Modern data science techniques, such as machine learning, are valuable tools to capitalize on our ever-growing data resources in diabetes research. To do this efficiently and to translate the latest methodology to clinical research, interdisciplinary efforts are needed. The event will focus on collaboration and networking opportunities, facilitating knowledge dissemination on different topics in diabetes research and data science between participants and experts with diverse backgrounds.
The Data Science Spring School & Challenge is the first joint event organised by the Danish Diabetes Academy and the Danish Data Science Academy.
Participation in the Data Science Spring School & Challenge is free. Accommodation, meals and training costs will be covered by the organisers. You will only have to cover travel costs yourself.
- Time & place
- Who can attend?
- Detailed description
- Programme & speakers
- Additional information
Dates: March 23-25 (Bootcamp) & May 23 (Closing event)
Place: Scandic Hotel, Vejle, Denmark
Applications are welcome from both academic institutions, hospitals, and the private sector (health-tech start-ups, pharmaceutical industry, etc.). The Spring School aims to bring together early career professionals (pre-graduate students, PhD students, junior postdocs or similar level in the industry), who are:
(1) Researchers or clinicians working within any field of diabetes and endocrinology, including both clinical, basic and translational and interdisciplinary work, who are interested in learning about how machine learning and other techniques in data science can revolutionize clinical research and practice,
(2) Data scientists (incl. computer scientists, bioinformaticians, mathematicians, statisticians, or similar) interested in clinical/biomedical research and in applying their data science skills to tackle real-world challenges in clinical research.
Participants are expected to have an open mindset to work in interdisciplinary groups in a collaborative setting.
Please note, that you are not guaranteed a seat in the training programme. Priority might be given to PhD students employed at Danish research and health institutions. However, a number of seats are reserved for participants in the life science industry or those from abroad. The DDA reserves the right to select participants based on the participants’ profiles to increase diversity and to modify the number of seats available in the programme.
Please note that there are only 32 available seats in the programme. Applicants will be notified about admittance within two weeks from the registration deadline.
Please note that the Spring School includes two physical events (March 23-25 & May 23) and participation in both is necessary.
You are welcome to write to Adam Hulmann to confirm eligibility before applying (firstname.lastname@example.org)
The purpose of the Spring School is to gather early-career professionals from different disciplines who will work in interdisciplinary groups to tackle real-world challenges in health research. The event will focus on collaboration and networking opportunities, facilitating knowledge dissemination on different topics in data science and diabetes research.
One month before the bootcamp, participants will get access to an online education platform, where they can prepare for the challenge in their own pace and according to their motivation level and current skillset. Prior to the bootcamp, the participants will have the opportunity to discuss expectations and course recommendations in an informal online meeting.
Bootcamp (23-25 March)
The 3-day bootcamp will bring together a diverse group of participants and experts working in diabetes research and care, or data science. Participants will be assigned to small groups based on their profiles at the beginning of the bootcamp. A data science challenge will be introduced on Day 1. The challenge will include a clinical dataset associated with a specific clinical topic. The groups will then themselves decide on the direction of their project and will receive guidance from the invited experts at the event. Project examples include development of prediction models, online implementations or visualizations of already existing prediction models, educational materials (e.g. tutorial, video, podcast on the topic), or any form of product that creates value with clinical relevance.
Throughout the bootcamp, talks will be given on data science from various aspects of clinical research. Participants will spend a significant amount of time working in groups, both working on their project and reflecting on the talks/workshops during the bootcamp. Furthermore, social activities (including quizzes with prizes, movie night, etc.) will be held to facilitate networking and team building.
After the bootcamp, the groups will continue working on the projects independently in their own pace, and are encouraged to reach out to experts from the bootcamp as well as their own networks. In addition to finalising the project work, these two months allow to create new or strengthen existing connections between the participants’ networks. Participants will have access to the online education platform throughout the whole process to gain further skills relevant to their projects.
