Conferences and Workshops of interest for AIROYoungers

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Displaying conferences 51 - 75 of 102 in total
Start date End date Description Location Country Url
01 Jun 2020 05 Jun 2020 Modern optimization for Transportation

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Organized by École des Ponts ParisTech with the support of Air France.


Claudia Archetti (University of Brescia) Solution methods for recent and challenging routing problems

Michel Bierlaire (Ecole Polytechnique Fédérale de Lausanne) Behavioral optimization

Luce Brotcorne (Inria Lille) Bilevel Programming and its applications to Network Pricing and Energy Management

Stein W. Wallace (NHH - Norvegian School of Economics) Handling randomness in logistics modelling

Practical Informations

Location: Villa Clythia in Fréjus, a wonderful resort of the French Riviera.

Dates: June 1-5, 2020

Schedule: To be announced

Registrations: will open in January 2020 (and will be announced on DMANET)


Organizers: Frédéric Meunier and Axel Parmentier (

Sponsor: The “Operations Research and Machine Learning” chair of Air France and École des Ponts ParisTech

Fréjus France Go
31 May 2020 03 Jun 2020 Column Generation 2020

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COLUMN GENERATION 2020 will take place in Sainte-Adèle, Québec, Canada, in the Hôtel Mont-Gabriel located in the beautiful Laurentians region about 70km North of Montréal. The workshop will start on Sunday, May 31 in the late afternoon with registration and a welcome reception and will end before dinner on Wednesday, June 3. It is sponsored by the GERAD research center (through its FRQNT Strategic Cluster grant) and by the Centre de Recherches Mathématiques (CRM). It is part of CRM's thematic semester on the Mathematics of Decision Making.

Like the successful Column Generation 2008 (Aussois, France), Column Generation 2012 (Bromont, Canada) and Column Generation 2016 (Búzios, Brazil), Column Generation 2020 aims at bringing together researchers from operations research, mathematical programming, and computer science which are active in solving large-scale integer programs via column generation. The workshop reflects the state-of-the-art of theory, applications, and implementation. It is informal in character and meant as a place of active research and exchange. There will be a single track of about 30 presentations.

Participation in Column Generation 2020 is BY INVITATION ONLY.

For more information, please contact

Sainte-Adèle Canada Go
18 May 2020 21 May 2020 Complex Networks: Theory, Methods, and Applications

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Many real systems can be modeled as networks, where the elements of the system are nodes and interactions between elements are edges. An even larger set of systems can be modeled using dynamical processes on networks, which are in turn affected by the dynamics. Networks thus represent the backbone of many complex systems, and their theoretical and computational analysis makes it possible to gain insights into numerous applications. Networks permeate almost every conceivable discipline —including sociology, transportation, economics and finance, biology, and myriad others — and the study of “network science” has thus become a crucial component of modern scientific education.

The school “Complex Networks: Theory, Methods, and Applications” offers a succinct education in network science. It is open to all aspiring scholars in any area of science or engineering who wish to study networks of any kind (whether theoretical or applied), and it is especially addressed to doctoral students and young postdoctoral scholars. The aim of the school is to deepen into both theoretical developments and applications in targeted fields.

Como Italy Go
02 May 2020 06 May 2020 Integer Symposium on Combinatorial Optimization

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ISCO (Integer Symposium on Combinatorial Optimization) is a biannual symposium whose aim is to bring together researchers from all the communities related to combinatorial optimization, including algorithms and complexity, mathematical programming, operations research, stochastic optimization, graphs and combinatorics. Quality papers on all aspects of combinatorial optimization, from mathematical foundations and theory of algorithms to computational studies and practical applications, are solicited.

Each ISCO conference is preceded or followed by a Spring School lectured by international Combinatorial Optimization searches. This school is dedicated to PhD students but the lectures are also opened to older colleagues.

The sixth issue will be held in Montreal, Canada goes to Marrakesh, Morocco in March 2020 organized with Bernard Gendron, from Université de Montréal. The ISCO conference will combined with the Optimization Days of the GERAD.
The spring school title is "Data science and combinatorial optimization" and will be offered by Andrea Lodi from Polytechnique Montréal.
School: 2 and 3 of May 2020
Conference: 4-6 May, 2020

Montréal Canada Go
20 Apr 2020 24 Apr 2020 NATCOR Course on Heuristic and Approximation Algorithms

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Heuristic and Approximation Algorithms are Operational Research tools that provide solutions to real-world optimisation problems across a wide range of application areas despite their inherent complexity and uncertainty. Heuristic algorithms include a range of techniques from simple ‘rules of thumb’ to more sophisticated methods inspired on physical and natural processes. Heuristics can provide good-quality solutions (though not necessarily optimal) in practical computational time to otherwise intractable problems. Approximation algorithms are designed to guarantee solutions of given quality based on worst-case analysis. The course features the main techniques for heuristic and approximation algorithms as well as an insight into data science and machine learning in the context of heuristic optimisation. The course is delivered by a set of experts in the field with strong publication records and experience in the design and deployment of these methods on real-world problems.

