Conferences and Workshops of interest for AIROYoungers

Displaying conferences 1 - 25 of 89 in total
Start date End date Description Location Country Url
07 Jul 2021 09 Jul 2021 Joint EUROPEAN CONFERENCE ON STOCHASTIC OPTIMIZATION and COMPUTATIONAL MANAGEMENT SCIENCE Conference

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Joint EUROPEAN CONFERENCE ON STOCHASTIC OPTIMIZATION and COMPUTATIONAL MANAGEMENT SCIENCE Conference
7-9 July 2021, Venice, Italy

The organizers are delighted to invite you to ECSO – CMS 2021 that will be held in Venice, Italy, 7-9 July 2021, at the Department of Economics - Ca’ Foscari University of Venice, in the San Giobbe Economics Campus.

The event was originally planned for July 2020 but due to the COVID-19 situation the conference is rescheduled to the next year.

ECSO - CMS 2021 is jointly organized by the Department of Economics of Ca’ Foscari University of Venice, the CMS Journal and the EURO Working Group on Stochastic Optimization.

ECSO 2021 is the 3rd edition of a stream of conferences organized by the EURO Working Group on Stochastic Optimization (EWGSO). The previous editions were held in Paris (2014) and Rome (2017). The scope of the conference is to bring together researchers and professionals in Stochastic Optimization and its applications in different fields spacing from economics and finance to supply chain, logistics, etc.

CMS 2021 is the 17th edition of an annual meeting associated with the journal of Computational Management Science published by Springer. The aim of the conference is to provide a forum for theoreticians and practitioners from academia and industry to exchange knowledge, ideas and results in a broad range of topics relevant to the theory and practice of computational methods in management science.

This joint event will provide a forum for fruitful discussions and interactions among researchers and professionals from industry and institutional sectors on decision making under uncertainty in a complex world. The conference will be within the scopes of both CMS and EWGSO and, in particular, it will focus on models, methods and computational tools in stochastic, robust and distributionally robust optimization and on computational aspects of management science with emphasis on risk management, valuation problems, measurement applications. Traditional fields of application, such as finance, energy, water management, logistics, supply chain management, and emerging ones, such as healthcare, climate risk and sustainable development, will be included.

CONFIRMED PLENARY SPEAKERS
DARINKA DENTCHEVA, Stevens Institute of Technology (USA)
DAVID MORTON, Northwestern University (USA)
GAH-YI BAN, London Business School (UK)
DANIEL KUHN, École polytechnique fédérale de Lausanne (CH)
GIORGIO CONSIGLI, Università di Bergamo (I)
WARREN POWELL, Princeton University (USA)


Venue: Department of Economics, Ca’ Foscari University of Venice
San Giobbe Campus – Cannaregio 873, 30121 Venice, Italy

Webpage: www.unive.it/ecsocms2021
Conference Secretariat: ecsocms2021@unive.it
Conference hashtag: #ecsocms2021

Call for papers and deadlines will be announced in Autumn

A Best Student Paper Prize will be awarded. Papers should be nominated via e-mail by the students’ supervisors (ecsocms2021@unive.it). The program will include a devoted session for presenting the best papers to compete for the prize, such that the jury could make the final choice. The paper does not have to be published. The papers should be principally authored by the student, but co-authors are permitted as long as their contributions are clarified. Only registered participants’ papers will be considered for the prize.
Jury for the Student Best Paper Prize: Stein-Erik Fleten (NTNU Norwegian University of Science and Technology), Milos Kopa (Charles University of Prague), Francesca Maggioni (University of Bergamo), Ruediger Schultz (University Duisburg-Essen).

We are looking forward to seeing you in Venice.

Best Regards,
Diana Barro, Stein-Erik Fleten and Martina Nardon

Organizing and Program Committee Chairs

Department of Economics, Ca’ Foscari University of Venice Italy Go
28 Sep 2020 02 Oct 2020 Deep Reinforcement Learning

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Organizers: Lihong Li (Google Brain; chair), Marc Bellemare (Google Brain)

The success of deep neural networks in modeling complicated functions has recently been applied by the reinforcement learning community, resulting in algorithms that are able to learn in environments previously thought to be much too large. Successful applications span domains from robotics to health care. However, the success is not well understood from a theoretical perspective. What are the modeling choices necessary for good performance, and how does the flexibility of deep neural nets help learning? This workshop will connect practitioners to theoreticians with the goal of understanding the most impactful modeling decisions and the properties of deep neural networks that make them so successful. Specifically, we will study the ability of deep neural nets to approximate in the context of reinforcement learning.

All events take place in the Calvin Lab auditorium.