Closing event (23 May)
During this 1-day event, the groups can showcase their developments and share their experience from the entire process with each other, field experts and the organisers. Participants will get feedback on their projects from their peers, as well as from experts in the field of data science and clinical research. To support education, the event will also feature keynote speakers.
Participants can actively contribute to the format of the final event during the bootcamp.
- Learn about basic concepts in both clinical research and data science
- Work in close collaboration in diverse groups and learn about methods and cultures in each other’s fields
- Get an overview of technological challenges and opportunities in diabetes research and care
- Have opportunities to establish networks across the DDA and the DDSA
- Have the opportunity to develop their skills individually using an online education platform throughout the entire program
- Learn about how to identify and validate needs in a clinical setting to create valuable data-driven solutions
- Meet entrepreneurs and learn about the start-up ecosystem
- Learn how state-of-the-art machine learning techniques are used in clinical research and practice
- Be able to discuss the ethical aspects of AI in medicine and identify sources of bias
- Participate in a career development session with a focus on pathways between academia and industry
- Get an opportunity to interact with experts from diverse backgrounds (academia & industry) in an informal environment during social activities
- Get feedback on their challenge project and get support to make it openly available for the public
- Michael Alexander Riegler, Chief Research Scientist, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Katharina Herzog*, Lead Data Scientist, Novo Nordisk, Copenhagen, Denmark
- Matthew Fenech, Co-Founder & Chief Medical Officer, Una Health (start-up), Berlin, Germany
- Sys Zoffmann Glud & Pernille Kølbæk, Managing Director & Senior Teaching Fellow, BioMedical Design Programme, DK
- Martin Vesterby, Health Tech Adoption Manager, Copenhagen Health Tech Hub, DK
- Anders Björkelund, Data Scientist/Research Engineer, Lund University, Sweden
- Aasa Feragen, Professor, DTU Compute, DK
- Ajenthen Ranjan, MD, Steno Diabetes Center Copenhagen, DK
- Thiusius Rajeeth Savarimuthu, Professor, SDU Robotics, DK
*also co-host of the event
- Adam Hulman, Senior Data Scientist, Steno Diabetes Center Aarhus (email@example.com)
- Katharina Herzog, Lead Data Scientist, Novo Nordisk, Copenhagen
4 February 2022
Please note that the information collected at registration will only be used to achieve the most diverse group of participants in case the number of applicants exceeds the number of available seats. Information in your motivation letter will be used to assign the groups and to tailor the programme and the workload to participants. Applications will NOT be ranked.
Please submit a motivation letter (max. 1 page) briefly describing the followings:
(1) Describe your current position and place of work (e.g. PhD student in diabetes epidemiology at Aarhus University; junior software developer at X medtech startup):
- If you are NOT a PhD student, then explain the level of your position (e.g. how long have you been in your current position; when did you graduate/finish your PhD).
(2) Describe your level of experience with the followings (if any):
- Analysis of clinical/biomedical data
- Any machine learning method (not necessarily programming them)
- Programming in Python
- Any deep learning library (e.g. PyTorch, tensorflow, fastai, …)
- Any cloud computing platform (e.g. AWS, Google Cloud, …)
- Leadership, organizational skills, project management
(3) How many hours do you expect / are you willing to spend with the individual preparation and the group work between the two events?
Please be informed that the organizers might contact applicants in e-mail to better understand some of the above-mentioned aspects of their profiles.
Please indicate if you need accommodation when you register.
Please note that it is free of charge to participate in the course however the DDA will charge a no-show fee of 250 DKK if you do not show up and have not unregistered from the course prior to its start.
A course certificate will be sent to all participants on request at the end of the course. Full participation is required to attain ECTS points. ECTS points: 2.4
Covid-19 Code of Conduct
To ensure safe events, the DDA’s activities are organized in compliance with our Interim COVID-19 Code of conduct.