Courses & internships

Social media and crowdsourcing for social good

Project Summary: The student will contribute to a multidisciplinary initiative for the use of social media and mobile crowdsourcing for social good. Several projects are available. Specific topics include:

* Social media analytics
* Visualization of social and crowdsourced data
* Smartphone apps for mobile crowdsourcing

Students will be working with social computing researchers studying European and developing cities.

Contact: Prof. Daniel Gatica-Perez

A human-centered approach to understand local news consumption

Project Summary: The goal of the project is the design and implementation a framework to study the consumption of local news in the Swiss multicultural context. The project will include a combination of research methods for experimental design and data analysis, and will be done in the context of the AI4Media European project (A European Excellence Center for Media, Society, and Democracy). The main tasks of the project include: literature review; identification of local news sources; mixed-method experimental design; experiments and data analysis; and writing.

Contact: Prof. Daniel Gatica-Perez

Swiss Alpine Lakes & Citizen Science

Project Summary: In the context of the CLIMACT UNIL-EPFL initiative, we aim at cataloging all Swiss Alpine Lakes located above 2000 meters, including collection of water samples and in-depth analysis of their microbial diversity, with a citizen science approach to engage citizens and increase awareness regarding environmental conservation. The project will build upon existing data sources (including Wikipedia and government data), and computational tools including natural language processing, computer vision, and visualization to build online interactive functionalities that can be used as part of citizen science activities.

Contact: Prof. Daniel Gatica-Perez

Privacy-preserving machine learning methods for diversity-aware mobile computing

Project Summary: The goal of the project is to study privacy-preserving machine methods in the context of mobile, diversity-aware computing systems that support the local needs of communities. The project will include work in machine learning, mobile data analysis, and will be done in the context of the multidisciplinary WeNet European project. The main tasks of the project include: literature review; algorithm design and implementation; experiments and data analysis; and writing.

Contact: Prof. Daniel Gatica-Perez

A FATE framework for diversity-aware mobile computing

Project Summary: This project will study and propose a methodology to characterize and validate machine learning methods in the context of diversity-aware mobile computing from the FATE perspective (fairness, accountability, transparency, ethics.) Recent approaches include Google’s model cards for model reporting or Microsoft’s Guidelines for Human-AI interaction. The project will provide a set of best practices for this domain. The main tasks of the project include: literature review; method design and implementation; experiments and data analysis; and writing.

Contact: Prof. Daniel Gatica-Perez

The European AI Act and its impact on European cities

Project Summary: The April 2021 proposal by the European Commission on AI regulation (The AI Act) will impact many sectors of the economy and have important societal implications. This project will study this proposal, analyze its possible effects on how European cities use AI as part of their mission, and make recommendations for the future. The project will be done in the context of the multidisciplinary ICARUS European project, which involve a number of actors in cities and non-governmental organizations. The main tasks of the project include: literature review; conceptual analysis; data collection; data analysis, and writing.

Contact: Prof. Daniel Gatica-Perez

Robot Learning and Interaction

Project Summary: The Robot Learning and Interaction Group proposes various Master and Semester projects with topics related to robotics, machine learning, adaptive control and human-robot interaction.

The list of projects is available here.

Supervisor: Dr. Sylvain Calinon

Keywords: robotics, machine learning, adaptive control, human-robot interaction

Human-Robot Interfaces for Interactive Robot Programming

Project Summary: For robots to be widely adopted across industries and beyond structured manufacturing environments,it is critical for them to be programmable by a wide range of users. Growing research on End UserProgramming (EUP) for robotics aims to address this problem with novel user interfaces, programminglanguages, and techniques to aid or fully automate robot programming.

In this project, you will design a Human-Robot Interface for Robot Programming, integrating anexisting programming framework based on iterative Linear Quadratic Regulator (iLQR) [1] on a roboticmanipulator, the FRANKA EMIKA Panda. The interface will allow users to compose robot programsdefined as sequences of components like goal poses or constraints (e.g., maintaining a gripper orientation)which, in turn, inform the generation of executable robot trajectories.

