Track / Overview

It is expected that the continued development and improvement of AI methods - by industry or university research - will make them even more powerful, and their demand in academic research will further grow. However, along with the progressive development of AI methods, their theoretical and algorithmic complexity as well as their requirements on practical implementations will grow to leverage their full potential. 

For these reasons, it is becoming increasingly difficult for domain scientists to keep pace with the latest developments in AI and apply the methods themselves. This divergence between the rapid development of AI methods and the complexity of their application on the one side and the high demand for AI applications in domain sciences on the other side has been recognized by universities and research institutes. Therefore, specialized units offering, e.g. machine learning support for domain scientists, have been established to fill this gap.

Nevertheless, even for expert data scientists, the application of AI methods to scientific research problems is fraught with challenges. Every scientific domain has established its unique language, standards and mindset. Additionally, when used in scientific domains, the behavior of the AI models must be constrained by domain-specific laws, e.g. physics-constrained ML applications, and choosing a suitable method is not always straightforward. The chosen methods must often be adapted or further developed to fit the specific use-cases.

With this track, we want to provide a platform for the stakeholders in such collaborations: data scientists, software engineers and domain scientists, and give them a chance to share their experiences. 

The speakers in this track will present specific technical and scientific projects they have worked on, highlighting one or more of the following topics:
a) Benefits and challenges of this collaboration model
b) Use cases of collaboration on specific projects
c) Challenges in applying machine learning to various scientific domains
d) Scaling up and professionalization of machine learning applications together with researchers
e) Opportunities for further improving this collaboration model

We strongly believe that this track will bring a lot of value and new input to those engaged in such collaborations and will further improve such synergies.

Track / Schedule


With Tarun Chadha, Thomas Wuest & Franziska Oschmann

A View of SDSC Projects: Understanding Swiss Parliamentary Data

With Fernando Perez-Cruz

A Machine Learning Collaboration with Neuroscience: Opportunities & Challenges

With Francesco Casalegno

Large-Scale Synaptic Resolution Brain Mapping in Academia-Industry Collaborations

With Michał Januszewski

What Makes Interdisciplinary Research Collaborations Work: Experiences from the Center for Data Analytics

With Rodrigo Cerqueira Gonzalez Pena

Machine Learning Based Analysis of Biomedical Microscopy Images

With Simon F. Nørrelykke

From Prototyping to Production: How SIS is Supporting AI in Research!

With Franziska Oschmann


Text Crunching Center (TCC): Data-Driven Methods for Linguists, Social Science and Digital Humanities

With Gerold Schneider

Machine Learning Climate Simulations

With Aris Marcolongo

Machine Learning for Economics and Social Sciences: Applications and Software

With Achim Ahrens

Data Scientists Supporting Scientific Researchers: A Win-Win Situation

With Matteo Tanadini

Panel Discussion: Benefits, Challenges and the Way Forward for AI Collaborations in Academic Research

With Simon F. Nørrelykke, Achim Ahrens, Aris Marcolongo, Guillaume Obozinski, Francesco Casalegno, Gerold Schneider, Rodrigo Cerqueira Gonzalez Pena, Tilia Ellendorff, Matteo Tanadini, Michał Januszewski, Thomas Wuest, Franziska Oschmann & Tarun Chadha

Track / Speakers

Franziska Oschmann

Data Scientist, Scientific IT Services, ETH Zürich

Thomas Wuest

Group Leader, ETH Zurich

Tarun Chadha

Data Scientist, Scientific IT Services, ETH Zurich

Achim Ahrens

Senior Data Scientist

Simon F. Nørrelykke

Image Analyst, ETH Zurich

Aris Marcolongo

Scientific Collaborator, FernFachHochSchule Schweiz (FFHS) & Science IT-Support, University of Bern

Francesco Casalegno

Machine Learning Section Manager, Blue Brain Project

Gerold Schneider

PD, University of Zurich

Matteo Tanadini

Senior Data Scientist, Zurich Data Scientists GmbH

Michał Januszewski

Staff Research Scientist, Google

Fernando Perez-Cruz


Guillaume Obozinski

Deputy Chief Data Scientist, Swiss Data Science Center (EPFL & ETH Zürich)

Rodrigo Cerqueira Gonzalez Pena

Data Analyst, University of Basel

Tilia Ellendorff

Postdoc, Universität Zürich

Track / Co-organizers

Franziska Oschmann

Data Scientist, Scientific IT Services, ETH Zürich

Thomas Wuest

Group Leader, ETH Zurich

Tarun Chadha

Data Scientist, Scientific IT Services, ETH Zurich

AMLD EPFL 2022 / Tracks & talks

AMLD Keynote Session – Monday morning

Marcel Salathé, Lenka Zdeborová, Carmela Troncoso, Chiara Enderle, Patrick Barbey, Thomas Wolf, Gunther Jansen, Laure Willemin, Simon Hefti, Arthur Gassner

10:00-12:00 March 28Auditorium A

AI & Physics

Francesca Mignacco, Gert-Jan Both, Michael Unser, Thomas Asikis, Dalila Salamani, Pietro Rotondo, Tom Beucler, Giulio Biroli

12:30-18:00 March 285BC

AI & Pharma

Asif Jan, Jonas Richiardi, Patrick Schwab, Naghmeh Ghazaleh, Alexander Büsser, Carlos Ciller, Caibin Sheng, Silvia Zaoli, Félix Balazard, Giulia Capestro, Marianna Rapsomaniki, Martijn van Attekum

13:30-17:30 March 281BC

AMLD / Global partners