Florian Buettner
Professor for Bioinformatics in Oncology
Goethe University Frankfurt
German Cancer Consortium (DKTK)
German Cancer Research Center (DKFZ)
Florian Buettner is a professor at Goethe-University Frankfurt and the German Cancer Consortium (DKTK)/German Cancer Research Center (DKFZ). Having earned his PhD in physics he spent several years as a postdoc and principal investigator at the Helmholtz Center for environmental health Munich and the European Bioinformatics Institute, Cambridge. Prior to joining Frankfurt University, he worked as a guest scientist at the Helmholtz Center Munich, in addition to his appointment as senior scientist for industrial AI and probabilistic machine learning at Siemens AG. His research interests are focused on the intersection of multi-omics bioinformatics, machine learning and oncology.
Interests
- Machine Learning (ML)
- Precision Oncology
- Multi-omics data integration
Latest
- Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors
- Explanatory Model Monitoring to Understand the Effects of Feature Shifts on Performance
- Federated Continual Learning Goes Online: Leveraging Uncertainty for Modality-Agnostic Class-Incremental Learning
- How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning
- Provably Better Explanations with Optimized Aggregation of Feature Attributions
- Through the Eyes of the Expert: Aligning Human and Machine Attention for Industrial AI
- ERC Consolidator Grant for Florian!
- Paper accepted at AAAI 2023!
- A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models
- Encoding Domain Knowledge in Multi-view Latent Variable Models: A Bayesian Approach with Structured Sparsity
- Multimodal analysis methods in predictive biomedicine
- Test Time Augmentation Meets Post-hoc Calibration: Uncertainty Quantification under Real-World Conditions
- Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition
- Paper accepted at ECCV 2022!
- Paper accepted at Cancer Cell 2022!
- Better Uncertainty Calibration via Proper Scores for Classification and Beyond
- Inflammatory fibroblasts mediate resistance to neoadjuvant therapy in rectal cancer
- Single cell analyses identify a highly regenerative and homogenous human CD34+ hematopoietic stem cell population
- The proteogenomic subtypes of acute myeloid leukemia
- Paper accepted at UAI!
- Hierarchical Domain Invariant Variational Auto-Encoding with weak domain supervision
- Multi-output Gaussian Processes for uncertainty-aware recommender systems
- Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration
- Towards trustworthy predictions from deep neural networks with fast adversarial calibration
- Post-hoc Uncertainty Calibration for Domain Drift Scenarios
- Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression
- TIMELY: Improving Labeling Consistency in Medical Imaging for Cell Type Classification
- Ano-rectal wall dose-surface maps localize the dosimetric benefit of hydrogel rectum spacers in prostate cancer radiotherapy
- Document informed neural autoregressive topic models with distributional prior
- Metabolic regulation of pluripotency and germ cell fate through α-ketoglutarate
- Design and selection of machine learning methods using radiomics and dosiomics for normal tissue complication probability modeling of xerostomia
- Document informed neural autoregressive topic models
- Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets
- texttovec: Deep contextualized neural autoregressive models of language with distributed compositional prior
- Texttovec: Deep contextualized neural autoregressive topic models of language with distributed compositional prior
- A divergent population of autoantigen-responsive CD4+ T cells in infants prior to β cell autoimmunity
- cgcorrect: a method to correct for confounding cell–cell variation due to cell growth in single-cell transcriptomics
- f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq
- Non-targeted metabolomic approach reveals two distinct types of metabolic responses to telomerase dysfunction in S. cerevisiae
- Parotid gland mean dose as a xerostomia predictor in low-dose domains
- Prospective identification of hematopoietic lineage choice by deep learning
- Vitamin A-retinoic acid signaling regulates hematopoietic stem cell dormancy
- destiny: diffusion maps for large-scale single-cell data in R
- Unbiased prediction and feature selection in high-dimensional survival regression
- Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations
- Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
- Computational assignment of cell-cycle stage from single-cell transcriptome data
- Decoding the regulatory network of early blood development from single-cell gene expression measurements
- Diffusion maps for high-dimensional single-cell analysis of differentiation data
- RAMONA: a Web application for gene set analysis on multilevel omics data
- Single-cell transcriptomic reconstruction reveals cell cycle and multi-lineage differentiation defects in Bcl11a-deficient hematopoietic stem cells
- Feature ranking of type 1 diabetes susceptibility genes improves prediction of type 1 diabetes
- Molecular phenotypic profiling of a Saccharomyces cerevisiae strain at the single-cell level
- Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data
- A modular framework for gene set analysis integrating multilevel omics data
- Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis
- A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst
- Novel approaches to improve the therapeutic index of head and neck radiotherapy: an analysis of data from the PARSPORT randomised phase III trial
- The dose–response of the anal sphincter region–an analysis of data from the MRC RT01 trial
- Modeling late rectal toxicities based on a parameterized representation of the 3D dose distribution
- Assessing correlations between the spatial distribution of the dose to the rectal wall and late rectal toxicity after prostate radiotherapy: an analysis of data from the MRC RT01 trial (ISRCTN 47772397)
- Using dose-surface maps to predict radiation-induced rectal bleeding: a neural network approach
- Dosimetric impact of motion in free-breathing and gated lung radiotherapy: A 4D Monte Carlo study of intrafraction and interfraction effects
- Optical trapping dynamics for cell identification