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Repositioning drugs for a rare disease
Case studies
Repositioning drugs for a rare disease
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.
Lucas joined the SDSC's industry cell as a Data Scientist in November 2020, having previously worked in data related roles at the New York State Attorney and at Ericsson. He holds a BSc in Economics from Bocconi University, a MSc in Urban Science and Informatics from New York University as well as a MSc in Machine Learning from KTH Royal Institute of Technology. Over the course of his academic and professional career he has worked on a variety of topics, from computer vision tasks for automated driving to financial fraud detection to generating data driven insights to inform urban policy decisions.
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.
Job title standardization through entity alignment of knowledge graphs
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.
Lucas joined the SDSC's industry cell as a Data Scientist in November 2020, having previously worked in data related roles at the New York State Attorney and at Ericsson. He holds a BSc in Economics from Bocconi University, a MSc in Urban Science and Informatics from New York University as well as a MSc in Machine Learning from KTH Royal Institute of Technology. Over the course of his academic and professional career he has worked on a variety of topics, from computer vision tasks for automated driving to financial fraud detection to generating data driven insights to inform urban policy decisions.
Data science for enhancing resource efficiency in manufacturing processes
Clément became part of the SDSC team in January 2019, assuming the role of a Data Scientist with an emphasis on industry-oriented projects. He holds a Bachelor of Science degree in Physics, acquired in 2016 from the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. Following this, he earned a Master of Science in Computational Science and Engineering in 2018, also from EPFL. Throughout his academic journey, Clément concentrated on leveraging Data Science and Machine Learning techniques to enhance efficiency in industrial processes. Subsequently, he developed a specialized interest in Generative AI, with a particular focus on Natural Language Processing (NLP), especially in the application of Large Language Models for innovation.
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