Laboratoire d'informatique de l'École polytechnique

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Full Stack Developer

The Big Data Analytics Team at Ecole Polytechnique is seeking a full stack developer to assist in developing a demonstration system for explainable anomaly detection on data streams. The appointment is expected to be a contract for 2 months, with possible renewal for additional months.

As a full stack developer, you will design and develop a Web portal providing the user with a visual interface to compare the results of various anomaly detection and explanation methods on time series data. You will be working across the full tech stack, on both the Python backend and JavaScript client side. The work to be done on the Python backend ranges from handling and parsing the user’s inputs to building the appropriate links and bridges between already developed inference and evaluation modules and the visual Web-based reporting presented to the user.

While the contract allows the developer to work remotely, there are also plenty of opportunities to interact with the research team at Ecole Polytechnique, who has the focus on efficient machine learning and statistical analysis methods on Big Data. Ecole Polytechnique is a French public institution of higher education and research, located in Palaiseau 45 minutes southwest of Paris. It is considered the most prestigious engineering school in France, with well-known educational programs in science and engineering. Among its alumni are three Nobel prize winners, one Fields Medalist, three Presidents of France, and many CEOs of French and international companies.

Requirements: * Master degree in Computer Science or related areas * Solid frontend development skills in JavaScript. * Good knowledge of a data visualization framework (e.g., D3.js, Chart.js, …) is preferred * Solid backend Python development skills * Good knowledge of client server communication * Basic knowledge of Deep Learning is preferred * Independent thinking and solution development, while being collaborative in technical discussions * Sufficient English language skills; please send your application in English

The position is available immediately. Application is open until the position is filled.
We look forward to your application!
Contact: Jessica Gameiro

Post-doctoral positions in 3D Computer Vision / Machine Learning and Computer Graphics / Geometry Processing

Two postdoc positions are available at the LIX research laboratory of Ecole Polytechnique in France, in the areas of Geometry Processing and Computer Vision. For details please visit the following website:

http://www.lix.polytechnique.fr/~maks/job_offers.html

Candidates for post-doctoral positions must have a PhD at the starting time of the post-doc, and should have publications in top venues in either Computer Graphics, Geometry Processing (e.g., SIGGRAPH, Eurographics, SGP) or Computer Vision (CVPR, ECCV, NIPS, etc.). The expected duration of the post-doc is 2 years.

The project will be supervised by Maks Ovsjanikov.

Please refer to the website mentioned above for information on the positions and the application procedure.

Post doc at LIX

The Big Data Analytics Team at Ecole Polytechnique is seeking a postdoc to work on the topic of “Explainable Anomaly Detection on Data Streams”. The appointment is for one year, with possible renewal for additional months.

I. Overall Project

As enterprise information systems are collecting event streams from various sources, the ability of a system to automatically detect anomalous events and further provide human readable explanations is of paramount importance. In the EXAD project, we develop an “Explainable Anomaly Detection” framework that harnesses the power of latest machine learning techniques for anomaly detection while being able to return human-readable explanations for interpreting prediction results, thereby increasing the trust of model predictions and providing valuable, actionable information to enterprise users. To do so, our system employs a range of novel techniques, including new human-interpretable dimensionality reduction methods, fast explanation discovery methods, and a new benchmark for comparing various anomaly detection and explanation discovery methods in real-world use cases.

The postdoc is expected to further extend the EXAD system, in particular, to develop a benchmark in the new area of financial data analysis, and to develop new data encoding schemes, as well as improved explainable anomaly detection methods, for this new domain.

The postdoc will find an active and collaborative environment at Ecole polytechnique, with world-renowned researchers in data analytics systems, data mining, machine learning, and statistics. Ecole Polytechnique is a French public institution of higher education and research, located in Palaiseau 45 minutes southwest of Paris. It is considered the most prestigious engineering school in France, with well-known educational programs in science and engineering. Among its alumni are three Nobel prize winners, one Fields Medalist, three Presidents of France, and many CEOs of French and international companies.

  1. Skills Required from the Applicant
  • A PhD degree in areas related to data science, big data analytics, and data stream analytics.

  • Knowledge of recent machine learning techniques for anomaly detection or related statistical data analytics, as well as data management systems, is preferred.

  • The application is also expected to have strong programming experience in Python and related machine learning libraries.

  1. Application

The position is available immediately. Application is open until the position is filled. The interested applicant is asked to submit a resume and 2 names of references to Prof. Yanlei Diao (yanlei.diao@polytechnique.edu) and Ms. Jessica Gameiro (jgameiro@lix.polytechnique.fr).

Local limit of random discrete surface with (or without !) a statistical physics model.

Speaker: Marie Albenque (Combi team)
Location: Amphi Sophie Germain (+ https://inria.webex.com/inria/j.php?MTID=m0270b19d056bea68e6414df36ce4956b)
Date: Thu, 16 Dec 2021, 13:00-14:00

Random planar maps (which correspond to planar graphs embedded in the plane) are a very natural model of random discrete surface, which have been widely studied in these last 30 years (in particular by several members of the Combi team !). In my talk, I will present some results of convergence — in the local limit sense, as introduced by Benjamini and Schramm – for those models. I will try to give an overview of this field: I will first consider models of maps which are sampled from a uniform distribution, for which many results are available. I will then move to random planar maps sampled with a weight which comes from a statistical physics model, for which many problems are still open and yield fascinating research perspectives.