For more details, see the following document
Supervisors: Dr. Jean-Marc Odobez, Dr. Sylvain Calinon
Advisors and point of contact: Dr. Mattia Racca

6D Pose Estimation for Robotic Manipulation Tasks

Project Summary: For robots to interact with their environment in a safe and efficient manner, they need robust ways of estimating the pose (location, orientation) of the objects to be manipulated. This is usually done with RGB-D cameras as input sensors, i.e. cameras that provide not only the color of each pixel (the RGB part) but also how far it is from the camera (the D for “depth”).

Most recent methods leverage deep learning architecture to achieve such estimation, leveraging convolutional neural networks and fully-connected layers.

In this semester project, you will use referenced methods (Wang et al 2019, He et al 2020 & 2021), testing their performance and usability on the available datasets. In particular, you will take advantage of the YCB Object Dataset, a popular robotics dataset used to evaluate 6D pose estimation techniques (Calli et al, 2015). This dataset, avaialble in the lab, consists of real-life items and their corresponding 3D model (in the form of point clouds or textured meshes). You will then integrate the methods in a lab setup, where the input images come, in real time, from a RBG-D camera (Intel Realsense d415, Kinect Azure). Finally, you will showcase the robustness of the methods, by having the estimated 6D pose of YCB items used as input for a robot grasping task.For more details about the tasks and goals, see the following link:

Supervisor: Dr. Jean-Marc Odobez
Advisors and point of contact: Anshul Gupta (research assistant), Dr. Mattia Racca

Interaction Manager for Human-Robot Interactions

Project Summary: This project aims to provide an easy way to create dialogues for human-robot interactions. For example, a robot introducing itself when someone looks at it, telling a joke if the person asks for one, and then using the person’s laughter to learn whether the joke was a good one. In this project (semester or master), you will use RASA, a commercial dialogue management system used in many chatbots on the web to handle turn-taking discussions. You will interface this system with a simulated robot as well as a real Pepper robot to create scenarios allowing people to interact with Pepper in different ways and ensuring that Pepper’s responses are appropriate both verbally and non-verbally. For this, you will need to use Pepper’s sensors to make sense of the world and understand people’s speech, relay this information to the dialogue manager in RASA, and treat the outputs of the dialogue manager to create real robot behaviors (speech and gestures).

For more details, see the following link to pdf
Supervisors: Dr. Jean-Marc Odobez, Dr. Emmanuel Senft
Point of contact: Dr. Emmanuel Senft

Introduction à l'apprentissage machine (EE-311) Lecturer(s): Michael Liebling

Ce cours présente une vue générale des techniques d'apprentissage automatiques, passant en revue les algorithmes, le formalisme théorique, et les protocoles expérimentaux.

Where: EPFL Language: French

Urban Thermodynamics (CIVIL-309) Lecturer(s): Jerôme Kämpf (Khovalyg Dolaana)

This course introduces the analysis of urban areas from a thermodynamics perspective, considering the heat exchange between different urban elements (buildings, vegetation, water surfaces, ground, and environment). Urban heat island effect and outdoor comfort topics are also discussed.

Where: EPFL Language: English

Automatic Speech Processing (EE-554) Lecturer(s): Mathew Magimai Doss

The goal of this course is to provide the students with the main formalisms, models and algorithms required for the implementation of advanced speech processing applications (involving, among others, speech coding, speech analysis/synthesis, and speech recognition).

Language: English

Computational Social Media (DH-500) Lecturer(s): Gatica-Perez Daniel

The course integrates concepts from media studies, machine learning, multimedia and network science to characterize social practices and analyze content in sites like Facebook, Twitter and YouTube. Students will learn computational methods to infer individual and networked phenomena in social media.

Language: English

Image processing II (MICRO-512) Lecturer(s): Liebling Michael, Sage Daniel, Unser Michaël, Van De Ville Dimitri Nestor Alice

Study of advanced image processing; mathematical imaging. Development of image-processing software and prototyping in JAVA; application to real-world examples in industrial vision and biomedical imaging.

Language: English

Genomics and bioinformatics (BIO-463) Lecturer(s) Luisier Raphaëlle & Jacques Rougemont

This course covers various data analysis approaches associated with applications of DNA sequencing technologies, from genome sequencing to quantifying gene expression, transcription factor binding and chromosome conformation.

