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:

Thesis

  • “End-to-end approach to classification in unstructured spaces with application to judicial decisions”

    Supervisor: Robert Wrembel (Poznan University of Technology)

    Ph.D. thesis

  • “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

2021

“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)

 

“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

 

“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

2020

“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)

 

“On Integrating and Classifying Legal Text Documents”

Alexandre Quemy and Robert Wrembel

International Conference on Database and Expert Systems Applications (DEXA)

 

“Two-stage Optimization for Machine Learning Workflow”

Alexandre Quemy

Information Systems

2019

“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

2018

“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

 

“AI for the legal domain: an explainability challenge”

Alexandre Quemy

PhD Student Research Competition, IFIP World Computer Congress

 

“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

2017

“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

2015

“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)

 

“True Pareto Fronts for Multi-objective AI Planning Instances”

Alexandre Quemy and Marc Schoenauer

Evolutionary Computation in Combinatorial Optimization (EvoCOP)

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

  • “Graph-based linear explanation for supervised machine learning models”

    2018 - 2019

    Pawel Mroz, Master Thesis

  • “Design and implementation of a technique to assess regressions associated to GitHub Pull Request”

    Sum. 2019

    Laetitia Beignon, Internship

  • “Improving predictions of the European Court of Human Rights decisions”

    Spr. 2019

    Amadeusz Masny, Internship

  • “Hyperparameter Tuning: state-of-the-art and benchmarking”

    Spr. 2019

    Sylwia Wronia, Internship

  • “Hyperparameter optimization of Split-and-Merge, a semantic partitioning algorithm”

    Sum. 2018

    Pawel Rzonca, Internship

Teaching

  • “Theoretical Machine Learning”

    2018 - 2019

    Lectures at IBM Krakow Software Lab

  • “IBM Watson Services Overview”

    2016 - 2019

    Regular presentation at polish universities

Talks

2020

“A Better Approach to Data Science: the example of COVID 19”

HackYeah, Online Webinar

 

“PCI Passthrough with Consumer GPU”

IBM Vitality Talks Cracow, Poland

2019

“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

2018

“Towards Data Pipeline Optimization”

PyData Warsaw Cracow, Poland

 

“AI for the legal domain: an explainability challenge (extended)”

IBM Vitality Talks Cracow, Poland

 

“Artificial Intelligence Microservices for NLP”

IBM Vitality Talks Cracow, Poland

2017

“Can we really compare our algorithms? Beyond worst-case time complexity”

IBM Vitality Talks Cracow, Poland

 

“IBM Watson Services in Scala”

ScalaSphere Cracow, Poland

 

“Data Science Techniques for Law and Justice: Current State of Research and Open Problems”

IBM Vitality Talks Cracow, Poland

2016

“Intelligent Home Automation, combining IoT and Machine Learning”

KrakYourNet 7 Cracow, Poland

 

“CESTAC: Stochastic estimation and control of rounding floatting point errors”

IBM Vitality Talks Cracow, Poland

 

“General Parallel File System (GPFS) presentation and administration”

IBM Vitality Talks Cracow, Poland