MOPRED Symposium - Multimodal Data Analysis
10.10.2023 @ 10:00 - 18:00
Program
Preliminary Overview
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Welcome
Welcome and opening remarks Marcel Schulz and Florian Buettner
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Pathways and Gene Functions
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Scientific Talks
Talks from: Andreas Chiocchetti, Katharina Imkeller, Thomas Oellerich, Ingo Ebersberger, Lena Wiese, Timothy Warwick, Sareh Ameri Far, Nina Baumgarten
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Group Photo
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Lunch Break
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ML Methods for Mulit-modal Integration
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Scientific Talks
Talks from: Maren Wehrheim, Arber Qoku, Marcel Schulz, Sikander Hayat, Zahra Moslehi, Kevin de Azevedo, Fatemeh Behjati, Nico Pfeifer
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Poster Session and Coffee
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Applications in Disease
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Scientific Talks
Talks from: Eva herrmann, Nadine Flinner, Christel Kamp, Ramachandra M Bhaskara, Kathi Zarnack
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End
Keynote Speakers
Exciting Insides
Prof. Dr. Nico Pfeifer
Topics
Relevant Key Concepts
Representation Learning
Representation learning automates feature extraction from raw data, enhancing model performance in tasks like natural language processing and computer vision.
Graph Neural Networks
Graph Neural Networks (GNNs) leverage relationships in graph-structured data to improve tasks like node classification and link prediction by updating node representations based on their neighbors.
Mechanistic Models
Mechanistic models describe system behaviors based on fundamental principles, simulating complex dynamics for fields like epidemiology and engineering.
Statistical Approaches
Statistical approaches use mathematical methods to analyze data, making inferences and predictions to uncover patterns and inform decision-making.
Registration
Open Until:
8.9.2023Abstract Notification Until:
