ENID | Enabling Innovation with Data Science at ETH Zurich
Dr. 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.
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.
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.
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.
Valerio started his career working for 7 years as a particle-physics researcher at CERN. There, he used state-of-the-art techniques to extract information from data, especially to search for traces of dark matter in particle collisions. Since 2016, he has worked in 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 Mai 2022 as a Principal Data Scientist with the mission of accompanying industrial partners and other institutions through their data science journey.
Thibaut holds a B.Sc in Computer Science from HEIG-VD. Before joining the SDSC, he worked in startups where he developped a diverse skill set combining cloud infrastructure, database and application development. Thibaut is very enthusiatic about new technologies and best coding practices and he is looking forward to supporting the team and its projects.
Alessandro joined the SDSC in March 2019 as a data scientist focused on industry collaborations. His mission is to support corporates in leveraging the power of their data by adopting analytical approaches and data-centric solutions. His background is in biomedical engineering, with a PhD in neuroscience from the University of Tübingen. Before joining the center, he worked as a postdoc at the Max Planck Institute for Biological Cybernetics, at the EPFL Laboratory of Cognitive Neuroscience in Geneva, and as data scientist for a private ecommerce company.
Presentation
In today’s world, data is everywhere, yet the ability to harness its potential for informed decision-making, and consequently, its significant impact on business, remains elusive to most organizations. But how should an organization start to become data-driven? What are the best practices for implementing data science models that benefit the entire organization? And how to negotiate executive buy-in for data science initiatives?
This 5-day course focuses on achieving impact and innovation with data science. It features theoretical lectures on selected applications of data science, e.g. natural language processing and computer vision, and practical lectures on leveraging data science within a business context, such as project management, impact evaluation, performance metrics, stakeholder management.
Details
Target Audience
Experienced professionals and executives wishing to steer data science initiatives and generate business impact. The course will be given in English.
Objectives
- Understand the foundational principles and techniques of data science within the broader context of artificial intelligence (AI) and machine learning, including deep learning applications
- Be able to objectively assess complexity and scalability of AI use cases
- Acquire the tools to manage AI projects from scoping to Minimum Viable Product (MVP) solution deployment
- Explore real-world applications through hands-on machine learning assignments and discover concrete data science applications
- Connect and share with other industry professionals
Instructors
- Prof. Olivier Verscheure, Executive Director, Swiss Data Science Center (SDSC) - EPFL & ETH Zurich
- Dr. Silvia Quarteroni, Head of Innovation, SDSC - EPFL & ETH Zurich
- Dr. Anna Fournier, Principal Data Scientist, SDSC - ETH Zurich
- Dr. Matthias Galipaud, Senior Data Scientist, SDSC - ETH Zurich
- Dr. Dan Assouline, Senior Data Scientist, SDSC - EPFL
- Dr. Saurabh Bhargava, Principal Data Scientist, SDSC - ETH Zurich
- Lucas Chizzali, Senior Data Scientist, SDSC - ETH Zurich
- Christian Schneebeli, Senior Data Scientist, SDSC - ETH Zurich
- Arshjot Khehra, Data Scientist, SDSC - ETH Zurich
- Dr. Valerio Rossetti, Principal Data Scientist, SDSC - EPFL
- Thibaut Loiseau, Machine Learning Engineer, SDSC - EPFL
- Dr. Alessandro Nesti, Principal Data Scientist, SDSC - EPFL
Certification
A certificate of attendance will be delivered at the end of the course.
Organisation
The Swiss Data Science Center (SDSC) is a strategic focus area of the ETH domain, with data professionals located at Ecole Polytechnique Fédérale de Lausanne (EPFL), the Eidgenössische Technische Hochschule Zürich (ETH) and Paul Scherrer Institut (PSI).
For any other course-related questions, please contact Dr. Anna Fournier at anna.fournier@sdsc.ethz.ch
Program Director
Prof. Olivier Verscheure, Executive Director, Swiss Data Science Center (SDSC)
Dates and schedule
This 5 day course will take place at the following dates:
- January 17, 2025
- January 24, 2025
- January 31, 2025
- February 7th, 2025
- February 28th, 2025
Course venue
ETH Zurich "Swiss AI Tower" - Andreasturm, Andreasstrasse 5, 8092 Zurich-Oerlikon, Switzerland
Room 15 at the 14th floor
Required Material
Participants should bring their own laptop (installation of KNIME Analytics Platform free software is necessary for hands-on experience)
Course fee
4000.- Swiss Francs
10% special discount for ETH employees and alumni, as well as SDSC partners
Registration deadline
December 31st, 2024.
The number of participants is limited.
Registration
- ETH Zurich - School for Conitinuing Education: Course information.
- Direct Registration through Online Application Form
Programme
DAY 1 : INTRODUCTION TO DATA SCIENCE AND DIGITAL TRANSFORMATION
- Data science history, terminology and basic concepts, overview of learning tasks
- Digital transformation – becoming data-driven, project management strategies and tools
- Hands-on session with no-code platform (KNIME) – supervised learning
- Legal and ethical aspects of AI
DAY 2 : FUNDAMENTALS OF MACHINE LEARNING (PART 1)
- Strength and limitations of different supervised learning algorithms (including deep learning) and performance metrics, with hands-on session (KNIME)
- Best practices for industrialisation of solutions and reusability of digital assets
- Presentation of use cases delivered by SDSC
DAY 3 : FUNDAMENTALS OF MACHINE LEARNING (PART 2)
- Strength and limitations of different algorithms for unsupervised learning and time series forecasting, with hands-on session (KNIME)
- Canvassing exercise – how to start a project on the right track
- Fostering adoption: model explainability techniques, AB testing for business impact assessment
DAY 4 : NATURAL LANGUAGE PROCESSING (NLP) AND GENERATIVE AI
- History of NLP, algorithms and applications, with hands-on session (KNIME and ChatGPT)
- Generative AI in NLP and other areas. State-of-the-art LLMs, such as GPT-4
- Prompt engineering workshop
- Presentation of use cases delivered by SDSC
- Group discussion and feedback on canvassed projects by participants
DAY 5 : COMPUTER VISION
- Group discussion and feedback on canvassed projects by participants
- Computer Vision (CV) algorithms and applications
- Presentation of use cases delivered by SDSC
- Feedback and course closing
Other events
Personalized Health Technologies 2024
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.
