Projects
RuralUS: Ultrasound adapted to resource limited settings
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.
Topics • Machine Learning • Health
ViewCausal Inference Using Observational Data: A Review of Modern Methods
Partner: armasuisse Partner contact: Albert Blarer EPFL laboratory: Chair of Biostatistics EPFL contact: Prof. Mats J. Stensrud In this report we consider several real-life scenarios that may provoke causal research questions. As we introduce concepts in causal inference, we reference these case studies and other examples to clarify ideas and provide examples of how researchers are approaching topics using clear causal thinking.
Topics • Machine Learning
ViewHarmful Information Against Humanitarian Organizations
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.
Topics • Machine Learning • Government & Humanitarian
ViewAdversarial 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.
Topics • Device & System Security • Machine Learning • Government & Humanitarian
ViewADAN
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.
Topics • Device & System Security • Machine Learning
ViewWhat If....? Pandemic Policy Decision Support System
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.
Topics • Machine Learning • Health • Government & Humanitarian
ViewDistributed Privacy-Preserving Insurance Insight-Sharing Platform
Partner: Swiss Re Partner contact: Sebastian Eckhardt EPFL laboratory: Laboratory for Data Security (LDS) EPFL contact: Prof. Jean-Pierre Hubaux, Juan Troncoso, Romain Bouyé The collection and analysis of risk data are essential for the insurance-business model. The models for evaluating risk and predicting events that trigger insurance policies are based on knowledge derived from risk data. The purpose of this project is to assess the scalability and flexibility of the software-based secure computing techniques in an insurance benchmarking scenario and to demonstrate the range of analytics capabilities they provide. These techniques offer provable technological guarantees that only authorized users can access the global models (fraud and loss models) based on the data of a network of collaborating organizations. The system relies on a fully distributed architecture without a centralized database, and implements advanced privacy-protection techniques based on multiparty homomorphic encryption, which makes it possible to efficiently compute machine-learning models on encrypted distributed data.
Topics • Privacy Protection & Cryptography • Machine Learning • Finance
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