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.
Upcoming Seminar
Data Science Seminar goes virtual
Data Science Seminars on Youtube. Watch out!
- 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
Data Science Driven Surgical Oncology program of the NCT Heidelberg
