The Data Train Team

Data Train courses: Operator Track - Data Scientist:  Deep learning

In his research he deals with inverse problems, machine learning, image and signal processing, computational engineering, parameter indentification and computational pathology.

Contact:
otero(at)math.uni-bremen.de

Website:  ORCID, More

Data Train courses: Starter Track:  Data protection and licenses

His research focuses on data protection and information law: Research depends on the free use of research data while the (exclusivity) interests of those whose personal data are to be processed for research purposes or those who own the copyright for study or similar data are opposed to this. In this context, it is task of the law to resolve this conflict.

Contact:
bbuchner(at)uni-bremen.de

Website:  ORCID, More

Data Train courses:Operator Track - Data Scientist

Contact:
brannath(at)uni-bremen.de

Website: More

Data Train courses: Operator Track – Data Steward: Reproducibility in science: How and why?

His research explores ways to leverage the diverse and rapidly growing techniques in data science, machine learning and statistical modelling towards data-driven analytics for the benefit of tropical marine sciences. His team bring together expertise across domains of habitat mapping, computer vision, machine learning, neural networks, biogeochemistry, benthic ecology, probabilistic analyses, stakeholder utility networks, software development, optical physics, sensor systems and platforms, engineering of data acquisition and analytical workflows.

Contact:
arjun.chennu(at)leibniz-zmt.de

Website: ORCID, More

Data Train courses: Operator Track – Data Scientist Track: Quantitative analyses for data science

Thorsten Dickhaus studied mathematics in Aachen and Düsseldorf, and he received his Dr. rer. nat. degree in mathematics and application areas from Heinrich-Heine-University Düsseldorf in 2008. Afterwards, he worked in Berlin, first as a PostDoc at the Berlin Institute of Technology, then as a junior professor at the Humboldt-University and finally as a scientific staff member at the Weierstrass Institute for Applied Analysis and Stochastics. Since March 2015, Thorsten Dickhaus is Full Professor and Head of the Working Group “Mathematical Statistics” at Faculty 3: Mathematics and Computer Science at the University of Bremen. Since 2018 he is the Vice Dean of Academics of the Faculty of Mathematics and Computer Science. His research interests include the development of statistical methods and their applications, in particular to high-dimensional and complex structured data from the life sciences and from economics.

Contact:
dickhaus(at)uni-bremen.de

Website: ORCID, More

Data Train courses: Starter Track: Asking the right research questions in data science, Operator Track – Data Scientist Track: Causal learning

Her research deals with the statistical modelling of and methods for data analysis. Specifically, she aims at developing statistical approaches to address questions about the consequences of (possibly hypothetical) interventions, e.g. by how much would a given increase in physical activity reduce childhood obesity? This kind of inference is known as causal inference. The particular statistical challenge consists of adequately addressing weaknesses and limitations of the data, such as lack of, or imperfect, randomization, systematic selection or drop-out etc. These need to be accounted for by suitable models and methods. A key prerequisite is the in-depth understanding and scrutinizing of the underlying assumptions so that these can be made plausible, either empirically or based on subject matter knowledge. Furthermore, Prof Didelez focusses on time-structured data, such as cohort data or event-history or survival data. She has developed new approaches for causal path analysis, dynamic graphical models and causal discovery. The new methods have applications in epidemiology or public health, such as for analysing the causes and effects of childhood obesity, or for cancer prevention or dementia research.

Contact:
didelez(at)leibniz-bips.de

Website: ORCID, More

Data Train courses: Starter Track: Managing confidential data

Martin Dörenkämper has been working on evaluation of weather and wind farm data in the context of wind energy research since more than 10 years. After his undergraduate and graduate studies in meteorology at the Universities of Hamburg and Oklahoma, his PhD research at the University of Oldenburg focused on energy meteorology. His current research addresses the improvement and validation of industry-suited models for wind energy siting and wind farm yield analysis applications. This work includes working with multi-dimensional as well as time-series based data of various complexity and confidentiality levels. At Fraunhofer IWES Martin coordinates joint research projects with industrial and academic partners.

