Data Science Seminar
The NCT Data Science Seminar is a campus-wide effort bringing together thought-leading speakers and researchers in the field of data science to discuss both methodological advances as well as medical applications.
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Upcoming heidelberg.ai talk
Upcoming Online Seminar
Data Science Seminar goes virtual
Data Science Seminars on Youtube. Watch out!
- 16.10.2023: Alexander Krull - Image Denoising and the Generative Accumulation of Photons
- 04.10.2023: Qi Dou - Image-based Robotic Surgery Intelligence
- 26.07.2023: Tristan Bereau - The role of multiscale modeling in molecular discovery
- 28.06.2023: Gael Varoquaux - Medical AI: addressing the validation gap
- 31.05.2023: Christoph Lippert - Unleashing the Genetic Architecture of Heritable Traits
- 17.05.2023: Chen Chen - Advancing deep medical image segmentation with adversarial data augmentation
- 19.04.2023: Jan Stühmer - Interpretable Representations and Neuro-symbolic Methods in Deep Learning
- 22.03.2023: Stefan Bauer: Neural Causal Models
- 08.03.2023: Uwe Korn - Best practices for parallelizing data pipelines
- 25.01.2023: Shehoofeh Azizi - Large Language Models Encode Clinical Knowledge
- 16.11.2022: Martin Dugas - MDM & OpenEDC: Next-generation study databases in medicine
- 02.11.2022: Anirban Mukhopadhyay - Reverse Engineering the Doctor’s Mind
- 19.10.2022: Magnus Rattray - Probabilistic modelling of transcription dynamics in whole embryos and singel cells
- 29.06.2022: Annika Reinke - (Bench)mark: Pitfalls in AI Validation
- 01.06.2022: Tommaso Calarco - Quantum technologies: the second quantum revolution
- 18:05.2022: Mathias Unberath - Advances in Scene Reconstruction and Tracking for Endoscopic Surgery
- 05.05.2022: Simon Kohl - Highly accurate protein structure prediction with AlphaFold
- 06.04.2022: Robert Geirhos - How can we narrow the gap between human and machine vision?
- 23.03.2022: Sotirios A. Tsaftaris - The building blocks of a Big AI in healthcare
- 23.02.2022: Julia Schnabel - AI-enabled imaging
- 26.02.2022: Ulf Leser - Data Science for Supporting Molecular Tumor Boards
- 17.12.2021: Florian Büttner - Trustworthy machine learning in oncology
- 17.11.2021: Jean-Philippe Vert - Machine Learning for Single Cell Omics
- 03.11.2021: Merle Behr - Statistical recovery of compositional discrete structures
- 22.10.2021: Matthias Baumhauer - Cognition Guided Reporting in Radiology
- 06.10.2021: Smita Krishnaswamy - Geometric and Topological Approaches to Representation Learning in Biomedical Data
- 22.09.2021: Marie-Louise Timcke - Data Visualization – Potential and Pitfalls
- 28.07.2021: Andrea Vedaldi - Discovering actionable interpretations from raw visual data: from 2D clustering to 3D reconstruction
- 30.06.2021: Stefanie Speidel - Quantifying surgical expertise
- 16.06.2021: Dmitry Kobak - Neighbour embeddings for scientific visualization
- 02.06.2021: Piotr Antonik - Machine learning at the speed of light
- 19.05.2021: Jenia Jitsev - Improving Transfer Learning via Large-Scale Model Pre-Training
- 21.04.2021: Phillip Richter-Pechanski - Natural Language Processing in the Clinical Domain
- 24.03.2021: Fred Hamprecht - Signed graph partitioning
- 10.03.2021: Nicola Rieke - Federated Learning for Healthcare – Collaborative AI without Sharing Patient Data
- 24.02.2021: Anja Butter - Boosting high energy physics with generative networks
- 27.01.2021: Jens Kleesiek - Digital Medicine and Real-World Machine Learning Applications
- 16.12.2020: David Kügler - What to learn in instrument pose estimation
- 02.12.2020: Hannes Kenngott - Smart Hospital – Data Science Enabling Infrastructure and Concepts
- 18.11.2020: Karsten Borgwardt - Machine Learning in Medicine: Early Recognition of Sepsis
- 04.11.2020: Paul F. Jäger - nnU-Net: Automated Design of Deep Learning Methods for Biomedical Image Segmentation
- 21.10.2020: Philipp Mann - From development to a certified medical product: Bringing ai-solutions to the patient
- 07.10.2020: Michael Bronstein - Deep learning on graphs: successes, challenges, and next steps
- 29.07.2020: Andreas Krug - Training and understanding artificial neural networks with cognitive neuroscience inspired methods
- 08.07.2020: Ben Glocker - Uncertainty, causality and generalization: Attempts to improve image-based predictive modelling
- 01.07.2020: Charlotte Bunne - Learning across Incomparable Spaces (in Biomedical Applications)
- 17.06.2020: Christina Heinze-Deml - Active Invariant Causal Prediction: Experiment Selection through Stability
- 20.05.2020: Elisabeth Hoppe - Deep Learning-based Magnetic Resonance Fingerprinting
- 06.05.2020: Max Welling - Learning Equivariant and Hybrid Message Passing on Graphs
- 22.04.2020: Lynton Ardizzone - Safety, Robustness and Explainability in Supervised Tasks using Invertible Neural Networks
- 08.04.2020: Veronika Cheplygina - Ten Simple Rules for using Twitter as a Scientist
- ELLIS against COVID-19
- Deep Learning State of the Art (2020) | MIT Deep Learning Series
Physical Seminars
- 11.03.2020: Emre Kavur - A story of a challenge and how to keep it impartial
- 26.02.2020: Mathias Niepert - Graph Neural Networks for (Bio-)Medical Applications
- 12.02.2020: Titus Brinker - Von der Klinik in die Pathologie: Das Potential künstlicher Intelligenz in der Hautkrebsdiagnostik
- 29.01.2020: Patrick Scholz, Jan Sellner, David Zimmerer - Recent developments in 3 domains of data science
- 06.11.2019: Manuel Wiesenfarth - Unraveling uncertainty in benchmarking: Methods and open-source toolkit for analyzing and visualizing challenge results
- 21.10.2019: Teodor Grantcharov - Using data to enhance teamwork, team performance, and patient safety in the OR
Data Science Seminar Questionnaire
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The Data Science Seminar series is supported by
Intelligent Systems in Surgical Oncology program of the NCT Heidelberg
