA-replication to regulating the heartbeat. The stability of biomolecular complexes depend heavily on the involved... (read more)

Projects 2020

Imaging & Diagnostics

Scalable Cell-Tracking with Learnable Combinatorial Optimization
Scalable Cell-Tracking with Learnable Combinatorial Optimization

Cell segmentation and tracking is the problem of processing a time series of (3D) images showing development of an organism (e.g... (read more)

Integration of omics data to discover biomarker signatures for hypoxia and radioresistance
Integration of omics data to discover biomarker signatures for hypoxia and radioresistance

Tumour hypoxia, a state of low oxygen levels in certain tissue regions, seems to play a prognostic role for loco-regional tumour... (read more)

Visualization for MRI-Based Psychiatric Diagnosis
Visualization for MRI-Based Psychiatric Diagnosis

Since reliable biomarkers for psychiatric diagnosis and treatment indication are lacking, such diagnostics and prognostics is mainly based on structural interviews... (read more)

Surgery & Intervention 4.0

Human Rules - AI brains: Automated CTV delineation for head and neck cancers
Human Rules - AI brains: Automated CTV delineation for head and neck cancers

The precise spatial delineation of cancerous and healthy tissue in radiation therapy is necessary to prevent side effects and the reoccurrence... (read more)

Cooperative multi-agent reinforcement learning for next-generation cognitive robotics in laparoscopic surgery
Cooperative multi-agent reinforcement learning for next-generation cognitive robotics in laparoscopic surgery

Laparoscopic surgery is a team effort. A surgeon and her assistant(s) collaborate to solve a shared task working individually and as... (read more)

Robust data-driven prediction of complications in minimally-invasive surgery
Robust data-driven prediction of complications in minimally-invasive surgery

Death within 30 days after surgery has recently been found to be the third-leading cause of death worldwide. In this context... (read more)

Models for Personalized Medicine

Explainable Artificial Intelligence in Life Science: An Application to Omics Data
Explainable Artificial Intelligence in Life Science: An Application to Omics Data

As it is becoming progressively challenging to wholly analyse the ever-increasing amounts of generated biomedical data (e.g., CT scans, X-ray images... (read more)

Bayesian Deep Learning for Radiogenomics Analysis of Cancer
Bayesian Deep Learning for Radiogenomics Analysis of Cancer

In recent years, biomedical data have been increasingly available. In particular, the costs for procuring omics data, including genome and exome... (read more)

ML based parameterization to simulate tissue and tumor development as emerging property from single cell events
ML based parameterization to simulate tissue and tumor development as emerging property from single cell events

A deep understanding of tissue growth as emerging behavior from single-cell events might lead to new insights for different scientific fields... (read more)

Data Mining and Uncertainty Quantification in disease diagnosis
Data Mining and Uncertainty Quantification in disease diagnosis

The accurate diagnosis of patients with rare diseases is important but challenging, since the prevalence of them is very low. This... (read more)

Projects 2019

Imaging & Diagnostics

Generating Medical Imaging Reports from 3D Radiological CT Scans using Image Captioning Techniques
Generating Medical Imaging Reports from 3D Radiological CT Scans using Image Captioning Techniques

While Deep Learning approaches, in particular Convolutional Neural Networks (CNNs), are now also being widely adopted in medical image analysis, only... (read more)

Camera-Invariant Spectral Image Analysis in Interventional Healthcare
Camera-Invariant Spectral Image Analysis in Interventional Healthcare

Death within 30 days after surgery has recently been found to be the third-leading cause of death worldwide. One of the... (read more)

An Automated Framework to Facilitate Biological Image Processing with Deep Learning Methods
An Automated Framework to Facilitate Biological Image Processing with Deep Learning Methods

Fluorescence microscopy has several inherent limitations, which are dictated by basic optical and physical laws as well as compromises arising from... (read more)

Scalable 3D+t Tracking by Graph-based and Deep Learning Methods
Scalable 3D+t Tracking by Graph-based and Deep Learning Methods

Many biomedical imaging devices are able to collect time series of high resolution 3D data (3D+t). A typical task in these... (read more)

Design Principles Underlying Fault Tolerance in Control Systems of Genetically Varying Model Organisms
Design Principles Underlying Fault Tolerance in Control Systems of Genetically Varying Model Organisms

Compared to man-made technical control systems, biological control systems exhibit a remarkable fault tolerance over wide ranges of environmental cues as... (read more)

Image-Guided Adaptive Radiation Therapy (IGART) Based on Massive Parallelism and Real-Time Scheduling
Image-Guided Adaptive Radiation Therapy (IGART) Based on Massive Parallelism and Real-Time Scheduling

The image shows the challenge of the project in my view: the first part shows a volume rendering of a lung... (read more)

Anomaly Detection Using Unsupervised Learning for Medical Images
Anomaly Detection Using Unsupervised Learning for Medical Images

An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning... (read more)

Surgery & Intervention 4.0

Robust Tissue Classification with Multispectral Imaging
Robust Tissue Classification with Multispectral Imaging

Machine learning-based decision support can potentially improve the quality of healthcare by providing physicians with the right information at the right... (read more)

Uncertainty Quantification in Radiation Therapy
Uncertainty Quantification in Radiation Therapy

Radiation therapy is one of the cornerstones in modern cancer treatment being applied in 50 % of all patients. It is a... (read more)

Lifelong Machine Learning in Surgical Data Science
Lifelong Machine Learning in Surgical Data Science

Current Machine Learning algorithms, especially Deep Neural Networks (DNNs), have shown to be successful tools in areas particularly relevant for Life... (read more)

Quantitative Photoacoustic Imaging with a Learning-to-simulate Approach
Quantitative Photoacoustic Imaging with a Learning-to-simulate Approach

Photoacoustic imaging (PAI) is an emerging modality that has the potential to provide tomographic images of blood oxygenation - an important... (read more)

Models for Personalized Medicine

Distributed Ledger Technology for Life Science
Distributed Ledger Technology for Life Science

Modern life sciences with their highly sensitive omics data sets face several challenges with regard to data storage and sharing. On... (read more)

Augmenting Physician Workflow to Personalize Care Decisions by Predicting Next Steps and Informational Needs in (Precision) Oncology
Augmenting Physician Workflow to Personalize Care Decisions by Predicting Next Steps and Informational Needs in (Precision) Oncology

With the possibility of whole genomic sequencing for oncologic patients, many processes in their treatment have to be adapted. Physicians in... (read more)

Integrative Analysis of Patient Multi-omics Data
Integrative Analysis of Patient Multi-omics Data

Multi-omics, the generation of omics-profiles with multiple assays on the same set of biological samples, is a fundamental experimental design pattern... (read more)

Publications


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Steering Committee


Spokespersons

Ralf Mikut

Ralf Mikut

Karlsruhe Institute of Technology

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Klaus Maier-Hein

Klaus Maier-Hein

German Cancer Research Center

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Michael Gertz

Michael Gertz

Heidelberg University

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Elected Members

Coordination Office


Coordination

Nicole Merkle

Nicole Merkle

Karlsruhe Institute of Technology

Gregor Köhler

Gregor Köhler

German Cancer Research Center

Daniel Walther

Daniel Walther

German Cancer Research Center

Administration

Nina Kraft

Nina Kraft

German Cancer Research Center

Data Science Groups


Achim Streit

Achim Streit

The Streit team is enabling data-intensive science through generic informatics R&D in the areas data management, data analytics, federated computing and scheduling.