Contact:
martin.doerenkaemper(at)iwes.fraunhofer.de

Website: ORCID, More

Data Train courses: Starter Track: Computer science basics for data science

Rolf Drechsler received the Diploma and Dr. phil. nat. degrees in computer science from the Johann Wolfgang Goethe University in Frankfurt am Main, Germany, in 1992 and 1995, respectively. He worked at the Institute of Computer Science, Albert-Ludwigs University, Freiburg im Breisgau, Germany, from 1995 to 2000, and at the Corporate Technology Department, Siemens AG, Munich, Germany, from 2000 to 2001. Since October 2001, Rolf Drechsler is Full Professor and Head of the Group of Computer Architecture, Institute of Computer Science, at the University of Bremen, Germany. In 2011, he additionally became the Director of the Cyber-Physical Systems Group at the German Research Center for Artificial Intelligence (DFKI) in Bremen.

His current research interests include the development and design of data structures and algorithms with a focus on circuit and system design. He is an IEEE Fellow.

From 2008 to 2013 he was the Vice Rector for Research and Young Academics at the University of Bremen. Since 2018 he is the Dean of the Faculty of Mathematics and Computer Science. He is one of the founders and currently the spokesperson of the Data Science Center at University of Bremen (DSC@UB).

Contact:
drechsler(at)uni-bremen.de

Website: ORCID, More

Data Train courses: Operator Track – Data Steward: Getting started in R, Operator Track – Data Steward: Erste Schritte mit MATLAB

Working with large data sets, the use of complex methods (especially from the areas of statistics, econometrics, optimization, operations research, simulation and machine learning), the use of statistical software (especially Matlab / Octave / Freemat, R, Stata, Python, Excel / VBA) and the transfer of research results to software tools that can be used in practice is part of his research.

Contact:
cfieberg(at)uni-bremen.de

Website: More

Data Train courses: Starter Track: Data and information management, Operator Track – Data Steward Track: How to write a data management plan?

His interdisciplinary team of geologists, biologists, engineers, and software developers located at the AWI and MARUM has a national and international proven track record in research data management, data logistics and data science.

Contact:
frank.oliver.gloeckner(at)awi.de

Website: ORCID, More

Data Train courses: Starter Track: Managing confidential data

Julia Gottschall has been working on the acquisition and evaluation of wind data in the context of wind energy research for more than 15 years. After completing her doctorate in applied physics at the University of Oldenburg, she worked at various research institutes in Germany and abroad, particularly in applied and industry-related research and development. Her work includes the application of mathematical models for the reconstruction of wind field parameters as well as the management of complex data sets, which are often subject to confidentiality requirements. One focus of her work is the application of wind lidar measurement technology, which in recent years has provided a completely new approach to the description of wind fields relevant to wind energy use and at the same time represents a new challenge with regard to the handling of the data obtained in this way. Julia Gottschall represents IWES on these topics in various international committees (including IEC – International Electrotechnical Commission, IEA Wind TCP – International Energy Agency Wind Technology Collaboration Programme)..

Contact:
Julia.gottschall(at)iwes.fraunhofer.de

Website: ORCID, More

Data Train courses: Operator Track – Data Scientist Track: Computational social sciences

Her research deals with the statistical modelling of and methods for data analysis. Specifically, she aims at developing statistical approaches to address questions about the consequences of (possibly hypothetical) interventions, e.g. by how much would a given increase in physical activity reduce childhood obesity? This kind of inference is known as causal inference. The particular statistical challenge consists of adequately addressing weaknesses and limitations of the data, such as lack of, or imperfect, randomization, systematic selection or drop-out etc. These need to be accounted for by suitable models and methods. A key prerequisite is the in-depth understanding and scrutinizing of the underlying assumptions so that these can be made plausible, either empirically or based on subject matter knowledge. Furthermore, Prof Didelez focusses on time-structured data, such as cohort data or event-history or survival data. She has developed new approaches for causal path analysis, dynamic graphical models and causal discovery. The new methods have applications in epidemiology or public health, such as for analysing the causes and effects of childhood obesity, or for cancer prevention or dementia research.