Further details about this workshop will be posted in due course. Enquiries may be sent to the organizers at this address.

Berkeley California Go
21 Sep 2020 25 Sep 2020 NATCOR Course on Forecasting & Predictive Analysis

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Introduction

The week long course will focus on the principles of time-series forecasting, and recent developments in predictive analytics. Many examples will be used and there will be opportunities for participants to use models ‘hands-on’ and to experiment with real data. Links between forecasting, predictive analytics and decision making under uncertainty will be emphasised. The relevance of forecasting to non-stationary environments will be discussed, for example in inventory, simulation and optimisation models.

Location and Session Leaders

Sessions will be led by experts from the Lancaster Centre for Forecasting, calling on external experts if necessary. The overall co-ordinator will be Professor John Boylan.

Course Structure

The course will be subdivided into three sections, as follows:

•Time series modelling
•Econometric models
•Predictive analytics

This forms a natural sequence, moving from univariate models to classical multivariate models to modern methods which address both univariate and multivariate data.

The session on time series modelling will commence with the simplest methods, with opportunities for participants to experiment with parameter choices and to appreciate the scope for parameter optimisation methods to improve forecast accuracy. Model-based approaches will be covered, as well as criteria for choosing between forecasting models. A more general discussion will be held on error measurement and concerns about the reproducibility of forecasting research.

The session on econometric models will start with an introduction to the concepts involved, but will assume some knowledge of regression modelling. It will cover diagnostic analysis of regression models and model choice. This session will also cover some of the important technicalities of econometric time-series models, such as spurious regression, cointegration and modelling non-stationary data. It will conclude with a broader discussion of asymmetric loss functions and forecaster behaviour.

The final session on predictive analytics will cover some key topics for time series analysis. This will include data mining, with a focus on time series clustering for data exploration, and time series classification. It will also cover artificial intelligence for forecasting, focussing on Artificial Neural Networks and recent developments in ensemble methods for forecasting.

Lancaster University UK Go
21 Sep 2020 24 Sep 2020 17th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research

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We invite you to attend the 17th International Conference on the
Integration of Constraint Programming, Artificial Intelligence, and
Operations Research (https://cpaior2020.dbai.tuwien.ac.at/ ). The aim of
the conference is to bring together interested researchers from
Constraint Programming (CP), Artificial Intelligence (AI), and
Operations Research (OR) to present new techniques or applications and
to provide an opportunity for researchers in one area to learn about
techniques in the others. A main objective of this conference series is
also to give these researchers the opportunity to show how the
integration of techniques from different fields can lead to interesting
results on large and complex problems.

The online conference will take place from September 21 to 24, 2020.

The registration is free of charge:
https://cpaior2020.dbai.tuwien.ac.at/registration/

The conference program is available here:
https://easychair.org/smart-program/CPAIOR2020/index.html

The conference program includes four invited talks and a Master Class on
the topic ?Recent Advances in Optimisation Paradigms and Solving
Technology?:

Invited Talks:

? Margarida Carvalho (University of Montreal, Canada): ?Algorithmic
approaches for integer programming games and a story on policy making?

? Georg Gottlob (University of Oxford, UK, and TU Wien, Austria):
?Hypertree Decompositions: Questions and Answers?

? Sebastian Pokutta (Technische Universit?t Berlin, Zuse Institute
Berlin, Germany): ?Restarting Algorithms: Sometimes there is Free Lunch?

? Peter Stuckey (Monash University, Australia) ?Combinatorial
Optimisation for Multi-Agent Path Finding?


Master Class ("Recent Advances in Optimisation Paradigms and Solving
Technology"):

? Laurent Perron and Fr?d?ric Didier (Google Paris, France) ?Constraint
Programming?

? Armin Biere (Johannes Kepler University, Linz, Austria):
?Satisfiability (SAT)?

? G?nther Raidl and Andrea Schaerf (TU Wien, Austria, and University of
Udine, Italy): ?(Meta)Heuristics and Hybridisation?

? In?s Lynce (University of Lisbon, Portugal): ?MaxSAT, Multi-Objective
Optimisation, and Parallelism?

? Timo Berthold (Fair Isaac Germany GmbH, Germany):
?Mixed-IntegerProgramming?

? Marie Pelleau (Universit? Nice Sophia-Antipolis, France): ?Numerical
Constraint Programming?


Best regards,
CPAIOR 2020 PC Chairs and the Local Organizing Committee

Online Go
14 Sep 2020 26 Sep 2020 CO@Work 2020

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CO@Work is a summer school organized by TU Berlin in cooperation with the Berlin Mathematical School and with support of the Berlin Mathematics Research Center MATH+.

