Interdisciplinary Machine Learning Projects Across Campus

As part of the Machine Learning Course CS-433, students can bring their ML skills to practice by joining forces with any lab on the EPFL campus, or other academic institutions, across any disciplines and topics of their choice.

In the four editions so far, 310 collaborative projects have been successfully completed, across 105 different labs of EPFL and other institutions.

The project is done in a group of 3, and counts 30% to the grade of the course. Detailed project description.

Call for projects: If your lab (from any academic institution in CH) wants to host a group of student for the next iteration of the course, please fill the contact form here.

Past projects: The interdisciplinary ‘ML4Science’ projects performed last years across campus were (including reproducibility challenge):


Reproducibility Challenge:


Reproducibility Challenge:


  • Machine Learning for Invisibility Cloak Architecture
  • Predicting PV Array Power Output Using an All-Sky Camera
  • Predicting bacteria evolution during cell cycle observations using a Recurrent Neural Network
  • Searching for Similar YouTube Channels
  • Stress Classification from Biosignals using Neural Networks
  • Crystal-Structure Descriptor for Binary Materials Based on Coordination Numbers
  • Polymers identification using nanopores
  • ML for Science – Generate images of Mesopotamian artifacts
  • Machine Learning for Toxicological Testing
  • Global gene expression analysis: determine hormone signalling activation in human breast cancer samples
  • Yeast Cell Segmentation with U_Net: Effects of Weight Maps and Attention Gates
  • Machine Learning for LHC performance optimization
  • Product Life Cycle Prediction Using Machine Learning
  • Clustering estrogen receptor-positive breast cancer tumors based on hormonal response type
  • Identifying the shape of the worms based on the hand-annotated images dataset
  • Applause Recognition of Live Concert Recordings for the MetaMedia Center
  • Machine Learning for EDS Data Decomposition
  • Twitter Astroturfing Detection
  • Removing Noise From Microscope Images Without Ground Truth
  • U-Net for Yeast Cell Segmentation
  • C elegans segmentation
  • FCN for neuronal semantic segmentation
  • Machine Learning Project 2 – Optimal Option Exit Strategies with Neural Networks
  • Bio Product Classifier
  • Segmenting yeast cells from microscope images
  • Instance Segmentation of Yeast Cells using Mask R-CNN
  • A machine learning approach to determine chemical shifts in NMR spectroscopy data
  • Detecting rooftop solar PV installations using CNN
  • A Physical-Interactive Pac-Man Game for Stroke Rehabilitation
  • Logistic Hairdressing
  • Prediction Forces for a Flapping Wing system using Linear Regression and Neural Networks Methods
  • Exploratory Analysis with MOOC data used for Blended Learning
  • Predicting Energy Building Consumption
  • Anomaly detection for energy consumption time series
  • Segmentation of Cracks on Laboratory Images
  • The _place_ (physical and conceptual) of advertising in video game magazines
  • Image Classification: Distinguish Murine from Human Cells in PDXs Models
  • PLIER for single-cell RNA sequencing data
  • Identifying Outer Divertor Legs On Images From Tokamak Experiments
  • Gravitational Lens Finding
  • VNAV – No GPS, No Problem
  • Earthquake Detection from Seismological Data
  • Detecting Astroturfing Bots on Twitter
  • Feature identification of MANTIS data
  • ObsBox Instability Clustering
  • Photovoltaic Power Production in Swiss Communes
  • Detection of Strong Gravitational Lenses with Convolutional Neural Networks
  • Nano Manufacturing with ML and Raman Spectroscopy
  • Motion Prediction on Drosophila Using a Seq2seq Model
  • Segmentation of spinal cord images
  • Avalanche Intelligence
  • Pac-Man Error Project
  • Building Classification using Google Street-View
  • U-Net application for yeast cell segmentation
  • Landmark Based Visual Navigation for Drones
  • Classification: Crashes and Disruptions in Plasma Experiments
  • Automating Route Setting in Climbing via Deep Generative Models
  • Leading Edge Suction Prediction of a Dynamically Pitching Airfoil with Trailing Edge Flap
  • Aerodynamical parameters estimation from velocity, pressure and noise sensors data using temporal convolutional neural network
  • Machine Learning for Science: Diffraction detection in rock structures
  • Classifying amino acid modifications using nanopore sequencing data
  • Microscopical Image Restoration of C. elegans using Noise2Noise
  • ML challenge : Strong Gravitational lensing
  • Instance segmentation
  • Invisibility cloak
  • Pacman collaboration with CHILI lab
  • Enchordings – Harmony Embeddings