Basics of complexity and optimization theory as well as computer algorithms. Some reading material is provided to students a few weeks in advance to the start of the course.

On completion of the course, students should have a working knowledge of the theory, design, implementation and applications of the main heuristic methods and approximation algorithms, as well as an insight into their interplay with data science and machine learning in the context of optimisation scenarios.

– Understanding of the fundamental theory underlying approximation algorithms and the main heuristic optimisation methods (e.g. local search, metaheuristics, hyper-heuristics, evolutionary algorithms, etc.).
– Awareness of the strengths and limitations of different heuristic optimisation methods.
– Ability to critically evaluate the applicability and quality of different heuristic optimisation methods.
– Capability for designing and developing appropriate heuristic methods to different optimisation problems.
– Awareness of existing software tools for the rapid prototyping of heuristic optimisation methods.
– Understanding of the fundamentals of data science and machine learning in the context of heuristic optimisation.

Introduction to Optimization, Complexity Theory, Approximation Algorithms, Local Search, Meta-heuristics, Multi-objective Heuristics, Hyper-heuristics, Evolutionary Algorithms, Hybrid Heuristics, Big Data and Machine Learning.

A few formative assessments throughout the course in the form of quizzes and practical exercises, in addition to a summative test at the end of the course.

Nottingham UK Go
02 Apr 2020 03 Apr 2020 Workshop on prediction and optimisation

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The aim of this workshop is to provide a platform for exchange of ideas, raise awareness of recent developments, and stimulate discussion at the interface of prediction and optimisation.
Confirmed Plenary Speakers:
Dick den Hertog (Tilburg University, Netherlands)
Ruud Teunter (University of Groningen)
Dolores Romero Morales (Copenhagen Business School, Denmark)

Lancaster University UK Go
30 Mar 2020 03 Apr 2020 Spring School on Mathematical Statistics

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March 30 - April 03, 2020
MPI für Mathematik in den Naturwissenschaften Leipzig

As statistics becomes more prominent in a world where data volume and computational power rapidly increase, the need for strong mathematical methods in statistics is evident. This is a one week school on Mathematical Statistics aimed at PhD students, advanced Master's students, or early career researchers interested in statistics and its applications.

We will have three leading experts in diverse domains of the field of mathematical statistics giving lecture courses on their areas of expertise. They will discuss recent developments and set up frontiers for exciting new research. Confirmed speakers are:

Holger Dette (Ruhr University Bochum)
Mathias Drton (Technical University Munich)
Jonas Peters (University of Copenhagen)
Invited speakers are:

Anna Klimova (Technical University Dresden)
Richard McElreath (Max Planck Institute for Evolutionary Anthropology)
Axel Munk (University of Göttingen)
During this week participants will learn relevant topics in mathematical statistics including: functional data analysis, classical mathematical statistics, causality in machine learning, and distributional robustness. The lectures will be complemented with practical problem sessions and discussions to put theory into practice.

There will also be a poster session for interested young participants. A limited amount of funding is available for those who present a poster. If you would like to apply for funding please fill out the necessary boxes during registration. Only complete applications will be taken into consideration. The application deadline is January 24, 2020.

Join us in Leipzig, the music capital of eastern Germany (and "European City of the Year 2019"), in March 2020.

Leipzig Germany Go
20 Jan 2020 24 Jan 2020 9th Winter School on Network Optimization

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The 9th edition of the Winter School on Network Optimization will take place at Hotel Estoril Éden, Estoril, from the 20th to the 24th of January 2020. Its main objective is to provide an opportunity for PhD students to get together and attend high level courses in the field of Network Optimization. Non-PhD students are welcome to attend the school, but the number of participants is limited and priority will be given to PhD students.

Invited lecturers are:
- Elena Fernandez (Universidad de Cadiz) - Formulations for Location-Routing
- Arie Koster (RWTH Aachen University) - Robust Network Optimization
- Ivana Ljubic (ESSEC Business School of Paris) - Branch-and-Benders-cut algorithms: modern implementations of Benders Decomposition
- Mario Ruthmair (University of Vienna))- Optimization in Social Networks
- Hande Yaman (KU Leuven) - Hub Location Problems

Further information at the link.

Estoril Portugal Go
19 Jan 2020 24 Jan 2020 Winter School on Data Science, Optimization and Operations Research

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The objective of the Winter School is to expose the audience to modern topics on Data Science, Optimization and Operations Research. Every year, two prominent researchers are invited to provide tutorials on selected topics, and to discuss some of their recent research with the students. Designed for doctoral education, the course is open to academic researchers (professors, researchers, PhD students) and professionals (from industry and public authorities), interested in optimization and operations research.

The course is organized by Prof. Michel Bierlaire, Transport and Mobility Laboratory (TRANSP-OR), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique de Lausanne (EPFL). It takes place in Zinal, a ski resort in the Swiss Alps. The special environment triggers a specific atmosphere that encourages scientific and personal exchanges among the participants.

In addition to the lectures, workshops will be organized every day where the students will have the opportunity to work on recent papers of the invited lecturers, under their guidance.

- Prof. Negar Kiyavash (ISyE, Georgia Tech)
- Prof. Marco Campi (University of Brescia)

Hotel Europe, Zinal Switzerland Go
13 Jan 2020 17 Jan 2020 6th International Winter School on Big Data

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BigDat 2020 will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of big data, which covers a large spectrum of current exciting research and industrial innovation with an extraordinary potential for a huge impact on scientific discoveries, medicine, engineering, business models, and society itself. Renowned academics and industry pioneers will lecture and share their views with the audience.

Most big data subareas will be displayed, namely foundations, infrastructure, management, search and mining, security and privacy, and applications (to biological and health sciences, to business, finance and transportation, to online social networks, etc.). Major challenges of analytics, management and storage of big data will be identified through 2 keynote lectures and 24 four-hour and a half courses, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Interaction will be a main component of the event.

An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles.

Master's students, PhD students, postdocs, and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, BigDat 2020 is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen and discuss with major researchers, industry leaders and innovators.

BigDat 2020 will take place in Ancona, a city founded by Greek settlers and today one of the main ports on the Adriatic Sea. The venue will be:

Department of Information Engineering
Marche Polytechnic University
Via Brecce Bianche 12
60131 Ancona
3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another.

Department of Information Engineering, Marche Polytechnic University, Ancona Italy Go
12 Jan 2020 17 Jan 2020 Low-rank models 2020

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One-week winter school on low-rank models, in the Swiss Alps from 12 to 17 January 2020.
The topic of the school is low-rank models and their use in numerical optimization and approximation. Specific focus will be given to modern applications in data science. Students will attend lectures and hands-on tutorials by leading experts.

Speakers (in alphabetical order) and topic of lectures:

Nicolas Boumal (Princeton University)
An introduction to optimization on smooth manifolds
Lieven De Lathauwer (KU Leuven)
An introduction to tensor decompositions and applications
Ivan Oseledets (Skoltech)
Low-rank tensor approximations and deep learning
Reinhold Schneider (TU Berlin)
Low rank approximation for high dimensional PDEs: Hamilton Jacobi Bellmann and variational Monte Carlo
Madeleine Udell (Cornell University)
Big data is low rank: models, theory, and algorithms
Venue and dates:
Low-rank Models 2020 takes place in Villars-sur-Ollon (VD), Switzerland, from the 12th to the 17th of January 2020, at the Eurotel Victoria

Low-Rank Models 2020 is financially supported by the Institute of Mathematics at EPFL, the Section of Mathematics at the University of Geneva, the EPFL Doctoral Program in Mathematics, and the Swiss Doctoral Program in Mathematics of the CUSO.

Eurotel Victoria, Villars-sur-Ollon (VD) Switzerland Go
10 Jan 2020 12 Jan 2020 Winter School on Machine Learning – WISMAL 2020

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Nicolai Petkov, program director
Nicola Strisciuglio, publicity
Carlos Travieso, local organisation

The Winter School on Machine Learning will take place on 10-12 January 2020.

This short winter school consists of several tutorials that present different techniques of Machine Learning. Each tutorial is two hours, introducing the main theoretical concepts and giving typical practical applications and their implementation in popular programming environments.

The participation in the winter school is free of charge for registered participants in APPIS 2020. The number of participants in the winter school is limited to 100 and early registration is encouraged to secure a place in the winter school.

The fee for participation is 250 Euro (before 13th December, and 325 Euro after 13th December), and it includes free registration to APPIS 2020.

Registrations will open soon

IMPORTANT: Please register as participant to APPIS and, after confirming your email address, indicate in the second step of the registration form if you are going to participate to WISMAL and APPIS or only to WISMAL.

Deep Learning in the Wolfram Language – Markus van Almsick, Algorithms R&D, Wolfram Research
Prototype-based machine learning – Michael Biehl, University of Groningen
Clustering – Kerstin Bunte, University of Groningen
Multi-target prediction –Willem Waegeman, University of Gent
Convolutional Neural Networks and Deep Learning – Maria Leyva, Nicolai Petkov (University of Groningen), Nicola Strisciuglio (University of Groningen – University of Twente)
Recurrent Neural Networks: From Universal Function Approximators to Big Data Tool – Danilo Mandic, Imperial College London
Consensus learning – Xiaoyi Jiang, University of Muenster
Further tutorials may be added to the program soon.

Las Palmas de Gran Canaria Spain Go
10 Dec 2019 20 Dec 2019 Second Nepal Winter School in AI

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Provide students/professionals with the prerequisite fundamentals of mathematics, statistics, programming and paper reading skills required for starting a career in AI research
Provide overview of state-of-the-art research in ML and AI
Inspire early stage researchers from Nepal and the region to take on challenging high impact AI problems in visual recognition, natural language processing, medical science etc.
Provide a venue for sharing and discussing cutting-edge technological advances in AI among both veteran and young researchers from around the world
Create new opportunities for the participants and play an important role in democratizing AI by inspiring the next generation of leaders in AI from developing countries

The Nepal School consists of series of lectures, lab sessions, and scientific paper reading/writing sessions given by invited world-class experts. The experts include a number of alumni who graduated from the local engineering schools, and have good knowledge of the teaching methodology there and the key gaps that need to be filled. The social events (a day hiking and dinner) will provide ample opportunities for the like-minded people to network and collaborate.

Undergraduate students
Graduate students
Researchers/Engineers/Scientists from Academia and Industry

Pokhara Nepal Go
11 Nov 2019 15 Nov 2019 Latin American Meeting In Artificial Intelligence - KHIPU

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The best of AI for Latin America
Artificial Intelligence has the potential to dramatically improve the lives of people and increase the prosperity of businesses, but solving a problem as hard as intelligence will require a diversity of thought and the best minds from every corner of the globe. Our mission is to support the advancement of Latin American talent, research, and companies in AI through an annual event.

Khipu 2019 will be take place Nov 11-15 at the Facultad de Ingeniería at the Universidad de la República in Montevideo, Uruguay. The primary goals of the event are:
• To offer training in advanced machine learning topics, such as deep learning and reinforcement learning.
• To strengthen the machine learning community by fostering collaborations between Latin American researchers, and creating opportunities for connections and knowledge exchange with the broader international community.
• To grow awareness around how AI may be used for the benefit of Latin America

In the hopes of inspiring wide and diverse participation, there will be no registration fees for accepted students and Khipu will provide financial assistance for travel expenses to select applicants. The event format is largely inspired by the success of our friends at the Deep Learning Indaba. We are very thankful to our sponsors and speakers for their invaluable support.

Montevideo Uruguay Go
04 Nov 2019 08 Nov 2019 Winter School on Computational Data Science and Optimization

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Data-driven analytics methodologies are presently at the forefront of efficient decision making and decision support in many industries. One prominent set of examples of this state-of-the-art computational data science and optimization tools that made headway is optimizing energy generation, storage, transmission and delivery, and trading. In a sense, nowadays, the employment of computational data science methodologies form a necessary condition for such systems to remain sustainable in the long-run and thrive, as we make a global transition to knowledge-and- information-based economy. These applications spread from operational to strategic time horizons. To name a few, optimization combined with machine-learning methods is successfully used to improve the efficiency SAGD process in oil recovery; optimization models and methods play a key role in determining efficient energy storage and dispatch strategies for smart grids, as well as help determine effective layouts for wind and solar farms; quantitative modelling and optimization occupy a central role when trading (energy) financial derivatives. However, despite these recent advances there is still a large gap between the most recent and vastly superior analytical tools available, and their practical applications.

The focus of the winter school is to train a new batch of highly qualified personnel that are essential in bridging the existing industry-to-academia gap. In addition, bringing together optimization thinking with more traditional data science approaches will help generate ideas for new approaches or improvements in existing approaches.

Registration Instruction:
Please register for each event you are interested in attending. Registering for one event does not enroll you for the entire Focus Program.

