OpenPulse Community Rollout Workshop


Carlos Vivar Ríos joined the SDSC in 2023, where he is part of the Open Research Data and Engagement Unit (ORDES). As a multidisciplinary data engineer, he brings a diverse background in biology, cognitive sciences, and bioinformatics from the University of Malaga. His multifaceted professional career spans several disciplines, including genomics at RIKEN in Yokohama, multidimensional image analysis in microscopy at the University of Lausanne (UNIL), and cellular biology modeling at INRIA in Lyon. Carlos has been involved in a variety of projects, such as analyzing astrocyte calcium dynamics, de novo sequencing Solea senegalensis, drug repurposing for Alzheimer's based on GWAS studies, conducting geospatial analysis for linguistic corpora, and assessing drought through remote sensing. He is dedicated to advancing reproducible research methods and actively supports the open science movement.

Presentation
Even though open-source research software is essential to modern science, it can be difficult to see the work and research communities that support it within academic institutions.
OpenPulse is an open research data platform created by EPFL Open Science and the Swiss Data Science Center. Through useful indicators of activity, collaboration, maintenance, and community engagement around software, it aims to make research software communities and the work they do more visible and valued.
After a successful first event in November 2025, where participants tested ideas in open hardware, reproducible machine learning, open education, data visualization, open-source policy, and open-source governance, OpenPulse is now ready to be rolled out to the community.
Join us to explore the dashboards and examine research software projects through CHAOSS-inspired indicators. Share your feedback, engage, and discuss possibilities.
Target audience :
We welcome researchers, software developers, research software engineers, data scientists, research administrators, IT and library professionals, and anyone interested in how research software communities are built, maintained, evaluated, and supported.
No coding experience is required.
Technical, conceptual, strategic, and community perspectives are equally welcome.
Details
Date: 16.06.26
Time: 09:30 - 13:30
Location: SDSC offices, Campus Biopôle Lausanne
Programme
- Introduction: What do open-source research software communities look like, and why study them through the lens of community engagement?
- Live OpenPulse demo by the Swiss Data Science Center
- OpenPulse booth & hands-on dashboard exploration
- Community roundtable on OpenPulse governance, maintenance, and sustainability.
- Together, we will ask: How do research software communities grow and sustain themselves? How are decisions made? Who maintains pull requests and bug trackers? What kinds of contributions are visible, and which ones are still missed?
- Lunch discussion and closing remark
More information here.
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Prof. Olivier Verscheure is the director and founder of the Swiss Data Science Center (SDSC). Olivier also co-leads a joint training program between EPFL and HEC Lausanne, specifically designed for senior executives. Since 2018, Olivier has been a member of the Board of Directors of Lonza, a global leader in the life sciences sector. This company provides products and services to the pharmaceutical, biotechnology, and specialized healthcare industries.Olivier began his career at IBM Research after earning his Ph.D. in computer science from EPFL. He held several research and leadership positions at the IBM T. J. Watson Research Center in New York and co-created and co-directed the IBM Research center in Dublin, Ireland, before joining the EPFL in 2016.


Silvia holds an MSc in Computer Science from EPFL and a PhD in Computer Science from the University of York, UK. She has been a senior research fellow at the University of Trento and later at Politecnico di Milano, Italy. Here, she had the chance to work on Marie Curie and ERC projects relating to natural language processing. From 2012 to 2019, she was a Senior Manager and NLP expert at ELCA Informatique Switzerland, whose AI department she helped create and expand. Silvia joined the Swiss Data Science Center in 2019 and is currently its Chief Transformation Officer, in charge of the team leading organizations to digital transformation.


Anna joined SDSC as a Data Scientist focusing on industry collaborations in July 2019. She completed her PhD in Bioinformatics at the University of Luxembourg, where she analysed large-scale heterogeneous datasets and leveraged multiple disciplines: Statistics, Network Analysis, and Machine Learning. Before joining SDSC, Anna worked as a Data Scientist at Deloitte Luxembourg, with a focus on computer vision and time-series analysis.Currently, Anna is a Principal Data Scientist based at the ETH Zurich office, where she leads biomedical collaborations with industry partners. Anna works on a range of projects: protein properties prediction, biomanufacturing optimization, statistical model evaluation and others.


Matthias Galipaud obtained his PhD in evolutionary biology in 2012 from the University of Burgundy in Dijon (France), and held postdoctoral positions as a mathematical biologist at the university of Bielefeld (Germany) and the university of Zurich, where he researched the evolutionary theories of aging and mate choice. In 2020, he became a data scientist, developing machine learning solutions for startups in Switzerland and Australia before joining the SDSC Innovation Team in November 2022.


Dan received an MSc in civil and environmental engineering from UC Berkeley and a Ph.D. from EPFL, where he developed models combining machine learning and geographic information systems to estimate renewable energy potentials on a large scale. After serving as a researcher/data scientist at Unisanté (Lausanne) and completing a one-year postdoc at the Quebec Artificial Intelligence Institute (Mila) in Montréal, Dan joined the SDSC Innovation team. His work has generally been focusing on crafting and tailoring machine learning methods and deep learning architectures for a variety of domains, most notably the spatio-temporal modeling and forecasting of environmental and energy related variables, as well as multiple applications in public health research.

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Before joining SDSC, Arshjot Khehra received his MSc in Artificial Intelligence from USI Lugano, where he completed his thesis on hierarchical graph reinforcement learning. Previously, he worked for 4+ years across India and Singapore gaining data science experience in insurance, logistics, and manufacturing sectors. He also holds a BSc in Industrial Engineering from PEC Chandigarh. Over the course of his career, Arshjot worked on a wide array of projects, such as, handwritten text recognition and generation, voice matching across phone call recordings, policy lapse rate prediction for customer retention, and automated insurance claim processing.


Matthias Galipaud obtained his PhD in evolutionary biology in 2012 from the University of Burgundy in Dijon (France), and held postdoctoral positions as a mathematical biologist at the university of Bielefeld (Germany) and the university of Zurich, where he researched the evolutionary theories of aging and mate choice. In 2020, he became a data scientist, developing machine learning solutions for startups in Switzerland and Australia before joining the SDSC Innovation Team in November 2022.


Valerio started his career working for 7 years as a particle-physics researcher at CERN. In 2016, he moved to consulting, applying data science in several industries. First, he joined the Quant team of Ernst & Young in Geneva. Later, he created his own company, SamurAI sàrl, providing consulting services for his clients. He also has a passion for teaching very complex subjects in simple terms. That is why he particularly enjoys offering training programs to private companies and universities. Valerio joined the SDSC in May 2022 as a Principal Data Scientist with the mission of accompanying industrial partners and other institutions through their data science journey.

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Guillaume Obozinski graduated with a PhD in Statistics from UC Berkeley in 2009. He did his postdoc and held until 2012 a researcher position in the Willow and Sierra teams at INRIA and Ecole Normale Supérieure in Paris. He was then Research Faculty at Ecole des Ponts ParisTech until 2018. Guillaume has broad interests in statistics and machine learning and worked over time on sparse modeling, optimization for large scale learning, graphical models, relational learning and semantic embeddings, with applications in various domains from computational biology to computer vision.
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