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Ali Sunyaev

Ali Sunyaev

The Sunyaev team designs, develops, and evaluates reliable and purposeful software and information systems within the scope of information security solutions and innovative health IT applications.

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Alin Albu-Schäffer

Alin Albu-Schäffer

The Albu-Schäffer team addresses in cooperation with the Asfour team medical robotics and the related data acquisition and Interpretation.

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Anne Koziolek

Anne Koziolek

The Koziolek team researches how to ease development of data-intensive systems, esp. the selection of appropriate machine learning algorithms with respect to suitability and performance.

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Benedikt Brors

Benedikt Brors

The Brors team develops algorithms for personalized medicine, cancer epigenetics and single-cell sequencing and applies them in a clinical context as well as in large international consortia.

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Carsten Dachsbacher

Carsten Dachsbacher

The research group developes methods for interactive visualization, high performance computer graphics, and radiative transport simulations.

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Carsten Rother

Carsten Rother

We (C. Rother, B. Savchynskyy, U. Köthe) work on machine learning and combinatorial optimization (IP, LP) - e.g. 2D/3D tracking, explainable ML, NN+Graphical Models

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Emilia Grass

Emilia Grass

Our objective is to strengthen the resilience of healthcare systems against cyber-attacks by using stochastic programming, simulation and machine learning.

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Filip Sadlo

Filip Sadlo

The Visual Computing Group develops novel techniques for visual data analysis, with a focus on topological analysis and feature extraction.

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Franziska Mathis-Ullrich

Franziska Mathis-Ullrich

The Mathis-Ullrich team aims to increase efficiency and patient-safety during medical procedures through innovative robotic instruments, as well as robot-assistance and machine learning for surgical intervention.

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Holger Fröning

Holger Fröning

Performance, energy-efficiency and programmability: HPC/HPA, deep learning, reconfigurable logic. Example 1: deep learning on embedded systems. Example 2: simplified multi-GPU programming

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Klaus Maier-Hein

Klaus Maier-Hein

The Maier-Hein team develops machine learning algorithms, mathematical modelling approaches for computational image understanding and large-scale information processing.

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Lena Maier-Hein

Lena Maier-Hein

The Maier-Hein team aims to improve the quality of interventional healthcare and its value computationally. It supports the physician throughout the entire process of disease diagnosis, therapy and follow-up with the right information at the right time.

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Martin Frank

Martin Frank

The Frank group aims at bringing modern mathematical techniques into practice. These techniques include modeling, simulation, optimization, inverse problems and uncertainty quantification.

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Melanie Schienle

Melanie Schienle

The Schienle team develops novel statistical methods for interpretable machine learning, including networks, treatment effects and prediction uncertainty.

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Michael Beigl

Michael Beigl

The Beigl team develops AI-driven analytics and Big Data methods and systems to solve problems in application domains.

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Michael Gertz

Michael Gertz

The Gertz team focuses on novel models and techniques in support of information extraction, data/text mining, machine learning, and network analysis for heterogeneous data.

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Oliver Stegle

Oliver Stegle

The Stegle group develops and applies statistical approaches and methods based on machine learning for analysing high-dimensional molecular data modalities.

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Pascal Friederich

Pascal Friederich

The AiMAT group works on the development and application of machine learning methods for materials science and chemistry.

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Peter Sanders

Peter Sanders

The Sanders team develops basic toolbox algorithms and software libraries for handling large data sets in a scalable way.

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Rainer Stiefelhagen

Rainer Stiefelhagen

The Stiefelhagen team investigates methods to analyse images using weak supervision, for example by jointly analysing medical images and their associated clinical reports.

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Ralf Mikut

Ralf Mikut

The Mikut team analyses 2D, 3D and 3D+t biomedical images and related data using image analysis and machine learning methods.

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Robert Strzodka

Robert Strzodka

The chair Application Specific Computing focuses on efficient interactions of mathematic, algorithmic and architectural aspects in heterogeneous high performance computing (GPUs, FPGAs, many-core).

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Shiva Faeghi

Shiva Faeghi

Our research focuses on development and application of computational methods to analyze and optimize healthcare processes.

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Stefan Riezler

Stefan Riezler

The Riezler group develops statistical machine learning methods for ambiguous and noisy data, with a focus on natural language processing.

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Tamim Asfour

Tamim Asfour

The Asfour team develop data-driven methods and algorithms for skill learning, motion generation and prediction in the context of robot-assisted surgery and exoskeletons.

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Till Bärnighausen

Till Bärnighausen

Till Bärnighausen is Alexander von Humboldt Professor and Director of the Heidelberg Institute of Global Health (HIGH), Heidelberg University, Germany. He is also Senior Faculty at the Africa Health Research Institute (AHRI) in South Africa and fellow at the Harvard Center for Population and Development Studies. Till's research focuses on creating and evaluating global health interventions. His research has been funded by the US National Institutes of Health, European Union, German Research Foundation, Alexander von Humboldt Foundation, Wellcome, Bill & Melinda Gates Foundation, Clinton Health Access Initiative, Else-Kröner-Fresenius Foundation, USAID, UNAIDS, WHO, KfW, World Bank. Till has previously worked as professor at Harvard T.H. Chan School of Public Health; as medical doctor in Germany, China and South Africa; and as management consultant for McKinsey & Company. Till holds doctoral degrees in international and population health (Harvard) and history of medicine (Heidelberg), and master degrees in health systems management (LSHTM), financial economics (SOAS), and innovation and entrepreneurship (HEC Paris).

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Ullrich Köthe

Ullrich Köthe

Ullrich Köthe's team works on interpretable machine learning methods that help to extract knowledge from data in the natural and life sciences. Our speciality are invertible neural networks.

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Vincent Heuveline

Vincent Heuveline

The EMCL focuses on uncertainty quantification (UQ) in scientific computing, high performance and data intensive computing, with main application focuses in medical engineering.

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Life Science Groups


Alexander Schug

Alexander Schug

The Schug lab develops and applies methods for molecular simulation and analysis of genomic data to address questions in biological and medical research.

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Anne-Kristin Kaster

Anne-Kristin Kaster

Our aim is to obtain genomes from new bacterial and archaeal taxa with no sequenced representatives and providing direct link information between cell's phylogenetic and metabolic markers by matching phylogeny and function. We unravel novel metabolisms, ecophysiology and mechanisms of energy conservation among uncultured microorganisms and in addition investigating minimal genome requirements and syntrophic interactions as well as genetic and phenotypic heterogeneity among cells, cell-to-cell variations, horizontal gene transfer and evolutionary pressure.

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Christof M. Niemeyer

Christof M. Niemeyer

The Niemeyer team is focussing on the development of biointerfaces for applications in fundamental cell biology, such as cell signaling and development

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Daniel Durstewitz

Daniel Durstewitz

The Durstewitz group develops statistical machine learning methods at the mathematical and algorithmic levels, with a focus on nonlinear time series analysis, nonlinear dynamical systems, and generative recurrent neural networks. Our main application domains are functional neuroimaging and electrophysiological data, as well as smartphone-based ecological momentary assessments, in psychiatry and neurology.

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Daniel Hübschmann

Daniel Hübschmann

Our research focuses on (a) pattern recognition in biological data, multi-omics analysis and data integration with special application to cancer genomics as well as (b) translational and personalized oncology, molecular tumor boards, biomarker development and data analysis for clinical trials.