Fields Institute, Stewart Library, Toronto Canada Go

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The BELIEF school is a biennial event organized by the Belief Functions and Applications Society (BFAS) that offers a unique opportunity for students and researchers to learn about fundamental and advanced aspects of the theory of belief functions, also referred to as Dempster-Shafer theory, or evidence theory.

The school will be organized around a set of lectures by prominent researchers as well as a tutorial session focused on the practical use and implementation of belief functions. Lectures will gradually tackle basic to more advanced theoretical concepts. They will also highlight the links with other uncertainty theories such as imprecise probabilities, random sets or rough sets, and present some of the belief functions applications in various domains including information fusion, inference and machine learning. An optional final test will conclude the school providing successful participants with a certificate.

The 5th edition of the school will take place in Certosa di Pontignano located in Siena, Tuscany, Italy from the 27th to the 31st of October 2019.

La Certosa di Pontignano, Siena Italy Go
07 Oct 2019 10 Oct 2019 Simulation Modelling for Business Research

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Abstract and Learning Objectives
Business research increasingly considers wicked problems and complex dynamic systems. Analytical models of such problems and systems quickly become untraceable and unsolvable. Given increasing computational power, simulation models provide an alternative tool. They can fuel studies tracing the long-term evolution of systems and comparing the outcomes of alternative scenarios. However, successfully applying simulation modelling for business research requires expertise on applicable simulation paradigms, approaches to model validation and the analysis of stochastic results.

Date of Event:
7. bis 10. Oktober 2019

Christian-Albrechts-Universität zu Kiel
Wilhelm-Seelig-Platz 1
Raum 505
24118 Kiel


Prof. Dr. Catherine Cleophas
CAU Kiel

To get an overview of the amount of the participation fee and to register for the course, please use this link:
You can also send an email to prodok(at)vhbonline(dot)org.

Registration deadline: 08. September 2019

Kiel Germany Go
07 Oct 2019 11 Oct 2019 InfraTrain Autumn School 2019

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INFRATRAIN is a series of events in INFRAstructure research and policy TRAINing designed for graduate scholars, practitioners, and policy makers. They are designed to promote the training of graduate scholars (advanced Master students, PhD-students, post-docs) as well as junior practitioners and policy makers (from ministries, regulatory agencies, and the private sector).

INFRATRAIN is designed as a forum for exchanging ideas of established research and work in progress, whereas the mutual development of new ideas by on-site communication is also seen as a major objective. Thereby INFRATRAIN covers the theoretical and applied topics most relevant for modern European infrastructure policy.

This year’s INFRATRAIN event will take place from October 7 to 11, 2019, at TU Berlin. It is dedicated to the issue of Sustainable Infrastructure Modeling: Numerical Models and Data Analysis.

Participants are offered alternative training sessions with a small number of participants (for the topics of the training sessions click here). Beside that, attendees can present their own research work in seminars. Keynote lectures of high level researchers complete the program.

InfraTrain is coordinated by the Workgroup for Economic and Infrastructure Policy (WIP) at TU Berlin, and the German Institute for Economic Research (DIW Berlin). Scientific coordinators of INFRATRAIN are Prof. Dr. Christian von Hirschhausen (TU Berlin/DIW Berlin), Jens Weibezahn (TU Berlin), and Nicole Wägner (DIW Berlin)

TU Berlin Germany Go
03 Oct 2019 11 Oct 2019 Autumn School on Machine Learning

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The Autumn school on Machine Learning will be held in Tbilisi, Georgia, October 3-11, 2019. The school will be organized by the International Black Sea University with the support of Shota Rustaveli National Science Foundation of Georgia (SRNSFG). The intended audience of the autumn school includes BSc, MSc and PhD students, researchers as well as industry professionals.

Organizing Committee

Anna Chutkerashvili, Georgian Technical University / Tbilisi State University
Besik Dundua, International Black Sea University / Tbilisi State University
Cihan Mert, International Black Sea University (chair)
Mikheil Rukhaia, International Black Sea University / Tbilisi State University

Tbilisi Georgia Go
30 Sep 2019 04 Oct 2019 Thematic Einstein Semester on Algebraic Geometry Varieties, Polyhedra, Computation

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Fall School
This is the webpage of the fall school of the thematic Einstein semester Algebraic Geometry, which is devoted to the study of algebraic geometry and of its applications and is organised by Peter Bürgisser (TU Berlin), Gavril Farkas (HU Berlin) and Christian Haase (FU Berlin).

