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Helmholtz Summer School - From Data to Knowledge

→ Europe/Berlin
Description

If you are looking for the third edition of our Incubator Summer Academy, you can end your search right here! As of this year, the jointly organized summer school by the five Information & Data Science platforms  Helmholtz AI, Helmholtz Imaging, HIFIS, HIDA and HMC, is rebranded to the 

HELMHOLTZ SUMMER SCHOOL – FROM DATA TO KNOWLEGDE.

All platforms have teamed up to design a course program that not only covers a great variety of skills, tools, and topics in the field of Information & Data Science suitable for participants with varying degrees of previous knowledge. We compiled courses and lectures and transferred them into a coordinated two-week summer school program, allowing participants to select courses that best suit their interest and experience level. An overview of the offered courses and lectures can be found here!

The Incubator Summer Academy is open to all researchers and staff in the Helmholtz Association. Additionally, a small number of seats are reserved for Master students, doctoral and postdoctoral researchers from other research institutions and universities.

Registration

Registration opens August 12 at noon and closes August 25, 2024.

Here is some information you need to know before you register. 

  1. Have a look at the courses, lectures, and timetable. Choose the course(s) you would like to attend keeping in mind that 
    • some courses require certain previous experience/knowledge  and
    • you should not pick two courses/ lectures that overlap timewise.
  2. If you want to register for a course or lecture, you can do so by clicking on → Register here ← in the course overview. Alternatively, you can click on the respective "Apply for registration" or "Register" button in the list of registration forms at the bottom of this general information page.
  3. You will be directed to the registration form for the respective course/ lecture. Fill it in and click on "Apply".
  4. After registration, you will have received a registration notice via e-mail. Please note that this is NOT yet a confirmation.
  5. Please make your own calendar entries for the courses / lectures you applied for so that you make sure to reserve the time.
  6. If you are selected as a participant for the course(s) of your choice, you will be notified about two - three weeks prior to the start of the Helmholtz Summer School.
  7. The event will take place in Gathertown. The link to our space will be shared with you shortly before the start of the event.

Questions?

Write us at hida-courses@helmholtz.de

Contact
  • Mon, September 16
    • 1
      Opening event Auditorium

      Auditorium

      Join our exciting Opening event!

      We will kick off with a keynote from Dr. Christian Debes from Spryfox titled Explainable AI with real-life applications:
      Explainability is a critical aspect of many data science applications, referring to a machine learning model’s ability to clarify its workings and outputs. Explainable AI (XAI) is data scientist’s trusty sidekick, offering a collection of tools and techniques designed to demystify and enhance trust and acceptance of these models among data scientists, subject matter experts, and users alike.
      This presentation will introduce you to XAI, demonstrating its role in identifying (un-)conscious biases and establishing trust and explaining why it is an indispensable skill for anyone developing or working with AI. We’ll also explore real-world applications of XAI, including a pet insurance disease prediction model and a churn prediction model for HR.

      No registration needed, everyone is welcome in the auditorium of our Gathertown space!

      The keynote will be followed by a discussion round among the five platforms, highlighting their useful offers to you.

      Dr. Christian Debes is co-founder and head of data analytics & AI at Spryfox GmbH as well as lecturer for data science at the Technical University of Darmstadt, Germany (since 2011). He has 15 years of industrial data science experience, prior to his time at Spryfox he worked in different industries where he built up and led large AI teams. His previous positions included head of people data science at Merck and director data science at AGT International.
      Dr. Debes received his PhD in 2010 from Technical University of Darmstadt, Germany in the field of machine learning. He is recipient of the 2013 IEEE Data Fusion Award and has more than 30 journal and conference papers in signal processing and machine learning. Dr. Debes is senior member of the IEEE and served as area editor for the IEEE Signal Processing Magazine and Elsevier’s Signal Processing.

      Speakers: Dr Anna-Lisa Doering (HIDA), Christian Debes (SPRYFOX GmbH), Fredo Erxleben (Helmholtz-Zentrum Dresden-Rossendorf), Mathieu Seyfrid, Mirl Trösch (Geomar Helmholtz Research Center), Sara Krause-Solberg (Helmholtz Imaging, DESY), Dr Stephanie Schworm (HIDA)
    • Course 13 (HIFIS): First steps with Python Room 1

      Room 1
      • 2
        First steps with Python (Part 1)

        During the first day of this workshop, you will deal with the following apsects:

        • Setting up a Python project: You learn how a basic project is set up and explore two approaches to Python programming: using the REPL and writing Python files.

