Projects Information Day 2022

By
Swiss Data Science Center
June 1, 2022
Share this post

Collaborative Project Information Day 2022

The goal of the SDSC Collaborative Projects is to help researchers and domain experts leverage the state-of-the-art in data science and at the same time, aim to support the application of techniques developed in research labs working on data science methods to real-world scenarios. The scope of these Collaborative Data Science Projects is representative of the diversity of the research undertaken within the ETH Domain.

In 2022, 14 projects were accepted in the main track, three projects in the Large Scale Infrastructure (LSI) track, and another three projects were co-funded with the Strategic Focus Area on Personalized Health and Related Technologies (PHRT). To learn more about PHRT, click here.

This webinar starts with a presentation of the 6th call. Then, the projects from the 5th call are presented individually.

Replay

  • 00:00 - Presentation of the 6th Call
  • 59:23 - EXPECTmine | Mining Toxicity and High-Resolution Mass Spectrometry Data for Linking Exposures to Effects.
  • 62:10 - ML-SPOCK | Machine Learning Supported system for Performance assessment of steel structures under extreme Operating Conditions and management of risK.
  • 1:04:10 - PHENOMINE | Extracting dynamic ideotypes from seasonal image time series of wheat taken in the field.
  • 1:07:00 - DAAAD_Bridges | Domain-aware-AI Augmented Design of Bridge Structures.
  • 1:09:26 - WATRES | A Data-Driven approach to estimate WATershed RESponses.
  • 1:11:25 - CHEMSPEC | Cost-effective chemical speciation monitoring of particulate matter air pollution.
  • 1:13:35 - PAIREDHYDRO | Machine learning for the components fatigue prediction in hydropower generation.
  • 1:16:55 - DATSSFLOW | Data Science and Mass Movement Seismology: Towards the Next Generation of Debris Flow Warning.
  • 1:19:26 - ML Fusion | Machine Learning for Disruption Prediction in Tokamaks.
  • 1:21:53 - DS4MS | LSI Track | Data Science for Multiplexing Spectrometers.
  • 1:24:04 - LAMP | LSI Track | Lensless Actinic Metrology for EUV Photomasks.
  • 1:26:45 - sc_Drug | co-funded with PHRT | Predictive models of cell type drug sensitivity in Acute Myeloid Leukemia.
  • 1:30:01 - CLIMIS4AVAL | LSI Track | Real-time cleansing of snow and weather data for operational avalanche forecasting.
  • 1:32:08 - DrSCS | co-funded with PHRT| Predicting subclonal drug response from single-cell sequencing for precision oncology.

Speakers

Presentation of the 6th call

  • Dr. Guillaume Obozinski
  • Prof. Fernando Perez-Cruz
  • Dr. Michele Volpi
  • Dr. Ekaterina Krymova
  • Dr. Benjamín Béjar

EXPECTmine

  • Prof. Dr. Juliane Hollender

ML-SPOCK

  • Prof. Dr. Dimitrios Lignos

PHENOMINE

  • Prof. Dr. Achim Walter

DAAAD_Bridges

  • Dr. Ing. Michael Kraus

WATRES

  • Dr. Paolo Benettin

CHEMSPEC

  • Dr. Satoshi Takahama

PAIRED-HYDRO

  • Dr. Elena Vagnoni

DATSSFLOW

  • Dr. Fabian Walter

ML Fusion

  • Dr. Olivier JL Sauter

DS4MS

  • Dr. Daniel Gabriel Mazzone

LAMP | LSI Track

  • Dr. Iacopo Mochi

sc_Drug | co-funded with PHRT

  • Prof. Dr. Didier Trono

CLIMIS4AVAL | LSI track

  • Prof. Dr. Jürg Schweizer

DrSCS | co-funded with PHRT

  • Alexandre Coudray

About the author

Share this post

More blog posts

October 10, 2024

Data-Driven Control Methods in Energy and Manufacturing

Data-Driven Control Methods in Energy and Manufacturing

The Swiss Data Science Center (SDSC), in collaboration with Empa and Bühler Group, co-organized a workshop to explore data-driven control methods in energy and manufacturing. The event brought together experts from academia and industry to discuss cutting-edge approaches like Rule-Based (RB) Controllers, Model Predictive Control (MPC) and Reinforcement Learning (RL). Participants examined real-world applications and addressed the challenges of adopting these advanced methods in production environments.
Our News
July 19, 2024

Eawag | Establishing machine learning as alternative to fish testing

Eawag | Establishing machine learning as alternative to fish testing

Researchers at Eawag and the Swiss Data Science Center have trained AI algorithms with a comprehensive ecotoxicological dataset. Now their machine learning models can predict how toxic chemicals are to fish.
Our News
May 1, 2024

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data

We’ve developed a smart solution for wind tunnel testing that learns as it works, providing accurate results faster. It provides an accurate mean flow field and turbulence field reconstruction while shortening the sampling time.
Blog

Contact us

Let’s talk Data Science

Do you need our services or expertise?
Contact us for your next Data Science project!