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Felix Nickel

Felix Nickel

Our goal is to establish Precision Surgery with Digital Technologies using Computer Vision, Advanced Imaging, Robotic Surgery, Artificial Intelligence, 3D Printing, and Extended Reality.

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Frauke Gräter

Frauke Gräter

We ask how tissues and the molecules therein sense mechanical force. To this end, we use and develop molecular simulations across scales, and complement those by biophysical experiments.

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Gerd Ulrich Nienhaus

Gerd Ulrich Nienhaus

Advanced optical microscopy: Method development for live microscopy (superresolution, light sheet), data analysis, biophysical research

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Hannes Kenngott

Hannes Kenngott

The Kenngott Team develops computer-assisted surgery, clinical decision support systems, medical robotics, cyber-physical systems in surgery and machine learning on medical images and data.

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Heinz-Peter Schlemmer

Heinz-Peter Schlemmer

Multiparametric and multimodal oncologic imaging; whole-body imaging; high and ultra-high field MRI; PET/MR hybrid imaging; dual-energy CT; US;

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Jens Kleesiek

Jens Kleesiek

The Kleesiek team develops machine learning algorithms for radiological and other clinical data. An emphasis lies on the translation of these computational methods into clinical settings.

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Joacim Rocklöv

Joacim Rocklöv

Our research focuses on using data science for studying drivers and predicting the emergence of infectious disease spreading among animals, insects, and humans. Specifically, we use and develop process-based modelling, machine learning and combination approaches integrating data sources from different disciplines and domains across biological, environment, and social domains to explain, forecast and project risks. Within this area we are engaged in larger international networks and projects on infectious disease ecology and dynamics, pandemic preparedness and climate change impacts and adaptation.

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Johannes Bracher

Johannes Bracher

We develop statistical methods for infectious disease epidemiology, with a specific focus on multi-model short-term forecasting of disease spread.

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Julio Saez Rodriguez

Julio Saez Rodriguez

Saez lab integrates big ('Omics') data with mechanistic molecular knowledge into statistical and machine learning methods to dissect disease mechanisms.

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Junyan Lu

Junyan Lu

Dr. Lu’s team focuses on developing and applying statistical/machine learning tools for mining mass spectrometry proteomic/metabolomic data as well as integrating them with multi-omics and clinical data, in order to identify cancer biomarkers and drug targets.

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Jürgen Debus

Jürgen Debus

Our goal is to establish precision medicine in Radiation Oncology with innovative high-precision protocols with ions and photons and molecular stratification of patients in over 40 trials.

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Lennart Hilbert

Lennart Hilbert

The Hilbert lab investigates information processing in dense DNA suspensions, as seen inside the cell nucleus, using microscopy, image analysis, and physical modelling.

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Mark E. Ladd

Mark E. Ladd

The Ladd team develops new and optimizes existing biomedical imaging methods (MRI, CT, PET, optical imaging and ultrasound) for diagnostic and therapeutic procedures.

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Martin Wagner

Martin Wagner

The Wagner team investigates methods of artificial intelligence and cognitive robotics to improve surgical treatment with a strong focus on clinical translation.

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Matthias Schlesner

Matthias Schlesner

The Schlesner team develops and applies methods for data analysis, visualization and integration to explores omics data and address questions in basic and translational cancer research.

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Michael Baumann

Michael Baumann

The Baumann team integrates omics data, pathological data and radiological imaging for personalized oncology.

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Oliver Jäkel

Oliver Jäkel

Our team is developing novel physical and mathematical methods to advance and improve radiation therapy and to analyze and predict its outcome.

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Oya Kuseyri Hübschmann

Oya Kuseyri Hübschmann

Our research focuses on study of inborn errors of neurotransmitter metabolism. We (a) work with and on international patient registries with a deep phenotyping approach and (b) develop prototypic scoring systems for clinical severity, drug response and outcome prediction.

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Pavlo Lutsik

Pavlo Lutsik

We develop methods and tools enabling data-driven insights into cancer biology through the epigenomes of tumor cells. Our major research topics include integrative analysis of large epigenomic data sets, deconvolution approaches for tumour heterogeneity inference, novel experimental techniques for epigenome profiling, as well as generic infrastructure for reproducible bioinformatics.

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Rebecca Wade

Rebecca Wade

Our main aim is to develop and apply computer-aided methods to study how biomolecules, such as proteins, interact. The methods make use of three-dimensional macromolecular structures and combine multiscale approaches based on physicochemical principles with those of bio-/chemo-informatics and machine learning.

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Sabine Jung-Klawitter

Sabine Jung-Klawitter

We are interested in inborn errors of metabolism with a special focus on defects in neurotransmitter biosynthesis and metabolism including defects in the biosynthesis of dopamine, serotonin, and GABA, as well as in tetrahydrobiopterin (BH4) biosynthesis. We use patient-specific induced pluripotent stem cells (iPSCs), CRISPR-generated isogenic control iPSC lines and differentiated somatic cell types (neurons, glia) and organoids to elucidate the pathophysiology of the diseases. We are especially interested in (a) gaining a deeper understanding of the complex pathophysiology of the diseases, (b) obtaining better genotype/phenotype correlations and (c) the identification of new therapeutic targets and diagnostic markers.

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Stefan Kölker

Stefan Kölker

The Kölker team in cooperation with the Heuveline team aims at improving the diagnostic quality of newborn screening programs.

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Uwe Spetzger

Uwe Spetzger

Our goal is the ongoing development of computer-assisted neurosurgery and robotics. Optimization of visualization techniques combined with functional intraoperative mapping, as well as simulation and machine learning for neurosurgical interventions.

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Uwe Strähle

Uwe Strähle

The Strähle lab investigates the regulatory mechanisms controlling development and regeneration and how these processes are disturbed in various genetic disease models.

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Veronique Orian-Rousseau

Veronique Orian-Rousseau

The Orian-Rousseau team is investigating signaling pathways involved in tumor progression and metastasis as well as in liver diseases. The impact of the microenvironment on these diseases is also under scrutiny.

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Training Summary


Overview

The goal of the HIDSS4Health curriculum is that its doctoral researchers become experts in data science and in at least one health-related field. It will include the ability to communicate with experts from both sides and to transfer knowledge from data to life science and vice versa. All lectures, courses, retreats and summer schools are held in English.

Supervision Concept

All doctoral researchers will receive feedback from a Thesis Advisory Committee (TAC). This consists of an interdisciplinary team of at least three Principal Investigators (PI), including PIs with data science and life science backgrounds from different institutions. TAC meetings will discuss the progress of the doctoral research project, the publication concept, working plans, cooperation potential with other doctoral researchers, planning of the mobility phase, as well as the supervision quality and career perspectives of the doctoral researcher. Although each doctoral researcher will belong to the main supervisor’s group, we recommend an associate membership in the group of a second PI, including an official second affiliation, regular stays in this group (such as once a week or one week per month) and integration in lab meetings etc. We anticipate that this will foster closer integration between data and life science expertise and a deeper understanding of both aspects.

Degree

In general, the doctoral thesis degree will be awarded by the informatics faculty of KIT or Heidelberg University, depending on your main supervisor’s individual affiliation. Each doctoral researcher who successfully finishes all training modules and the doctoral thesis project will receive a certificate as “Data Scientist”, including a confirmation regarding specialization in the health-related application domain. This certificate is a joint activity of the Faculty for Informatics at KIT and the Faculty for Mathematics and Computer Science at Heidelberg University.

