According to the Chinese peasant calendar, 2023 will be the YEAR OF THE RABBIT. For this occasion, we would like to design a wall calendar with your help and fill it with your artwork!
In addition to the drawing, the works should also contain a suitable Chinese saying (“Chengyu”) or poem and be thematically related to the YEAR OF THE RABBIT.
From the submitted works, twelve pictures plus a cover picture will be selected by a 4-member jury. Printed works will be rewarded with a 50 € voucher (Bösner/Hugendubel) and three calendars.

Are you interested? Fill out the entry form and submit it with your work.



Only one artwork may be submitted per person. Maximum size is Din A3, submission can also be made digitally as a PNG / PDF file with 600 dpi.
In addition to the drawing, the artwork must contain a poem or a Chinese saying (“Chengyu”) and be thematically related to the Year of the Rabbit.
The saying or poem can be written in long or short characters. A self-written poem should not contain less than 10 characters but should also not be too long.
To participate, the conditions of participation must be accepted (see below)

Gheorghe awarded Alberta Machine Intelligence Institute Scholarship

Our very own, Gheorghe Lisca, was awarded a highly competitive scholarship from the Alberta Machine Intelligence Institute with the Alberta University in Edmonton, Canada.

Gheorghe will visit AMII and present his work on Deep Reinforcement Learning for Plausible Motion Synthesis in Biomechanics in the research group of “the father” of Reinforcement Learning, Prof. Richard Sutton. The scholarship covers the travelling to and the accommodation in Edmonton during the AMII AI Week workshop 24 – 27th of May.

Well done Gheorghe!


PRECISION: Profile Extraction from Clinical Insights for Smart Individualized Oncology



Over the past decades, early diagnosis, new drugs and more personalised treatment have led to impressive increases in survival rates of cancer patients. Yet, chemotherapy-induced peripheral neuropathy (CIPN), one of the most disabling side effects of commonly used chemotherapeutic drugs, is a severe problem in oncology leading to dose reduction, treatment delay or discontinuation. CIPN causes long-lasting disturbances of daily functioning and quality of life in a considerable proportion of patients. With an increasing number of cancer survivors, more attention is being paid to persistent sequelae of tumour treatment and supportive measures used as adjuncts to mainstream cancer care to control symptoms and enhance well-being.

CIPN describes the damage to the peripheral nervous system incurred by a patient who has received a chemotherapeutic agent that is known to be neurotoxic. Independent of the mechanisms of action, the targeted impact of such agents is on axonal transmission with consequences leading up to neuronal apoptosis.

Computational Oncology Approach

The incidence of peripheral neuropathy differs significantly across chemotherapeutic agents. The prevalence and severity of CIPN are dependent upon several factors relating to both drug pharmacokinetics (e.g., PK, cumulative dose and treatment duration) and pharmacodynamics (e.g., PD, mechanisms of toxicity and patient characteristics). Typically, in vitro and in vivo tests are performed to investigate drug toxicity. In comparison to such experimental approaches, computational methods, including machine learning and structural alerts, have shown great advantages since they are green, fast, cheap, accurate, and most importantly they could be done before the patient receive the therapy. Although such approaches exploit patient-specific risk factors that have been associated with the susceptibility for developing CIPN.

We are aware of no publicly available, validated in silico system to predict the development of CIPN in individual patients. The goal of this project is to develop a clinical decision-support system to predict CIPN development. Using simulation, mechanistic modelling, and machine learning we will develop and validate predictive models to quantify the risk of developing CIPN and deploy a standalone clinical decision-making system that will improve cancer treatment and survivorship care planning.













The project proposes a new digital intervention to support clinical oncologists in designing CIPN-aware cancer therapies. Understanding low-level tumor biology and patient genetics can provide unique insights that were previously impossible, to identify genetic predictors of CIPN. Correlating such information with models of tumor growth and patient’s clinical peculiarities, the system can isolate the most relevant cancer response patterns to adjust therapy parameters. The overall goal of the project is to fuse the insights gained at each of these levels in order to build a decision support system that predicts the development of CIPN in a particular patient. The system will learn the governing drug PK/PD differential equations directly from patient data by combining key pharmacological principles with neural ordinary differential equations refined through simulations for improved temporal prediction metrics. Furthermore, by incorporating key PK/PD concepts into its architecture, the system can generalize and enable the simulations of patient responses to untested dosing regimens. These emphasize the potential such a system holds for automated predictive analytics of patient drug response. The end result of the project will be a simple app that will collect physician inputted patient clinical parameters and eventually health records and will provide, given the therapy drug choice a prediction of the severity of CIPN before the chemotherapy regimen. Additionally, the physician and patient will get interpretable insights and a clear explanation of how each of the factors affects the prognosis. The entire processing and data aggregation and learning will happen in the cloud using GDPR-aware data handling.
















The project is based on large-scale data fusion that provides a multi-faceted profile of the patient, drugs, tumor and how they interact. This will finally support personalized intervention and adaptive therapies that maximise efficacy and minimize toxicity.

New Journal Article accepted in Frontiers in Artificial Intelligence

Our lab’s collaboration with the Life Sciences Department at TU Munich and the Interdisciplinary Breast Center at the Helios Klinikum Munich West resulted in a new research article published in the prestigious Frontiers in Artificial Intelligence – Medicine and Public Health (Mathematical and Computational Oncology).

The work on Data-driven Discovery of Mathematical and Physical Relations in Oncology Data using Human-understandable Machine Learning introduces a new framework for mathematical and computational oncology tools that exploit network approaches for the mathematical modeling, analysis, and prediction of cancer development and therapy design. Well done!

The preprint is available at: https://www.biorxiv.org/content/10.1101/2021.08.13.456200v1 

New journal article accepted in MDPI Symmetry

Our lab’s collaboration with IBM Research Zurich and the Computer Science Department at the University of Surrey resulted in a new perspective article published in the prestigious MDPI Symmetry under Networks in Cancer: From Symmetry Breaking to Targeted Therapy.

This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. Well done!

The full article is available at: https://www.mdpi.com/2073-8994/13/9/1559 

New journal article accepted in IEEE Sensors

Our latest work on Learning Insights from a Single Motion Sensor for Accurate and Explainable Soccer Goalkeeper Kinematics has been accepted for publication in the prestigious IEEE Sensors Journal.


This work is part of PERSEUS Project (Platform for Enhanced virtual Reality in Sport Exercise Understanding and Simulation, No. ZF4017410SS9) funded by the Central Innovation Program for Small and Medium-sized Enterprises (ZIM) from the German Federal Ministry for Economic Affairs and Energy (BMWi).


The preprint is available at:


Well done team and collaborators!

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