Projects

Sep 2020Aug 2024

Status:Ongoing

ADHes: Attacks and Defenses on FPGA-CPU Heterogeneous Systems

FPGAs are essential components in many computing systems. With conventional CPUs, FPGAs are deployed in various critical systems, such as wireless base stations, satellites, radars, electronic warfare platforms, and data centers. Both FPGAs and CPUs have security vulnerabilities; integrating them together presents new attack opportunities on both sides. In this project, we investigate the attacks made possible by closely integrating FPGAs with CPUs in heterogeneous computing platforms.

Type Device & System Security
Partner Armasuisse
Partner contact Vincent Lenders
EPFL Laboratory Parallel Systems Architecture Laboratory (PARSA)
Aug 2023Jun 2024

Status:Ongoing

Citizen-Centered Digitalization in Healthcare:​ Overcoming Barriers for Electronic Patient Record (EPR) Implementation

In Switzerland, the importance of electronic patient records (EPR) is widely acknowledged, yet its widespread adoption remains limited. This research project aims to uncover the reasons behind this low adoption rate by analyzing the perspectives of stakeholders as well as examining the technologies underlying the current system. It seeks to identify strategies to overcome adoption barriers and provide insights that can inform policies and practices.

Type Policy
Author Sanja Tumbas
Aug 2023Feb 2024

Swiss Digital Service Public: Towards a Digital Policy

The advent of digitalization has profoundly transformed societal and economic structures by reshaping traditional value chains into digital platform-driven networks, impacting various sectors, including public services in Switzerland. This project aims to examine the influence of digital platforms on Swiss public services and proposes recommendations for a digital policy.

Type Policy
Author Matthias Finger, Melanie Kolbe-Guyot
Feb 2022Feb 2024

Graph Embedding Methods for Scalable Knowledge Graph Completion

Knowledge graphs have recently attracted significant attention in scenarios that require exploiting large-scale heterogeneous data collections. When graph sizes reach high orders of magnitude a delicate balance between performance and computational cost might is required. This project presents an approach to construct a model that generates meaningful graph representations while maintaining the scalability and prediction performance as significant as possible.

Type Machine Learning
Partner Swisscom
Partner contact Samuel Benz, Daniel Dobos
EPFL Laboratory Laboratory for Information and Inference Systems (LIONS)
Apr 2023Jan 2024

Making Commercial e-Transactions Flow Within and Across Borders

International trade transaction, especially in cross-border B2B contracting, often still rely on paper-based processes and physical exchanges. While electronic contracting technologies like digital signatures offer reliability and efficiency, their adoption is limited. This project aims to address the issue by exploring legal, technological, and institutional solutions.

Type Policy
Author Leonila Guglya
Jan 2023Dec 2023

Using Artifical Intelligence to Explore the Prognostic Value of Macroscopy in Liver Cancer

Liver cancer ranks third in terms of cancer-related mortality. Hepatocellular carcinoma (HCC) accounts for 90% of primary liver cancers. Tremendous efforts have been pursued to establish HCC prognostic, including clinical, radiological, pathological and even molecular readouts. Regardless of the strategy, the performance of these tools remains modest. Recent data using artificial intelligence (AI) on HCC histology (microscopy) have revealed promising results. We aim to submit pictures of liver cancers specimen to AI models to generate algorithms allowing to establish prognosis in a large-scale study including centers from North America, Europe and Asia.

Type Machine Learning, Health
Partner CHUV
Partner contact Ismail Labgaa
EPFL Laboratory Machine Learning and Optimization Laboratory (MLO), Intelligent Global Health Research Group
Jan 2023Dec 2023

Machine-Learning Prognostication in Patients Undergoing Surgery for Hepatocellular Carcinoma (Liver Cancer)

Liver cancer is the second deadliest malignancy. It essentially accounts hepatocellular carcinoma (HCC). Surgery with liver resection is the main curative option but unfortunately, it is only recommended in patients with early HCC. Prognosis of HCC is particularly challenging and results from numerous attempts using various strategies remain relatively poor.Artificial intelligence (AI) has demonstrated unmatched value to decipher complex traits and mechanisms. This multicentric effort will include 8 Academic centers from the United States, Europe and Asia, allowing to generate a large-scale dataset of patients undergoing liver resection for HCC. We aim to investigate the input of AI to improve prognostication of these patients.

