MLO Lab

Machine Learning in Oncology

Who we are

Our mission is the application and collaboration driven development of interpretable and statistically sound machine learning methods for understanding disease heterogeneity. As part of the DKTK/DKFZ and hosted by Frankfurt University we thrive to use machine learning for accelerating progress in personalised oncology.

We build on probabilistic machine learning to address computational challenges in three areas: translational single-cell genomics, computational proteomics and the integration of multi-omics data.

Team

Researchers

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Florian Buettner

Professor for Bioinformatics in Oncology

Machine Learning (ML), Precision Oncology, Multi-omics data integration

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Zahra Moslehi

Postdoctoral Researcher

Machine learning, Bioinformatics, Probabilistic modeling, Metric learning, Omics data analysis

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Sebastian Gruber

Ph.D. Candidate

Calibration, Loss functions, Uncertainty Quantification

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Arber Qoku

Ph.D. Candidate

Probabilistic Machine Learning, Multi-omics Data Integration, Survival Analysis

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Sarmad Ahmad Khan

Ph.D. Candidate

Bioinformatics, Multi-omics Data Integration, Machine Learning, Oncological Studies, Calibration

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Kevin De Azevedo

Ph.D. Candidate

Bioinformatics, Omics Data Analysis, Machine Learning, Multi-omics Data Integration

Students

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Yannick Ace Schlund

Student

Mixture of Factor Analyzers with Informative Priors for Explaining Latent Clusters in Biomedical Applications

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Lukas Kuhn

Student

Post-hoc Uncertainty Calibration of Neural Networks via Kernel Density Estimation

Guest Researchers

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Andreas Kopf

Guest Scientist

Machine Learning (ML), Deep Learning, Probabilistic Modeling

Alumni

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Xiaoyan Feng

Student

Encoding Pathway Gene Sets with Sparse Priors for Inferring Explainable Latent Variables

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Tim-Oliver Ewald

Student

Simulation and Evaluation of Distribution Shifts on Tumor Images

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Tim Diedrich

Student

Hidden Functional Activity in JOANA

Teaching

In the summer term 2022 we offer Introduction to AI, have a look at the Vorlesungsvereichnis here. If you would like to take the class, please sign up in moodle. Note that the course is taught in German.

In the winter term 2021/2022 we offer a seminar on AI in Medicine. Please contact me directly if you are interested/have any questions. Further information will be provided via moodle: https://moodle.studiumdigitale.uni-frankfurt.de/moodle/course/view.php?id=2046. The course is now open for self-registration - please only register in moodle if you are assigned to this seminar. This seminar will be offered again in the winter term 2022/2023 (held in English).

In the summer term 2021 we offered Introduction to AI, have a look at the Vorlesungsvereichnis here.

Thesis Topics

We currently have no thesis topics to offer to BSc and MSc students.

Open Positions

We are hiring!

We are looking for a motivated student with excellent skills in R to join a collaborative project for developing an open source software to support the analysis of multi-omics data. See full post!

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