Cyril joined the SDSC in 2022. He holds a Ph.D. in computational biology from Sorbonne University and the Institut Pasteur in Paris. There, he developed tools to detect changes in the 3D conformation of chromosomes and analyzed how it is altered during infection by bacterial pathogens. Before that, he received an MSc. in Bioinformatics from the University of Lausanne, where he worked on evolutionary genomics in insects. Over time, Cyril developed a strong interest in open and transparent scientific research. He is eager to promote good software and data management practice in research.
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.
Daniel worked as a postdoctoral researcher on critical event prediction for the University Hospital in Zurich. In addition, Daniel has worked as a postdoctoral researcher in Lausanne, delivering algorithms for Bayesian inference in big panel data. Previously in Paris, he developed models for automated scientific discovery. He obtained a Ph.D. from the University of Edinburgh, funded by a Microsoft Research scholarship. His interest relates primarily to attacking applied biomedicine problems from different angles, frequentist statistics, Bayesian statistics, and Machine Learning.
Quentin graduated with an engineering degree in mathematics and computer science from École des Ponts ParisTech in 2019. After a 6-month experience at the Center for Data Science of the New York University working on applied Machine Learning for medical imaging, he did a PhD in Statistics at Gustave Eiffel University (Paris). During his PhD, Quentin worked on random graphs and selective inference. His recent cross-disciplinary collaborations involve applications in biology and hydrology.
SDSC Hackathon: ORD for the Sciences
Valerio started his career working for 7 years as a particle-physics researcher at CERN. There, he used state-of-the-art techniques to extract information from data, especially to search for traces of dark matter in particle collisions. Since 2016, he has worked in 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 Mai 2022 as a Principal Data Scientist with the mission of accompanying industrial partners and other institutions through their data science journey.
Luis is originally from Spain, where he completed his bachelor's studies in Electrical engineering, and the Ms.C. on signal theory and communications, both at the University of Seville. During his Ph.D. he started focusing on machine learning methods, more specifically message passing techniques for channel coding, and Bayesian methods for channel equalization. He carried it out between the University of Seville and the University Carlos III in Madrid, also spending some time at the EPFL, Switzerland, and Bell Labs, USA, where he worked on advanced techniques for optical channel coding. When he completed his Ph.D. in 2013, he moved to the Luxembourg Center on Systems Biomedicine, where he switched his interest to neuroscience, neuroimaging, life sciences, etc., and the application of machine learning techniques to these fields. During his 4 and a half years there as a Postdoc, he worked on many different problems as a data scientist, encompassing topics such as microscopy image analysis, neuroimaging, single-cell gene expression analysis, etc. He joined the SDSC in April 2018. As Lead Data Scientist, Luis coordinates projects in various domains. Several projects focus on the application of natural language processing and knowledge graphs to the study of different phenomena in social and political sciences. In the domains of architecture and engineering, Luis is responsible for projects centered on the application of novel generative methods to parametric modeling. Finally, Luis also coordinates different projects in robotics, ranging from collaborative robotic construction to deformable object manipulation.
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.
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.
Data-Driven Control Methods for Energy and Manufacturing
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.
Carl holds a Ph.D in Mathematics from École des Ponts ParisTech and Université Gustave Eiffel in Paris. He has broad interests in statistics and stochastic control, and works on reinforcement learning, generative methods and time series forecasting, with applications in various domains such as energy, finance and physics. He worked with EDF R&D and Finance des Marchés de l’Energie (FiME) laboratory on applications of machine learning to risk management, including time series generation and deep hedging. He joined the SDSC in 2022 as a senior data scientist in the academic team at École Polytechnique Fédérale de Lausanne (EPFL).
Victor joined as a Data Scientist in the SDSC Innovation team in 2023. He holds a Bachelor's degree in Mechanical Engineering (B.Eng.) from the University of Pretoria in South Africa, as well as Master's degrees in Robotics and Mechatronics (M.Sc.) and Artificial Intelligence (M.Sc.) from KU Leuven in Belgium.Prior to joining SDSC, he worked for several years as a consultant at Capgemini Engineering and as an R&D Engineer at Toyota Motor Europe. Within the Advanced Powertrain and Target Setting team at Toyota, Victor played a crucial role in the pre-development of innovative electric and fuel-cell vehicles. His responsibilities included leading the development and deployment of Natural Language Processing (NLP) tools and pipelines, data science and machine learning, building data analytics dashboards, statistical forecasting, powertrain design, optimal control system design, and strategic technical target setting. He is passionate about leveraging his combined Engineering and Data Science knowledge to solve complex problems in the industry.
After earning a MSc in Theoretical Physics at University of Padua, Giulio graduated in Quantitative Finance from Bocconi University. Before joining the SDSC industry cell in June 2021, he spent a few years working in the financial sector, where he mainly dealt with the application of machine learning to financial risk management. When not coding, Giulio spends his free time playing bass guitar, hiking and cooking.
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