Contact:
nikolitsa.grigoropoulou(at)uni-bremen.de

Website: ORCID, More

Data Train courses: Operator Track - Data Scientist.

Contact:
janek-goedeke(at)uni-bremen.de

Website:  ORCID

Data Train courses: Operator Track – Data Scientist Track: Visualization in science: Principles & critical reflections, Operator Track – Data Scientist Track: Visual analytics using GIS

In her work, she often experiences that scientific data and research results are often understandable for experts only. Here, visualization is key: it transforms numbers in symbols or graphics and has the ability to decode complex interrelations even to non-experts.

Contact:
antonie.haas(at)awi.de

Website: ORCID, More

Data Train courses: Starter Track: Digital ethics

His research interests are Ethics, Digital Ethics, Ethics of Technology, History of Philosophy, Cultural History.

Contact:
bhaferkamp(at)uni-bremen.de

Website: More

Data Train courses: Starter Track: Machine Learning basics

Lena Happ completed two bachelor degrees in mathematics and physics from the University of Göttingen before she pursued an interdisciplinary master in environmental science at the University of Cologne. Already during her studies she worked in a consulting company for wind and solar energy in Brazil, researching methods for the prediction of wind speeds using artificial neural networks.
After completing her studies, she worked for more than four years at Beckhoff Automation. First, the focus of her work was on the analysis and management of data from wind turbines. It then shifted to the development of algorithms for the real-time analysis of streaming data from control systems, mainly for clustering and classification.
Now she is part of the Sea Ice Physics Section of the Alfred Wegener Institute and the Helmholtz School for Marine Data Science (MarDATA), pursuing a PhD at the University of Bremen. In her project she is developing machine learning models for the classification of sea ice surfaces based on satellite radar altimeter data.

Contact:
lena.happ(at)awi.de

Data Train courses: Operator Track – Data Steward: Git/GitHub

As a software engineer at the Alfred Wegener Institute, his involvement with the O2A (Observation to Archive) data flow framework is integral to its operation. O2A is essential in supporting scientific research by providing efficient data streaming and transfer capabilities within a collaborative workspace. Our commitment to the FAIR Data Principles ensures the preservation and accessibility of our data through the PANGAEA World Data Centre. In addition, our portal solutions provide easy access to metadata, data and data products, streamlining the discovery and re-use process.

His responsibilities include the management of critical components of the O2A framework. Nico oversees the O2A Registry, an essential repository for platform, device and sensor information, ensuring its continuous improvement through regular updates, and manages the O2A Projects platform, which simplifies the process for researchers to secure storage and computing resources.

He is currently leading the development of a new infrastructure to improve the interoperability of O2A tools. This initiative aims to improve scientific workflows and promote more efficient collaboration and data sharing within the research community.

Contact:
nico.harms(at)awi.de

Website: ORCID, More

Data Train courses: Starter Track: Managing qualitative data, Operator Track – Data Steward: Data preparation

He is a sociologist (Dipl.-Soz.) who obtained a PhD in Economics and Social Sciences (Dr. rer. pol.) in 2014 from the Bremen International Graduate School of Social Sciences (BIGSSS) at the University of Bremen. He has worked as a postdoctoral researcher in several international research projects at the University of Bremen and the Humboldt-Universität zu Berlin and published extensively on the topics of social policy and consumer bankruptcy. Since 2018 he works as a domain expert for social sciences at the Research Data Center (RDC) Qualiservice at the University of Bremen. At Qualiservice he is responsible for various aspects of data management and data curation and he is teaching in the areas of empirical research methods and data management. Since January 2021 he is also coordinating the measure “Generation of qualitative data – RDM portfolio für qualitative social research” as part of the KonsortSWD within the National Research Data Infrastructure (NFDI).