        • Importing: Since projects often get distributed over multiple files or require code from other sources, we will investigate how to import code from other files or libraries.

        • Variables, Assignments, and Data Types: Get to know the basic constructs for storing and manipulating information in a program. Understand what data types are and how they influence how information is processed.

        • Conditionals: It is often necessary to check conditions and act accordingly. This section will cover expressing those conditions and how to control in which order they get checked and how to react to them.

        • While - Loops: Loops are a good choice when it comes to repeating actions. In this section, the "while"-loop will be introduced as a method of repeating code based on condition.

        Speaker: Fredo Erxleben (Helmholtz-Zentrum Dresden-Rossendorf)
    • Course 10 (Helmholtz Imaging): Regularization in Image Reconstruction: From Model to Data Driven Methods Room 2

      Room 2
      • 3
        Lecture: Regularization in Image Reconstruction: From Model to Data Driven Methods
        Speaker: Prof. Martin Burger (Helmholtz Imaging, DESY)
      • 4
        Tutorial: Regularization in Image Reconstruction: From Model to Data Driven Methods (Part 1)

        In the first part of the tutorial we briefly introduce the world of inverse problems. Most importantly we learn about the radon transform and regularization for inverse problems. Small hands on tasks throughout the tutorial challenge your understanding of the material.

        Speakers: Lukas Weigand (Helmholtz Imaging, DESY), Samira Kabri (Helmholtz Imaging, DESY), Tim Roith (Helmholtz Imaging, DESY)
        Package10-Instructions.pdf
        Package10-Setup-Test.ipynb
        Package10_Tutorial.zip
  • Tue, September 17
    • Course 18 (HIFIS): Introduction to Git and GitLab Room 3

      Room 3
      • 5
        Introduction to Git

        This day of the workshop covers the following topics:

        • Introduction to Version Control
        • Initial Git Setup
        • Creating a Repository
        • Tracking Changes
        • Exploring History
        • Ignoring Things
        • Feature branch workflow
        Speaker: Tobias Schlauch (DLR / HIFIS)
    • Course 4 (Helmholtz AI): Introduction to Machine Learning with scikit-learn Room 4

      Room 4
      • 6
        Introduction to Machine Learning with scikit-learn (Part 1)
        Speakers: Helene Hoffmann, Peter Steinbach (HZDR)
    • Course 10 (Helmholtz Imaging): Regularization in Image Reconstruction: From Model to Data Driven Methods Room 2

      Room 2
      • 7
        Tutorial: Regularization in Image Reconstruction: From Model to Data Driven Methods (Part 2)

        In the second part of the tutorial we learn about deep learning for inverse problems. We start with an end-to-end approach emplyoing a U-Net to then introduce the concept of plug-and-play regularization. We finally introduce the basics of sampling and uncertainty quantification. Again, these insights are applied to CT reconstruction in small hands on tasks.

        Speakers: Lukas Weigand (Helmholtz Imaging, DESY), Samira Kabri (Helmholtz Imaging, DESY), Tim Roith (Helmholtz Imaging, DESY)
        models.py
        Package10_Tutorial_Part_2.zip
        utils.py
    • Course 13 (HIFIS): First steps with Python Room 1

      Room 1
      • 8
        First steps with Python (Part 2)

        On Day 2 of this course, you will deal with the following aspects:

        • Functions: Splitting parts of programs off into self contained, reusable blocks is a good way to handle complexity and allow for parts of a program to also be used in other projects.
        • For - Loops: Introducing the second kind of loop, the "for"-loop is well suited to iterate over a set of data or repeat a set of instructions a given amount of times.
        Speaker: Fredo Erxleben (Helmholtz-Zentrum Dresden-Rossendorf)
  • Wed, September 18
    • Course 18 (HIFIS): Introduction to Git and GitLab Room 3

      Room 3
      • 9
        Introduction to GitLab

        This day of the workshop covers the following topics:

        • Introduction to GitLab
        • Working with remote repositories in GitLab
        • GitLab contribution workflow using issues and merge requests
        • GitLab Contribution Workflow: Team Exercise
        Speaker: Tobias Schlauch (DLR / HIFIS)
    • Course 4 (Helmholtz AI): Introduction to Machine Learning with scikit-learn Room 4

