Projects

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.

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.

Partner: CHUV

Partner contact: Ismail Labgaa

EPFL laboratory: Machine Learning and Optimization Laboratory (MLO), intelligent Global Health Research group

EPFL contact: Mary-Anne Hartley

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.

Partner: Microsoft

Partner contact: Dimitrios Dimitriadis, Emre Kıcıman, Robert Sim, Shruti Tople

EPFL laboratory: Data Science Lab (dlab)

EPFL contact: Prof. Robert West, Valentin Hartmann, Maxime Peyrard

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.

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.

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.

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).

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.

Partner: CHUV

Partner contact: Ismail Labgaa

EPFL laboratory: Machine Learning and Optimization Laboratory (MLO), intelligent Global Health Research group

EPFL contact: Mary-Anne Hartley

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.

Partner: ICRC, CHUV

Partner contact: Mary-Anne Hartley

EPFL laboratory: Machine Learning and Optimization Laboratory (MLO), intelligent Global Health Research group

EPFL contact: Mary-Anne Hartley

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.

Partner: Swisscom

Partner contact: Dan-Cristian Tomozei

EPFL laboratory: Signal Processing Laboratory (LTS4)

EPFL contact: Prof. Pascal Frossard, Nikolaos Dimitriadis

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.

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.

Partner: ICRC, funded by HAC

Partner contact: Fabrice Lauper

EPFL laboratory: Distributed Information Systems Laboratory (LSIR)

EPFL contact: Prof. Karl Aberer, Rebekah Overdorf

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.

Partner: Cyber-Defence Campus (armasuisse)

Partner contact: Ljiljana Dolamic

EPFL laboratory: Signal Processing Laboratory (LTS4)

EPFL contact: Prof. Pascal Frossard

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.

Partner: ICRC, funded by HAC

Partner contact: Vincent Graf

EPFL laboratory: Security and Privacy Engineering Laboratory (SPRING)

EPFL contact: Prof. Carmela Troncoso, Wouter Lueks

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.

Partner: armasuisse

Partner contact: Gérôme Bovet

EPFL laboratory: Signal Processing Laboratory (LTS4)

EPFL contact: Prof. Pascal Frossard

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.

Partner: Swiss RE

Partner contact: Mary-Anne Hartley

EPFL laboratory: Machine Learning and Optimization Laboratory (MLO), intelligent Global Health Research group

EPFL contact: Mary-Anne Hartley, Prof. Martin Jaggi, Prakhar Gupta, Giorgio Mannarini, Francesco Posa

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.

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.

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.

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.

Partner: armasuisse

Partner contact: Gérôme Bovet

EPFL laboratory: Signal Processing Laboratory (LTS4)

EPFL contact: Prof. Pascal Frossard

Modulation recognition state-of-the-art architectures use deep learning models. These models are vulnerable to adversarial perturbations, which are imperceptible additive noise crafted to induce misclassification, posing serious questions in terms of safety, security, or performance guarantees at large. One of the best ways to make the model robust is to use adversarial learning, in which the model is fine-tuned with these adversarial perturbations. However, this method has several drawbacks. It is computationally costly, has convergence instabilities and it does not protect against multiple types of corruptions at the same time. The objective of this project is to develop improved and effective adversarial training solutions that tackle these drawbacks.

Partner: Swissquote

Partner contact: Serge Kassibrakis

EPFL laboratory: Swiss Finance Institute @ EPFL

EPFL contact: Prof. Damir Filipovic, Alexis Marchal

We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short- term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps and volatility bursts. As a result, the network learns how to disentangle the two. Then it is applied to out-of-sample simulated data and delivers results that considerably differ from the benchmark: we obtain fewer spurious detection and identify a larger number of true jumps. When applied to real data, our approach for jump screening allows to extract a more precise signal about future volatility.

Partner: Swissquote

Partner contact: Serge Kassibrakis

EPFL laboratory: Swiss Finance Institute @ EPFL

EPFL contact: Prof. Damir Filipovic, Alexis Marchal

We develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection of bubbles. We show the outperformance of our algorithm over the existing statistical method in a laboratory created with simulated data. We then apply the network classification to real data and build a zero net exposure trading strategy that exploits the risky arbitrage emanating from the presence of bubbles in the US equity market from 2006 to 2008. The profitability of the strategy provides an estimation of the economical magnitude of bubbles as well as support for the theoretical assumptions relied on.

Partner: Swissquote

Partner contact: Serge Kassibrakis

EPFL laboratory: Swiss Finance Institute @ EPFL

EPFL contact: Prof. Damir Filipovic, Alexis Marchal

I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated with higher equity returns and a drop in realized volatility. The method creates an attention score by quantifying how much the Chair needs to rely on reading internal documents to be able to answer a question. This is accomplished by building a novel dataset of video images of the press conferences and leveraging recent deep learning algorithms from computer vision. This alternative data provides new information on nonverbal communication that cannot be extracted from the widely analyzed FOMC transcripts. This paper can be seen as a proof of concept that certain videos contain valuable information for the study of financial markets.