Alexandre Quemy

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

1. End-to-end approach to classification in unstructured spaces with application to judicial decisions Supervisor: Robert Wrembel (Poznan University of Technology) Ph.D. thesis
2. Insertion of adaptive modalities in the mono or multi objectives evolutionary planner Divide-and-Evolve Supervisor: Marc Schoenauer (Inria), Christian Gout (INSA) Master thesis

1. True Pareto Fronts for Realistic Multi-Objective AI Planning Instances Alexandre Quemy To be submitted to International Conference on Automated Planning and Scheduling (ICAPS), 2021
2. A Physical Approach to Classification Alexandre Quemy To be submitted to International Conference on Machine Learning (ICML), 2021
3. Cautiously Making Friends with AI: Machine Learning for human rights research and practice Helga Molbæk-Steensig, Alexandre Quemy AI & Human Rights: Friend or Foe?, The Erasmus School of Law, together with the Jean Monnet Centre of Excellence on Digital Governance, 2021
4. 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), 2021
5. ECHR-OD: On Building an Integrated Open Repository of Legal Documents for Machine Learning Applications Alexandre Quemy and Robert Wrembel Information Systems, 2021
6. GBEx, towards Graph-Based Explainations Paweł Mróz and Alexandre Quemy and Mateusz Ślażyński and Krzysztof Kluza and Paweł Jemioło International Conference Tools with Artificial Intelligence (ICTAI), 2020
7. On Integrating and Classifying Legal Text Documents Alexandre Quemy and Robert Wrembel International Conference on Database and Expert Systems Applications (DEXA), 2020
8. Two-stage Optimization for Machine Learning Workflow Alexandre Quemy Information Systems, 2020
9. Binary Classification In Unstructured Space With Hypergraph Case-Based Reasoning Alexandre Quemy Information Systems, 2019
10. 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, 2019
11. 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, 2018
12. AI for the legal domain: an explainability challenge Alexandre Quemy PhD Student Research Competition, IFIP World Computer Congress, 2018
13. 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, 2018
14. Data Science Techniques for Law and Justice: Current State of Research and Open Problems Alexandre Quemy Advances in Databases and Information Systems (ADBIS) Workshops and Short papers, 2017
15. Solving Large MultiZenoTravel Benchmarks with Divide-and-Evolve Alexandre Quemy and Marc Schoenauer and Vincent Vidal and Johann Dréo and Pierre Savéant Learning and Intelligent Optimization (LION), 2015
16. True Pareto Fronts for Multi-objective AI Planning Instances Alexandre Quemy and Marc Schoenauer Evolutionary Computation in Combinatorial Optimization (EvoCOP), 2015

1. IBM Innovation Award Restlessly reinvent – our company and ourselves, 2019
2. Best Paper, International Workshop On Design, Optimization Languages and Analytical Processing of Big Data, Lisbon, 2018
3. Grant from the Polish Academy of Science IFIP World Computer Congress PhD Student Research Competition, 2018
4. IBM Analytics Hero Award Restlessly reinvent – our company and ourselves, 2018
5. Best Paper, International Workshop On Design, Optimization Languages and Analytical Processing of Big Data, Vienna, 2018
6. IBM Manager’s Choice Award x2 Dare to Create Original Ideas, 2016

Students and interns under my supervision:

1. Graph-based linear explanation for supervised machine learning models Pawel Mroz, Master Thesis, 2018 - 2019
2. Design and implementation of a technique to assess regressions associated to GitHub Pull Request Laetitia Beignon, Internship, Sum. 2019
3. Improving predictions of the European Court of Human Rights decisions Amadeusz Masny, Internship, Spr. 2019
4. Hyperparameter Tuning: state-of-the-art and benchmarking Sylwia Wronia, Internship, Spr. 2019
5. Hyperparameter optimization of Split-and-Merge, a semantic partitioning algorithm Pawel Rzonca, Internship, Sum. 2018

I have participated in the following graduate and undergraduate courses:

1. Theoretical Machine Learning Lectures at IBM Krakow Software Lab, 2018 - 2019
2. IBM Watson Services Overview Regular presentation at polish universities, 2016 - 2019

1. A Better Approach to Data Science: the example of COVID 19 HackYeah, Online Webinar, 2020
2. PCI Passthrough with Consumer GPU IBM Vitality Talks Cracow, Poland, 2020
3. Is practical AutoML more than CASH? GHOST Day: a practical machine learning conference Poznan, Poland, 2019
4. Towards Data Pipeline Selection and Optimization IBM CEE Regional Technical Exchange Budapest, Hungary, 2019
5. Towards Data Pipeline Optimization PyData Warsaw Cracow, Poland, 2018
6. AI for the legal domain: an explainability challenge (extended) IBM Vitality Talks Cracow, Poland, 2018
7. Artificial Intelligence Microservices for NLP IBM Vitality Talks Cracow, Poland, 2018
8. Can we really compare our algorithms? Beyond worst-case time complexity IBM Vitality Talks Cracow, Poland, 2017
9. IBM Watson Services in Scala ScalaSphere Cracow, Poland, 2017
10. Data Science Techniques for Law and Justice: Current State of Research and Open Problems IBM Vitality Talks Cracow, Poland, 2017
11. Intelligent Home Automation, combining IoT and Machine Learning KrakYourNet 7 Cracow, Poland, 2016
12. CESTAC: Stochastic estimation and control of rounding floatting point errors IBM Vitality Talks Cracow, Poland, 2016
13. General Parallel File System (GPFS) presentation and administration IBM Vitality Talks Cracow, Poland, 2016