Times and Venue
The school will take place from the 30th of September 2019 to the 4th of October 2019 at Arnimallee 3 (Room SR 019 and SR 024) in the campus of the Freie Universität Berlin.

Greg Blekherman (Georgia Tech) - Convex and Algebraic Geometry of Sums of Squares on Varieties
A polynomial with real coefficients is called nonnegative if it takes only nonnegative values. For example, any sum of squares of polynomials is obviously nonnegative. The study of the relationship between nonnegative polynomials and sums of squares is a classical area in real algebraic geometry. The minicourse will be about the convex cones of nonnegative polynomials and sums of squares on a variety. Convex-geometric considerations, such as duality and facial structure of these cones will lead to new insights in algebraic geometry. The main questions we will consider are: when are all nonnegative polynomials sums of squares, and the number of squares needed to write a sum of squares.
Dawei Chen (Boston College) - Moduli of differentials and Teichmüller dynamics
An abelian differential defines a flat metric with conical singularities such that the underlying Riemann surface can be realized as a polygon with edges pairwise identified via translation. Varying the shape of such polygons induces a GL(2,R)-action on the moduli space of abelian differentials, called Teichmüller dynamics, whose study has provided fascinating results in many fields. I will give an introduction to this beautiful subject, with a focus on a combination of algebraic, analytic, combinatorial and dynamical viewpoints as well as some recent developments.
Giorgio Ottaviani (University of Florence) - Tensor Rank and Complexity
The asymptotic complexity of the matrix multiplication algorithm is one of the basic open problems in Complexity Theory. In other words, how many operations are needed to multiply two nxn matrices when n is large? The seminal work by Strassen and Bini translated this problem into the computation of the rank and the border rank of certain tensors, which can be studied with tools from Algebraic Geometry, especially in the symmetric case. The symmetric rank of a monomial can be computed by algebraic tools, and already this elementary case is not trivial. In the lectures we will review some important constructions and results in this area, and we will introduce the asymptotic rank, which in turn has a distinguished role in quantum computation. We will expose the recent computation of the symmetric border rank of a monomial by Christandl, Gesmundo and Oneto, by a clever use of the asymptotic rank.
Bernd Sturmfels (MPI Leipzig/Berkeley) - D-Modules and Holonomic Functions
In algebraic geometry we study the solutions of polynomial equations. This is equivalent to studying solutions of linear partial differential equations with constant coefficients. In these lectures we explore the more general case of partial differential equations whose coeffients are polynomials. The letter D stands for the Weyl algebra, and a D-module is simply a left module over D. We focus on left ideals, or D-ideals. These are the systems of linear PDE whose solutions we care about.
Functions in several variables can be modeled by the PDE they satisfy. A comprehensive class of functions with excellent algebraic properties are the holonomic functions. They are encoded by holonomic D-modules. These are useful for many applications, e.g. in geometry, physics and statistics. Course participants will learn how to work with holonomic functions.

Participants must apply at this link. We are working to acquire funding to offer support for travel or accommodation to some participants. The registration will close on the 1st of September 2019.
Due to space limitations and the overwhelming interest in our Fall School we might not be able to accommodate everybody who registered; we reserve the right to reject applications. We will send out notifications in early September.

For any question about this event, you may contact the email address or one of the organisers of the fall school: Daniele Agostini (HU Berlin), Thomas Krämer (HU Berlin), Marta Panizzut (TU Berlin), Rainer Sinn (FU Berlin).

Freie Universität Berlin, Berlin Germany Go
09 Sep 2019 13 Sep 2019 Course on Combinatorial Optimization

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EURO is pleased to announce the availability of a limited number of bursaries for PhD students to attend NATCOR (National Taught Course Centre in Operational Research) courses in the UK to PhD students on a number of different topics in OR.

The NATCOR course fees and accommodation (typically bed and breakfast) will be funded by EURO for successful candidates.

Applicants must be PhD students, from one of the EURO member countries or studying in one of the EURO member countries. (see Applicants must have good English Language skills as all NATCOR courses will be in English. Preference will be given to students in their first or second years but all are welcome to apply. Preference will also be given to applicants who have not previously received support from EURO for NATCOR or other PhD schools.

To apply for a bursary, candidates from a EURO member society country, or studying in a EURO member society country, should submit a zip file containing the application form, their curriculum vitae (including their academic track record), a letter outlining their motivation to attend, and a letter of recommendation from their supervisor to Applicants should declare on their application if they are receiving any additional support from their PhD funding body or other sponsor.

