Fabian Theis
(Helmholtz Centre, Germany)
Fabian Theis uses artificial intelligence to unlock the secrets of human cells. How do they work together, and what goes wrong at the cellular level in diseases? With the help of single-cell sequencing, in particular, he and his team are able to model the diversity of cells and their activities. He uses machine learning and deep learning for predictions in biology and biomedicine.
Fabian holds two master’s degrees in mathematics and physics and two doctorates in physics and computer science.
Fabian began his career as head of the „Signal Processing and Information Theory“ research group at the Institute of Biophysics in Regensburg. In 2006, as a Bernstein Fellow, he led a junior research group at the Bernstein Center for Computational Neuroscience at the Max Planck Institute for Dynamics and Self-Organization in Göttingen. One year later, he became a research group leader at the Institute of Bioinformatics at the Helmholtz Center in Munich, and two years later, he became an adjunct professor of mathematics in systems biology at the Technical University of Munich.
In between, Fabian held international guest research positions at the Department of Architecture and Computer Technology at the University of Granada, Spain, at the RIKEN Brain Science Institute in Wako, Japan, at FAMU/FSU in Florida, USA, and at TUAT’s Laboratory for Signal and Image Processing in Tokyo, Japan.
Today, Fabian heads the Computational Health Center at Helmholtz Munich, under which six institutes conduct research on artificial intelligence with a focus on health. In his second endeavor, Fabian holds the Chair for „Mathematical Modeling of Biological Systems“ at the Technical University of Munich. As Scientific Director of HelmholtzAI, Fabian Theis coordinates numerous initiatives and networks, including:
– Analysis Working Group of the Human Cell Atlas
– Network “SingleCellOmics Germany” (SCOG)
– Munich School for Data Science (MUDS)
– ELLIS Munich for outstanding researchers
Carolina Wählby
(Uppsala University, Sweden)
I received a MSc in Molecular Biothechnology in 1998, and during my MSc thesis work at the Karolinska Institute I was fascinated by how cells can be studied using microscopy, and continued as a PhD student in digital image processing at Uppsala University, focusing on methods for finding cells and extracting quantitative measurements from digital microscopy data. After completeing my PhD in 2003, I did a postdoc in genetics and pathology, with emphasis on methods development. I joined the Broad Institute of Harvard and MIT i USA in 2009, and worked with algorithms for analysis of large scale experiments on model organisms such as C. elegans worms and zebrafish to evaluate the effect of new potential drugs. I returned to Sweden and SciLifeLab and became full professor in quantitative microscopy at the centre for image analysis, Dept. of Information Technology, in 2014. My reserach group develops digital image processing and analysis methods for analysis of different types of microscopy image data with applications in the life sciences and medicine, both basic and more application oriented research.
Shantanu Singh
(The Broad, USA)
Shantanu Singh is a senior group leader in the Imaging Platform at the Broad Institute. He leads a data science group that develops computational and statistical methods to create fingerprints of genes, chemicals, and diseases from microscopy images of cells. Using assays like Cell Painting that capture a broad range of their morphological properties, cellular populations are characterized at single-cell resolution to discover similarities and differences among treatments. This work has the potential to transform how both the targets and therapies for disease are identified.
After completing his Ph.D. at Ohio State in computer science, Shantanu joined the Imaging Platform, inspired by the group’s vision to make cell morphology as computable as genomes. He has previously worked in research groups at Mercedes-Benz R&D, GE Global Research, and Lawrence Livermore National Laboratory, where he applied computer vision and machine learning techniques to a wide range of problems in road safety, cell biology, and geospatial imaging.
Slava Ziegler
(MPI-Dortmund, Germany)
– Diploma in Biochemistry (Ruhr University Bochum)
– PhD in Biochemistry (Max Planck Institute of molecular physiology and Ruhr University Bochum)
– Postdoctoral researcher at the University Hospital of Duesseldorf, Institute of Pathology, Duesseldorf, Germany
– Project group leader in the department of Chemical Biology at the Max Planck Institute of molecular physiology
Gregory Way
(University of Colorado, USA)
Greg is the Principal Investigator (PI) of the Way Lab. He is an Assistant Professor in the Department of Biomedical Informatics and a member of the Center for Health AI in the School of Medicine at University of Colorado Anschutz. He sets forward the lab’s scientific path of inquiry, acquires funding, and establishes a lab environment where scientists of all backgrounds can flourish. He is currently interested in developing morphology as a systems biology readout of disease states to link molecular information with higher order phenotypes in order to improve drug discovery and translational research. He is an optimist who believes that the next generation of biological discoveries will require lots of data, lots of compute, reproducible software, and a lot more diverse people with diverse ideas to forge an equitable and prosperous path forward for humanity.
Before founding the Way Lab in 2021, Greg earned a B.S. in Biology and Environmental Studies from The College of New Jersey, an M.S. in Biology from Saint Joseph’s University, and a Ph.D. Genomics and Computational Biology from The University of Pennsylvania. His PhD research applied machine learning to genomic and transcriptomic data. He later moved to the Broad Institute of MIT and Harvard where he worked as a postdoc in the Imaging Platform studying image-based profiling, high-throughput assay development, software engineering, and drug discovery. Greg’s overarching mission is to reduce human suffering through biomedical data science methods and applications.