TU Berlin BMS MATH+
FICO Gurobi GAMS

This block course targets master students (in their final year), PhD students, and Post-docs interested in the use of combinatorial optimization and mathematical programming in concrete applications from practice. The course schedule covers two weeks with lectures and exercises from 9 am to 5 pm for Monday to Friday, plus half day programs on Saturdays. We plan to have around 25 distinguished lecturers from all over the world, including developers and managers of seven leading companies in the field of mathematical optimization.

Date: September 14 - 26, 2020
Place: Zuse Institute Berlin (ZIB)
ECTS: 10 credit points

Application is open now and will be closed once we reach the capacity limit of 150 participants.

The language of the course is English. There is no participation fee.

Apply here
We will be able to supply a limited number of travel support grants to students from emerging countries. If you want to apply for a grant, please send an e-mail with a detailed CV and a recommendation letter by a faculty member to coaw-support@zib.de.
If you need assistance for obtaining a German visa, please contact coaw-visa@zib.de.

Confirmed Speakers
Timo Berthold, FICO Xpress
Ksenia Bestuzheva, ZIB
Bob Bixby, Gurobi Optimization
Ralf Borndörfer, FU Berlin
Christina Burt, Water Corporation
Philipp Christophel, SAS
Bistra Dilkina, University of Southern California
Leon Eifler, ZIB
Ivet Galabova, University of Edinburgh
Leona Gottwald, ZIB
Julian Hall, University of Edinburgh
Thorsten Koch, TU Berlin
Pawel Lichoki, Google
Andrea Lodi, GERAD, Polytechnique Montréal
Marco Lübbecke, RWTH Aachen
Robert Luce, Gurobi Optimization
Stephen Maher, University of Exeter
Christoph Moll, Siemens
Marc Pfetsch, TU Darmstadt
Sebastian Pokutta, ZIB and TU Berlin
Güvenç Şahın, Sabancı University
Sabine Seelenmayer, SAP
Stefan Vigerske, GAMS
Sven Wiese, Mosek
Previous Workshops
CO@Work2020, will be the sixth incarnation of this workshop series, and the fourth one being held in Berlin.

Berlin 2015
From September 28 to October 10, 2015 more than 160 students from 29 countries, covering all continents except Antarctica, participated in the course held at Zuse Institute Berlin.
Berlin 2009
From September 21 to October 9, 2009 many students from all over the world participated in the course held at the Zuse Institute Berlin.
Berlin 2005
From October 4-15 more than 100 students out of 10 countries participated in the course held at the Zuse Institute Berlin.
Görlitz 2006
From September 3-15 parts of the course where discussed during the Görlitz summer school of the German National Academic Foundation.
Beijing 2006 From September 25 to October 6 more than 40 students from all over China attended the course as part of the Workshop Optimization Methods and Applications at the Morningside Center of Mathematics, Chinese Academy of Sciences.

Berlin Germany Go
31 Aug 2020 04 Sep 2020 Theory of Reinforcement Learning Boot Camp

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Organizers: Csaba Szepesvári (DeepMind & University of Alberta; chair), Emma Brunskill (Stanford University), Sébastien Bubeck (MSR), Alan Malek (MIT), Sean Meyn (University of Florida), Ambuj Tewari (University of Michigan), Mengdi Wang (Princeton)

The Boot Camp is intended to acquaint program participants with the key themes of the program. It will consist of five days of tutorial presentations from leading experts in the topics of the program.

All events take place in the Calvin Lab auditorium.

Further details about this boot camp will be posted in due course. Enquiries may be sent to the organizers at this address.

Berkeley California Go
31 Aug 2020 05 Sep 2020 Mediterranean Machine Learning Summer School

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The Mediterranean Machine Learning (M2L) summer school will be structured around 6 days of keynotes, lectures and practical sessions. The program will include social or cultural activities to foster networking and to promote the hosting city. Participants will be encouraged to (optionally) present their work at evening poster sessions during the school and to interact with the main sponsors at their stands/booths during the coffee breaks throughout the week.
Local and international AI experts will teach lectures and laboratories. State-of-the-art content and code will be accessible to all school participants.
Location and dates:
The next edition of the Mediterranean Machine Learning (M2L) summer school will take place in Milan, Italy, from the 31st of August to the 5th of September 2020. The school will be located in the spaces of Università degli Studi di Milano-Bicocca.
Audience:
The target audience will consist primarily of Master and Doctoral students, academics, and practitioners from all around the world, with a focus on the Mediterranean area. The school will be advertised with a public call and participants will be selected on the basis of merit and to promote diversity. We aim to have around 100 attendees from higher education, with technical background and some understanding and practical experience of machine learning.