Reproducibility Challenge:


  • Quality of Life in Swiss Cities based on OpenStreetMap
  • Compressive Sensing MRI using Deep Learning
  • Predicting the density of lightning activity from atmospheric and geographic features
  • Machine Learning for Science: Quantum Machine Learning
  • Solar Panel Recognition and Segmentation on Swiss Map using Convolutional Neural Networks
  • ML for crystal structure determination as an alternative to NMR spectroscopy
  • Human Behavior Modelling
  • Domain-invariant defect detection using deep learning
  • Comparing classification techniques for metabolic kinetic models selection
  • Spatially-Inferred Graphical Models for fMRI Data Mining
  • Crowded enzyme kinetics using simulation and machine learning
  • Quality of life in Swiss Cities
  • Ultrathin section segmentation
  • Correlations between cognitive performance and sensory stimuli in the work environment
  • Autism Diagnostic based on Machine Learning
  • Human performance modelling according to indoor temperature and light (quantity and colour)
  • Automatic Harmonization using Recurrent Neural Networks
  • Chord recognition on Beethoven string quartets
  • Machine Learning Privacy
  • Quality of Life Clustering of Swiss Cities from Insurance and Demographic Data
  • Machine learning for air quality measurement and modeling
  • The Case for Bagged Neural Networks: Evidence from Outlier Detection using Autoencoder Ensembles
  • Chord Prediction with The Annotated Beethoven Corpus
  • Predicting Forces on a Flapping Wing Model using Machine Learning
  • Brain Tissue Segmentation
  • Clustering and Predicting Swiss cities based on Insurance Data
  • Predicting the material properties of the arterial wall of a mouse
  • Predicting organic carbon with infrared spectra
  • 3D Pointclouds Super-resolution for Digital humanities
  • Segmentation Of Silicon Wafers For Electron Microscopy Using Mask-RCNN
  • Classifying Nanopore Readings with Deep Learning
  • Wind Profile Prediction in an Urban Canyon: a Machine Learning Approach
  • A Stem Cell Classifier for Single Cell RNA Sequencing Data
  • Deep Convolutional Neural Networks for Cell Segmentation in Bright-Field Microscopy Images
  • Predicting Aerosol Particles: Sulfate, Nitrate and PM2.5
  • Healthy aging: age group prediction from chunking strategies during motor sequence learning
  • Multi-lingual text classifier for social media data
  • Implementation of an Improved Model for the Prediction of Effective Rate Constants in the Presence of Crowders
  • Architecture of Feelings
  • Evaluating the quality of videos through machine learning
  • Classifying segmentation defects in mutant zebrafish embryo
  • Fingerprinting DNS-over-HTTPS traffic
  • Statistics of Turkish researchers after the 2016 Coup d’Etat attempt
  • Analysis of the dismissal of Turkish researchers after the 2016 Coup d’Etat attempt
  • Evaluating the risk of relapse in melanoma
  • Improving Generalization and Stability of Generative Adversarial Networks
  • Meta-learning with differentiable closed-form solvers
  • A Resizable Mini-batch Gradient Descent based on a Multi-Armed Bandit
  • Detecting Adversarial Examples via Neural Fingerprinting
  • Learning Neural PDE Solvers with Convergence Guarantees
  • AutoLoss: Learning Discrete Schedules for Alternate Optimization
  • MAE : Mutual Posterior-Divergence Regularization for Variational Autoencoders
  • Hyper-Regularization: An Adaptive Choice for the Learning Rate in Gradient Descent
Links to the 3 additional official ‘classic’ project competitions options, which we offer each year on AIcrowd: