Research
Summary
My doctoral thesis is entitled "End-to-end approach to classification in unstructured spaces with application to judicial decisions" and focused both on theoretical and practical Machine Learning. I try to reduce the need for expertise required in the usual Machine Learning workflow as it is the first obstacle to the adoption of artificial intelligence solutions.
A detailed summary of my doctoral thesis is available here.
My main contributions are:
- A new mathematical theory for classification, with "good" properties (explainability, no metric required, no hyperparameter,...) based on hypergraphs and metric learning,
- A generic method to automate most of data preparation using standard hyperparameter tuning techniques,
- The largest curated datasets about the legal domain, on which I reached over 94% accuracy predicting the outcome of a judgment.
My current research interests span different areas of Machine Learning and Artificial Intelligence:
- Stochastic Sequence Hypergraphs for classification,
- Explainability of Machine Learning models,
- AutoML & Automated data preparation,
- Application of AI to the justice domain.
Previously I also worked on:
- Online hyperparameter tuning,
- Multiobjective discrete optimization.
I serve or served as reviewers for the following journals and conferences:
- Fuzzy Information and Engineering, Taylor & Francis.
- Data & Knowledge Engineering, Elsevier.
- Computing, Springer.
- Expert Systems With Applications, Elsevier. [Certificate]
- Data Analytics solutions for Real-Life APlications (DARLI-AP), workshop collocated with EDBT/ICDT Joint Conference. [2020] [2019]
Thesis
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“End-to-end approach to classification in unstructured spaces with application to judicial decisions”
Supervisor: Robert Wrembel (Poznan University of Technology)
Ph.D. thesis
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“Insertion of adaptive modalities in the mono or multi objectives evolutionary planner Divide-and-Evolve”
Supervisor: Marc Schoenauer (Inria), Christian Gout (INSA)
Master thesis
Publications
“True Pareto Fronts for Realistic Multi-Objective AI Planning Instances”
Alexandre Quemy
To be submitted to International Conference on Automated Planning and Scheduling (ICAPS)
“A Physical Approach to Classification”
Alexandre Quemy
To be submitted to International Conference on Machine Learning (ICML)
“Paradiseo: From a Modular Framework for Evolutionary Computation to the Automated Design of Metaheuristics”
Johann Dreo, Arnaud Liefooghe, Sébastien Verel, Marc Schoenauer, Juan Merelo, Alexandre Quemy, Benjamin Bouvier, Jan Gmys
Genetic and Evolutionary Computation Conference (GECCO)
“ECHR-OD: On Building an Integrated Open Repository of Legal Documents for Machine Learning Applications”
Alexandre Quemy and Robert Wrembel
Information Systems
“Two-stage Optimization for Machine Learning Workflow”
Alexandre Quemy
Information Systems
“Binary Classification In Unstructured Space With Hypergraph Case-Based Reasoning”
Alexandre Quemy
Information Systems
“Data Pipeline Selection and Optimization”
Alexandre Quemy
International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP) @ International Conference on Extending Database Technology/International Conference on Database Theory (EDBT/ICDT) Joint Conference
“Binary Classification With Hypergraph Case-Based Reasoning”
Alexandre Quemy
International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP) @ International Conference on Extending Database Technology/International Conference on Database Theory (EDBT/ICDT) Joint Conference
“Unsupervised Video Semantic Partitioning Using IBM Watson and Topic Modelling”
Alexandre Quemy and Krzysztof Jamrog and Marcin Janiszewski
International Workshop on Data Analytics solutions for Real-LIfe APplications (DARLI-AP) @ International Conference on Extending Database Technology/International Conference on Database Theory (EDBT/ICDT) Joint Conference
Awards
IBM Innovation Award
Restlessly reinvent – our company and ourselves
2019
Best Paper, International Workshop On Design, Optimization
Languages and Analytical Processing of Big Data
2018, Lisbon
Grant from the Polish Academy of Science
IFIP World Computer Congress PhD Student Research Competition
2018
IBM Analytics Hero Award
Restlessly reinvent – our company and ourselves
2018
Best Paper, International Workshop On Design, Optimization
Languages and Analytical Processing of Big Data
2018, Vienna
IBM Manager’s Choice Award x2
Dare to Create Original Ideas
2016
Teaching & Supervision
Supervision
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“Graph-based linear explanation for supervised machine learning models”
2018 - 2019
Pawel Mroz, Master Thesis
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“Design and implementation of a technique to assess regressions associated to GitHub Pull Request”
Sum. 2019
Laetitia Beignon, Internship
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“Improving predictions of the European Court of Human Rights decisions”
Spr. 2019
Amadeusz Masny, Internship
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“Hyperparameter Tuning: state-of-the-art and benchmarking”
Spr. 2019
Sylwia Wronia, Internship
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“Hyperparameter optimization of Split-and-Merge, a semantic partitioning algorithm”
Sum. 2018
Pawel Rzonca, Internship
Teaching
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“Theoretical Machine Learning”
2018 - 2019
Lectures at IBM Krakow Software Lab
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“IBM Watson Services Overview”
2016 - 2019
Regular presentation at polish universities
Talks
“Is practical AutoML more than CASH?”
GHOST Day: a practical machine learning conference Poznan, Poland
“Towards Data Pipeline Selection and Optimization”
IBM CEE Regional Technical Exchange Budapest, Hungary
“AI for the legal domain: an explainability challenge (extended)”
IBM Vitality Talks Cracow, Poland
“Artificial Intelligence Microservices for NLP”
IBM Vitality Talks Cracow, Poland
“Can we really compare our algorithms? Beyond worst-case time complexity”
IBM Vitality Talks Cracow, Poland
“Data Science Techniques for Law and Justice: Current State of Research and Open Problems”
IBM Vitality Talks Cracow, Poland
“Intelligent Home Automation, combining IoT and Machine Learning”
KrakYourNet 7 Cracow, Poland
“General Parallel File System (GPFS) presentation and administration”
IBM Vitality Talks Cracow, Poland