      Room 4
      • 10
        Introduction to Machine Learning with scikit-learn (Part 2)
        Speakers: Helene Hoffmann, Peter Steinbach (HZDR)
    • Course 13 (HIFIS): First steps with Python Room 1

      Room 1
      • 11
        First steps with Python (Part 3)

        During the last day of this course, you will be dealing with the following aspects:

        • Tuples: Tuples are a great way to bundle up multiple values. Learn how to employ them and take advantage of Python's automatic Packing/unpacking feature.
        • Lists: Another very useful data type is the List, a sorted collection of data. In this section we introduce some basic functionality and learn where to find more detailed information for this data type and many others.
        • Finalizing the project: We will put some finishing touches on our example project to make it ready for a first release. Further, possible future learning paths will be outlined.
        Speaker: Fredo Erxleben (Helmholtz-Zentrum Dresden-Rossendorf)
    • Course 9 (Helmholtz AI): Effective Use of Large Language Models and Prompt Engineering Room 2

      Room 2
      • 12
        Effective Use of Large Language Models and Prompt Engineering
        Speakers: Alexandre Strube (Helmholtz AI), Ilhem Isra Mekki (Helmholtz AI)
  • Thu, September 19
    • Course 2 (HMC): Introduction to Scientific Metadata Room 3

      Room 3
      • 13
        Fundamentals of Scientific Metadata: Why context matters - PART 1
        Speakers: Dr Silke Gerlich (HMC), Theresa Schaller (HMC/ HZDR)
    • Course 4 (Helmholtz AI): Introduction to Machine Learning with scikit-learn Room 4

      Room 4
      • 14
        Introduction to Machine Learning with scikit-learn (Part 3)
        Speakers: Helene Hoffmann, Peter Steinbach (HZDR)
    • Course 16 (Helmholtz AI / HIDA): From Idea to I did: Playing Lean for Start- ups and Innovation Room 2

      Room 2
      • 15
        Lecture: Introduction to Lean Methodology for Startups

        We’ll introduce the principles and methodologies of the lean startup approach covering the crucial topics including conducting customer research, creating a minimum viable product (MVP), and reiterative development cycles. We’ll emphasise the importance of user feedback, rapid experimentation, and agile continuous improvement to build successful innovations.

        Speaker: Klaus Kammermeier
      • 16
        Workshop - Playing Lean

        Join us for a session of Playing Lean, an engaging hands-on "flight simulator" for Lean Startup and innovation. The creators of the game have partnered up with Alexander Osterwalder, inventor of the Business Model Canvas and one of the great minds of Lean Startup. The Playing Lean board game teaches highly valuable lessons on Lean Startup, creates interest in Business Model Canvas, goes deep on the Value Proposition Canvas or running lean with the Lean Canvas. Playing Lean is turn based and players in competing teams advance a fictional strategy, competing in the same industry. Playing Lean deploys a number of gamification practices: storytelling, social learning, motivation and reward structures, competition - all supported by a trained facilitator

        Speaker: Klaus Kammermeier
  • Fri, September 20
    • Course 2 (HMC): Introduction to Scientific Metadata Room 3

      Room 3
      • 17
        Fundamentals of Scientific Metadata: Why context matters - PART 2
        Speakers: Dr Silke Gerlich (HMC), Theresa Schaller (HMC/ HZDR)
    • Course 3 (HIDA / Helmholtz AI): From Idea to I did: Empowering Women Entrepreneurs Room 1

      Room 1
      • 18
        Keynote "From Idea to I did: Empowering Women Entrepreneurs"

        This keynote builds the central impulse and backbone for the workshop on “empowering women entrepreneurs”. This keynote sets the central theme and foundation for the workshop on “Empowering Women Entrepreneurs.” Prof. Dr. Stephanie Birkner will offer valuable insights into the complex landscape of female entrepreneurship, debunking prevalent myths and examining the critical factors that influence women’s entrepreneurial journeys. By drawing parallels between research cycles and startup creation, the discussion will illuminate the iterative and dynamic processes that underpin successful entrepreneurial ventures.

        Emphasizing the diversity of entrepreneurial thinking and actions, this talk will demonstrate how varied approaches contribute to a robust and innovative entrepreneurial ecosystem. Participants will discover and harness their innovative potential, understanding that entrepreneurial success is not a one-size-fits-all model but a personalized journey of creativity, resilience, and strategic action. This keynote aims to inspire and equip female entrepreneurs with the insights and tools necessary to navigate and thrive in the entrepreneurial world.

        Speaker: Stephanie Birkner (Hochschule Bremerhaven)
      • 19
        Workshop - Empowering Women Entrepreneurs

        The workshop will address the specific challenges encountered by women in the entrepreneurial domain, with a focus on openly discussing gender biases and stereotypes. The goal is to foster a supportive environment for participants to share experiences and strategies. Attendees will acquire practical knowledge and skills essential for entrepreneurial success, particularly in overcoming gender-specific obstacles in MINT-related careers. Through self-assessment exercises and interactive sessions, participants will identify their unique strengths and capabilities. An experienced female entrepreneur will be present to reflect and consult with you. The workshop aims to empower women to leverage these attributes, cultivating the self-confidence necessary for entrepreneurial endeavors.

        Speakers: Angela Kornau (Coach & Researcher), Barbara Bachus (ExoMatter)
    • Course 4 (Helmholtz AI): Introduction to Machine Learning with scikit-learn Room 4

      Room 4
      • 20
        Introduction to Machine Learning with scikit-learn (Part 4)
        Speakers: Helene Hoffmann, Peter Steinbach (HZDR)
    • Lecture 1 (HMC): HMC FAIR Friday
      • 21
        HMC FAIR Friday: The origin of data – The relevance of provenance in the context of FAIR
        Speaker: Mirl Trösch (Geomar Helmholtz Research Center)
  • Mon, September 23
    • Course 1 (HMC): Introduction to FAIR & Scientific Data Reuse (Multiple locations)

      (Multiple locations)
      • 22
        Lecture: Introduction to FAIR data Auditorium

        Auditorium

        The digital world forgets nothing, a common saying claims. However, anyone who has ever tried to find data that is only a few years old on the global web and then reuse it with up-to-date software knows that digital information can very well get lost. To ensure that this does not happen to valuable research data, numerous players in science are currently working on making this data as FAIR as possible - findable, accessible, interoperable and reusable.

        This lecture is open to anyone who is interested!

        Speaker: Mirl Trösch (Geomar Helmholtz Research Center)
      • 23
        Training Course: Reusability of Scientific Data Room 1

        Room 1

        In the training course "Reusability of Scientific Data," we focus on the 'R' in the FAIR Principles. This course was developed in collaboration with the Helmholtz Artificial Intelligence team and is specifically tailored to the Research Field Matter.

        By the end of the course, participants will have a comprehensive understanding of the importance and necessity of making data reusable, a key aspect of the FAIR principles. While the course is open to everyone, it is particularly aimed at newly hired postdocs and PhD students in the Research Field Matter who are at the beginning of their research. For these researchers, it is crucial to learn how to apply data reusability methods in the early stages of their projects. They will also gain hands-on experience in selected steps necessary for making research data reusable by others.

        Participants will understand the significance of the FAIR principles, particularly the reusability of data within the Research Field Matter and the AI field. Additionally, the course will feature a lightning talk on data reusability in the Research Field Matter.

        Speakers: Dr OEzlem OEzkan (HMC), Helene Hoffmann (Helmholtz AI), Till Korten (Helmholtz AI)
    • Course 19 (HIFIS): Continuous Integration with GitLab Room 3

      Room 3
      • 24
        Continuous Integration in GitLab (Part 1)

        Day 1: With continuous integration in GitLab, you can automate the building, testing, and deploying of your code. This day will focus on creating an initial GitLab CI pipeline.

        Speaker: Mr Christian Hueser (Helmholtz-Zentrum Dresden - Rossendorf e.V. (HZDR))
    • Course 14 (Helmholtz AI): Time-dependent Generative Models Room 2

      Room 2
    • Course 15 (Helmholtz AI): Fantastic Vision Language Models and how to compress them Room 4

      Room 4
      • 26
        Fantastic Vision Language Models and how to compress them
        Speaker: Yiran Huang (Helmholtz Munich)
    • Course 5 (Helmholtz AI): Introduction to Statistical Learning Room 2

      Room 2
      • 27
        Introduction to Statistical Learning Zoom

        Zoom
        Speaker: Tingying Peng (Helmholtz Munich)
  • Tue, September 24
    • Course 19 (HIFIS): Continuous Integration with GitLab Room 3

      Room 3
      • 28
        Continuous Integration in GitLab (Part 2)

        Building on day 1, you will learn advanced concepts of GitLab CI useful for optimizing the pipeline.

        Speaker: Mr Christian Hueser (Helmholtz-Zentrum Dresden - Rossendorf e.V. (HZDR))
    • Course 7 (Helmholtz AI): Explainable Artificial Intelligence Room 1

      Room 1
      • 29
        Introduction to eXplainable Artificial Intelligence
        Speakers: Francesco Campi (HAICU), Ilhem Isra Mekki (Helmholtz AI), Lisa Barros de Andrade e Sousa (Helmholtz AI), Sabrina Benassou
        Course GitHub Repo
    • Course 11 (Helmholtz Imaging): Introduction to Image Registration Room 4

      Room 4
      • 30
        Intro to image registration
        Speaker: Ella Bahry (Helmholtz Imaging, MDC)
    • Lecture 2 (Helmholtz AI): Fairness in machine learning Auditorium

      Auditorium
      • 31
        Fairness in machine learning
        Speaker: Niki Kilbertus
  • Wed, September 25
    • Course 6 (Helmholtz AI): Introduction to Uncertainty Quantification (UQ) in ML
    • Course 17 (HIFIS): Data processing with Pandas & Data plotting with Matplotlib Room 4

      Room 4
      • 33
        Introduction to Pandas

        This day will have the following content:

        • The Series Data Type: Learn about the fundamental data type used for labelled sequential data.
        • Introduction to DataFrames: Get to know a more advanced data type to deal with multi-dimensional data.
        • Accessing Data: Learn about multiple ways to access subsections of a Series or a DataFrame and singular elements in it.
        • Filtering Data: Learn how to create filter masks to separate out the interesting data.
        • Modifying Data: Learn how to add and manipulate existing data in a DataFrame or Series.
        • Hands-on Exercise (Pandas) Loading Data: Try out your newly learned skills on a real-live data set. In this section, you will learn how to load a complex data set and re-shape it into a usable form based on the provided metadata. The instructors are available to give advice and feedback.
        Speaker: Fredo Erxleben (Helmholtz-Zentrum Dresden-Rossendorf)
    • Course 12 (Helmholtz Imaging): Visualization of volumetric datasets Room 2

      Room 2
      • 34
        Visualization of volumetric datasets
        Speaker: Deborah Schmidt (Helmholtz Imaging, MDC)
  • Thu, September 26
    • Course 17 (HIFIS): Data processing with Pandas & Data plotting with Matplotlib Room 4

      Room 4
      • 35
        Introduction to Matplotlib

        The following content awaits you:
        - Introduction to Pyplot: Pyplot is a collection of shortcuts for common tasks. Learn how to use it to create basic plots.
        - Multiple Plots: Learn how to create multiple plots and arrange them in a single figure.
        - Object- oriented Style: Learn about an alternative approach to setting up plots.
        - Matplotlib & Pandas: Learn how you can combine Pandas and Matplotlib for quick plotting.
        - Hands-on Exercise (Matplotlib): Try out your newly learned skills on a set of real-live use cases. The instructors are available to give advice and feedback.

        Speaker: Fredo Erxleben (Helmholtz-Zentrum Dresden-Rossendorf)
    • Course 8 (Helmholtz AI): Dimensionality Reduction Room 1

      Room 1
      • 36
        A practical guide to dimensionality reduction (Part 1)
        Speakers: Elisabeth Georgii (Helmholtz Zentrum München), Francesco Campi (HAICU), Ilhem Isra Mekki (Helmholtz AI), Lisa Barros de Andrade e Sousa (Helmholtz AI)
  • Fri, September 27
    • Course 17 (HIFIS): Data processing with Pandas & Data plotting with Matplotlib Room 4

      Room 4
      • 37
        Hands - on Exercises with Pandas
        • Cleaning Data: The loaded data set still has quite a few inconsistencies, missing values and formatting peculiarities that need to be dealt with before it is ready for analysis.
        • Analyzing Data: Analyze the now cleaned data set to extract new knowledge about the climatic conditions at the chosen location.
        Speaker: Fredo Erxleben (Helmholtz-Zentrum Dresden-Rossendorf)
    • Course 8 (Helmholtz AI): Dimensionality Reduction Room 1

      Room 1
      • 38
        A practical guide to dimensionality reduction (Part 2)
        Speakers: Elisabeth Georgii (Helmholtz Zentrum München), Francesco Campi (HAICU), Ilhem Isra Mekki (Helmholtz AI), Lisa Barros de Andrade e Sousa (Helmholtz AI)
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