Contact:
jheuer(at)uni-bremen.de

Website: ORCID, More

Data Train courses: Starter Track: Managing qualitative data, Operator Track – Data Steward: Data preparation

She is member of the German Data Forum (RatSWD), advisory council to the federal government, and co-spokesperson of the Consortium for the Social, Behavioural, Educational, and Economic Sciences (KonsortSWD).

Contact:
betina.hollstein(at)uni-bremen.de

Website: ORCID, More

Data Train courses: Operator Track - Data Scientist

Contact:
sophia.hunger(at)uni-bremen.de

Website:  More

Data Train courses: Starter Track: Cryptography basics, Starter Track: Security & Privacy

He received his PhD from the University of Karlsruhe working on automating inductive theorem proving. In 1991 he moved to the Saarland University and joined the German Research Center for Artificial Intelligence (DFKI) in 1993. He guided various projects in Formal Methods and Security. Moving to Bremen in 2008, Dieter Hutter is now vice director of the Cyber-Physical-System Department at DFKI and honorary professor at Bremen University. He was co-initiator of the German DFG Priority Program on Reliably Secure Software Systems and speaker of the section on Formal Methods and Software Engineering for Safety and Security in the German Informatics Society. He is working in the areas of security, formal methods and change management. In particular, he is working on structuring mechanisms for information flow control supporting a formal notion of security in the large.

Contact:
dieter.hutter(at)dfki.de

Website: More

Data Train courses: Starter Track: Data protection and licenses

Data security is one of the central requirements and at the same time challenges for the secure handling of research data. In this context, his research focuses on the legal requirements for secure data storage and management principles to be implemented accordingly. He believes research data management is an important as well as difficult challenge, which can only be met if all the disciplines concerned closely cooperate.

Contact:
kipker(at)uni-bremen.de

Website: ORCID, More

Data Train courses: Operator Track – Data Steward Track: Data extraction from external online platforms using R

She received her Doctoral Degree (Dr.’in rer. pol.) with distinction (summa cum laude) at the University of Cologne. Afterwards, she was Post-Doctoral Researcher in the Department of Marketing and Brand Management (Prof. Dr. Franziska Völckner) at the University of Cologne.

Her research is empirical-quantitative, i.e., in all her projects she works with data (primary or secondary data) and deals with:

• Digital technologies to improve customer experience (gamification)
• Digital technologies for customer interaction (chatbot design, voice applications)
• Serious games (in employer branding)
• Influencer Marketing
• Sensory marketing and emotions
• Brand Activism

Contact:
kklein(at)uni-bremen.de

Website: ORCID, More

Data Train courses: Operator Track – Data Steward Track: Getting started with Python, Operator Track – Data Steward Track: Data preparation

He works on very high-resolution ocean and climate modelling, pre- and post processing of large amounts of geophysical data and interactive data analysis and visualization.

Contact:
nikolay.koldunov(at)awi.de

Website:  ORCID, More

Data Train courses: Operator Track – Data Steward Track: Data preparation

Development of autonomous algorithms for the characterisation and discrimination of arctic sea ice using satellite-borne radar systems, as well as the fusion of data from various instruments towards purely data driven analysis and the extrapolation of ground measurements to the scope of satellite acquisitions are part of his research.

Contact:
karl.kortum(at)dlr.de

Contact:
kloep(at)uni-bremen.de

Data Train courses: Starter Track: Data and information management, Operator Track – Data Steward: How to write a data management plan?

He spent the last seven years building a service-oriented infrastructure for supporting scientists in Biodiversity and Ecology with their data management tasks. He co-designed a unified data submission interface, established and coordinated a Help Desk, personally curated molecular sequence datasets and supported the preparation of Data Management Plans.

