| KEYWORD |
A fast Bayesian Artificial Intelligence reasoning engine for modeling and optimization tasks
keywords ARTIFICIAL INTELLIGENCE, OPTIMIZATION ALGORITHMS, PARALLEL COMPUTING, ARCHITECTURE SOFTWARE DISTRIBU, STATISTICAL ANALYSIS
Reference persons STEFANO DI CARLO, ALESSANDRO SAVINO
Research Groups DAUIN - GR-24 - SMILIES - reSilient coMputer archItectures and LIfE Sci, TESTGROUP - TESTGROUP
Thesis type APPLIED RESEARCH, RESEARCH / EXPERIMENTAL
Description Goals:
Build a C++ Bayesian Artificial Intelligence reasoning library resorting to all modern programming paradigms including advanced modelling features, paired with an extremal optimization engine.
Description:
The Bayesian reasoning is a field of the Artificial Intelligence deputed to the ability of taking decisions using uses the knowledge of uncertain prediction. We aim at developing a C++ library to support such tools in a modern fashion.
The library is going to be composed by two set of tools:
1. The Bayesian core: it provides all programming tools to generate and analyze BNs. The core is going to be build resorting to all available programming paradigm: multi-threading, mixed CPU-GPU computations, etc.
2. The Extremal Optimization engine: build on top of the core, the engine must resort to all analysis capabilities to exploit any kind of multi-objective optimization. The engine will include a meta-programming layer to describe the optimization rules to support the optimization with the maximum flexibility.
3. Extra: if feasible, the final code could be provided with a Python wrapper too.
Learned Outcomes: Advanced C/C++ programming, multi-thread and mixed computation, Bayesian Theory, Optimization algorithms.
Required skills C/C++ programming, basic parallel computing (from Operating Systems lectures).
Deadline 27/07/2023
PROPONI LA TUA CANDIDATURA