Anya-Aurore Mauron


My internship was hosted by IDUN Technologies, a pioneer in the development of in-ear EEG earbuds dedicated to the monitoring and interpretation of brain waves. Established in 2017 as a spin-off from the Federal Institute of Technology (ETH) in Zürich, IDUN Technologies is committed to enhancing individuals’ self-awareness by offering real-time, objective measurements of brain activity.

During the internship at IDUN Technologies, significant advancements were made in the development of a horizontal electrooculogram (HEOG) classifier and the generation of an automated daytime report. The HEOG classifier project aimed at real-time prediction of a subject’s gaze direction using EEG signals, employing various classification strategies such as algorithmic approaches, the EEG Conformer (a deep learning model), and simpler classifiers to improve accuracy and usability. The final granular HEOG classifier achieved a notable accuracy, demonstrating potential for practical applications despite certain limitations.
The daytime report project focused on creating a comprehsive analysis of EEG signals recorded throughout the day, presented in a downloadable PDF format. This report included quality assessments and detailed analyses of frequency bands, leveraging cloud-based machine learning platforms for deployment. The results from these projects contribute to the fields of EEG signal analysis and real-time gaze prediction, showcasing the practical applications of advanced data science and machine learning techniques in interpreting brain wave data.

Legend: Raw VS Filtered data. Labels correspond to the absolute angles of the gaze of the subject. Calibration time is necessary for the filter to be efficient.


Francesco Fainello


Logitech is a leading company in computer peripherals and software, known for its innovative products that enhance user experiences across different platforms. As the top employer of EPFL students, Logitech provides opportunities for interns to work on cutting-edge projects. This particular internship, supervised by the Audio ML team and the CTO office, focused on exploring deep learning solutions for speech separation in video conferencing products. The aim was to develop a deep learning framework capable of enhancing the user experience by effectively separating overlapping speech and improving audio quality in real-time. This involved developing a deep neural network to process multi-channel audio signals, extracting both spectral and spatial information to distinguish between different speakers. The work culminated in the creation of a Python-based model capable of separating and enhancing speech, even in scenarios with an unknown number of speakers. Through continuous support and weekly meetings, I was not only able to deepen technical skills in programming and deep learning but also to refine my communication skills through regular presentations and interactions within a structured corporate environment, providing valuable support and insights into organizational dynamics.


Giacomo Mossinelli


Procter&Gamble is the world’s largest consumer goods company, made of 100 000 employees with a turnover of $82.0 billion. I did my internship in the R&D department of their German Innovation Center (GIC) in Schwalbach am Taunus, near Frankfurt am Main.

During my 6 months long internship project I had the opportunity to focus on modeling and simulation tools to assist and improve the development of products made of Airlaid materials (figure 1).

More specifically, in my project I focused on the improvement and optimization of the numerical model for one of the most important metrics to describe the final product performance, so that it could be implemented together with the other already returned metrics. The result thus obtained is to have a single simulation that provides the fundamental information about the product without the need to invest resources in tests and experiments. The final step was to use what was built to create a virtual twin of a very intricate and relevant test method, which is composed of many different measurements. The outputs of this virtual experiment can finally be used to take developmental and business decisions.

Right from the beginning of the internship, I was charged with many responsibilities and project leadership. This allowed me to learn a lot from a technical point of view but, more importantly, from a professional, relational and organizational point of view. Indeed, to bring the project to completion it has been necessary to interact and collaborate with colleagues with different roles and backgrounds, thus making the social and communication component as crucial as the technical and engineering one.

Moreover, the opportunity to conduct experiments in the laboratory following the directions and tips of experts, with the aim of validating the predictions of numerical simulations, allowed for a practical approach in addition to the more theoretical and modeling approach of the strictly modeling part.

Figure 1: Portion of Airlaids manufacturing process


Kseniia Shevchenko


Alpiq is one of the main electricity producers in Switzerland with a large majority of its production coming from hydropower plants. Alpiq also has varied portfolio of nuclear, gas-fired, wind, solar power plants located throughout Europe. During my internship I worked in the Innovation and Projects team whose goal is to bring innovative digital solutions into production to optimize the operation of hydropower plants.

During my internship I worked on Alpiq’s predictive maintenance project which is aimed at preventing failures on the power plant detecting early detection of precursors. The core of the project is machine learning models trained for anomaly detection. I worked on multiple tasks, such as hypothesis testing, development and integration of new features (metrics) and physics-based models. The developed metric was based on statistical indicators derived from the residuals of the prediction model and proved to enriches and complements the existing functionality of the project. The goals of the physics-based model was to directly model the thermal behavior of and element of the machine using a discretized dynamical system.

During the internship I enhanced my skills in machine learning and software engineering. The internship allowed me to focus on specific domain problem – in maintenance of hydropower equipment and see how to integrate prior physics knowledge into machine learning models as well as how to interpret the results according to the construction and functioning of power plant.

This internship provided me an excellent learning experience and was very valuable both in improving and getting new technical skills and collaboration and communication in professional context in industry.


Sam Jegou


APCO Technologies specializes in designing and manufacturing high quality mechanical and electromechanical equipment for space, energy and industry applications. With around 400 employees, APCO focuses on providing innovative solutions and concepts, from the engineering phase to assembly and final testing, as well as on-site assistance.

During this internship I developed an algorithm that recommends collision avoidance maneuvers to satellites. These maneuvers are optimized to use as less propellant as possible, but still reaching low-risk criteria (distance between the two satellites and probability of collision). Another type of maneuver proposed is to change the ballistic coefficient of the satellite (via the solar panels orientation for instance) to modify its trajectory, as shown in the figure.