Contact:
ikostadi(at)gfbio.org

Website: ORCID, More

Data Train courses: Starter Track – Philosophical reflections on data science

He studied philosophy and history in Göttingen and wrote his dissertation on our understanding of knowledge at Johns Hopkins University. The focus of his research are epistemic standards which govern the quality of information we pass on to each other. For example, we expect each other not to lie or represent guesses as if they were established facts. Understanding these standards may also shed light on a range of related issues including the effects of misinformation, the idea of epistemic injustice, or the role of epistemic institutions like media outlets and universities. He is also interested in the development of active and inclusive teaching formats.

Contact:
lossau1(at)uni-bremen.de

Website: ORCID, More

Data Train courses: Starter Track: Overview about programming languages

His research covers the whole area of advanced system design, from theoretical foundations as found in category theory, through the development of tools for constructing or verifying hardware or software, to applications in practical areas such as robotics. The overarching theme of his work is how to reliably construct correct systems.

Contact:
clueth(at)uni-bremen.de

Website: ORCID, More

Data Train courses: Operator Track – Data Steward: How to write a data management plan?

Data have tremendous potential, meaning the data we produce can also provide diverse and unexpected benefits. Jimena Linares is interested in 1) optimizing the relationship we have with data and 2) exploring how to represent data so they are made FAIR to everyone.

Contact:
jlinares(at)gfbio.org

Website: ORCID, More

Data Train courses: Operator Track – Data Steward Track: Getting started with R, Operator Track – Data Steward Track: Erse Schritte in MATLAB

His research interests focus on the application of classical econometric methods or machine learning techniques in the field of asset pricing. The emphasis is on how innovative techniques can be used to better understand and predict asset price formation. The integration of classical econometrics with machine learning aims to enhance traditional financial market models through the use of advanced analytical methods, thereby contributing to a more precise valuation of assets.

Contact:
gliedtke(at)uni-bremen.de

Website: ORCID, More

Data Train courses: Operator Track – Data Steward Track: Data base skills

Sebastian Maneth's research interest lies in efficient storage and querying technology for semi-structured data. He is developing novel methods to compress graph structured data and methods that make possible to directly execute queries on compressed data, without prior decompression. Since recently he is also working on analysing eye tracking data using machine learning methods, for instance for biometrics.

Contact:
maneth(at)uni-bremen.de

Website: More

Data Train courses: Operator Track – Data Scientist Track: Hardware and system architectures for data science

Christopher Metz acquired a Bachelor degree in Business Informatics (2015) followed by a Master degree in Informatics – Distributed and Mobile Applications (2017) at Osnabrück University of Applied Science. After his studies, he worked as software developer at CKS Systeme GmbH, where one of his main tasks was to implement interfaces for e.g. medical equipment, web services, police offices or fire departments. Moreover, he was responsible for the development of products for the police and fire department. From October 2018 until April 2020, Christopher Metz worked as research associate in the project Environment, Health and Safety based on Artificial Intelligence at Osnabrück University of Applied Science. He was responsible for the establishment of a GPU cluster for deep learning models as well as the development of AI models for NLP (Natural Language Processing).
Christopher Metz contributes to the DSC with his extensive knowledge in developing and implementing data interfaces and neuronal networks, in particular deep learning. Additionally, he is responsible for the establishment of the DSC infrastructure. Moreover, Christopher Metz supports scientist from various disciplines with the development, programming, and implementation of data science technologies. His main research interests lie in AI-methods and infrastructure for high-performance computing.