Type Machine Learning, Health
Partner CHUV
Partner contact Ismail Labgaa
EPFL Laboratory Machine Learning and Optimization Laboratory (MLO), Intelligent Global Health Research Group
Jan 2023Dec 2023

Exploring Artificial Intelligence to Predict Complications after Major Digestive Surgery

Major digestive surgery is associated with a high comorbidity (i.e. high risk of complications after surgery). Anticipating Postoperative complications (POC) may help and guide clinicians in the postoperative management of surgical patients. Unfortunately, the available tools in clinical practice are of restraint value due to their limited accuracy. Recently, artificial intelligence (AI) has shown a meteoric rise in medicine, showing numerous clinical applications but its role to predict POC remains unknown. We aim to use AI to develop new models allowing to improve the prediction of POC in a dataset of >2000 patients undergoing major digestive surgery.

Type Machine Learning, Health
Partner CHUV
Partner contact Ismail Labgaa
EPFL Laboratory Machine Learning and Optimization Laboratory (MLO), Intelligent Global Health Research Group
Jan 2022Dec 2023

Invariant Federated Learning: Decentralized Training of Robust Privacy-Preserving Models

As machine learning (ML) models are becoming more complex, there has been a growing interest in making use of decentrally generated data (e.g., from smartphones) and in pooling data from many actors. At the same time, however, privacy concerns about organizations collecting data have risen. As an additional challenge, decentrally generated data is often highly heterogeneous, thus breaking assumptions needed by standard ML models. Here, we propose to “kill two birds with one stone” by developing Invariant Federated Learning, a framework for training ML models without directly collecting data, while not only being robust to, but even benefiting from, heterogeneous data.

Type Machine Learning
Partner Microsoft
Partner contact Dimitrios Dimitriadis, Emre Kıcıman, Robert Sim, Shruti Tople
EPFL Laboratory Data Science Lab (dlab)
Mar 2022Dec 2023

TMM – Leveraging Language Models for Technology Landscape Monitoring

The objective of the project is to perform online monitoring of technologies and technology actors in publicly accessible information sources. The monitoring concerns the early detection of mentions of new technologies, of new actors in the technology space, and the facts related to new relations between technologies and technology actors (subsequently, all these will be called technology mentions). The project will build on earlier results obtained on the retrieval of technology-technology actors using state-of-the-art NLP approaches.

Type Machine Learning
Partner armasuisse
Partner contact Alain Mermoud
EPFL Laboratory Distributed Information Systems Laboratory (LSIR)
Jan 2022Dec 2023

Tyche: Confidential Computing on Yesterday’s Hardware

Confidential computing is an increasingly popular means to wider Cloud adoption. By offering confidential virtual machines and enclaves, Cloud service providers now host organizations, such as banks and hospitals, that abide by stringent legal requirement with regards to their client’s data confidentiality. Unfortunately, confidential computing solutions depend on bleeding-edge emerging hardware that (1) takes long to roll out at the Cloud scale and (2) as a recent technology, it is bound to frequent changes and potential security vulnerabilities. This proposal leverage existing commodity hardware combined with new programming language and formal method techniques and identify how to provide similar or even more elaborate confidentiality and integrity guarantees than the existing confidential hardware.

Type Privacy Protection & Cryptography
Partner Microsoft
Partner contact Adrien Ghosn, Marios Kogias
EPFL Laboratory Data Center Systems Laboratory (DCSL), HexHive Laboratory
Jan 2022Dec 2023

PAIDIT: Private Anonymous Identity for Digital Transfers

To serve the 80 million forcibly-displaced people around the globe, direct cash assistance is gaining acceptance. ICRC’s beneficiaries often do not have, or do not want, the ATM cards or mobile wallets normally used to spend or withdraw cash digitally, because issuers would subject them to privacy-invasive identity verification and potential screening against sanctions and counterterrorism watchlists. On top of that, existing solutions increase the risk of data leaks or surveillance induced by the many third parties having access to the data generated in the transactions. The proposed research focuses on the identity, account, and wallet management challenges in the design of a humanitarian cryptocurrency or token intended to address the above problems. This project is funded by Science and Technology for Humanitarian Action Challenges (HAC).