Language: English

Practical Course in Linear Algebra and Probability (M01) Lecturer(s): Théophile Gentilhomme, Ina Kodrasi
Language: English

Data structure and algorithms for AI (M02) Lecturer(s): Olivier Bornet
Language: English

Signal Processing (M03) Lecturer(s): Michael Liebling
Language: English

Foundations in statistics for AI (M04) Lecturer(s): Phil Garner, Ina Kodrasi
Language: English

Open Science and Ethics (M05) Lecturer(s): Sébastien Marcel, André Anjos
Language: English

Fundamentals in Machine Learning 1 (M06) Lecturer(s): Sébastien Marcel, André Anjos, Andre Freitas, Jean-Marc Odobez
Language: English

Introduction to Image Processing and Computer Vision (M07) Lecturer(s): Michael Liebling, Jean-Marc Odobez
Language: English

Fundamentals in Machine Learning 2 (M08) Lecturer(s): Sébastien Marcel, André Anjos, Andre Freitas, Jean-Marc Odobez
Language: English

Introduction to Speech Processing (M09) Lecturer(s): Mathew Magimai Doss
Language: English

Deep Learning (M10) Lecturer(s): Olivier Canévet
Language: English

Biometrics (A01) Lecturer(s): Sébastien Marcel
Language: English

Multimodal Computational Sensing of People (A02) Lecturer(s): Jean-Marc Odobez
Language: English

Natural Language Processing (A03) Lecturer(s): James Henderson
Language: English

Robotics (A04) Lecturer(s): Sylvain Calinon
Language: English

AI Company Strategy and Project Definition (P01) Lecturer(s): Olivier Bornet
Language: English

AI Project Development (P02) Lecturer(s): Olivier Bornet
Language: English

Biometrics (School of Criminal Justice (ESC)) Lecturer(s): Sébastien Marcel

This course introduces to the analysis, modelling and interpretation of biometric data for biometric person recognition, forensic biometrics, cybersecurity and behavioural biometrics in man-machine communication.

Language: French

Human-Robot Interaction and Collaborative Robotics (M4C1) Lecturer(s): Sylvain Calinon

This course presents the use of artificial intelligence and machine learning techniques in human-robot interaction applications. In particular, it will focus on techniques to transfer skills by demonstration, inspired by imitation mechanisms to teach new skills to robots with an intuitive interface for the end-user.

Language: English

Statistical, geometrical and dynamical representations of movement (M2C7) Lecturer(s): Sylvain Calinon

This course will present various ways of representing movement data and gestures in a mathematical manner, with the goal of analyzing, compressing or generating movements. Several examples of applications will be covered, from generation of manipulation skills in robotics to the analysis of motion capture data.

Language: English

Perception and learning from multimodal sensors (EE-623) Lecturer(s): Odobez Jean-Marc

The course will cover different aspects of multimodal processing (complementarity vs redundancy; alignment and synchrony; fusion), with an emphasis on the analysis of people, behaviors and interactions from multimodal sensor, using statistical models and deep learning as main modeling tools.

Where: EPFL Language: English

Digital Speech and Audio Coding (EE-719) Lecturer(s): Magimai Doss Mathew, Motlicek Petr

The goal of this course is to introduce the engineering students state-of-the-art speech and audio coding techniques with an emphasis on the integration of knowledge about sound production and auditory perception through signal processing techniques.

Where: EPFL Language: English

Fundamentals in statistical pattern recognition (EE-612) Lecturer(s): Anjos André, Marcel Sébastien, Canévet Olivier

This course provides in-depth understanding of the most fundamental algorithms in statistical pattern recognition as well as concrete tools (as source code) to PhD students for their work. It will cover regression, classification (MLP, SVM) and probability distribution modeling (k-Means, GMM, HMM).

Where: EPFL Language: English

Machine Learning for Engineers (EE-613) Lecturer(s): Calinon Sylvain, Odobez Jean-Marc

The objective of this course is to give an overview of machine learning techniques used for real-world applications, and to teach how to implement and use them in practice. Laboratories will be done in python using jupyter notebooks.

Where: EPFL Language: English

Deep Learning For Natural Language Processing (EE-608) Lecturer(s): James Henderson
The Deep Learning for NLP course provides an overview of neural network based methods applied to text. The focus is on models particularly suited to the properties of human language, such as categorical, unbounded, and structured representations, and very large input and output vocabularies.
Where: EPFL Language: English