Lectures, Workshops, Seminars

All incoming doctoral researchers start with the interdisciplinary course “Data Science & Health”. This is held by the PIs of the school and invited speakers who cover additional topics. The course consists of lecture-style elements and an interactive discussion and covers data science methods (such as clustering, image analysis, visualization, deep learning), domain-oriented methods (such as medical imaging, surgery) as well as ethical, legal and social implications. In addition, it will train researchers to consider the different views and needs of data science and health-related sciences, and the opportunities for interdisciplinary cooperation.

The following lecture course "Advanced Topics in Data Science & Health" has two objectives. On the one hand, it provides advanced doctoral researchers and postdocs with the opportunity to gain some experience in this type of teaching format. On the other hand, it will provide first-year doctoral researchers with an overview of recent scientific developments and the state of the art in the research fields of the presenters.

In addition, we will offer a set of specific scientific courses (for example, on Simulation & Optimization, Machine Learning, Uncertainty Quantification, Scientific Visualization, Python, Confocal Microscopy, International Zebrafish and Medaka Course) with a data and/or life science orientation.

The exact configuration of these compulsory elective courses is individually chosen by the doctoral researcher depending on thematic needs and existing skills.

Exchange Program

Each doctoral researcher of the school should complete a funded mobility phase of around three months, either at a leading international university or institution. The school supports the selection of appropriate partner institutions, for example, using existing international cooperation networks such as HeKKSaGOn (the Heidelberg – Kyoto – Karlsruhe – Sendai – Göttingen – Osaka network of German and Japanese universities) and InterACT (network of eight universities from Europe, Asia and America including KIT and Carnegie Mellon University). DKFZ has exchange programs with the Weizmann Institute of Science (Israel), the MD Anderson Cancer Center (USA) and partners of Cancer Core Europe.

Summer Schools

Each doctoral researcher is expected to attend at least one summer school, which will be held annually. These summer schools will be co-organized with other data science schools within HIDA, thus enabling all Helmholtz Information and Data Science School participants to network and encourage further collaboration.

Personal Skill Training

The school provides a wide portfolio of personal skills training (including leadership and language) as well as comprehensive support measures regarding internationalization, networking and career orientation. Courses such as “Introduction to German Academic Culture”, “Publishing in Scientific Journals”, and “Managing Projects” are currently offered. Furthermore, research school doctoral researchers will be encouraged to participate in the centrally-organized Helmholtz Transferable Skills Training program for doctoral researchers. Three modules, each lasting 2–3 days will provide training in (I) research skills development, (II) presentation and communication and (III) career and leadership.

Cross Theme Topics

Cross Theme Topics (CTTs) are a collaboration between 2–4 doctoral researchers working on related methodological problems, supported and mentored by postdocs and PIs of the school. The aim of a CTT is to leverage synergy potentials between projects (for example by sharing special skills of a doctoral researcher between projects) and to broaden the individual methodological understanding beyond the research areas. The ideal outcome of a CTT would be a joint paper, software tool or benchmark dataset. All doctoral researchers in HIDSS4Health will be encouraged to be involved in at least one CTT during his/her time in the school.

Training Program


Lectures

Data Science & Health – Winter Semester 2024/2025

This lecture is held by the PIs of the school and invited speakers who cover additional themes. The course consists of lecture-style elements and an interactive discussion. It covers data science methods (e.g., clustering, image analysis, visualization, deep learning), domain-oriented methods (e.g., medical imaging, surgery) as well as ethical, legal and social implications. The aim of the course is to introduce the doctoral researchers to the field and to give a basic understanding of all research areas of the school. In addition, it will train them to consider the different views and needs of data science and health-related sciences, and the opportunities for interdisciplinary cooperation.

Organization

The lecture series Data Science & Health is offered online via zoom. The lectures take place weekly and only attendance is documented. Participants can receive a confirmation of participation after attending 75% of all lectures. To count towards this, each lecture must be attended for at least 80% of its duration. Registration is required and can be completed via this form.

Dates

Date Time Location Lecturer Topic
24.10.2024 14:00–15:30 Zoom Nicole Merkle Knowledge Representation with the Semantic Web and Knowledge Graphs
31.10.2024 14:00–15:30 Zoom Emilia Grass Cybergeddon in Healthcare
07.11.2024 14:00–15:30 Zoom Joacim Rocklöv Integrated surveillance and predictions of climate-sensitive diseases
21.11.2024 14:00–15:30 Zoom Benedikt Brors Curse of Dimensionality: Implications for Machine Learning
28.11.2024 14:00–15:30 Zoom Katja Mombaur TBA
05.12.2024 14:00–15:30 Zoom Junyan Lu Multi-omics data integration for precision oncology
12.12.2024 14:00–15:30 Zoom Axel Loewe Synergies between mechanistic and data-driven models in medical research
19.12.2024 14:00–15:30 Zoom Ralf Mikut Time Series Analysis
16.01.2025 14:00–15:30 Zoom Alexander Schug Biomolecular Structure Prediction: From statistical models to AlphaFold and beyond
23.01.2025 14:00–15:30 Zoom Scott Thiebes Explainable artificial intelligence in the life sciences
30.01.2025 14:00–15:30 Zoom Johannes Bracher Evaluation of probabilistic forecasts in infectious disease modelling
06.02.2025 14:00–15:30 Zoom Oliver Stegle From multi-omics integration to causal discovery in molecular systems
13.02.2025 14:00–15:30 Zoom Nicole Merkle State Space Models and Mamba for Time Series Data and Large Language Models
Data Science & Health – Winter Semester 2023/2024

This lecture is held by the PIs of the school and invited speakers who cover additional themes. The course consists of lecture-style elements and an interactive discussion. It covers data science methods (e.g., clustering, image analysis, visualization, deep learning), domain-oriented methods (e.g., medical imaging, surgery) as well as ethical, legal and social implications. The aim of the course is to introduce the doctoral researchers to the field and to give a basic understanding of all research areas of the school. In addition, it will train them to consider the different views and needs of data science and health-related sciences, and the opportunities for interdisciplinary cooperation.

Organization

This year, the lecture Data Science & Health is offered online via zoom. The lecture takes place weekly and only attendance is documented. Currently, no further examinations are planned. Registration is required, a registration link can be requested via mail to office@hidss4health.de.

Dates

Date Time Location Lecturer Topic
2023-10-26 14:00–15:30 Zoom Pascal Friederich Bayesian Optimization for Autonomous Experiments
2023-11-02 14:00–15:30 Zoom Emilia Graß Cybersecurity in Healthcare
2023-11-09 14:00–15:30 Zoom Alexander Schug Inference on Biomolecular Data by Machine Learning
2023-11-16 14:00–15:30 Zoom Ralf Mikut Time Series Analysis
2023-11-23 14:00–15:30 Zoom Vincent Heuveline Uncertainty Quantification (UQ) for medical models
2023-11-30 14:00–15:30 Zoom Junyan Lu Multi-omics data integration for precision medicine
2023-12-07 14:00–15:30 Zoom Martin Frank Mathematical Foundations of Deep Learning
2023-12-14 14:00–15:30 Zoom Stefan Riezler Reproducibility of Machine Learning Research
2023-12-21 14:00–15:30 Zoom Rainer Stiefelhagen TBD
2024-01-11 14:00–15:30 Zoom Julio saez-Rodriguez Knowledge-based machine learning from omics data
2024-01-18 14:00–15:30 Zoom Joacim Rocklöv Integrated Surveillance and Predictions for Prepardness
2024-01-25 14:00–15:30 Zoom Johannes Bracher / Melanie Schienle Evaluation of probabilistic forecasts in infectious disease modelling
2024-02-01 14:00–15:30 Zoom Anne Kaster Single Cell Omics.
2024-02-08 14:00–15:30 Zoom Shiva Faeghi Process Mining in Healthcare
2024-02-15 14:00–15:30 Zoom Evangelia Christodoulou / Annika Reinke Validation Matters: Pitfalls and recommendations for model evaluation in Biomedical Imaging Analysis
Six Main Tasks in Image Processing – Summer Semester 2023

In this series of seminars, six key image processing tasks will be discussed, following a typical workflow in the image processing pipeline. Images are not always captured by a camera. Often, they must be tediously reconstructed from a series of projections or other non-image types of acquisitions. Different reconstruction algorithms allow for better image quality or can focus on specific properties of the objects under observation. Noise can be introduced at many steps in the image acquisition process. Denoising is therefore an essential step in most image processing workflows. Tracking individual objects over multiple time steps is a difficult task, but allows for the observation of temporal dynamics. Segmentation refers to the assignment of each pixel in an image to a specific category. In semantic segmentation, all pixels belonging to a cat are labeled "cat", and all pixels belonging to trees are labeled "tree". In instance segmentation, each pixel is additionally assigned to an object instance, making it possible to distinguish multiple cats and trees in an image. The visualization of otherwise difficult-to-interpret data, such as reconstructed 3D(+T) objects or high-dimensional image data, is essential for understanding the results. Finally, interpreting the results of AI-based image analysis algorithms is important: Why was a particular decision made? What structures in the images were responsible? What can AI tell us about the underlying problem?

This lecture series is held by imaging experts invited by the organizing schools and Helmholtz Imaging. The course consists of presentations and interactive discussions. It covers different imaging techniques in life sciences and soft matter. For an in-depth understanding of the subject, we recommend attending all lectures. Registration is mandatory for participation. Places are limited and in the case of overbooking, priority will be given to fellows (members) of the three schools. Participants demonstrating an attendance record of more than 70% can receive a certificate of participation.

The lecture series is offered online. It takes place (more or less) bi-weekly. Registration is required; a registration link can be requested via mail to sabine.niebel@helmholtz-imaging.de.

Further details and up-to-date information in case of changes: https://events.hifis.net/event/793/

Date Time Location Lecturer Topic
2023-05-04 14:00–15:30 Zoom Prof. Philip Kollmannsberger (HHU) Six Main Tasks in Image Processing: an Overview
2023-05-25 14:00–14:45 Zoom Dr. Christoph Lerche (FZJ) Tomographic Methods in Medical Imaging
2023-05-25 14:45–15:30 Zoom Dr. Tim-Oliver Buchholz (FMI) Restoring Noisy Microscopy Images
2023-06-15 14:00–15:30 Zoom Prof. Carsten Rother (HCI) Tracking of Objects: from One to Many
2023-06-29 14:00–15:30 Zoom Prof. Dagmar Kainmüller (MDC) Machine Learning for Image Segmentation
2023-07-06 14:00–15:30 Zoom Deborah Schmidt (MDC) Visualization
2023-07-27 14:00–15:30 Zoom Prof. Ullrich Köthe (IWR) Explainable Machine Learning
Advanced Topics in Data Science & Health – Summer Semester 2023

This lecture is held by doctoral researchers of HIDSS4Health in their second or third year. The course if offered online and mainly for the HIDSS4Health doctoral researchers, but other doctoral researchers interested in single topics can request a registration link via mail to office@hidss4health.de.

Date Time Location Lecturer Topic
2023-04-25 14:00–14:45 Zoom Tim Ortkamp Bayesian Hyperparameter Optimization
2023-04-25 14:45–15:30 Zoom Simon Warsinsky Improving Data Quality in Medical Image Annotation Tasks through Gamification
2023-05-02 14:00–14:45 Zoom Abdul Moeed Causality and Machine Learning
2023-05-02 14:45–15:30 Zoom Balazs Gyenes Model-based Reinforcement Learning
2023-05-09 14:00–14:45 Zoom Robin Fleige Multi-Parameter Bifurcation Analysis of Vector Fields
2023-05-09 14:45–15:30 Zoom Julia Münch Next Generation Sequencing - Application and Data Analysis
2023-05-16 14:00–14:45 Zoom Yannick Kirchhoff Self-configuring methods for biomedical image analysis tasks
2023-05-16 14:45–15:30 Zoom Marlen Neubert Machine Learning for Molecular Simulations
2023-05-23 14:00–14:45 Zoom Daniel Wolffram Probabilistic Forecasting
2023-05-23 14:45–15:30 Zoom Sebastian Pirmann Cancer Pharmacogenomics
2023-05-30 14:00–14:45 Zoom Reiner Dolp TBA
2023-05-30 14:00–14:45 Zoom Max Piochowiak Volume Visualization
2023-06-06 14:00–14:45 Zoom Marcel Meyer Boltzmann Generators
2023-06-06 14:00–14:45 Zoom Ahmad Bin Qasim TBA
2023-06-13 14:00–14:45 Zoom Jayson Salazar Rodriguez TBA
Data Science & Health – Winter Semester 2022/2023

This lecture is held by the PIs of the school and invited speakers who cover additional themes. The course consists of lecture-style elements and an interactive discussion. It covers data science methods (e.g., clustering, image analysis, visualization, deep learning), domain-oriented methods (e.g., medical imaging, surgery) as well as ethical, legal and social implications. The aim of the course is to introduce the doctoral researchers to the field and to give a basic understanding of all research areas of the school. In addition, it will train them to consider the different views and needs of data science and health-related sciences, and the opportunities for interdisciplinary cooperation.

Organization

This year, the lecture Data Science & Health is offered online via zoom. The lecture takes place weekly and only attendance is documented. Currently, no further examinations are planned. Registration is required, a registration link can be requested via mail to office@hidss4health.de.

Dates

Date Time Location Lecturer Topic
2022-10-25 14:00–15:30 Zoom Leonardo Ayala Optical imaging concepts enabled by deep learning
2022-11-08 14:00–15:30 Zoom Junyan Lu Multi-omics data integration for precision medicine
2022-11-15 14:00–15:30 Zoom Alexander Schug Data inference in molecular biology: from mutual information to alpha fold
2022-11-22 14:00–15:30 Zoom Hannes Kenngott Challenges and Opportunities of Digital Transformation in a University Hospital: prototypes and reality. Lecture is cancelled
2022-11-29 14:00–15:30 Zoom Anne Kaster Single Cell Omics. Lecture is cancelled
2022-12-06 14:00–15:30 Zoom Pascal Friederich Bayesian Optimization for Autonomous Experiments
2022-12-13 14:00–15:30 Zoom Ullrich Köthe Bayesian Inference with invertible neural networks
2022-12-20 14:00–15:30 Zoom Mark Ladd Imaging Physics in Oncology
2023-01-10 14:00–15:30 Zoom Ali Sunyaev Distributed Ledger Technology in genomics
2023-01-17 14:00–15:30 Zoom Martin Frank Mathematical Foundations of Deep Learning
2023-01-24 14:00–15:30 Zoom Michael Gertz Data Science for Text Analysis
2023-01-31 14:00–15:30 Zoom Shiva Faeghi Process Mining in Healthcare
2023-02-07 14:00–15:30 Zoom Ralf Mikut Time Series Analysis
2023-02-14 14:00–15:30 Zoom Emilia Grass Cybergeddon in Healthcare: Preparing for the Worst