Type Privacy Protection & Cryptography, Blockchains & Smart Contracts, Device & System Security, Finance, Government & Humanitarian
Partner ICRC
Partner contact TBD
EPFL Laboratory Decentralized Distributed Systems Laboratory (DEDIS)
Oct 2021Oct 2023

RuralUS: Ultrasound adapted to resource limited settings

Point-of-Care Ultrasound (PoCUS) is a powerfully versatile and virtually consumable-free clinical tool for the diagnosis and management of a range of diseases. While the promise of this tool in resource-limited settings may seem obvious, it’s implementation is limited by inter-user bias, requiring specific training and standardisation.This makes PoCUS a good candidate for computer-aided interpretation support. Our study proposes the development of a PoCUS training program adapted to resource limited settings and the particular needs of the ICRC.

Type Machine Learning, Health
Partner ICRC, CHUV
Partner contact Mary-Anne Hartley
EPFL Laboratory Machine Learning and Optimization Laboratory (MLO), Intelligent Global Health Research Group
Oct 2020Sep 2023

Multi-Task Learning for Customer Understanding

Customer understanding is a ubiquitous and multifaceted business application whose mission lies in providing better experiences to customers by recognising their needs. A multitude of tasks, ranging from churn prediction to accepting upselling recommendations, fall under this umbrella. Common approaches model each task separately and neglect the common structure some tasks may share. The purpose of this project is to leverage multi-task learning to better understand the behaviour of customers by modeling similar tasks into a single model. This multi-objective approach utilises the information of all involved tasks to generate a common embedding that can be beneficial to all and provide insights into the connection between different user behaviours, i.e. tasks. The project will provide data-driven insights into customer needs leading to retention as well as revenue maximisation while providing a better user experience.

Type Machine Learning, Digital Information
Partner Swisscom
Partner contact Dan-Cristian Tomozei
EPFL Laboratory Signal Processing Laboratory (LTS4)
May 2023Aug 2023

Assessment of image hashing technologies – Visual Hash

In Visual Hash Project EPFL partners with SICPA in order to provide guidance and use the technical expertise of scientists from Multimedia Signal Processing Group for assessing the performance of novel imaging technologies for security, privacy and digital identity.

Type Digital Information
Partner SICPA
Partner contact Víctor Martínez Jurado
EPFL Laboratory Multimedia Signal Processing Group (MMSPG)
Jan 2023Jul 2023

Cornerstones of a Swiss Digital and Data Policy

This project aims to develop economically based terminology and conceptualizations as well as a sound understanding of digital markets and their regulation in order to propose key values for a Swiss digital policy with a focus on data policy. The resulting framework for analyzing the origins and effects of challenges in digital policy from an economic perspective will then be applied to current political and regulatory initiatives in Switzerland and the EU. Recommendations are derived from this.

Type Policy
Author Matthias Finger
May 2021May 2023

Harmful Information Against Humanitarian Organizations

In this project, we are working with the ICRC to develop technical methods to combat social media-based attacks against humanitarian organizations. We are uncovering how the phenomenon of weaponizing information impacts humanitarian organizations and developing methods to detect and prevent such attacks, primarily via natural language processing and machine learning methods.

Type Machine Learning, Government & Humanitarian
Partner ICRC, funded by HAC
Partner contact Fabrice Lauper
EPFL Laboratory Distributed Information Systems Laboratory (LSIR)
Apr 2022Mar 2023

Adversarial Attacks in Neural Machine Translation Systems

Recently, deep neural networks have been applied in many different domains due to their significant performance. However, it has been shown that these models are highly vulnerable to adversarial examples. Adversarial examples are slightly different from the original input but can mislead the target model to generate wrong outputs. Various methods have been proposed to craft these examples in image data. However, these methods are not readily applicable to Natural Language Processing (NLP). In this project, we aim to propose methods to generate adversarial examples for NLP models such as neural machine translation models in different languages. Moreover, through adversarial attacks, we mean to analyze the vulnerability and interpretability of these models.

Type Device & System Security, Machine Learning
Partner armasuisse
Partner contact Ljiljana Dolamic
EPFL Laboratory Signal Processing Laboratory (LTS4)
Feb 2021Feb 2023

PriBAD: Private Biometrics for Aid Distribution

In this project, we work on providing a privacy-preserving biometric solution for humanitarian aid distribution. The project seeks to understand the requirements of aid distribution in emergency situation and design a solution that enables the use of biometrics without endangering the beneficiaries that need access to aid.

