CIS SV Retreat 2022 AI in life sciences: challenges and opportunities

The EPFL School of Life Sciences and the EPFL Center for Intelligent Systems are delighted to invite all EPFL professors, research staff and students to the first CIS-SV AI deepdive. We are looking forward to exchanging on AI in Life Sciences in general and to discuss challenges and opportunities .The event consists of keynotes, poster sessions and networking.

Please indicate whether you will present a poster and submit the title of your poster when registering for the event. Presenting a poster is strongly recommended but optional.
Format: Scientific poster – A0 (Portrait)
Print: Please note, that you will be in charge of printing the poster and bringing it on-site on the day of the event. 

Location: Building SV, EPFL – Room: SV1717

Program

*7 talks, each talk 15 min + 5 min Q&A

Collaborations established through CIS
Moderator: Dr Jan Kerschgens

Informed machine learning for medicine
Dorina Thanou
Creating an assistive environment for people with limited mobility using modular robot
Auke Ijspeert

Structural biology and protein design in the era of AI
Moderator: Prof. Pierre Vandergheynst

Paolo de los Rios
Lucas Rudden [Barth group]
Ilia Igashov [Correia group]
Lucien Krapp [Dal Peraro group]
Umberto Lupo [Bitbol group]

Replay: 

Adam Gosztola
Topological deep learning detects critical points in non-linear dynamical systems
 
Subham Choudhury
Reconstruction of Kinetic Models of Metabolism using Generative Adversarial Networks
 
Riccardo Tenderini
PDE-aware deep learning for inverse problems in cardiac electrophysiology
 
Beril Besbinar
Immunotheraphy Response Prediction with Hierarchical Representations of Whole-Slide Cell-Graphs
 
Bernadette Stolz
Multiscale topology characterizes dynamic tumor vascular networks
 
Benjamin Gallusser
Self-supervised representation learning for biomicroscopy videos via permutation-equivariant time arrow prediction
 
Steffen Schneider
Learnable latent embeddings for joint behavioral and neural analysis
 
GoverdeCasper
De novo protein design using structure prediction deep neural networks
 
Alexandre Coudray
Deconvolution of Drug Sensitivity Data
 
Alex Lederer
Towards a probabilistic framework for a manifold-based RNA velocity estimation
 
Andrea Levy
Accurate prediction of transition metal ion location including low-homology targets via deep learning
 
Ece Ozelci
Body axis morphogenesis of zebrafish embryos using robot-assisted tissue micromanipulation
 
Jose Antonio Vasquez Porto Viso
Image-based cell sorting using convolutional autoencoders
 
Steffen Schneider
Learnable latent embeddings for joint behavioral and neural analysis
 
Lucas Stoffl
Improving social behavior analysis with conditional top-down pose estimation
 
Alessandro Marin Vargas
Investigating the role of proprioception with task-optimized neural network models
 
Alberto Silvio Chiappa
Sensorimotor principles provide a strong inductive bias for learning locomotion with a changing body
 
Xiangxiao Liu
Neuromechanical Simulation of Zebrafish Visuomotor Coordination
 
Matthieu Marfoglia
NRI model for predicting and design of GPCR allostery
 
 
 
And more…
 

*8 talks, each talk 15 min + 5 min Q&A

Machine learning in single cell omic analyses
Moderator: Anne-Florence Bitbol
Felix Naef
Gioele La Manno
Bart Deplancke

Graph machine learning for biomedical discovery
Moderator: Prof. Pascal Frossard
Maria Brbic

Machine learning in microscopy
Moderator: Prof. Amir Zamir
Microscopy image analysis with (un)supervised machine learning 
Martin Weigert

Robotics
Moderator: Prof. Amir Zamir
Using collaborative robots in experiments
Ana Marija Jaksic

Neuroscience
Moderator: Prof. Nicolas Flammarion
Mackenzie Mathis
Pavan Ramdya

Replay

Adam Gosztola
Topological deep learning detects critical points in non-linear dynamical systems
 
Subham Choudhury
Reconstruction of Kinetic Models of Metabolism using Generative Adversarial Networks
 
Riccardo Tenderini
PDE-aware deep learning for inverse problems in cardiac electrophysiology
 
Beril Besbinar
Immunotheraphy Response Prediction with Hierarchical Representations of Whole-Slide Cell-Graphs
 
Bernadette Stolz
Multiscale topology characterizes dynamic tumor vascular networks
 
Benjamin Gallusser
Self-supervised representation learning for biomicroscopy videos via permutation-equivariant time arrow prediction
 
Steffen Schneider
Learnable latent embeddings for joint behavioral and neural analysis
 
GoverdeCasper
De novo protein design using structure prediction deep neural networks
 
Alexandre Coudray
Deconvolution of Drug Sensitivity Data
 
Alex Lederer
Towards a probabilistic framework for a manifold-based RNA velocity estimation
 
Ece Ozelci
Body axis morphogenesis of zebrafish embryos using robot-assisted tissue micromanipulation
 
Jose Antonio Vasquez Porto Viso
Image-based cell sorting using convolutional autoencoders
 
Steffen Schneider
Learnable latent embeddings for joint behavioral and neural analysis
 
Lucas Stoffl
Improving social behavior analysis with conditional top-down pose estimation
 
Alessandro Marin Vargas
Investigating the role of proprioception with task-optimized neural network models
 
Alberto Silvio Chiappa
Sensorimotor principles provide a strong inductive bias for learning locomotion with a changing body
 
Xiangxiao Liu
Neuromechanical Simulation of Zebrafish Visuomotor Coordination
 
Matthieu Marfoglia
NRI model for predicting and design of GPCR allostery
 

Neuroengineering Laboratory – Prof. Ramdya
Demo: Recording neural activity in behaving flies. Performing neuromechanical simulations of flies.

Laboratory for Biomolecular Modeling – Prof. Dal Peraro 
Demo: TBD

Laboratory of Protein Design and Immunoengineering – Prof. Correia 
Demo: TBD

Laboratory of Systems Biology and Genetics – Prof. Deplancke
Demo: IRIS–Interconnected robotic imaging and sequencing. IRIS is a robotic system that uses a microfluidics and imaging to combine the phenotype of a cell with the transcriptome of that same cell. This technology has the potential to allow prediction of the transcriptome of a cell from an image alone.

Laboratory of Neurodevelopmental Systems Biology – Prof. La Manno
Demo: Demo of HybISS data visualization

Experimental Evolutionary Neurobiology – Dr. Jaksic
Demo: Two-arm ABB Yumi collaborative robot and application for setting up a behavioral experiments

Biorobotics Laboratory (BioRob) – Prof. Ijspeert
Demo: Assistive robotics project + possibly other robots (Quadruped or swimming) + FARMS and neuromechanical simulations

Laboratory of protein and cell engineering – Prof. Barth
Demo: TBD

@ SV1204 – TBC

Organizing committee

Anne-Florence Bitbol
Pavan Ramdya
Patrick Barth
CIS team

Note: This event will be photographed. Please be advised that CIS may use photographs taken during events for publication in print and online. Please alert the photographer or CIS team member at the beginning of the event if you wish not to have your photograph taken. 

Contact

Any questions regarding the CIS SV Retreat ? Please contact us:
[email protected]