Contact:
cmetz(at)uni-bremen.de

Website: More

Since January 2025, Martin Mundt is professor for lifelong machine learning at the University of Bremen, where he leads the OWL-ML lab. OWL-ML’s central mission is to develop AI systems that continue learning throughout their lifecycles. This capability for lifelong machine learning – a key ability also of the human brain – is pivotal to render AI systems more adaptive, robust and inclusive. These aspects are becoming increasingly important, as present AI design is mostly static and thus dominated by the large amounts of resources and power of big companies.  In contrast, Martin believes that lifelong adaptivity is an essential missing element to make AI both technologically and socially more sustainable. As such, his view spans the entire lifecycle of AI systems, starting from how data is sourced and models are developed, all the way to how they are evaluated and who is affected by their outcomes. He holds a PhD in computer science and a Master’s of Physics from Goethe University Frankfurt. Martin is also a board member of directors at the non-profit organization ContinualAI and one of the core-organizers at Queer in AI, both of which aim to disseminate knowledge and foster public discourse on how AI affects different communities. With both technological advancement and societal benefit being vital to Martin, he currently also serves as Diversity, Equity and Inclusion (DEI) chair at CoLLAs 2025 and was previously DEI Chair at AAAI-24.

Contact: mundtm@uni-bremen.de

Data Train courses: Operator Track - Data Steward

Contact:
alexandra.nozik(at)leibniz-zmt.de

Website:  ORCID, More

Data Train courses: Operator Track – Data Steward Track: Data preparation/processing - Textual Data

As a mathematician with a PhD in bioinformatics, Manfred Nölte has worked for many years in applied and interdisciplinary fields. At the Bremen State and University Library, these experiences in Data Science were complemented by digitisation, full text generation and finally the digital humanities. Therefore, an advisory service for the Digital Humanities was established at SuUB Bremen for students, teachers and researchers in the humanities, cultural studies and social sciences.

Contact:
noelte(at)suub.uni-bremen.de

Data Train courses: Starter Track: Statistical thinking

She initiated the interdisciplinary graduate education program Data Train on “Research data management and data science” in 2019 to serve the upcoming needs in this area.

This education program is led by Iris Pigeot together with Frank Oliver Glöckner and Rolf Drechsler. As Co- Spokesperson of the consortium to set up a National Research Data Infrastructure for Personal Health Data (NFDI4Health), she links the graduate education program to this German - wide initiative and ensures the implementation of uniform standards for personal health data.

Contact:
pigeot(at)leibniz-bips.de

Website: ORCID, More

Data Train courses: Starter Track: Computer science basics for data science

Her research is in the area of application-oriented optimisation. She uses bio-inspired heuristic methods as well as methods from the area of machine learning to produce stable results for the given domain problem. She especially focusses on effectively using the implicit knowledge from  the application domain for the optimisation algorithms.

Contact:
christina.plump(at)dfki.de

Website: ORCID

Data Train courses: Starter Track: Managing qualitative data, Operator Track - Data Scientist Track: Data Preparation - Qualitative Data

Michaela Rizzolli, studied European Ethnology at the University of Innsbruck and received her PhD degree in 2016; she has been working in the RDM-field since 2019, gaining her first experiences at the CRC "Affective Societies" at the Free University of Berlin, where she assisted the CRC in all activities related to data management. Since May 2022, Michaela Rizzolli has been working as a research assistant (postdoc) at the Research Data Center Qualiservice, where she develops suitable workflows and services for the archiving and reuse of qualitative/ethnographic research data.

Her research interests include archiving and re-use of qualitative, sensitive research data; research data management in ethnographic research; research ethics; CARE principles.

Contact:
michaela.rizzolli(at)uni-bremen.de

Website: ORCID, More

Data Train courses: Starter Track: Philosophical reflections on data science, Operator Track – Data Scientist Track: Visualization in science: principles & critical reflections

He studied philosophy, physics, and mathematics in Heidelberg and Cambridge. In fact, a special trait of his research group is that all members have backgrounds in more than one academic discipline. The group is interested in questions about "how science works" and what values are pursued in science. Here special attention is paid to the role played by data and by artificial intelligence within different disciplines (such as physics and pharmaceutical science) and different research contexts (such as theory development, hypothesis generation, and problem solving).

