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.
News
Projects
Recent Publications
Team
Principal Investigators
Florian Buettner
Professor for Bioinformatics in Oncology
Machine Learning (ML), Precision Oncology, Multi-omics data integration
Postdoctoral Researchers
Zahra Moslehi
Postdoctoral Researcher
Machine learning, Bioinformatics, Probabilistic modeling, Metric learning, Omics data analysis
Adrien Jolly
Postdoctoral Researcher
Blood cell development, Modeling of cell population dynamics in vivo, Omics data analysis, Immune cell repertoire analysis
Giuseppe Serra
Postdoctoral Researcher
Machine Learning, Federated Continual Learning, Trustworthy and Explainable AI
Doctoral Researchers
Arber Qoku
Ph.D. Candidate
Probabilistic Machine Learning, Multi-omics Data Integration, Survival Analysis
Sarmad Ahmad Khan
Ph.D. Candidate
Bioinformatics, Multi-omics Data Integration, Machine Learning, Oncological Studies, Calibration
Kevin De Azevedo
Ph.D. Candidate
Bioinformatics, Omics Data Analysis, Machine Learning, Multi-omics Data Integration
Yihao Liu
Ph.D. Candidate
Spatial Transcriptomics, Bone Marrow Microenvironment, Probabilistic Artificial intelligence, Graph Theory
Research Scientists
Ali Yavuz Çakır
Research Scientist
Next Generation Sequencing Systems, Bioinformatics, Data Science, Human Genetics
Visiting Researchers
Helong Gary Zhao
Visiting Scientist
Hematological malignancies, Clonal hematopoiesis, Precision Oncology, Cancer Prevention
Alumni
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.
Alumni
| Student | Topic | Degree | Year |
|---|---|---|---|
| Xiaoyan Feng | Encoding Pathway Gene Sets with Sparse Priors for Inferring Explainable Latent Variables | Bachelor | 2021 |
| Tim-Oliver Ewald | Simulation and Evaluation of Distribution Shifts on Tumor Images | Bachelor | 2021 |
| Tim Diedrich | Hidden Functional Activity in JOANA | Bachelor | 2021 |
| Lukas Kuhn | Post-hoc Uncertainty Calibration of Neural Networks via Kernel Density Estimation | Bachelor | 2022 |
Open Positions
We currently have no open positions.
Contact
- buettner|med.uni-frankfurt.de
- Theodor-Stern-Kai 7
60596 Frankfurt am Main