Note

The lecture "Time Series Analysis" by Ralf Mikut will be held on 2023-02-07 (previously scheduled for 2023-01-17) and the lecture "Mathematical Foundations of Deep Learning" by Martin Frank will be held on 2023-01-17 (previously scheduled for 2023-02-07).
Imaging – From Organisms to Molecules – Summer Semester 2022

(Lecture Series on Imaging Methods and Applications in Life Sciences and Soft Matter)

The application areas of imaging in life sciences and soft matter are almost inexhaustible in number. Highly specialized techniques and methods have thus been developed over time for imaging and analyzing a wide variety of samples, giving us unique insights into their structures at all scales. Over the course of this lecture series, data scientists and domain scientists will introduce you to a great number of imaging techniques.

Organization

This lecture series is held by PIs of the organizing schools and Helmholtz Imaging. The lecture series is offered online. It takes place (more or less) bi-weekly. Registration is required, a registration link can be requested via mail to sabine.niebel@helmholtz-imaging.de. For more details, please visit the course website.

Dates

Date Time Location Lecturer Topic
2022-04-28 14:00–15:30 Online Mark Ladd Imaging Physics in Medicine
2022-05-12 14:00–15:30 Online Paul Jäger Analyzing Medical Images Using Machine Learning
2022-06-02 14:00–15:30 Online Michael Wagner Optical Coherence Tomography in Biofilm Research – Visualization and Characterization of the Mesoscopic Biofilm Structure
2022-06-23 14:00–15:30 Online Rudolf Merkel Basics of Light Microscopy for the Study of Cells
2022-06-30 14:00–15:30 Online Gerd Ulrich Nienhaus Advanced Fluorescence Microscopy
2022-07-07 14:00–15:30 Online Lennart Hilbert Microscopy Assessment of DNA-based Information Processing in Biological and Artificial Systems
2022-07-14 14:00–15:30 Online Carsten Sachse Imaging Biological Molecules by Electron Cryo-Microscopy (cryo-EM)
Advanced Topics in Data Science & Health – Summer Semester 2022

This lecture is held by doctoral researchers of HIDSS4Health in their second year. The course if offered online and mainly for the HIDSS4Health doctoral researchers, but other doctoral researchers interested in single topics can request a registration link via mail to office@hidss4health.de.

Date Time Location Lecturer Topic
2022-04-27 16:00–16:45 Zoom Verena Bitto Statistics done wrong
2022-04-27 16:45–17:30 Zoom Lucas-Raphael Müller From research code to production. Concepts and tools Lecture is cancelled
2022-05-04 16:00–16:45 Zoom Stefan Haller Lagrange Decomposition for Combinatorial Optimization Problems
2022-05-04 16:45–17:30 Zoom Alexandra Walter Optimization of Artificial Neural Networks: Mathematics behind Stochastic Gradient Descent
2022-05-11 16:00–16:45 Zoom Elaine Zaunseder The needle in the haystack - how to handle imbalanced medical data
2022-05-11 16:45–17:30 Zoom Philipp Toussaint Explainable AI - Methods for Interpretability and Explainability in ML and DL
2022-05-18 16:00–16:45 Zoom Philipp Wimmer Visualization of Scientific Data with Paraview
2022-05-18 16:45–17:30 Zoom Paul Maria Scheikl Reinforcement Learning for Surgical Robotics
2022-05-25 16:00–16:45 Zoom Vahdaneh Kiani Introduction to medical image registration
2022-05-25 16:45–17:30 Zoom Alejandra Jayme Hypernetworks for CNN or Bayesian Neural Networks
2022-06-01 16:00–16:45 Zoom Julian Herold Is Attention All You Need? (Intro to Transformers)
Data Science & Health – Winter Semester 2021/2022

This lecture is held by the PIs of the school and invited speakers who cover additional themes. The course consists of lecture-style elements and an interactive discussion. It covers data science methods (e.g., clustering, image analysis, visualization, deep learning), domain-oriented methods (e.g., medical imaging, surgery) as well as ethical, legal and social implications. The aim of the course is to introduce the doctoral researchers to the field and to give a basic understanding of all research areas of the school. In addition, it will train them to consider the different views and needs of data science and health-related sciences, and the opportunities for interdisciplinary cooperation.

Organization

This year, the lecture Data Science & Health is offered online. The lecture takes place weekly and only attendance is documented. Currently, no further examinations are planned. Registration is required, a registration link can be requested via mail to office@hidss4health.de.

Dates

Date Time Location Lecturer Topic
2021-10-19 14:00–15:30 Zoom Klaus Maier-Hein Medical Image Computing
2021-10-26 14:00–15:30 Zoom Ralf Mikut Time Series Analysis
2021-11-02 14:00–15:30 Zoom Alexander Schug Data Inference on Sequence Data: How to predict biomolecular structure and function
2021-11-09 14:00–15:30 Zoom Michael Beigl Wearable Health Data
2021-11-16 14:00–15:30 Zoom Mark Ladd Imaging Physics in Oncology
2021-11-23 14:00–15:30 Zoom Pascal Friederich Graph Neural Networks for Molecular Design
2021-11-30 14:00–15:30 Zoom Anne Kaster Single Cell Omics
2021-12-07 14:00–15:30 Zoom Lennart Hilbert Fluorescence microscopy and digital image processing in molecular cell biology
2021-12-14 14:00–15:30 Zoom Franziska Mathis-Ullrich Co-Operation with Surgical Robots
2021-12-21 14:00–15:30 Zoom Oliver Jäkel Why data sciences will be crucial for modern image guided radiotherapy
2022-01-11 14:00–15:30 Zoom Annika Reinke Next-generation biomedical image analysis competitions
2022-01-18 14:00–15:30 Zoom Michael Gertz Trends and Topics in Text Analytics
2022-01-25 14:00–15:30 Zoom Bogdan Savchynskyy Combinatorial Optimization Techniques for Bioimaging Lecture is cancelled.
2022-02-01 14:00–15:30 Zoom Oliver Stegle Machine learning for genomics
2022-02-08 14:00–15:30 Zoom Carsten Dachsbacher Introduction to Visual Data Science
Data Science & Health – Winter Semester 2020/2021

This lecture is held by the PIs of the school and invited speakers who cover additional themes. The course consists of lecture-style elements and an interactive discussion. It covers data science methods (e.g., clustering, image analysis, visualization, deep learning), domain-oriented methods (e.g., medical imaging, surgery) as well as ethical, legal and social implications. The aim of the course is to introduce the doctoral researchers to the field and to give a basic understanding of all research areas of the school. In addition, it will train them to consider the different views and needs of data science and health-related sciences, and the opportunities for interdisciplinary cooperation.

Organization

This year, the lecture Data Science & Health is offered online. The lecture takes place every two weeks and only attendance is documented. Currently, no further examinations are planned. Registration is required, a registration link can be requested via mail to office@hidss4health.de.

