workshop

Dr. Sonja Hänzelmann and Dr. Fabian Hausmann, Institute of Medical Systems Biology, UKE

In diesem intensiven 3-tägigen Kurs werden Sie in die Welt der Data Literacy (Die Fähigkeit, kompetent mit großen Datenmengen umzugehen) eingeführt. Der Kurs bietet eine praxisnahe Herangehensweise an biomedizinische Probleme, bei denen die Teilnehmer:innen lernen, wie sie relevante Erkenntnisse aus komplexen Datensätzen gewinnen können.

Topics:
Grundlagen der Dateninterpretation und Data Literacy: Verständnis von Schlüsselbegriffen und Konzepten im Bereich Data Literacy
Python für die Datenanalyse: Einführung in die Programmiersprache Python für Datenanalyse und -manipulation
Einführung in Data Mining-Techniken: Überblick über verschiedene Data Mining-Methoden und ihre Anwendungen im biomedizinischen Bereich.
Praktische Anwendung von Clustering, Klassifizierung und automatischer Mustererkennung
Anwendung auf biomedizinische Probleme: Bearbeitung eines ausgewählten biomedizinischen Problems durch ein Gruppenprojekt
Visualisierung und Interpretation der Ergebnisse: Effektive Kommunikation von Analyseergebnissen durch Datenvisualisierung
Interpretation und Diskussion der gewonnenen Erkenntnisse im biomedizinischen Kontext

Visualisierung von Daten fungiert als epistemisches Mittel; eine Visualisierung soll Aussagen über die Einzel- als auch die Gesamtdaten treffen sowie Erkenntnisse fördern. Vorgestellt werden zwei im UCLAB der Fachhochschule Potsdam entstandene dynamische und interaktive Viewer, die unterschiedliche Einstiege in kulturelle Datensammlungen anbieten und Objekte und ihre Relationen sichtbar machen. In ihrer Funktion als Analysewerkzeug und Erkenntnismittel erlaubt die Visualisierung dabei auch einen strukturierten und dynamischen Zugriff auf große Datenmengen.

Basis einer jeden Visualisierung ist die Strukturierung und semantische Anreicherung der Forschungsdaten. In dem Hands-On-Workshop wird eine Infrastruktur zur Erfassung und Kontextualisierung kleiderhistorischer Quellen, die mittels CidocCRM und weiterer Vokabulare strukturiert wurde, vorgestellt. Die Teilnehmenden werden angeleitet, mittels einer Collage-Technik eigene explorative Zugänge zu den Sammlungsobjekten zu erarbeiten. Im Anschluss folgt eine Diskussion über die entstandenen Visualisierungen und eine gemeinsame Reflektion über Datenpraktiken.

Referentin: Sabine de Günther (UCLAB, FH Potsdam)

Eine Veranstaltung im Rahmen der Veranstaltungsreihe „Digital Humanities – Wie geht das?“ des Referats für Digitale Forschungsdienste.

Die Teilnehmer:innenzahl ist beschränkt auf 15, daher wird um Anmeldung an forschungsdienste@sub.uni-hamburg.de gebeten.

Die Einführung von generativen KI-Tools wie ChatGPT hat im Hochschulkontext zu einer großen Verunsicherung geführt. Viele Studierende nutzen die Tools bislang gar nicht, manche sind unsicher, was erlaubt ist und andere nutzen sie womöglich bereits intensiv für das Studium, ohne genau über Funktionsweise und Grenzen informiert zu sein.

In diesem Workshop sind die Grundlagen des Einsatzes von generativer KI im Studiumskontext das Thema. Gemeinsam betrachten wir verschiedene Tools hinsichtlich ihrer Funktionsweise, Möglichkeiten und Grenzen. Ziel ist es, dass die Teilnehmenden anschließend für sich geklärt haben, ob und wie sie generative KI in ihrem Studium einsetzen wollen – und dadurch auch andere Studierende in dieser Entscheidung begleiten können. Dafür basiert ein großer Teil des Workshops auf Austausch unter den Teilnehmenden, weshalb er sich an Anfänger:innen, aber auch an erfahrenere gKI-Nutzende richtet.

Teilnahme und Anmeldung: 

Dieses Angebot richtet sich fachübergreifend an alle regulär Studierenden der Universität Hamburg.

