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Distributed Programming Laboratory LPD
The lab is teaching the following courses:
LPD offers master projects in the following areas:
- Dynamically Distributed Spatial Indexing: a project here would consist in studying existing spatial index data structures and algorithms, e.g., simple grids, Quadtrees, R-Trees etc., and how they may be dynamically distributed for indexing a large number of moving objects; please contact Benoit Garbinato to get more information.
- Multicore computing: a project here would consist for instance in designing and implementing efficient lock-based or lock-free shared objects; please contact Tudor David to get more information.
- Dynamic distributed computing: a project here would consist for instance in designing and implementing applications that would run in a simulation of a cloud with high churn, but possibly robust to arbitrary behavior of some of its components; please contact Matej Pavlovic to get more information.
- Distributed and Fault-tolerant algorithms: projects here would consist in designing failure detection mechanisms suited for large-scale systems, real-time systems, and systems with unreliable communication or partial synchrony. This task also involves implementing, evaluating, and simulating the performance of the developed mechanisms to verify the achievable guarantees; please contact David Kozhaya 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 Retwis, or ShareJS) to recommender systems, static content storage services (à la Facebook's Haystack), or experimenting with well-known cloud serving benchmarks (such as YCSB); please contact Adrian Seredinschi for further information.
- Distributed database algorithms: a project here would consist in implementing and evaluating protocols that are running in today's database systems, e.g., 2PC, and comparing them with those protocols that can potentially be used in future database systems; please contact Jingjing Wang to get more information.
- Machine learning attacks privacy: a project here would consist in implementing attacks to privacy-preserving platforms using machine learning (e.g., a neural network); please contact Mahsa Taziki to get more information.
If the subject of a Master Project interests you as a Semester Project, please contact the supervisor of the Master Project to see if it can be considered for a Semester Project.
EPFL I&C duration, credits and workload information are available here. Don't hesitate to contact the project supervisor if you want to complete your Semester Project outside the regular semester period.