Università degli Studi di Milano-Bicocca, Milan Italy Go
24 Aug 2020 28 Aug 2020 Third Prague Summer School on Discrete Mathematics

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The third edition of Prague Summer School on Discrete Mathematics will be held on August 24-28, 2020 in the historical building of the Computer Science Department of Charles University in the very heart of Prague. Lecture series will be given by Subhash Khot (New York University) and Shayan Oveis Gharan (University of Washington).
The School is primarily intended for PhD students and early career researchers. There is no registration fee and a number of travel stipends will be available.
Previous Schools were held in Prague in 2016 and 2018.

Prague Czech Republic Go
03 Aug 2020 09 Aug 2020 MLSS Indonesia

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OUR MISSION
Indonesia has around 3,276 universities, 6,924,511 students, and 247,269 lecturers, yet this country is unnoticed when talking about research, especially in computer science, more specifically in Machine Learning.

According to NIPS 2017 (The biggest conference in Machine Learning) statistics here, none of the authors from 675 accepted papers were affiliated with Indonesian Institutions. This situation can change, India also had zero participant in 2006 but now regularly shows up in many top ML conferences. Brazil starts to follow.

Skills shortages, small funding, infrastructure, and even confidence could be the factors that affect the Indonesian participation levels in top machine learning conferences. Many ML experts, top universities, top conferences, summer schools, and industries are located far away from Indonesia, making it even harder for Indonesian students and researchers to get good exposures in machine learning. Moreover, currently there is no formal program in machine learning or artificial intelligence offered in Indonesian universities.

Therefore, inline as stated at mlss.cc, our mission is to bring the best ML/AI environments closer to Indonesia, so more people can taste machine learning directly from the experts, and eventually more Indonesian talents will get inspired.

Bandung Indonesia Go
29 Jul 2020 06 Aug 2020 Deep Learning and Reinforcement Learning Summer School 2020

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About the 2020 Deep Learning and Reinforcement Learning School

In 2005, CIFAR’s Learning in Machines & Brains program hosted its first Deep Learning and Reinforcement Learning Summer School in Toronto with the goal of fostering the next generation of AI researchers. Many of the former students are now leaders at some of the top tech firms and university labs.

Today, the DLRL Summer School is a part of both the CIFAR Learning in Machines & Brains program and CIFAR Pan-Canadian AI Strategy’s National Program of Activities, and is delivered in partnership with Canada’s three national AI Institutes, Mila, Amii and the Vector Institute.

This year’s DLRL Summer School happens July 29 to August 6, 2020 in Montreal, Quebec, Canada. The event brings together graduate students, post-docs and professionals to cover the foundational research, new developments, and real-world applications of deep learning and reinforcement learning. Participants learn directly from world-renowned researchers and lecturers.

Related extracurricular activities will include an AI Career Fair, industry and partner-sponsored events, as well as tourism events.

Montréal Canada Go
27 Jul 2020 31 Jul 2020 4th International Summer School on Deep Learning

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DeepLearn 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 deep learning. Previous events were held in Bilbao, Genova and Warsaw.

Deep learning is a branch of artificial intelligence covering a spectrum of current exciting research and industrial innovation that provides more efficient algorithms to deal with large-scale data in neurosciences, computer vision, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, healthcare, recommender systems, learning theory, robotics, games, etc. Renowned academics and industry pioneers will lecture and share their views with the audience.

Most deep learning subareas will be displayed, and main challenges 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.

León, Guanajuato Mexico Go
27 Jul 2020 31 Jul 2020 4th Modelling Symposium: Introducing Deep Neural Networks

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We are pleased to announce the 4th Modelling Symposium which provides once more a mix of theoretical contents and application-oriented analyses. The next symposium will cover Deep Neural Networks (DNNs) including basic introductions into DNNs, common building blocks, design patterns and architectures, best practices, optimization, applications etc. To this end, we welcome a new tutor -- Prof. Dr. Sebastian Stober.

Goal: Please note that DNNs are complex and that this course will help you to get started with DNN analyses. The workshop provides a general introduction into DNNs covering a wide range of topics. After the 4 days you should have an overview of different DNNs, their strength and weaknesses and which parameters of the model might be important and which ones you might have to tweak. The course will also help you to make decisions about which information/parameter can be important in steps XY and it also helps you to better understand the DNN literature (e.g. whether author's omitted important information about the presented models).