Dates

Date Time Location Lecturer Topic
2020-11-02 14:00–15:30 Zoom Bogdan Savchynskyy Combinatorial Optimization Techniques for Bioimaging
2020-11-02 15:45–17:15 Zoom Frank Ückert Medical Informatics in Translational Oncology
2020-11-16 14:00–15:30 Zoom Ralf Mikut Time Series Analysis
2020-11-16 15:45–17:15 Zoom Alexander Schug Data Inference on Sequence Data
2020-11-30 14:00–15:30 Zoom Holger Fröning Any Growth is Bounded – On the Future of Performance Scaling
2020-11-30 15:45–17:15 Zoom Benedikt Brors The n<<p paradigm in omics data analysis
2020-12-14 14:00–15:30 Zoom Franziska Mathis-Ullrich Minimally-Invasive Robots for Medicine
2020-12-14 15:45–17:15 Zoom Martin Frank Mathematical Foundations of Deep Learning
2021-01-11 14:00–15:30 Zoom Michael Beigl Wearable and mobile health data
2021-01-11 15:45–17:15 Zoom Filip Sadlo Introduction to Visual Data Science
2021-01-25 14:00–15:30 Zoom Peter Sanders Parallel Algorithms for Dummies
2021-01-25 15:45–17:15 Zoom Achim Streit Introduction to Distributed and Parallel Computing
2021-02-08 14:00–15:30 Zoom Lena Maier-Hein Surgical Data Science
2021-02-08 15:45–17:15 Zoom Michael Gertz Trends and Topics in Text Analytics
Data Science & Health – Winter Semester 2019/2020

This lecture is held by the PIs of the school and invited speakers who cover additional themes. The course consists of lecture-style elements and an interactive discussion. It covers data science methods (e.g., clustering, image analysis, visualization, deep learning), domain-oriented methods (e.g., medical imaging, surgery) as well as ethical, legal and social implications. The aim of the course is to introduce the doctoral researchers to the field and to give a basic understanding of all research areas of the school. In addition, it will train them to consider the different views and needs of data science and health-related sciences, and the opportunities for interdisciplinary cooperation.

Organization

The lectures take place at the following locations:

Dates

Date Time Location Lecturer Topic
2019-10-22 09:45–11:15 KIT Ralf Mikut Time Series Analysis
2019-10-22 11:30–13:00 KIT Lennart Hilbert Fluorescence microscopy and digital image processing in molecular cell biology
2019-11-05 09:45–11:15 Mathematikon Carsten Rother Image-based Machine Learning
2019-11-05 11:30–13:00 Mathematikon Klaus Maier-Hein Radiologic Data Science
2019-11-19 09:45–11:15 KIT Achim Streit Introduction to Distributed and Parallel Computing
2019-11-19 11:30–13:00 KIT Peter Sanders Parallel Algorithms for Dummies
2019-12-03 09:45–11:15 Mathematikon Holger Fröning Any Growth is Bounded – On the Future of Performance Scaling
2019-12-03 11:30–13:00 Mathematikon Lena Maier-Hein Surgical Data Science
2019-12-17 09:45–11:15 KIT Franziska Mathis-Ullrich Minimally-Invasive Robots for Medicine
2019-12-17 11:30–13:00 KIT Tamim Asfour Data-Driven Learning of Sensorimotor Skills for Robots and Exoskeletons
2020-01-07 09:45–11:15 Mathematikon Michael Gertz Introduction to Text Analysis
2020-01-07 11:30–13:00 Mathematikon Robert Strzodka Essential Performance Considerations in Programming
2020-01-21 09:45–11:15 KIT Anne Koziolek Requirements Engineering for Data-driven Solutions
2020-01-21 11:30–13:00 KIT Ali Sunyaev Introduction to Decentralized Data Management with Distributed Ledger Technology
2020-02-04 09:45–11:15 DKFZ Martin Frank Mathematical Aspects of Uncertainty Quantification
2020-02-04 11:30–13:00 DKFZ Filip Sadlo Introduction to Visual Data Science

Seminars

Scientific Writing CourseWorkshop Content
  • Five stages of Publishing
  • Who is my reader?
  • Destination and Roadmap
  • Building structure and connectivity
  • From scientific report to a scientific argument
  • Scientific writing in English – words, sentences, and paragraphs

Training Methods
This is a highly interactive online training workshop with extensive elements of partner work, exercises, group discussion, and including some offline homework tasks. We use innovative online tools combined with proven didactic techniques to reproduce as much as possible a real workshop situation. We place a special emphasis on sharing and learning from participants’ own expertise and experience. To increase impact and applicability, we work with real-life cases from the participants whenever possible. We may ask participants to reserve time for individual preparatory work before the workshop, and between each session.

Workshop Goal
The goal of this workshop is to help publishing scientists develop a more impartial,analytical view of scientific writing, to better understand their readers as the focus for their scientific communication, and to make them more efficient writers and editors. Their writing will no longer be driven by a standard formula for How? to write a paper, but will be inspired by the question Why? Participants will develop a deeper understanding of the structure of scientific papers, with a renewed focus on the purpose of each section and the connections between them. They will gain a global framework for conceptualising the entire publishing process, how to create an expectation in the reader and then deliver on that expectation, and how to make the qualitative jump from a passive scientific account to an active scientific argument. Finally, we will explore some common problems of language construction that make scientists’ writing unclear, and why we are prone to these problems; we will practice some intuitive editing tools to address them.

This course will take place online on 06. – 08. February 2023 from 9:30-13:00 each day. A total of 16 people can participate. If you would like to participate, please contact (office@hidss4health.de) by 08. November 2022 at the latest.

Managing a Constructive Relationship between PhD Students and their Supervisors

You are currently doing your doctorate and it often seems difficult to manage the relationship between yourself and your supervisor? You need support and/or feedback, but it’s hard to get an appointment at all? You feel left alone with your doctoral thesis? You work very hard and sometimes even feel drained? It’s time to clarify the relationship and to find a satisfactory way to manage the expectations between both parties, PhD students and their supervisors.

The workshop takes place online on March 3rd 2022, from 9 a.m. to 5 p.m. and includes a lot of practical sessions.

For registration, please visit the course website.

Internal

HIDSS4Health Retreat

On the HIDSS4Health Retreat all HIDSS4Health doctoral researchers present and discuss their projects and potentials for collaborations.

Next: September 12–14 2022, Bad Urach (internal HIDSS4Health Event)

Doctoral Researcher General Assembly

General assembly event for the doctoral researchers of the school, 13:00–17:00 (internal event).

HIDSS4Health General Assembly

General assembly event of the school, 09:00–12:00 (internal event).

Apply


Overall Process

The HIDSS4Health research school performs an annual candidate selection process. Suitable candidates are selected in a three-step process: (1) the written application, (2) a virtual one-day selection event for short-listed applicants, and (3) matching projects and candidates. Evaluation criteria for the candidates are an excellent academic record and a strong motivation for the interdisciplinary nature of the projects. The institutions are committed to increasing the percentage of female scientists and encouraging female applicants to apply. The next application call is anticipated for July 2025/August 2025.

Requirements

We are looking for excellent graduates holding master degrees in computer science, mathematics, engineering, physics or related quantitative sciences (e.g., bioinformatics or medical informatics).
As an international research school, we require our doctoral researchers to be fluent in English (German is optional). If you are neither a native German nor a native English speaker, we therefore ask for a proof of language skills in form of a certificate (IELTS, TOEFL or CAE) or a certified statement that the studies in your previous university degree were taught in English.