Important Dates
Deadline for applications: January 15, 2019
Notification of acceptance: February 4, 2019

University of Southampton England Go
01 Sep 2019 04 Sep 2019 12th International Workshop on Computational Optimization (WCO19)

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We invite original contributions related to both theoretical and practical aspects of optimization methods. The list of topics includes, but is not limited to:

* combinatorial and continuous global optimization
* unconstrained and constrained optimization
* multiobjective and robust optimization
* optimization in dynamic and/or noisy environments
* optimization on graphs
* large-scale optimization, in parallel and distributed computational environments
* meta-heuristics for optimization, nature-inspired approaches and any other derivative-free methods
* exact/heuristic hybrid methods, involving natural computing techniques and other global and local optimization methods
* numerical and heuristic methods for modeling

The applications of interest are included in the list below, but are not limited to:

* classical operational research problems (knapsack, traveling salesman, etc)
* computational biology and distance geometry
* data mining and knowledge discovery
* human motion simulations; crowd simulations
* industrial applications
* optimization in statistics, econometrics, finance, physics, chemistry, biology, medicine, and engineering.
* environment modeling and optimization


The best WCO19 paper will be awarded during the social dinner of FedCSIS2019.
The best paper will be selected by WCO19 co-Chairs by taking into consideration the scores suggested by the reviewers, as well as the quality of the given oral presentation.


Submission and Publication

* Authors should submit draft papers in PDF format.
* The total length of a paper should not exceed 10 pages for regular paper and 4 pages for short papers (IEEE style). IEEE style templates are available at
* Papers will be refereed and accepted on the basis of their scientific merit and relevance to the workshop.
* Accepted and presented papers will be published in the Conference Proceedings and included in the IEEE Xplore database and submitted for different indexations (Communication and Position papers will only appear in the conference proceedings).
* Extended versions of selected papers presented at WCO19 will be published in edited books of the series "Studies of Computational Intelligence", Springer.


Important dates:

Paper submission (sharp / no extensions) : May 14th, 2019
Position paper submission : June 4th, 2019
Author notification : June 25th, 2019
Final paper submission and registration : July 10, 2019
Conference date : September 1-4, 2019


Organizing Committee

Stefka Fidanova, Bulgarian Academy of Science, Bulgaria
Antonio Mucherino, IRISA, University of Rennes 1, France
Daniela Zaharie, West University of Timisoara, Romania

Leipzig Germany Go
10 Aug 2019 16 Aug 2019 Data Science Meets Optimisation (DSO) Workshop at IJCAI-19 (the 28th International Joint Conference on Artificial Intelligence)

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Call for Papers - Data Science Meets Optimisation (DSO) Workshop at IJCAI-19 (the 28th International Joint Conference on Artificial Intelligence)
August 10-16, 2019, Macao, China

Extended submission deadline: May 27, 2019
(New!!!!) Special issue in the Annals of Mathematics and Artificial Intelligence
Keynote speaker: Prof. dr. Holger Hoos (Leiden University, NL)

*Important dates*

Submission deadline (extended): May 27, 2019
Notification of acceptance: June 15, 2019


Data science and optimisation are closely related. On the one hand, many problems in data science can be solved using optimisers, on the other hand optimisation problems stated through classical models such as those from mathematical programming cannot be considered independent of historical data. Examples are ample. Machine learning often relies on optimisation techniques such as linear or integer programming. Algorithms may be complete, approximative or heuristic and may be applied in on-line or off line settings. Reasoning systems have been applied to constrained pattern and sequence mining tasks. A parallel development of metaheuristic approaches has taken place in the domains of data mining and machine learning. In the last decades, methods aimed at high level combinatorial optimisation have been shown to strongly profit from configuration and tuning tools building on historical data. Algorithm selection has since the seventies of the previous century been considered as a tool to select the most appropriate algorithm for a given instance. Empirical model learning uses machine learning models to approximate the behaviour of a system, and such empirical models can be embedded into an optimisation model for efficiently finding an optimal system configuration.

The aim of the workshop is to organize an open discussion and exchange of ideas by researchers from Data Science and Operations Research domains in order to identify how techniques from these two fields can benefit each other. The program committee invites submissions that include but are not limited to the following topics:
- Applying data science and machine learning methods to solve combinatorial optimisation problems, such as algorithm selection based on historical data, speeding up (or driving) the search process using machine learning, and handling uncertainties of prediction models for decision-making.
- Using optimisation algorithms in developing machine learning models: formulating the problem of learning predictive models as MIP, constraint programming (CP), or satisfiability (SAT). Tuning machine learning models using search algorithms and meta-heuristics. Learning in the presence of constraints.
- Embedding methods: combining machine learning with combinatorial optimization, model transformations and solver selection, reasoning over Machine Learning models.
- Formal analysis of Machine Learning models via optimization or constraint satisfaction techniques: safety checking and verification via SMT or MIP, generation of adversarial examples via similar combinatorial techniques.
- Computing explanations for ML model via techniques developed for optimization or constraint reasoning systems
- Applications of integration of techniques of data science and optimization.