Die Anmeldung erfolgt über das nachstehende Anmeldeformular. Wenn Sie zum Termin verhindert sind, melden Sie sich bitte wieder ab, damit andere Personen ggf. nachrücken können (max. Teilnehmendenzahl: 15).

Die Workshops finden in der Jungiusstraße statt (Raumnummer folgt). 

Institutions

• DDLitLab, ISA-Zentrum

learn_bAIome offers workshops and trainings in biomedical AI/data science with tailored formats that take into account background, programming skills and intensity to provide unique, focused, and effective courses. These courses are free and open to students, clinicians, and researchers across academic institutions in Hamburg.

Lecturer: Dr. Lorenz Adlung, I. Department of Medicine, Hamburg Center for Translational Immunology (HCTI), bAIome, UKE

Prerequisites: Intrinsic motivation to learn about infection and inflammation using your computer.

Description: This workshop is open to all students, researchers and clinicians who want to learn how we use (“big”?) data and computational modelling for discovery and rational intervention in infection and inflammation. In today’s biomedical research, the bottleneck has shifted, and for the first time, data generation is no longer the rate-limiting step in scientific progress, but rather: data analysis. We will discuss current trends and show how we can use mathematical concepts and analytical thinking to address unmet clinical needs in influenza infection and inflammatory bowel disease. The workshop will be in presence and therefore each participant should bring their own laptop or ipad.

Topics:

• “big” data and code repositories
• mathematical concepts relevant to infection and inflammation
• computational modelling of murine influenza infection
• identification of treatment responders in inflammatory bowel disease
• discussion of the future of bAIomedical research

Dr. Behnam Yousefi, Institute of medical systems biology, UKE

This workshop is for students, researchers, and clinicians keen to learn the R programming language and data analysis relevant to biomedicine. The course is designed to be practical and comprehensive with no specific background requirements. We will focus on fundamentals of data analysis with examples of real-life data in biomedicine, such as gene expression. By the end of the course, participants will be familiar with the essentials of data analysis, including statistical tests, linear regression, principal component analysis, clustering and data visualization. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics:
Basics of R programming language
Statistical tests
Linear regression
Principal component analysis (PCA)
Clustering
Data visualization

Dr. Behnam Yousefi, Institute of medical systems biology, UKE

This workshop is for students, researchers, and clinicians keen to learn the R programming language and data analysis relevant to biomedicine. The course is designed to be practical and comprehensive with no specific background requirements. We will focus on fundamentals of data analysis with examples of real-life data in biomedicine, such as gene expression. By the end of the course, participants will be familiar with the essentials of data analysis, including statistical tests, linear regression, principal component analysis, clustering and data visualization. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics:

Basics of R programming language
Statistical tests
Linear regression
Principal component analysis (PCA)
Clustering
Data visualization

Dr. Behnam Yousefi, Institute of medical systems biology, UKE

This workshop is open to students, researchers, and clinicians keen to learn the essentials of machine learning and implementing it via Python. The aim of the course is to provide a comprehensive map of machine learning (and deep learning) methods with no specific background requirements. A little background in python can be helpful, though. We will focus on fundamentals of machine learning, validation methods, linear and nonlinear models, and feature reduction. The students will also get familiarized with the Python packages of Sci-kit Learn and Pytorch. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics
Types of machine learning: supervised and unsupervised
Validation  metrics and cross validation
Introduction to linear and nonlinear models include: Linear regression, Random forest, support vector machines, deep neural networks.
Feature reduction.
Regularization.

learn_bAIome offers workshops and trainings in biomedical AI/data science with tailored formats that take into account background, programming skills and intensity to provide unique, focused, and effective courses. These courses are free and open to students, clinicians, and researchers across academic institutions in Hamburg.

Lecturer: Dr. Fatemeh Hadäghi, Institute of Computational Neuroscience, UKE

Prerequisites A basic understanding of neural networks and machine learning concepts is expected as well as a familiarity with Python and basic programming skills.