Magdeburg Germany Go
27 Jul 2020 31 Jul 2020 MESS 2020

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Many challenging applications in Science and Industry can be formulated as optimization problems. Due to their complexity and hardness often they cannot be solved in an exact manner within a reasonable time; thus approximate algorithms become the main alternatives to solve them thanks to their ability to efficiently explore large search spaces.

Metaheuristics are successful techniques able to solve such complex and hard optimization problems that arise in human activities, such as economics, industry, or engineering, and constitute a highly diverse family of optimization algorithms, each of which shows individual properties, and different strengths.

The international Metaheuristics Summer School is aimed at qualified and strongly motivated MSc and PhD students; post-docs; young researchers, and both academic and industrial professionals to provide an overview on the several metaheuristics techniques, and an in-depth analysis of the state-of-the-art. The main theme of the 2020 edition is “Learning and Optimization from Big Data”, therefore MESS 2020 wants to focus on (i) Learning for Metaheuristics; (ii) Optimization in Machine Learning; and (iii) how Optimization and Learning affect the Metaheuristics making them relevant in handling Big Data.

The courses will be held by world renowned experts in the field, and will be inspected practical aspects on complex combinatorial optimization problems, as well as examples of their successful real-world applications. The participants will have plenty of opportunities for debate and work with leaders in the field, benefiting from direct interaction and discussions in a stimulating environment. They will also have the possibility to present their recently results and/or their working in progress through oral or poster presentations, and interact with their scientific peers, in a friendly and constructive environment.

All participants to the school will be involved in the “Metaheuristics Competition”, where each of them, individually or divided in working groups, they will must develop a metaheuristic solution on the given problem. The top three of the competition ranking will receive the MESS 2020 prize. Further, the students, whose algorithms will rank in the five top of the competition ranking, will be invited to submit a report/manuscript of their work to be published in the special MESS 2020 Volume of the AIRO Springer Series.

MESS 2020 will involve a total of 36-40 hours of lectures, therefore in according to the academic system, all PhD and master students attending to the summer school will may get 8 ECTS points. Further, during the summer school the students will tackle homework, or project development.



Important Dates



Application Deadline: March 5, 2020

Notification acceptance: March 30, 2020

Early Registration: by May 1, 2020

Late Registration: from May 2, 2020

Catania Italy Go
27 Jul 2020 31 Jul 2020 Advanced Methods in Operations Research for Logistics and Transportation

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Advanced Methods in Operations Research for Logistics and Transportation
In the context of decision-making processes, we use Operations Research methods to understand, predict and optimize the behavior of real-life systems through mathematical models. Operations Research techniques are designed to tackle problems with practical meaning, which are typically very complex. These models and methods have been applied in numerous contexts such as defining public and private policies, and planning processes for government and industry with an exceptionally broad spectrum.

General Objectives
To promote scientific collaboration in research lines that focus on advanced methods in Operations Research
To present state-of-the-art methodologies and problems within the context of Logistics and Transportation.

Bogotà Colombia Go
27 Jul 2020 31 Jul 2020 PhD Summer School - "Digital transformation of mobility systems: Operations Research models and methods"

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PhD-SUMMER SCHOOL "DIGITAL TRANSFORMATION OF MOBILITY SYSTEMS: OPERATIONS RESEARCH MODELS AND METHODS"

We invite interested PhD students to apply for the summer school on “Digital Transformation of Mobility Systems – Operations Research Models and Methods”. The summer school will take place from July 27th-31st 2020 at the Heilbronn-campus of the Technical University of Munich.

Speakers:
· Claudia Archetti: MILP Formulations for Mobility Problems
· Maximilian Schiffer: Recent and Future Trends in Mobility and Transportation Systems
· Marlin Ulmer: Sequential Decision Processes for Mobility and Transportation
· Thibaut Vidal: Heuristics for Vehicle Routing Problems: Trends, Challenges and Prospects

The summer school is sponsored by TUM School of Management. Participants have to pay a fee of 150€ to cover expenses related to the social program including the conference dinner.

Deadline for applications is March 31, 2020.
Further information and application: https://www.gs.tum.de/adone/summerschool/

TUM, Munich Germany Go
26 Jul 2020 31 Jul 2020 ORAHS 2020

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The EURO Working Group on Operational Research Applied to Health Services (ORAHS) was formed in 1975 as part of a program for developing special interest groups within the European branch, EURO, of the International Federation of Operational Research Societies, IFORS. The group currently has 242 members from 30 countries, mainly from Europe but also from overseas (e.g., USA, Canada, and Brazil). The group meets for a week each summer in a different, mainly European, host country. Meetings are open to anyone with a quantitative background and/or an interest in the subject area. However, the number of participants can be limited to ensure that an open debate is possible. The objectives of the group are to impart ideas, knowledge and experience on the application of OR approaches and methods to problems in the health services area, mutual support of members, co-operation on joint projects and stimulation of approaches and attitudes in the field of applied Operational Research (http://orahs.di.unito.it/about.html).

Location (air-conditioned):
University of Vienna, Austria
Faculty of Business, Economics, Statistics Oskar-Morgenstern-Platz 1, A-1090 Vienna

Important Dates:
 Conference: July 26th –31st, 2020
 Abstract submission deadline: February 21st, 2020
 Notification of acceptance: March 31st, 2020
 Deadline for early registration: April 30th, 2020
 Deadline late of registration: May 20th, 2020

Outline:
The academic sessions, which consist of a combination of plenaries and parallel streams with oral presentations, will take place from Sunday, 26th, to Friday, 31st July 2020. There is also a poster session for students with the best poster being awarded a prize. In addition, we have two outstanding keynote speakers: Prof. Dr. Peter Zweifel, Professor emeritus of the Department of Economics of the University of Zurich, and Prof. Greg Zaric PhD, Faculty Director of Management Science, Ivey Business School, Canada (plus Canada Research Chair in Health Care Management Science). There is also a round table entitled "Challenges and Development in Health Care Prevention” with distinguished national and international researchers and policy makers, moderated by Prof. Dr. Robert Fitzgerald, Head of the Karl Landsteiner Institute for Anaesthsiology and Intensive Care Medicine and Head of the Health Political Forum (https://www.gesundheitspolitischesforum.at/). The social program offers an attractive introduction to Austrian culture and the Austrian heritage (city tour, Schönbrunn Palace tour, Danube boat trip).

Local Organizing Committee
 Marion Rauner, University of Vienna, Austria
 Patrick Hirsch, University of Natural Resources and Life Sciences, Vienna, Austria
 Margit Sommersguter-Reichmann, University of Graz, Austria
Supported by
 Sabine Grahsner, University of Vienna (Secretary)
 Lorena Reyes-Rubiano, University of Natural Resources and Life Sciences (Web-Master)
 Heads of Austrian OR Society Working Groups (ÖGOR) (e.g., Walter Gutjahr, Tina Wakolbinger)

International Scientific Board Members
 Ines Marques (Joint Coordinator), Portugal
 Sally Brailsford, Great Britain
 Mike Carter, Canada
 Maria Captivo (2014), Portugal
 Fermín Mallor Giménez (2016), Spain
 Joe Viana and Fredrik Dahl (2018), Norway Information/Registration: https://orahs2020.univie.ac.at/
 Roberto Aringhieri (Joint Coordinator), Italy
 Leonid Churilov, Australia
 Evrim Didem Gunes and Tugba Cayirli (2013), Turkey  Patrick Soriano (2015), Canada
 Christos Vasilakis (2017), Great Britain

University of Vienna Austria Go
20 Jul 2020 31 Jul 2020 2020 Gene Golub SIAM Summer School

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Theory and Practice of Deep Learning
The eleventh Gene Golub SIAM Summer School will take place from 20 - 31 July 2020 at the African Institute for Mathematical Sciences (AIMS) South Africa in Muizenberg, a small seaside suburb of Cape Town, South Africa
The focus of the school will be the theory, implementation, and application of deep learning based on neural nets with many layers. Students will learn the mathematical underpinnings of deep learning using a combination of functional analysis and optimization theory. They will be introduced to applications that include computer vision, nonlinear programming, and forecasting, in addition to attending lectures by practitioners of deep learning in industry.
The summer school will include an important computing component, in which students will implement deep learning algorithms primarily using Python with TensorFlow and Keras. High performance computing resources will be provided by the AIMS supercomputing facility.
Info:
Application deadline :
1 February 2020
Applicants selection :
by 15 March 2020
Learn more:
SIAM Web Site
Contact us : siam2020@aims.ac.za
The summer school is being organised by:

Bubacarr Bah
AIMS South Africa

Coralia Cartis
University of Oxford

Kasso Okoudjou
University of Maryland, College Park
The intended audience is graduate students, meaning anyone studying beyond a three- or four-year undergraduate degree. Applicants are expected to have done a significant amount of mathematics and some computing in their studies in order to have the required mathematical and computational background for the summer school. Ideal candidates will be working on a research project that requires the use of deep learning methods.
Applicants selected to participate will pay no registration fee, will be provided with standard student accommodation and meals and their travel expenses will be fully covered.

Muizenberg South Africa Go
19 Jul 2020 23 Jul 2020 Euro PhD Summer School in Sustainable Supply Chains

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Welcome to Lisbon, to Técnico and to the 2020 Euro PhD Summer School in Sustainable Supply Chains. For one week, PhD students and young researchers have the opportunity to learn more on how operational research methods can support organizations on the design, planning and operation of supply chains towards sustainability goals.

Lisboa Portugal Go
10 Jul 2020 19 Jul 2020 EURO PhD School on Data Driven Decision Making

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This EURO PhD School (EPS) will focus on giving participants advanced training on Data Driven Decision Making. There will be a methodological as well as an applied component to this EPS. Methodological training on the role of Mathematical Optimization in Data Science will be given in the format of lectures and computer workshops. The lectures will highlight the mathematical and statistical modeling and numerical optimization behind data analysis and data visualization tools. The computer workshops will make this knowledge actionable. Applications of the acquired knowledge to the modeling of specific industrial problems will be presented by professionals from industry and worked out by the PhD students. Mathematical and statistical models and numerical solution approaches will be developed and communicated, following a collaborative approach, in which the PhD students will work in small groups under the guidance of the instructors.

WHERE

Institute of Mathematics of the University of Seville

WHEN

Friday to next Sunday
10-19 July 2020

Institute of Mathematics of the University of Seville Spain Go
06 Jul 2020 17 Jul 2020 Bocconi Summer School in Advanced Statistics and Probability

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Program


Reproducibility in data science
The goal of this fast-paced course is to expose PhD-level statistics and machine learning students to current research topics in statistical inference for large-scale data sets, focusing on methods with finite-sample frequentist guarantees.
This work is motivated by the growing reliance of many applied fields on the automatic analysis of large amounts of data in order to make scientific discoveries and inform high-stakes decisions.
In particular, there is growing awareness of a widespread reproducibility crisis in science, and novel statistical methods are needed to ensure that reported discoveries are reproducible and are not spurious discoveries resulting from the multiple-comparisons problem (“data snooping”).
We will begin by introducing the frequentist multiple hypothesis testing problem and exploring a variety of general methods for addressing it.
Next, we will frame the model selection problem as a multiple hypothesis testing problem and explore some of the inferential challenges and recent solutions in this setting.
We will conclude by exploring how conditional independence testing relates to causality and discussing how to calibrate arbitrary machine learning algorithms to ensure valid predictive inference.

Instructors

Lectures:
Chiara Sabatti (Department of Statistics, Stanford University, US)

Tutorials:
Stephen Bates (Department of Statistics, Stanford University, US)
Matteo Sesia (Department of Statistics, Stanford University, US)

Format
Morning: 3 hours/day lectures
Afternoon: 2 hours/day supervised tutorials as well as individual and team work.

Moreover, there will be a poster session, where participants, upon previous request, may present their research. A welcome cocktail will be offered during the poster session. More detailed info to follow.

Room and board
Accommodation is included in the registration fee.
The students will be hosted at the Guest House of Villa del Grumello and at the Ostello Bello.
The organizing committee will take care of the reservation.
Working days’ lunches are included in the registration fees.

Attendance and final certificate
Full attendance of the activities of the summer school is mandatory for the participants.
Subject to a positive participation to the program, an attendance certificate will be awarded by Università Bocconi, mentioning that the 2020 edition of the Summer School is offered in collaboration with University of Oxford and Imperial College London.

Como Italy Go
06 Jul 2020 17 Jul 2020 EURO PhD summer schools on Multiple Criteria Decision Aiding/Making (MCDA/MCDM)

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PhD summer schools on Multiple Criteria Decision Aiding/Making (MCDA/MCDM) have been jointly organized by the International Society on Multiple Criteria Decision (MCDM) making and EURO Working Group on Multicriteria Decision Aiding (EWG-MCDA) regularly since 1983. The summer school brings together around 50 PhD students from all over the world and leading scholars of MCDA/MCDM at a venue where all participants live, work, and socialize together for a two-week period. This event has been very successful in educating future generations of MCDA/MCDM scholars and facilitating networking among participants.

Ankara Turkey Go
01 Jul 2020 01 Jul 2020 Workshop on Data and Decisions in the COVID19 times

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SAVE-the-DATE!!!!
July 1, 2pm-5pm (CEST)

Online "Workshop on Data and Decisions in the COVID19 times"

jointly organised by IMUS-Instituto de Matemáticas de la Universidad de Sevilla and Copenhagen Business School, within the H2020 RISE NeEDS – Network of European Data Scientists, www.riseneeds.eu.

