All across Roche, there are business challenges and decisions that can be tackled through the use of data. Healthcare Xplorers offers students and PhDs in the field of bioinformatics, business mathematics, physics and engineering the chance to solve real challenges from Roche business areas where data plays a crucial role and to submit their own ideas and research interests in the data sphere. Many of the original Healthcare Xplorers are still working hard at Roche one year on. In this article miniseries, we catch up with some Healthcare Xplorers and learn about their experience in Roche.
“I look forward to putting what I know into practice and making an impact – even in a small way – to help progress healthcare and ultimately better patients’ lives.”
“I realized if we did not have this algorithm, the amount of time we would spend trying to find the right solution would be astronomical.”
“I was so excited to be provided with a platform where I can implement my skills in a real world scenario.”
In realisation
Gene set analysis is crucial for interpreting the results of RNASeq experiments. However, most signatures do not get used because of poor annotation.
In realisation
Let’s seek powerful and flexible algorithms for computer-assisted high-throughput liquid-chromatography (HPLC).
In realisation
Crack the code of data literacy and culture as part of our team. Data and information is the heart of our future and we want to crack the code together with you.
In realisation
Roche is one of the major players in in-vitro diagnostics, with a plethora of assays, technologies and customers. We are curious to learn from your ideas: how could we profile and segment our customers to better understand their needs?
In realisation
We believe that multi-biomarker panels have the potential to enhance the diagnosis, prognosis and monitoring of a broad range of diseases effectively. We are curious to learn from your ideas: how could we identify hidden patterns in our wealth of diagnostics assay data?
In realisation
Machine Learning is gaining ground in the development of medical and diagnostic products. However, unfolding its full potential is hampered by lack of interpretability of “black box” models. So let’s build interpretable ML models for automated quality assessment of instrument results.
In realisation
Artificial Intelligence (AI) is divided into probabilistic and logics-based approaches. Machine-readable clinical practice guidelines form a prime use case for bringing these two approaches together. Only combined AI approaches enable us to understand, explain, and trust the hints and paths provided by the system which ultimately leads to better outcomes for patients.
Martin Dostler
Groupleader Mass Spec Assay Development
Alina Drumm
Global Manager Data Literacy and Data Culture
Dr. Ole Eigenbrod
Semantic Data Integration Scientist, Roche Information Solutions
Dr. Andrea Geistanger
Subchapterlead – MassSpec Biostatistics
Dr. Thomas Helmbrecht
Data Scientist @ The Cube
Dr. Manuela Hummel
Biostatistician
Dr. Michael Laimighofer
Expert Data Science @ The Cube
Dr. Chiahuey Ooi
Principal Scientist, Predictive Modeling and Data Analytics
Nadja Schäfer
Global Lead Data Culture & Data Literacy
Dr. Lara Schneider
Data Scientist @ The Cube
In realisation
— Focus: PHC
Develop machine learning-based framework for digital biomarkers discovery: A generic platform for digital biomarkers development and analysis using patient surveys and wearables data.
Dr. Kamran Farooq
Senior Data Scientist @ Data Insights Squad , GIS
In realisation
— Focus: PHC
Real world population-level oncology data contains information that can change the way patients are treated. Do you want to pave the way to the future of healthcare?
In realisation
— Focus: PHC
Millions of patients around the world rely on the precision and accuracy of our diagnostics devices and lab instruments every day. We are curious to learn from your ideas: how could we monitor the quality of our products and detect emerging issues as early as possible?
In realisation
— Focus: PHC
Shifting the focus to patients in the clinical workflow is key for a meaningful and patient-centric healthcare transformation. For this, we would like to explore the changes in the patient’s quality of life along with the care flow. We foresee that data sharing will be key to foster collaboration. Would you take that journey with us?
Marta Batlle
Start-IT, Machine Learning
Celia Bel
Senior Data Scientist, Roche Information Solutions
Danilo Guerrera,
Data Scientist, Machine Learning
Dr. Michael Laimighofer
Team Lead – Data Science @ The Cube
Dr. Carsten Magnus
Principle Data Scientist, Roche Information Solutions
Enrique Vidal Ocabo,
Senior Data Scientist, Roche Information Solutions
Dr. Lara Schneider
Data Scientist @ The Cube
In realisation
— PHC
The fundamental understanding of the human organism and how it is perturbed by variants and drugs forms the basis of delivering tailored care. How can we predict the specific effect a drug has on its known cellular target(s) based on its cellular interaction network?
In realisation
— PHC
How will trends affect lives and behaviors and influence the daily lives of our patients? How connected will our world be in the future and what could that mean for the future solutions for diabetics
In realisation
— PHC
Interested in machine learning, and analysing digital health datasets to uncover hidden disease progression? Want to make a tangible impact on the lives of people living with neurological diseases? Then help us apply deep learning methods to develop better digital measurements of impairment!
In realisation
— Lab Automation
Do you want to help improve the testing capacity of medical laboratories and hospitals? Do you want to have a direct impact on society’s ability to test for and thereby track diseases faster? Have a look at the data set and convince us of your solution proposal!
In realisation
— Process and Manufacturing Analytics
You love diving really deep into data and business topics which are linked to real value chain problems. You like to work on real, incomplete, dirty data sets, using complex methods while explaining it in simple words. Welcome to the world of enterprise data science!
Frank Dondelinger
Data Analysis Lead MS, Digital Biomarkers, pREDi
Marcin Elantkowski
Principal Associate Data Analyst, Digital Biomarkers, pREDi
Dr. Charlotta Fruechtenicht
Senior Data Scientist, PHC Analytics
Romain Guerre
Software Engineer, Sample Quality
Dr. Frank Kienle
Digital Strategy Manager, Materials & Business Process Management
Dr. David C. Krey
Senior Innovation Lead, Diabetes Care Global R&D Innovation
Jim Lefevere
International Business Leader Pre-Diabetes & OAD, Diabetes Care Global Strategy & Customer Solutions
In realisation
Are you curious about cancer immunotherapy everyone in the oncology world is talking about? Do you love using and advancing your skills to analyze complex data and shedding light on current research questions?
In realisation
Computational methods can help to unravel the complexity of the tumor microenvironment of individual patients. We ask for your help to assess such methods to enhance our understanding of cancer heterogeneity and to improve patient classification.
In realisation
Cancer patients often do not know what to do when they get their initial diagnosis and are often overwhelmed with the situation. We are curious to learn from your ideas how to enable patients to navigate through the jungle of information, challenges and hurdles, so that they can be in the driver’s seat of their patient journey inside the healthcare system.
In realisation
Support clinicians in decision-making processes through easy access of mutation dependent targeted therapy options by using NLP.
Dr. Franziska Braun
Senior Data Scientist, Pharma Research and Early Development Informatics
Dr. Markus Bundschus
Head Data Science Technologies, Data Office
Dr. Lars Hummerich
Head of Oncology Innovations, Oncology Innovations
Jan Riekmann
Group Lead Data Insights, Data Office