data analytics

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

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

We are excited to announce a guest talk by Alexandra Diehl from the University of Zurich, who will present her latest research on the visualization and communication of weather forecasts. Alexandra is a senior researcher and lecturer in the Multimedia and Visualization group at UZH's Department of Computer Science. Her work focuses on efficient analysis, decision-making, and communication of high-impact weather events (HIWE) and their associated risks.

Talk Title: Visualization Research for Weather Forecast Communication and Analysis

Abstract: This talk will cover Alexandra's recent contributions to developing efficient visualization tools for analyzing and communicating weather forecasts and characterizing HIWEs. Additionally, she will discuss her current efforts in citizen data analysis and the challenges of effectively communicating severe weather events through participatory citizen science.

Speaker Bio: Alexandra Diehl holds a Ph.D. in Computer Science from the University of Buenos Aires and has extensive experience in data visualization, visual analytics, and geographic information systems. She has been a postdoctoral researcher in the Data Visualization and Analysis Group at the University of Konstanz and currently works with Prof. Dr. Renato Pajarola's group at UZH.

We look forward to seeing many of you at this insightful talk.