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-  * [[education/ca_2018|Concurrent Algorithms]] (theory & practice)
+  * [[education/ca_2020|Concurrent Algorithms]] (theory & practice)
   * [[education/da|Distributed Algorithms]] (theory & practice)
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 DCL offers master projects in the following areas:
-  * **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.
+  * **[[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.
-  * **Distributed computing using RDMA and/or NVRAM**: contact [[https://people.epfl.ch/igor.zablotchi|Igor Zablotchi]] for more information.
+  * **GAR performances on different datasets**:Robust machine learning on textual data and content recommendation is critical for the safety of social media users (harassment, hate speech, etc.), but also for the reliability of scientific use of natural language processing such for processing computer programs, chemistry and drug discovery. Text datasets are known to have long-tailed distributions, which poses specific challenges for robustness, while content recommendation datasets may feature clusters of similar users. The goal of this project is to better understand the properties of different datasets, and what makes a gradient aggregation rule (e.g. Krum, trimmed mean...) better than another, given a specific text dataset (conversational chatbots, translation, github code etc.). Contact [[https://people.epfl.ch/le.hoang|Lê Nguyên Hoang]]  for more information.
-  * **[[Distributed ML|Distributed Machine Learning]]**
+  * **Strategyproof collaborative filtering**:In collaborative filtering, other users' inputs are used to generalize the preferences of a given user. Such an approach has been critical to improve performance. However, it exposes each user to being manipulated by the inputs of malicious users, which is arguably currently occurring on social medias. In this theoretical project, we search for Byzantine-resilient and strategyproof learning algorithms to perform something akin to collaborative filtering. This would also have important applications for implicit voting systems on exponential-size decision sets. Contact [[https://people.epfl.ch/le.hoang|Lê Nguyên Hoang]] for more information.
-  * **Robust Distributed Machine Learning**: The goal of such project is to work on the design and implementation of algorithms and systems to improve the robustness of distributed ML schemes that would tolerate poisoned data, software bugs as well as hardware failures. The practical work will be done on TensorFlow or Pytorch. Contact [[https://people.epfl.ch/arsany.guirguis|Arsany Guirguis]] or [[https://people.epfl.ch/sebastien.rouault|Sébastien Rouault]] 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.
-  * **Consistency in global-scale storage systems**: We offer several projects in the context of storage systems, ranging from implementation of social applications (similar to [[http://retwis.redis.io/|Retwis]], or [[https://github.com/share/sharejs|ShareJS]]) to recommender systems, static content storage services (à la [[https://www.usenix.org/legacy/event/osdi10/tech/full_papers/Beaver.pdf|Facebook's Haystack]]), or experimenting with well-known cloud serving benchmarks (such as [[https://github.com/brianfrankcooper/YCSB|YCSB]]); please contact [[http://people.epfl.ch/dragos-adrian.seredinschi|Adrian Seredinschi]] or [[https://people.epfl.ch/karolos.antoniadis|Karolos Antoniadis]]  for further information.
+  * **Distributed computing using RDMA and/or NVRAM.** RDMA (Remote Direct Memory Access) allows accessing a remote machine's memory without interrupting its CPU. NVRAM is byte-addressable persistent (non-volatile) memory with access times on the same order of magnitude as traditional (volatile) RAM. These two recent technologies pose novel challenges and raise new opportunities in distributed system design and implementation. Contact [[https://people.epfl.ch/igor.zablotchi|Igor Zablotchi]] for more information.
+  * **Robust Distributed Machine Learning**: With the proliferation of big datasets and models, Machine Learning is becoming distributed. Following the standard parameter server model, the learning phase is taken by two categories of machines: parameter servers and workers. Any of these machines could behave arbitrarily (i.e., said Byzantine) affecting the model convergence in the learning phase. Our goal in this project is to build a system that is robust against Byzantine behavior of both parameter server and workers. Our first prototype, AggregaThor(https://mlsys.org/Conferences/2019/doc/2019/54.pdf), describes the first scalable robust Machine Learning framework. It fixed a severe vulnerability in TensorFlow and it showed how to make TensorFlow even faster, while robust. Contact [[https://people.epfl.ch/arsany.guirguis|Arsany Guirguis]] for more information.
+  * **Consistency in global-scale storage systems**: We offer several projects in the context of storage systems, ranging from implementation of social applications (similar to [[http://retwis.redis.io/|Retwis]], or [[https://github.com/share/sharejs|ShareJS]]) to recommender systems, static content storage services (à la [[https://www.usenix.org/legacy/event/osdi10/tech/full_papers/Beaver.pdf|Facebook's Haystack]]), or experimenting with well-known cloud serving benchmarks (such as [[https://github.com/brianfrankcooper/YCSB|YCSB]]); please contact [[http://people.epfl.ch/dragos-adrian.seredinschi|Adi Seredinschi]] or [[https://people.epfl.ch/karolos.antoniadis|Karolos Antoniadis]]  for further information.
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 EPFL I&C duration, credits and workload information are available [[https://www.epfl.ch/schools/ic/education/|here]]. Don't hesitate to contact the project supervisor if you want to complete your Semester Project outside the regular semester period.
+===== Collaborative Projects =====
+The lab is also collaborating with the industry and other labs at EPFL to offer interesting student projects motivated from real-world problems. With [[http://lara.epfl.ch|LARA]] and [[interchain.io|Interchain Foundation]] we have several projects:
+  - **[[https://dcl.epfl.ch/site/cryptocurrencies|AT2]]:** Integration of an asynchronous (consensus-less) payment system in the Cosmos Hub.
+  - **[[https://github.com/cosmos/ics/tree/master/ibc|Interblockchain Communication (IBC)]]:** Protocols description (and optional implementation) for enabling the inter-operation of independent blockchain applications.
+  - **[[http://stainless.epfl.ch|Stainless]]**: Implementation of Tendermint modules (consensus, mempool, fast sync) using Stainless and Scala.
+  - **[[https://github.com/viperproject/prusti-dev|Prusti]]:** Implementation of Tendermint modules (consensus, mempool, fast sync) using Prusti and the Rust programming language.
+  - **[[https://tendermint.com/docs/spec/reactors/mempool/functionality.html#mempool-functionality|Mempool]]** performance analysis and algorithm improvement.
+  - **Adversarial engineering:** Experimental evaluation of Tendermint in adversarial settings (e.g., in the style of [[http://jepsen.io/analyses/tendermint-0-10-2|Jepsen]]).
+  - **Testing**: Generation of tests out of specifications (TLA+ or Stainless) for the consensus module of Tendermint.
+  - **Facebook Libra comparative research**: Comparative analysis of consensus algorithms, specifically, between HotStuff (the consensus algorithm underlying [[https://cryptorating.eu/whitepapers/Libra/libra-consensus-state-machine-replication-in-the-libra-blockchain.pdf|Facebook's Libra]]) and Tendermint consensus.
+Contact [[adi@interchain.io|Adi Seredinschi]] (INR 327) if interested in learning more about these projects.