Dr. Holger Hennig

I analyze big biomedical data with machine learning, in particular deep learning, with a focus on the image analysis of cells; current projects include diagnosis and prognosis of various diseases in clinical studies such as blood cancer.

Research interest

Short biography

I'm a research scientist at the Dept. of Systems Biology and Bioinformatics at the University of Rostock, Germany and an affiliate at the Imaging Platform at the Broad Institute of Harvard and MIT, USA. My main area of research is the analysis of cellular images in high-throughput with a focus on Imaging Flow Cytometry (IFC). Recently, we applied cutting-edge machine learning to develop label-free cell cycle analysis for high-throughput IFC [Blasi et al., Nature Communications (2016). Hennig et al., Methods (2017)]. Now, in several ongoing research projects on immune system cells, we study blood stem cell differentiation, and investigate diseases such as leukemia and severe allergies in clinical studies. We strive to advance precision medicine by applying the latest machine learning techniques, including deep learning, which is recently achieving impressive successes, to identify signatures of various disease states in the data.


28 publications in peer-reviewed journals. View all publications (google scholar profile):



Research Projects

DeepBioSeq: Deep Learning for Next Generation Sequencing Data

Deep learning technologies are making an impact, particularly with image analysis and object detection. Applications to Next Generation Sequencing data are however still at an early stage ...


DeepMRI: Analysis of phase III clinical trial data, including MRI image classification using artificial intelligence

Regenerative therapies using stem cells for the repair of heart tissue have been at the forefront of preclinical and clinical development during the past 16 years. To build upon this progress, the Phase III clinical trial PERFECT was designed to assess clinical safety and efficacy of intramyocardial CD133+ bone marrow stem cell treatment combined with coronary artery bypass graft for induction of cardiac repair.


FlowML: Diagnostic Tools for Image-Based Flow Cytometry using Machine Learning

Imaging flow cytometry(IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation into clinical practice.


Selected publications

Diagnostic potential of imaging flow cytometry

Doan M, Vorobjev I, Rees P, Filby A, Wolkenhauer O, Goldfeld AE, Lieberman J, Barteneva N, Carpenter AE, Hennig H

Trends in Biotechnology

Data analysis strategies for image-based cell profiling

Caicedo J,…, Hennig H,…, Carpenter AE

Nature Methods 14, 849 (2017)

Cardiac Function Improvement and Bone Marrow Response Outcome Analysis of the Randomized Perfect Phase III Clinical Trial of Intramyocardial CD133 + Application After Myocardial Infarction

Steinhoff G, Nesteruk J, Wolfien M, ... , Hennig H, ... , Wolkenhauer O

EBioMedicine 22, 208-224 (2017)

An open-source solution for advanced imaging flow cytometry data analysis using machine learning

Hennig H, Rees P, Blasi T, Kamentsky L, Hung J, Dao D, Carpenter A E, Filby A

Methods 112, 201 (2017)

Label-free cell cycle analysis for high-throughput imaging flow cytometry

Blasi T, Hennig H, Summers HD, Theis FJ, Davies D, Filby A, Carpenter AE, Rees P

Nature Communications 7, 10256 (2016)

Synchronization in human musical rhythms and mutually interacting complex systems

Hennig H

Proceedings of the National Academy of Sciences of the USA 111, 12974-12979 (2014)


  • Society of Biomolecular Imaging and Bioinformatics (SBI2)
  • International Society for Advancement of Cytometry (ISAC)
  • Contributor to the TMF (Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V.)
  • Contributor to the GMDS (German Association for Medical Informatics, Biometry and Epidemiology e.V.)

Awards and Distinctions

  • Amnis Travel Stipend from Merck-Millipore (09/2016)
  • Postdoctoral Fellowship (DFG) for research at Harvard University. Grant no. HE 6312/1-2 (06/2012)
  • Postdoctoral Fellowship (DFG) for research at Harvard University. Grant no. HE 6312/1-1 (06/2011)
  • Research stipend from Boston University (05/2008 – 09/2008)
  • Otto-Haxel award from the University of Heidelberg, awarded for an outstanding diploma (master) thesis in theoretical physics (07/2004)