SDSC-Connect: Translating AI to Clinical Practice

Oksana is a disruptive innovator bringing her positive energy to projects. Driven by her curiosity and can-do attitude she excels in industrial and academic contexts. Oksana earned her PhD in Life Sciences and Bioinformatics from the University of Lausanne after two MSc in Bioinformatics and in Information Systems from the University of Geneva. For more than 10 years, she has been committed to actively promoting the value of data science and advocating the best practices for reproducible and ethical research. She believes that Swiss Data Science Center is a key player in building a competitive data economy in Switzerland leveraging its innovative potential and renown commitment to quality.

As an EPFL Life Science Engineer, my main interest is to do science with an impact.FAIR principals guide my work style, and I strive for user-centric infrastructure to encompass data science in the biomedical and governmental spheres. I have experience in Global Health, working with multi-hospital surveillance system for pandemics, as well as training data scientist (thegraphcourses.org).My core side-interests lie in ocean conservation notably cetacean conservation, biodiversity, and untreated health problematics from lower and middle income countries.I have solid hard skills in problem-solving, data engineering in AI/ML, and have developed soft skills in creativity and social integration. I have acquired domain knowledge in a diversity of fields: from biology-related sciences such as human gut microbiology, epidemiology, and environmental sciences, as well as social sciences such as anthropology and psychology.I am always happy to engage with new people on innovative and impactful thematics so please do reach out !

Stefan has a background in Biology and decided to move towards evolutionary bioinformatics for both his MSc and PhD.Over the years, he developed a passion for the entire data analysis process: from collecting data, to analyzing and presenting results. Presentations, particularly opportunities for public speaking, are activities he enjoys since he values communication a lot. In order to follow this passion and deepen his knowledge on systems to collect and manage data, he joined SDSC in 2023 as a Biomedical Data Engineer.Outside work, Stefan is an avid reader of sci-fi books (but not only!), enjoys swimming, running, and biking both competitively and casually and enjoys plenty of activities with friends, especially when beer is involved.

Almut Lütge joined the ORDES team in Zurich as Biomedical Data Engineer, in January 2024.

Almut did both her Bachelor and Master in molecular biotechnology with a major in bioinformatics at the University of Heidelberg in Germany.

After her masters she worked as a research assistant on population genetics at the NTNU in Trondheim, Norway.

In 2018 Almut started her PhD about benchmarking of single cell analysis tools at the University of Zürich, followed by a short PostDoc in pharmaceutical immunology at ETH Zürich.

Almut enjoys data-driven problem-solving and highly value open science.

Roberto holds an M.Sc. and a Ph.D. in Particle Physics from the University of Torino, Italy. He has worked for several years in fundamental research as a senior fellow and data scientist at the CERN Experimental Physics division and on a research project supported by the Belgian National Fund for Scientific Research (FNRS). In 2018 he moved to EPFL to work on data mining and Machine Learning techniques for the built environment and renewable energies. He has started and led multiple collaborations with academic and industry partners in the energy domain. Roberto joined the SDSC in September 2021 as a Principal Data Scientist with the mission of accompanying industries, NGOs and international organizations through their data science journey.

Rok obtained a B.A. in Physics from Washington University in St.Louis in 2003. After obtaining his PhD in theoretical Astrophysics from the University of Washington in 2010, Rok spent several years as a Postdoctoral researcher at the Institute for Computational Science, University of Zürich. Seeking new challenges, he moved to the ETH Scientific IT Services group, where he helped researchers across different ETH domains solve their (big) data analysis problems. He specialized in optimizing and scaling up data analysis tasks by mapping them to high-performance computing resources. Since July 2017 he has been at the Swiss Data Science Center developing Renku, the Center's data science platform.

As an EPFL Life Science Engineer, my main interest is to do science with an impact.FAIR principals guide my work style, and I strive for user-centric infrastructure to encompass data science in the biomedical and governmental spheres. I have experience in Global Health, working with multi-hospital surveillance system for pandemics, as well as training data scientist (thegraphcourses.org).My core side-interests lie in ocean conservation notably cetacean conservation, biodiversity, and untreated health problematics from lower and middle income countries.I have solid hard skills in problem-solving, data engineering in AI/ML, and have developed soft skills in creativity and social integration. I have acquired domain knowledge in a diversity of fields: from biology-related sciences such as human gut microbiology, epidemiology, and environmental sciences, as well as social sciences such as anthropology and psychology.I am always happy to engage with new people on innovative and impactful thematics so please do reach out !

