His thesis developed interpretable generative machine learning models for travel demand analysis.
For his Ph D thesis, Dr Jules Heuberger did research at CHDR on the subject 'The clinical pharmacology of performance enhancement and doping detection in sports'.
In the past, three techniques have been developed for improving an SPE’s ability to adapt.
Those techniques are classified based on applications’ requirements on exact or approximate results: stream partitioning, and re-partitioning target exact, and load shedding targets approximate processing.
Stream partitioning strives to balance load among processors, and previous techniques neglected hidden costs of distributed execution.
Load Shedding lowers the accuracy of results by dropping part of the input, and previous techniques did not cope with evolving streams.
Committee Alexandros Labrinidis, Department of Computer Science, University of Pittsburgh (co-chair) Panos Chrysanthis, Department of Computer Science, University of Pittsburgh (co-chair) Jack Lange, Department of Computer Science, University of Pittsburgh (member) Andy Pavlo, Department of Computer Science, Carnegie Mellon University (member) TITLE: A Procrustean Approach to Stream Processing Abstract: === START === The increasing demand for real-time data processing and the constantly growing data volume have contributed to the rapid evolution of Stream Processing Engines (SPEs), which are designed to continuously process data.
Low operational cost and timely delivery of results are both objectives of paramount importance for SPEs.
From his research, Jules concluded that for most of the substances prohibited by the World Anti-Doping Agency (WADA) there is no evidence of performance enhancement.
Considering doping has become an important issue in the sports world, Jules' research received a lot of attention, both positive and negative.