Colleagues from CARTO, Instituto de Estadística y Cartografía de Andalucía, KU Leuven, Centraal Bureau voor de Statistiek, Danmarks Statistik, Università degli Studi di Milano, Universidad de Chile, and Universidad de Sevilla will present contributions from Data Science, Official Statistics and Mathematical Optimization to enhance Data Driven Decision Making in the COVID19 times.

Confirmed speakers are:
Sandra Benítez Peña, Jonas Klingwort, Alessandra Micheletti, Cristina Molero del Río, Laust Mortensen, Klass Nelissen, Hector Ramirez Cabrera, Reme Sillero Denamiel

To register, follow the link
https://riseneeds.eu/2020/06/19/needs-workshop-on-data-and-decisions-in-the-covid19-times/

Announcements
https://twitter.com/DoloresRomeroM/status/1274020104305610753?s=20
https://www.linkedin.com/posts/dolores-romero-morales-77b49035_save-the-date-july-1-2pm-5pm-cest-activity-6679808000103976960-Tzvg
https://www.facebook.com/permalink.php?story_fbid=1454552651394052&id=100005179936730

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01 Jul 2020 08 Jul 2020 School on "Graph Theory, Algorithms and Applications"

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School on "Graph Theory, Algorithms and Applications"
International School of Mathematics -- "Guido Stampacchia"
Centre "Ettore Majorana" for Scientific Culture
Erice, Sicily (Italy)
July 1-8, 2020
http://www.graphalgorithms.org/erice2020

LECTURERS:
* Claudia Archetti, Essec Business School, France
* Daniel Delling, Apple Inc., United States
* Devdatt Dubashi, Chalmers University of Technology, Sweden
* Michael Juenger, University of Cologne, Germany
* Silvio Lattanzi, Google Zurich, Switzerland
* Andrea Lodi, Polytechnique Montr?al, Canada
* Petra Mutzel, TU Dortmund University, Germany
* Juan Jos? Salazar Gonz?lez, University of La Laguna, Spain
* M.Grazia Speranza, Univeristy of Brescia, Italy
* Bob Tarjan, Princeton University, United States
* Milkkel Thorup, University of Copenhagen, Denmark
* Daniele Vigo, University of Bologna, Italy

DIRECTORS OF THE SCHOOL:
Prof. Raffaele Cerulli, University of Salerno
Dr. Andrew V. Goldberg, Amazon.com Inc.
Prof. Giuseppe F. Italiano, LUISS University
Prof. Robert E. Tarjan, Princeton University

DIRECTORS OF THE COURSE:
Prof. Franco Giannessi, University of Pisa

LOCATION:
Erice is among the oldest cities in Sicily. The town is placed on the
homonymous mount Eryx, religious center of the Elimi, which is famous
for its temple where the Phoenicians worshipped Astarte, the Greeks
Aphrodite and the Romans Venus. Throughout history Erice was contended
by many different populations, and each of them left a palpable sense
of history. Erice is nowadays an enchanting wonderfully preserved
Mediaeval town offering the most breathtaking views in Sicily.

APPLICATION:
Advanced undergraduates, MS and PhD students, and young scientists (35
or under) interested in graph algorithms are encouraged to apply.
Qualified candidates should complete their application on the School
Website (http://www.graphalgorithms.org/erice2020) by April 10, 2020.
Application material includes a short CV and optional recommendation
letters. Space is limited. Acceptance notifications will be sent
around the end of April 2020.

REGISTRATION:
The registration fee for the School is 800 Euro. It includes meals and
accommodation for 8 days, i.e., from July 1 (evening) to July 8
(morning).

FURTHER DETAILS:
More information is available on the School Web Site
(http://www.graphalgorithms.org/erice2020)

--
Erice 2020 - Directors of the Course

International School of Mathematics "Guido Stampacchia" - Centre "Ettore Majorana" for Scientific Culture, Erice Italy Go
29 Jun 2020 12 Jul 2020 2020 Deep Learning Summer School

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Welcome & orientation

Courses about deep learning

Chinese language and culture classes

Visits and cultural outings: Great Wall, National Museum, dinner in a typical Chinese restaurant etc.

Visits of IT company: Google, Sogou, Baidu…

Farewell party

Tsinghua University, Beijing China Go
29 Jun 2020 03 Jul 2020 4th DS3 - Data Science Summer School

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This fourth edition of Data Science Summer School (DS3) is co-organised by the Data Science Initiative of École polytechnique and DATAIA Institute, in the quiet and charming outskirts of Paris.
The primary focus of the event is to provide a series of courses and practical sessions covering the latest advances in the field of data science.
The event is targeted for students (MSc2, PhD), postdocs, academics, members of public institutions, and professionals.

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