Contact:
sieroka(at)uni-bremen.de

Website: ORCID, More

Data Train courses: Starter Track: What does ChatGPT actually know? – An application-oriented introduction into Large Language Models

Contact:
karen.scholz(at)mevis.fraunhofer.de

Data Train courses: Starter Track: Data science and big data, Operation Track - Data Steward: Data preparation / processing - Image Data

He accomplished his Bachelor studies in Scientific Programming at FH Aachen and his simultaneous qualification in Mathematical-technical Software Development in 2010 at RWTH Aachen University. In 2013 he received his Master degree in Artificial Intelligence at Maastricht University. Since 2013 he is employed as research associate in the team of Synthetic Aperture Radar (SAR) oceanography at the Remote Sensing Technology Institute of German Aerospace Center (DLR) in Bremen, Germany. He is responsible for integrating the team’s research and development work into operational prototype software for robust and fast processing of SAR data.

His research comprises the automatic detection and classification of ship signatures on SAR imagery. As PhD student at Helmut Schmidt University, Hamburg he also elaborates on the automatic recognition of ship’s wake signatures of moving vessels.

Contact:
bjoern.tings(at)dlr.de

Website: More

Data Train courses: Starter Track: About the meaningfulness of data

His research focuses on statistical modeling and programming, but also on philosophy of science and basic questions of psychology. "Instead of focusing on exploring the nature of the data and the questions that concern scientists, statisticians tend to commit errors of the third kind, i.e., giving exact answers to the wrong questions, which is perhaps the most serious of the three kinds of errors." - Tukey (1962), cited in: Clark, C.A. (1963). Hypothesis testing in relation to statistical methodology (p.469). Review of Educational Research, 33, 455-473.

Contact:
hans-christian.waldmann(at)uni-bremen.de

Website : ORCID, More

Data Train courses: Operator Track – Data Steward Track: How to write a Data Management Plan?

Her scientific areas of interest are Biodiversity Informatics, Database Management, Scientific Data Curation, and Project Management. Over the past 13 years, she has been able to observe and accompany the increasing significance of Scientific Data Management in her work with scientists and collection curators in the field of Biology. By providing support and training in the data management software "Diversity Workbench", as well as the joint development of work and data flows along the data life cycle in the infrastructure project "German Federation for Biological Data" (GFBio), she helped to ensure that data are handled according to FAIR principles. In the last two years, she completed the master's program in Digital Data Management at the Humboldt University of Berlin and the Potsdam University of Applied Sciences, giving her an in-depth insight into Scientific Data Management outside the Natural Sciences, such as Library and Information Sciences.

Contact:
weibulat(at)snsb.de

Website: ORCID, More

Data Train courses: Operator Track – Data Scientist Track: Evaluating ML/AI algorithms

Beforehand, in 2019, he completed his PhD on the topic “Model Selection and Evaluation in Supervised Machine Learning“ within the DFG-funded research training group  π³ at the University of Bremen. His research is concerned with medical diagnosis and prognosis applications, in particular with statistical methods for the evaluation of medical tests and AI-based prediction models. At Fraunhofer MEVIS he also contributes to different applied research projects by developing statistical and predictive models, for instance to enable innovative clinical decision support systems.

Contact:
max.westphal(at)mevis.fraunhofer.de

Website: ORCID, More

Data Train courses: Starter Track: Asking the right research questions in data science, Operator Track – Data Scientist Track: Causal learning

Contact:
markus.wenzel(at)mevis.fraunhofer.de

Website: More

Data Train courses: Operator Track – Data Scientist Track: Machine learning

He has a research focus on statistical learning and interpretable machine learning and is interested in epidemiological applications to high-dimensional genetic data and longitudinal register data. Marvin is author of several machine learning R packages, e.g. for random forests and neural networks. He taught several machine learning courses at international conferences and at international universities.

Contact:
wright(at)leibniz-bips.de

Website: ORCID, More