Funding

All positions are fully-funded (e.g. “E13” TVöD or TV-L positions under applicable regulations of the participating institutions) according to the public sector salary system at German universities and research centers. It means a yearly gross income of more than 45.000 € and results in more than 26.000 € net income after taxes, own contributions to health insurance, pension insurance, unemployment insurance etc. Weekly working times are 39.5 hrs, the contract includes 30 paid leave days. However, since the German system is quite complicated, all financial details cannot be provided here but can be calculated here (sorry, only available in German).

Living in Heidelberg / Karlsruhe

Heidelberg and Karlsruhe are attractive cities located in the sunny south-western part of Germany within a traveling distance of around 50 kilometers. They are connected via a dense public transport network. More information on living costs can be found in the information pages of the German Academic Exchange Service (DAAD) or in a PDF booklet from the German Federal Ministry for Education and Research.

Diversity and Equal Opportunities

The provision of equal opportunities and diversity is a central concern of KIT, DKFZ and Heidelberg University. We are committed to enabling researchers to balance the demands of career and family life and therefore offer various options to staff, such as flexible working time models, family-friendly meeting times, re-entry after maternity leave, comprehensive childcare concepts, provision of Tele-offices, parent-child offices, and in-house childcare facilities. Funded places in local childcare facilities offer full-time and part-time child-care (for example during a conference or summer school). The networks of female scientists at the partners – “WiKIT” at the KIT and the Executive Women’s Initiative at the DKFZ – are professional networks of female scientists in leading positions. These networks are platforms of mutual exchange and aim at fostering networking among female scientists of various disciplines to improve the career perspectives and working conditions of female scientists in Germany. All measures promoting the compatibility of career and family life are monitored and advanced by the “familiengerechte Hochschule” (family-friendly university) audit and “Audit Beruf und Familie” (Career and Family Audit) at the KIT, Heidelberg University and DKFZ, respectively.

Becoming an Associated Doctoral Researcher

You are already funded and your doctoral research is on a topic that is relevant to the school? Apply for an association!

Supporting Early Career Female Scientist

Do you have interesting projects and want to supervise a PhD in the context of HIDSS4Health? Then apply now as we are looking for early career female scientists who want to become PIs eligible to apply for projects in HIDSS4Health (deadline October 2021).

Events


List of relevant events for the school hosted by the school itself or by partner institutions.

Upcoming Events

15
3
2021

Helmholtz GPU Hackathon

23
4
2021

HIDSS4Health Selection Event

  • 1 day
  • Online

Third round internal selection event for invited candidates (online format).

28
4
2021

Visualizing Science

17
5
2021

Presentation Skills Course

20
9
2021

HIDSS4Health Retreat

  • 3 days
  • Bad Herrenalb

Retreat for doctoral researchers of the school.

22
9
2021

Helmholtz Data Science Career Day

27
9
2021

Doctoral Researcher General Assembly

  • 1 day
  • DKFZ

General assembly event for the doctoral researchers of the school, 13:00–17:00 (internal event).

28
9
2021

Workshop Time and Self-Management

6
10
2021

HIDSS4Health General Assembly

  • 1 day
  • Online

General assembly event of the school, 09:00–12:00 (internal event).

20
10
2021

Workshop Time and Self-Management

18
11
2021

DASHH Data Science Colloquium

19
1
2022

Introduction to machine learning

19
1
2022

Social media

26
1
2022

HIDA Lecture @ HIDSS4Health

Data Science for Supporting Molecular Tumor Boards
Prof. Dr. Ulf Leser, Institute for Computer Science, Humboldt Universität zu Berlin

Molecular Tumor Boards are interdisciplinary teams of medical experts which come together to obtain therapy recommendations for complex oncological cases. In recent years, these recommendations are more and more based on molecular profiling of tumors, in particular the sequencing of cancer genes followed by the detection of genetic variants. The PREDICT project set out to develop systems and algorithms for supporting such decision-making. We will discuss the main results of this project i.e. (1) VIST, a search engine specifically constructed to find clinically relevant documents given a patient’s variant profile, (2) I-VIS, a framework for building integrated precision oncology databases, and (3) BRONCO, the first open and annotated corpus of German medical texts. Building these systems required advanced NLP technologies, the application of modern machine learning methods, and robust data integration technologies.

Ulf Leser is a full professor at the Institute for Computer Science at Humboldt-Universitaet zu Berlin. He obtained his PhD in Data Integration from Technische Universität Berlin. After app. three years in the software industry, he returned to academia as professor for Knowledge Management in Bioinformatics. His main research interests are semantic data integration, text mining, statistical Bioinformatics, and large-scale scientific data analysis. His group not only develops novel algorithms for these tasks but also applies them in a set of highly interdisciplinary collaborative projects for studying concrete biomedical questions, especially in cancer research. He is speaker of the Collaborative Research Center "Foundations of Workflows for Large-Scale Scientific Data Analysis (FONDA)" and of the collaborative research project "Comprehensive Data Integration for Precision Oncology (PREDICT)". From 2014-2019, he was speaker of the Research Training Group "Service-oriented Architectures for Health Care Systems (SOAMED)". Furthermore, he currently is principal investigator in the interdisciplinary graduate schools "Computational Methods for Precision Oncology (CompCancer)“ and “Helmholtz-Einstein International Berlin research school on Data Science“.

Register today!
1
2
2022

AI-HERO Hackathon for Energy-Efficient AI

23
2
2022

AI-enabled imaging

11:00 Am CET

3
3
2022

Managing a Constructive Relationship between PhD Students and their Supervisors

4
4
2022

Publishing in Scientific Journals

12
9
2022

HIDSS4Health Retreat 2022

  • 3 days
  • Bad Urach
6
2
2023

Scientific Writing Course

4
5
2023

Six Main Tasks in Image Processing: an Overview

25
5
2023

Tomographic Methods in Medical Imaging

25
5
2023

Restoring Noisy Microscopy Images

15
6
2023

Tracking of Objects: from One to Many

29
6
2023

Machine Learning for Image Segmentation

6
7
2023

Visualization

21
7
2023

Knowledge Representation with the Semantic Web and Knowledge Graphs

This lecture aims at introducing the Semantic Web in a nutshell and to show the possibilities it offers:
• An overview of applications of Semantic Web technologies.
• Elements or tools (e.g. RDF/RDFS, OWL, SHACL, JSON-LD, SPARQL) of the Semantic Web stack.
• Use cases how the Semantic Web follows the FAIR principles and can be applied (e.g. Named Entity Recognition (NER), Question Answering, Semantic Search, Expert Systems, Knowledge Discovery)
Join the lecture from 11:00 am to 12:15 pm. Register here!

27
7
2023

Explainable Machine Learning

Yearly Overview

News


HIDA


As domain scientists from all research fields need to be equipped with knowledge, methods and tools in the areas of Information & Data Science, a training and education initiative for researchers in the Helmholtz Association has been established. The Helmholtz Information & Data Science Academy (HIDA) will coordinate networking, education and training activities throughout the entire Helmholtz Association (video). It bundles the resources of all Information & Data Science Schools (HIDSS), including HIDSS4Health.

Contact


Office Karlsruhe

  Karlsruhe, Germany

  Phone: +49 721 608-28672

  Email: office@hidss4health.de

Office Heidelberg

  Heidelberg, Germany

  Phone: +49 6221 42-2327

  Email: office@hidss4health.de