Type Privacy Protection & Cryptography, Government & Humanitarian
Partner ICRC, funded by HAC
Partner contact Vincent Graf
EPFL Laboratory Security and Privacy Engineering Laboratory (SPRING)
Mar 2022Feb 2023

ARNO

State-of-the-art architectures for modulation recognition are typically based on deep learning models. However, recently these models have been shown to be quite vulnerable to very small and carefully crafted perturbations, which pose serious questions in terms of safety, security, or performance guarantees at large. While adversarial training can improve the robustness of the network, there is still a large gap between the performance of the model against clean and perturbed samples. Based on recent experiments, the data used during training could be an important factor in the susceptibility of the models. Thus, the objective of this project is to research the effects of proper data selection, cleaning and preprocessing of the samples used during training on robustness.

Type Device & System Security, Machine Learning
Partner armasuisse
Partner contact Gérôme Bovet
EPFL Laboratory Signal Processing Laboratory (LTS4)
Jan 2021Dec 2022

What If….? Pandemic Policy Decision Support System

After 18 months of responding to the COVID-19 pandemic, there is still no agreement on the optimal combination of mitigation strategies. The efficacy and collateral damage of pandemic policies are dependent on constantly evolving viral epidemiology as well as the volatile distribution of socioeconomic and cultural factors. This study proposes a data-driven approach to quantify the efficacy of the type, duration, and stringency of COVID-19 mitigation policies in terms of transmission control and economic loss, personalised to individual countries.

Type Machine Learning, Health, Government & Humanitarian
Partner Swiss RE
Partner contact Mary-Anne Hartley
EPFL Laboratory Machine Learning and Optimization Laboratory (MLO), Intelligent Global Health Research Group
Mar 2022Nov 2022

Technology Monitoring and Management (TMM)

Partner: armasuisse

Partner contact: Alain Mermoud

EPFL laboratory: Distributed Information Systems Laboratory (LSIR)

EPFL contact: Prof. Karl Aberer, Angelika Romanou

The objective of the TMM project is to identify, at an early stage, the risks associated with new technologies and develop solutions to ward off such threats. It also aims to assess existing products and applications to pinpoint vulnerabilities. In that process, artificial intelligence and machine learning will play an important part. The main goal of this project is to automatically identify technology offerings of Swiss companies especially in the cyber security domain. This also includes identifying key stakeholders in these companies, possible patents, published scientific papers.

Type Machine Learning
Apr 2021Mar 2022

Adversarial Attacks in Natural Language Processing Systems

Partner: Cyber-Defence Campus (armasuisse)

Partner contact: Ljiljana Dolamic

EPFL laboratory: Signal Processing Laboratory (LTS4)

EPFL contact: Prof. Pascal Frossard, Sahar Sadrizadeh

Recently, deep neural networks have been applied in many different domains due to their significant performance. However, it has been shown that these models are highly vulnerable to adversarial examples. Adversarial examples are slightly different from the original input but can mislead the target model to generate wrong outputs. Various methods have been proposed to craft these examples in image data. However, these methods are not readily applicable to Natural Language Processing (NLP). In this project, we aim to propose methods to generate adversarial examples for NLP models such as neural machine translation models in different languages. Moreover, through adversarial attacks, we mean to analyze the vulnerability and interpretability of these models.

Type Device & System Security, Machine Learning, Government & Humanitarian
Mar 2020Mar 2022

Technology Monitoring and Management (TMM)

Partner: armasuisse

Partner contact: Alain Mermoud

EPFL laboratory: Distributed Information Systems Laboratory (LSIR)

EPFL contact: Prof. Karl Aberer, Chi Thang Duong

The objective of the TMM project is to identify, at an early stage, the risks associated with new technologies and develop solutions to ward off such threats. It also aims to assess existing products and applications to pinpoint vulnerabilities. In that process, artificial intelligence and machine learning will play an important part. The main goal of this project is to automatically identify technology offerings of Swiss companies especially in the cyber security domain. This also includes identifying key stakeholders in these companies, possible patents, published scientific papers.

Type Machine Learning