Work
Research, engineering, and experiments spanning machine learning, network systems, and software. More GitHub projects coming soon.
My Master's thesis on predicting course enrollment using student-level trajectory modelling. We compared LSTMs, GRUs, LightGBM, and Course2Vec embeddings on 505 students and 51 courses over 11 years, and explored hierarchical reconciliation to make forecasts consistent across different planning levels.
A European consortium project on which I collaborate. In many AI use cases, the training stage is done on a central server, meaning that data is shared. Hence, we are developing a solution based on Federated Learning (without sharing any kind of data) integrating the VEDLIoT (also an EU-funded project) toolchain into our use case (voice recognition) within a 5G network setup.
A project for the UPC (Universitat Politècnica de catalunya) for implementing, configuring and running a 5G end-to-end setup using SDRs and OpenAirInterface5G, allowing the UPC to conduct a wide range of experiments/studies on the 5G network.
So far, the procedure for university enrollments it has been done manually. In addition, it has always required a great effort and experience on the part of the team that manages them. We are facing a complex problem, that is, if we wanted to automate this procedure, we could not apply a traditional approach nor any generic rule or algorithm to determine whether or not a student will enroll because of the behavior of each student is quite unpredictable. For this reason, it is proposed to apply Machine Learning with the purpose of generating and analyzing various predictive models based on the previous academic history of all students.