We invite the following submissions (all in the IJCAI proceedings format, see: ):
- Submission of original work up to 8 pages in length.
- Submission of work in progress (with preliminary results) and position papers, up to 6 pages in length.
- Published journal/conference papers in the form of a 2-pages abstract.
The program committee will select the papers to be presented at the workshop according to their suitability to the aims.
Contributors of the workshop will be invited to submit full versions of their papers for inclusion in a special volume of the Annals of Mathematics and Artificial Intelligence, published by Springer.
Those invited submissions will be subject to refereeing at the usual standards of the journal, and authors will receive more details with the acceptance notice.

Submissions through:

*Workshop organizers*

Patrick De Causmaecker (KU Leuven, BE),
Michele Lombardi (University of Bologna, IT),
Yingqian Zhang (TU Eindhoven, NL),

Macao China Go
15 Jul 2019 19 Jul 2019 2nd Advanced Course on Data Science & Machine Learning (ACDL)

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The 2nd Advanced Course on Data Science & Machine Learning (ACDL) is a full-immersion five-day residential Course at the Certosa di Pontignano (Siena – Tuscany, Italy) on cutting-edge advances in Data Science and Machine Learning with lectures delivered by world-renowned experts. The Course provides a stimulating environment for academics, early career researches, Post-Docs, PhD students and industry leaders. Participants will also have the chance to present their results with oral talks or posters, and to interact with their peers, in a friendly and constructive environment.
You will gain a heightened awareness for fields of data science and machine learning relevant for your activity and, perhaps most important, you will gain a place within an elite global network of data scientists and machine learning experts.
The Advanced Course is not a summer school suited only for younger scholars. Rather, a significant proportion of seasoned investigators are regularly present among the attendees, often senior faculty at their own institutions. The balanced audience that we strive to maintain in each Advanced Course greatly contributes to the development of intense cross-disciplinary debates among faculty and participants that typically address the most advanced and emerging areas of each topic.
Each faculty member presents lectures and discusses with the participants for one entire day. Such long interaction together with the small, exclusive Course size provides the uncommon opportunity to fully explore the expertise of each faculty, often through one-to-one mentoring. This is unparalleled and priceless.
The Course will involve a total of 36-40 hours of lectures, according to the academic system the final achievement will be equivalent to 8 ECTS points for the PhD Students and the Master Students attending the summer school.
The Certosa di Pontignano provides the perfect setting to a relaxed yet intense learning atmosphere, with the stunning backdrop of the Tuscan landscapes. World-class wines and traditional foods will make the Advanced Course on Data Science & Machine Learning the experience of a lifetime.

Certosa di Pontignano (Siena – Tuscany) Italia Go
07 Jul 2019 13 Jul 2019 ECMI Postgraduate / VI Iberian / NeEDS Modelling Week

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The ECMI Postgraduate / VI Iberian / NeEDS Modelling Week will be held at the Mathematical Institute of the University of Seville (Seville, Spain) on July 7th-13th, 2019. It is co-organized by the European Consortium for Mathematics and Industry (, the Spanish Network for Mathematics-Industry (, the Portuguese Network of Mathematics for Industry and Innovation (, and the H2020-MSCA-RISE NeEDS project (, and takes part of the satellite meetings to the 9th International Congress on Industrial and Applied Mathematics (July 15th-19th, 2019, Valencia, Spain).

The format of the Modelling Week is to spend one week working to solve real problems that can be tackled through mathematical modeling. Small groups of multinational Master students, Ph.D. students and junior researchers (like post-doctoral students) will be assigned to each problem in term of their preference and own skills on the first day of the event after the presentation of the problems. An instructor, that must be an expert in the area, of the proposed problem leads each of these groups. During the following four days students will work on solving the problems under the guidance of the instructor and industrial collaborators. Last day of the meeting will be devoted to the presentation of the results, which will be collected in the proceedings of the event.

To register for this modeling week, please follow the link
For information on competitive financial support, please follow the link

Instituto de Matemáticas de la Universidad de Sevilla, IMUS, Seville Spain Go