Talk by Rim Rammal: « Differential flatness for fractional order dynamic systems »

Speaker: Rim Rammal (Université Toulouse III - Paul Sabatier)
Location: Room Henri Poincaré
Date: Tue, 14 Dec 2021, 11:00-12:00

Abstract: Differential flatness is a property of dynamic systems that allows the expression of all the variables of the system by a set of differentially independent functions, called flat output, depending on the variables of the system and their derivatives. The differential flatness property has many applications in automatic control theory, such as trajectory planning and trajectory tracking. This property was first introduced for the class of integer order systems and then extended to the class of fractional order systems. This talk will present the flatness of the fractional order linear systems and more specifically the methods for computing fractional flat outputs.

Further details can be found on the webpage of the seminar: http://www.lix.polytechnique.fr/max/max-web/max/max-seminar.en.html

Talk by Mohab Safey El Din: « msolve: a library for solving multivariate polynomial systems »

Speaker: Mohab Safey El Din (Sorbonne Université)
Location: Room Grace Hopper
Date: Tue, 7 Dec 2021, 11:00-12:00

Abstract: In this talk, we present a new open source library, developed with J. Berthomieu (Sorbonne Univ.) and C. Eder (TU Kaiserslautern) named msolve, for solving multivariate polynomial systems through computer algebra methods. Its core algorithmic framework relies on Gröbner bases and linear algebra based algorithms. This includes J.-C. Faugère’s F4 algorithm, recent variants of the FGLM change of ordering and real root isolation.This talk will cover a short presentation of the current functionalities provided by msolve, followed by an overview of the implemented algorithms which will motivate the design choices underlying the library. We will also compare the practical performances of msolve with leading computer algebra systems such as Magma, Maple, Singular, showing that msolve can tackle systems which were out of reach by the computer algebra software state-of-the-art. If time permits, we will report on new algorithmic developments for ideal theoretic operations (joint work with J. Berthomieu and C. Eder) and change of orderings algorithms (joint work with J. Berthomieu and V. Neiger).

Further details can be found on the webpage of the seminar: http://www.lix.polytechnique.fr/max/max-web/max/max-seminar.en.html

Soutenance de thèse d'Isabella Panaccione : « Sur des algorithmes de décodage de codes géométriques au delà de la moitié de la distance minimale »

Speaker: Isabella Panaccione
Location: Amphi. Sophie Germain
Date: Ven. 3 déc. 2021, 09h30-11h30

Isabella Panaccione, doctorante dans l’équipe Grace, soutiendra sa thèse intitulée « Sur des algorithmes de décodage de codes géométriques au delà de la moitié de la distance minimale », le vendredi 3 Décembre à 9h30 à Inria Saclay, bâtiment Turing, salle Sophie Germain au niveau 0.

Algorithmic Law and Society

Location: HEC
Date: Wed, 1 Dec 2021, 08:00 - Fri, 3 Dec 2021, 18:00

DaSciM is co-organizing the workhop “Algorithmic Law and Society” which will take place to HEC from December 1st to the 3rd. Visit the website https://algorithmiclawandsociety.com and feel free to register.

Talk by Torsten Mütze: « Combinatorial generation via permutation languages ».

Speaker: Torsten Mütze (University of Warwick)
Location: Zoom
Date: Lun. 29 nov. 2021, 15h00-16h00

La prochaine séance du séminaire de combinatoire du plateau de Saclay aura lieu ce lundi 29 novembre à 15h en ligne (informations de connexion ci-dessous) avec retransmission en salle Philippe Flajolet du LIX. Nous aurons le plaisir d’écouter Torsten Mütze (University of Warwick) nous parler de « Combinatorial generation via permutation languages ». Le résumé et les informations de connexion sont disponibles ci-dessous.

Résumé : In this talk we present a versatile algorithmic framework for exhaustively generating a large variety of different combinatorial objects, based on encoding them as permutations. This framework provides a unified view on many known Gray code results and allows us to prove many new ones, and it yields efficient algorithms for computing Hamilton paths and cycles on large classes of polytopes. We give an overview of the ingredients of the framework, and we present two of its main applications: (1) the generation of pattern-avoiding permutations (see www.combos.org/jump); (2) the generation of lattice congruences of the weak order on the symmetric group. This talk is based on joint work with Liz Hartung, Hung P. Hoang, and Aaron Williams (SODA 2020).

Connexion:


Le programme du séminaire est disponible sur la page https://galac.lri.fr/fr/pages/combi-seminar.html

Best PhD thesis award for Natasha Fernandes (Comete)

Natasha Fernandez has won the CORE award for the best PhD thesis in CS in Australasia this year.

Natasha Fernandez was a PhD student in cotutelle between the University of Macquarie in Sidney, under the supervision of Annabelle McIver, and Inria/LIX, under the supervision of Catuscia Palamidessi. She defended her thesis in Spring this year.

Link to the announcement