Robin joined the SDSC in 2022. He received an MSc. in Management, Economics & Consumer Studies from the University of Wageningen in the Netherlands. After his studies, he developed himself into a consultant in the area of ontology & linked data modelling, working mostly in the domain of local and national infrastructure projects. He has a great interest in standardization efforts in the field of semantic web technology standards and is actively working at SDSC with clients and collaborators to stimulate their adoption.

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.

Saurabh Bhargava, joined the SDSC as a Principal Data Scientist in the Industry Cell at the Zürich office in 2022. Saurabh previously worked in the retail sector and the advertising industry in Germany. He lead and built various data products for customers using state of the art machine learning methods and industrializing them thereby adding value for the customers. He completed his PhD from ETH Zürich in June 2017 specializing in machine learning applications on Audio data. He obtained his Master’s and Bachelor’s degrees from EPFL and Indian Institute of Technology (IIT), Roorkee, India in 2011 and 2009 respectively. His interests and expertise are in combining state of the art data science and data engineering tools for building scalable data products.

Christian joined the SDSC in July 2021 as a data scientist in the industry cell. Before that he worked at the Media Technology Center at ETH Zurich to develop new ML technology for their industry partners. He completed his Master's degree in Mathematics at ETH Zurich (2017) with focus on algorithmics and machine learning. After his studies he worked as a online marketing data analyst in the news publishing business. His expertise lies in statistics, NLP and software engineering.

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.

Saurabh Bhargava, joined the SDSC as a Principal Data Scientist in the Industry Cell at the Zürich office in 2022. Saurabh previously worked in the retail sector and the advertising industry in Germany. He lead and built various data products for customers using state of the art machine learning methods and industrializing them thereby adding value for the customers. He completed his PhD from ETH Zürich in June 2017 specializing in machine learning applications on Audio data. He obtained his Master’s and Bachelor’s degrees from EPFL and Indian Institute of Technology (IIT), Roorkee, India in 2011 and 2009 respectively. His interests and expertise are in combining state of the art data science and data engineering tools for building scalable data products.

Nora earned her Ph.D. in Computer Science/Bioinformatics from the University of Tübingen, where she focused on the in silico design of peptide-based vaccines using combinatorial optimization and machine learning. During her postdoctoral fellowship at Memorial Sloan Kettering Cancer Center in New York, and later as a staff scientist at the New York Genome Center, she worked on metagenomics, infectious diseases, and cancer. In 2015, Nora joined NEXUS Personalized Health Technologies at ETH Zurich where her focus shifted towards the management of clinical and biomedical research data. In May 2024, she joined the Swiss Data Science Center as Head of Biomedical Data Science.

Irene joined the SDSC in August 2024 as a Senior Biomedical Data Scientist. Trained as an evolutionary biologist, she worked in academic research in Switzerland and abroad before transitioning to bioinformatics, where she specialized in supporting researchers with the analysis of next generation sequencing data. At SDSC, her focus is on data engineering, management, and analysis of biomedical data.

Xinrui obtained her Ph.D. in Computer Science from ETH Zürich in 2022 under the supervision of Prof. Gunnar Rätsch. During her doctoral study, she was devoted to applying machine learning algorithms to solving healthcare problems. Xinrui also holds a M.Sc. degree in Electrical Engineering from EPFL in Switzerland and a B.Eng. degree in Electronic Information Science and Technology from Tsinghua University in China. Before joining the SDSC, Xinrui worked for the NEXUS Personalized Health Technologies platform at ETH Zürich.

Sebastian Salzmann is a molecular biologist and data scientist who holds an MSc degree in Molecular Health Science from ETH (2016) and Datascience (2024) from ZHAW.

After graduating from his first master he worked in the Analytical Chemistry department at the Swiss Federal Institute of Aquatic Sciences (Eawag) as a research software engineer in environmental massspectrometry.

With a mixed background in the molecular life sciences, chemistry and information technology he is joining SDSC as a Biomedical Data Engineer to apply his skills in the domains of data engineering, management and analysis of biomedical data.

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.