Alongside the Aerostructures, MRO International and Ammotec divisions, RUAG Space is one of four business units of the technology group RUAG International. The Space division has 14 sites in Switzerland and worldwide with a total of over 1200 employees. RUAG Space is now the market leader as a supplier to Europe’s space industry and will soon rebrand itself as beyond gravity to focus solely on the space market.
The main objective of the internship was to determine if data science, and in particular artificial intelligence, could bring value to the space industry. As of today, only a handful of mechanisms are produced in large enough quantities to have a suitable dataset for AI. This is now changing with the creation of satellite « constellations ».
During my internship, I had the opportunity to work on several projects to apply different data science techniques, from classical signal processing to state-of-the-art deep learning models. The most challenging part was to create clean and unbiased datasets. For this, I had the opportunity to work directly in the clean room to gather data from the hardware itself. Once the datasets created, the goal was to analyze them with a specific goal, e.g. finding anomalies or predicting an outcome using AI.
As an example, here is a figure that I created showing a voltage drop test time series with its Fourier transform and its spectrogram. The goal here was to better understand the profile of the resistive noise that appeared on the slip rings of a solar array drive mechanism. Such visualization helped other engineers to better visualize the huge amount of data that was gathered in this project.
Cravero Baraja Maximo
Throughout my internship at the IBM Zürich Research Lab, I worked on developing feature engineering and machine learning techniques for Anti-Money Laundering (AML). More specifically I worked on scalable pattern detection and subgraph encoding techniques for financial transaction graphs, and the implementation of these detected features in Machine Learning (ML) architectures, namely boosting algorithms and Graph Neural Networks (GNNs).
These features are then used to extend the raw transaction graph information and improve fraud detection performance using classical ML algorithms. To capitalize on the relations provided between users making transactions, we also made use of GNNs which leverage topological information by propagating feature information through the graph.
Money laundering is a big challenge with large costs incurred by several banks. An effective detection algorithm would provide immense value, and the proof of concept I have worked on will serve as a reference for future iterations on novel AML algorithms developed at the research lab.
Fluxim is a company based in Winterthur, affiliated with the local university that sells simulation software and measurement hardware for research and development of displays, lighting and photovoltaic cells in industry and academia.
During my time there, I worked on the modeling of OLED pixels. Standard OLEDs are comprised of a stack of transparent materials. At each flat material interface, incident light is partially transmitted and reflected according to the Fresnel equations, typically resulting in only about 20% efficiency. One approach to enhance this low efficiency is to have sinusoidal material interfaces instead of flat ones. My job was to develop a prototype for simulating the efficiency of an OLED with sinusoidal interfaces through rigorous numerical modeling of Maxwell’s equations. The first step was to carry out a literature review on existing methods. Then, together, the company and I chose the preferred method which I developed in Matlab during the rest of the internship.
Below you may find a Matlab simulation of the spectral radiance of a flat OLED with respect to the in-plane wave vector and the thickness of one of the materials (for optimization purposes). Total internal reflection is visible beyond Brewster’s angle as postulated by Fresnel’s equations.
Empa, the Swiss Federal Laboratories for Materials Science and Technology is an interdisciplinary Swiss research institute for applied materials science and technology. In particular, I was working in the SymBioSys (Simulation Biological System) group located in the Empa’s center in St. Gallen (https://www.empa.ch/web/simbiosys).
This project was performed in collaboration with a sensor company.
In this project, I looked for using such sensor data to better predict how the life of each fruit and vegetable in a refrigerated truck or trailer evolves. For that purpose I developed digital twins of the cargo, based on measured air temperature and humidity data in fruit cold chains by commercial sensors. These data are fed into a physics-based model to provide theoretical estimates of key performance indicators such as average
cargo temperature, mass loss, and remaining fruit quality at the end of the chain, based on quality decay as a function of temperature.
Spiden is a startup company that aims to develop compact devices for continuous monitoring of biomarkers and drugs in bodily fluids. By bringing together experts in spectroscopy, electronics, fluidics, biomedical science and machine learning, Spiden aims to develop revolutionary medical technologies that will improve the lives of millions of people.
I worked at Spiden as a Data Science Intern for about 6 months. In my internship, I performed exploratory analysis of spectral and medical data, developed machine learning models for reconstruction of biomarker concentrations in bodily fluids, and investigated application of deep generative neural networks to production of synthetic spectra.
I greatly enjoyed my internship at Spiden. The team is very professional and experienced, and incredibly welcoming and friendly. Spiden offers a unique setting for multidisciplinary research, and I wholeheartedly recommend it to any EPFL student.
Logitech is a Swiss manufacturer of computer peripherals and software, with headquarters in Lausanne, Switzerland. The company has offices throughout Europe, Asia, Oceania, and the Americas, and is one of the world’s leading manufacturers of input and interface devices for personal computers (PCs) and other digital products. Logitech accounts more than 9000 employees, develops and markets personal peripherals for PC navigation, video communication and collaboration, music and smart homes. This includes products like keyboards, mice, tablet accessories, webcams, Bluetooth speakers, universal remotes and more. Recently, Logitech has developed a lot of products and services for gamers and the gaming area in general.
The aim of the project is the study of in-ear audios to detect and estimate the amount of water intake in daily life recording conditions. The first part of the project was to build a labeled database of in-ear audio recordings during actual or simulated activities of daily living (ADL). To do this, an experiment was performed over 29 subjects. Then, the second part of the project was to use the database to train machine learning models for the detection of sip event as well as amount estimation. For sip detection, the best models classify the sip event with an accuracy of 85% while for the amount estimation, best models build have a mean relative error around 30%.
Autodesk, is an American multinational software corporation that makes software products and services for the architecture, engineering, construction, manufacturing, media, education, and entertainment industries. I worked as a software engineer intern in the Plant 3D team of AutoCAD. Plant 3D is an AutoCAD-based industry-specific toolset for plant design. Customers can create and edit P&IDs and 3D models, and extract piping orthographics and isometrics in the product. My contribution involves two parts:
The first part is to identify the defects reported by QA and fix the issue. It will help the product makes better drawings. In these tasks, I benefit from what I learned in geometry processing and 3D math. This knowledge helps me a lot in my development.
The second part is that I create a tool to compare two similar isometric drawings. This tool can help developers to identify the difference quickly.
AXA is a multinational insurance company that insures large companies as well as private customers. AXA has software and data science focused divisions in Switzerland, Paris, Spain, and Singapore, the Swiss lab being located at the EPFL innovation park. At the EPFL innovation park the teams have worked on a range of topics, including computable contracts, document understanding, and the intersection of computer vision and satellite imagery.
My project was to automatically identify insurance related risk factors from satellite imagery using computer vision models. The main scientific contribution was the study of transfer learning techniques in object detection with limited training examples. I showed that by using a base training set that is closer to the target domain, we get better validation error on the low-shot object detection task. Additionally I made additions to existing low-shot fine tuning techniques that made training more robust.
Other notable pieces of work included a web based annotation platform that allows users to create polygonal annotations of objects in satellite images. By the end of the internship I had compiled 4 object detection models for the risk factors we identified, and showed how to go from raw address data to geo-referenced predictions of risk factor locations. I integrated my models with existing foot printing software, which allowed users to visualise model predictions on a google maps type interface, and process industrial sites of any size. The internship took me through all the stages of a data science project, from data collection to model building.