Description This workshop is open to students, researchers, and clinicians wanting to learn about recurrent neural networks (RNNs) and their applications in biomedical signal processing. RNNs are vital tools in the field of neural networks, especially known for their capability to manage sequential data. This workshop will provide an accessible introduction to RNNs, concentrating on their core concepts and various applications. We will explore how RNNs excel at capturing temporal dependencies through their unique recurrent connections, making them highly effective for a variety of tasks. Participants can expect to achieve a solid understanding of the basic principles and architecture of RNNs as well as the ability to identify suitable applications for RNNs and implement basic RNN models. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics

• Overview of RNN fundamentals and how they differ from other neural networks
• Key applications of RNNs in biomedical signal processing
• Reservoir computing (RC)
• Hands-on exercises and examples to illustrate RNN implementation and usage

learn_bAIome offers workshops and trainings in biomedical AI/data science with tailored formats that take into account background, programming skills and intensity to provide unique, focused, and effective courses. These courses are free and open to students, clinicians, and researchers across academic institutions in Hamburg.

This workshop is open to students, researchers, and clinicians wanting to learn how machine learning is applied for biomedical datasets, the different classes of machine learning algorithms that may be used, as well as the best practices in selecting and evaluating algorithms, and their limitations.  The aim of the course is to provide concepts and tools to navigate the use of machine learning in the biomedical landscape. The course will use biological datasets and there will be hands-on components as well as discussions. Participants should already have taken an introduction to machine learning and be familiar with Python programming. The workshop will be in presence and therefore each participant should bring their own laptop (no ipads).

Topics

• Taxonomy of machine learning algorithms
• Linear regression, logistic regression and related methods
• Decision trees
• Support Vector Machines
• Bias & Variance, curse of dimensionality
• Representation learning
• Neural networks and deep learning: MLPs, transformers, CNNs
• Applications to RNAseq and imaging data

In the humanities and cultural studies, OCR (Optical Character Recognition) and HTR (Handwritten Text Recognition) remain difficult tasks. All users have access to a free and simple-to-use tool through OCR4all to carry out their own OCR workflows. The fundamental ideas and concepts of OCR will be covered in this workshop, along with a brief overview of the OCR4all program.

- What kinds of files and data are necessary for OCR?
- How does the OCR or HTR workflow integration in OCR4all adapt according to the source material and the anticipated (human) effort?
- With regard to the content at hand, how much of the workflow can be automated?
- What is an OCR model, and how can one train a specific text recognition model?
- What level of recognition accuracy can be expected?
- How much work should be put into producing texts if they are going to be used later?

By the end of the session, all participants will be able to work independently on challenging OCR tasks thanks to the discussion and explanation of these and other topics.

The participants may use the offered sample texts as well as their own materials. There is no prerequisite for this training, and all skill levels can participate.

Speaker: Florian Langhanki (JMU)

The number of participants is limited to 15, so please register at forschungsdienste@sub.uni-hamburg.de.

This event is in the series "Digital Humanities – How does it work?" of the Department for Digital Scholarship Services. 

The German Conference on Bioinformatics (GCB) is an annual, international conference devoted to all areas of bioinformatics and meant as a platform for the whole bioinformatics community. Recent meetings attracted a multinational audience of approximately 250 participants each year.

The conference will take place at Bielefeld University. Besides the talks and poster sessions, ample networking opportunities including a conference dinner will be provided.
The conference starts in the afternoon of 30 September 2024 and is preceded by a workshop programme in the morning.

Looking forward to seeing you in Bielefeld!

Abstracts for presentations can be submitted until 6 May 2024, the submission of poster abstracts will close on 7 August 2024

Submit your abstract here!

Institution

• DECHEMA, Gesellschaft für Chemische Technik und Biotechnologie e.V.

Join us in person on September 23-24, 2024 at DESY in Hamburg for a workshop on Deep Learning in Inverse Problems, jointly organized by DESY, DFG, FoDL, the University of Hamburg, and Helmholtz Imaging. Register by 15 September at here.

This workshop focuses on novel developments on learning based methods for inverse problems, including supervised learning and unsupervised learning such as learned regularizations and generative models for Bayesian inversion. Particular emphasis will be put on the understanding of foundations of learning in this area as well as novel applications including problems in imaging and speech/audio processing.

Speakers include:

• Tatiana Bubba (University of Bath),
• Antoine Deleforge (INRIA Nancy),
• Berthy Feng (Caltech),
• Eloi Moliner (Aalto University Helsinki), and
• Ozan Öktem (KTH Stockholm).