Differences

This shows you the differences between two versions of the page.

--- education [2022/02/25 11:02]
fablpd
+++ education [2022/04/19 17:49]
fablpd
@@ Line 28: / Line 28: @@
   * **[[cryptocurrencies|Cryptocurrencies]]**: We have several project openings as part of our ongoing research on designing new cryptocurrency systems. Please contact [[rachid.guerraoui@epfl.ch|Prof. Rachid Guerraoui]].
-  * **On the design and implementation of scalable and secure blockchain algorithms**: Consensus has recently gained in popularity with the advent of blockchain technologies. Unfortunately, most blockchains do not scale due, in part, to their centralized (leader-based) limitation. We recently designed a promising fully decentralised (leader-less) algorithm that promises to scale to large networks. The goal of this project is to implement it in rust and compare its performance on AWS instances against a traditional leader-based alternative like BFT-Smart whose code will be provided. Contact [[https://people.epfl.ch/vincent.gramoli|Vincent Gramoli]] for more information.
-  * **Making Blockchain Accountable**: Abstract: One of the key drawback of blockchain is its lack of accountability. In fact, it does not hold participants responsible for their actions. This is easy to see as a malicious or Byzantine user typically double spends in a branch of blocks that disappears from the system, hence remaining undetected. Accountability is often thought to be communication costly: to detect a malicious participants who has sent deceitful messages to different honest participants for them to disagree, one may be tempted to force each honest participant to exchange all the messages they receive and cross-check them. However, we have recently designed an algorithm that shares the same communication complexity as the current consensus algorithms of existing blockchains. The goal of this project is to make blockchains accountable by implementing this accountable consensus algorithm and comparing it on a distributed set of machines against a baseline implementation. Contact [[https://people.epfl.ch/vincent.gramoli|Vincent Gramoli]] for more information.
+  * **Decentralized authentication for cryptocurrencies**: Current cryptocurrency systems use expensive cryptographic operations to authenticate users. These heavy computations limit the number of users and operations a system can serve concurrently which prevents it to scale. Our recent research shows that we can use a decentralized authentication algorithm to bypass the cryptographic bottleneck and make cryptocurrency systems faster and more available. This is a practical project which requires good knowledge of network programming, preferrably Rust otherwise C++, and of the basics of cryptography (hashing functions, asymmetric cryptography). Preferred skills include distributed algorithms and more advanced cryptography such as BLS signatures. Contact Pierre-Louis Roman <pierre-louis.roman@epfl.ch> for more information.
+  * **Topology-aware mempool for cryptocurrencies**:The mempool is a core component of cryptocurrency systems. It disseminates user transactions to the miner nodes before they reach consensus.Current mempools assume an homogeneous network topology where all machines have the same bandwidth and latency.This unrealitic assumption forces the system to progress at the same speed as the slowest node in the system. This project aims at implementing a mempool which exploits the heterogeneity of the network to speed up data dissemination for cryptocurrency systems. This is a practical project which requires good knowledge in network programming, either Go or C++, distributed algorithms. Contact Gauthier Voron <gauthier.voron@epfl.ch> for more information.
   * **Robust mean estimation**: In recent years, many algorithms have been proposed to perform robust mean estimation, which has been shown to be equivalent to robust gradient-based machine learning. A new concept has been proposed to define the performance of a robust mean estimator, called the [[https://arxiv.org/abs/2008.00742|averaging constant]] (along with the Byzantine resilience). This research project consists of computing the theoretical averaging constant of different proposed robust mean estimators, and to study their empirical performances on randomly generated vectors. Contact [[https://people.epfl.ch/sadegh.farhadkhani?lang=en|Sadegh Farhadkhani]] for more information.
@@ Line 37: / Line 38: @@
   * **Accelerate Byzantine collaborative learning**: [[https://arxiv.org/abs/2008.00742|Our recent NeurIPS paper]] proposed algorithms for collaborative machine learning in the presence of Byzantine nodes, which have been proved to be near optimal with respect to optimality at convergence. However, these algorithms require all-to-all communication at every round, which is suboptimal. This research consists of designing a practical solution to Byzantine collaborative learning, based on the idea of a random communication network at each round, with both theoretical guarantees and practical implementation. Contact [[https://people.epfl.ch/sadegh.farhadkhani?lang=en|Sadegh Farhadkhani]] for more information.
-  * **Decentralize Tournesol’s learning algorithms**: The [[https://tournesol.app/|Tournesol platform]] leverages the contributions of its community of contributors to assign a « should be more recommended » score to YouTube videos rated by the contributors, using a learning algorithm. Currently, the computations are performed on a central server. But as Tournesol’s user base grows, and as more sophisticated learning algorithms are considered for deployment, there is a growing need to decentralize the computations of the learning algorithm. This project aims to build a framework, which will enable Tournesol users to run part of the computations of Tournesol’s scores directly in their browsers. Contact [[https://people.epfl.ch/le.hoang/?lang=en|Lê Nguyên Hoang]] for more information.
-  * **Listening to the silent majority**: Vanilla machine learning from user-generated data inevitably favors those who generated the most amounts of data. But this means that learning algorithms will be optimized for these users, rather than for the silent majority. This research aims to correct for this bias, by trying to infer what data the majority would have likely generated, and by inferring what the models would have learned if the silent majority’s data was included in the training of the models. It involves both designing algorithms, proving correctness and implementing them. This research is motivated by the [[https://tournesol.app/|Tournesol project]]. Contact [[https://people.epfl.ch/le.hoang/?lang=en|Lê Nguyên Hoang]] for more information.
-  * **Should experts be given more voting rights?**: This is a question that Condorcet tackled in 1785, through what is now known as the jury problem. However, his model was crude and does not apply to many critical problems, e.g. determining if a video on vaccines should be largely recommended. This research aims to better understand how voting rights should be allocated, based not only on how likely voters are to be correct, but also on the correlations between the voters’ judgments. So far, it involves mostly a theoretical analysis. This research is motivated by the [[https://tournesol.app/|Tournesol project]]. Contact [[https://people.epfl.ch/le.hoang/?lang=en|Lê Nguyên Hoang]] for more information.
   * **Probabilistic Byzantine Resilience**:  Development of high-performance, Byzantine-resilient distributed systems with provable probabilistic guarantees. Two options are currently available, both building on previous work on probabilistic Byzantine broadcast: (i) a theoretical project, focused the correctness of probabilistic Byzantine-tolerant distributed algorithms; (ii) a practical project, focused on numerically evaluating of our theoretical results. Please contact [[matteo.monti@epfl.ch|Matteo Monti]] to get more information.