Conferences and Workshops of interest for AIROYoungers

Displaying conferences 26 - 50 of 95 in total
Start date End date Description Location Country Url
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
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
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

Online Denmark and Spain Go
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 REGML 2020

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COURSE AT A GLANCE
The course will be held from June 29th to July 3rd at DIBRIS (University of Genova, Italy)

Understanding how intelligence works and how it can be emulated by machines is an age old dream and arguably one of the biggest challenges in modern science. Learning, with its principles and computational implementations, is at the very core of this endeavor. Recently, for the first time, we have been able to develop artificial intelligence systems able to solve complex tasks considered out of reach for decades. Modern cameras recognize faces, and smart phones voice commands, cars can see and detect pedestrians and ATM machines automatically read checks. In most cases at the root of these success stories there are machine learning algorithms, that is, software that is trained rather than programmed to solve a task. Among the variety of approaches to modern computational learning, we focus on regularization techniques, that are key to high-dimensional learning. Regularization methods allow to treat in a unified way a huge class of diverse approaches, while providing tools to design new ones. Starting from classical notions of smoothness, shrinkage and margin, the course will cover state of the art techniques based on the concepts of geometry (aka manifold learning), sparsity and a variety of algorithms for supervised learning, feature selection, structured prediction, multitask learning and model selection. Practical applications for high dimensional problems, in particular in computational vision, will be discussed. The classes will focus on algorithmic and methodological aspects, while trying to give an idea of the underlying theoretical underpinnings. Practical laboratory sessions will give the opportunity to have hands on experience.


RegML is a 20 hours advanced machine learning course including theory classes and practical laboratory sessions. The course covers foundations as well as recent advances in Machine Learning with emphasis on high dimensional data and a core set techniques, namely regularization methods. In many respects the course is a compressed version of the 9.520 course at MIT.

Related courses:

MLCC 2019. A one week (crash) course of 10 lectures, including theoretical and practical sessions..
MIT 9.520 - Statistical Learning Theory and Applications. This is a term long course of roughly 25 lectures offered to graduate students at MIT.
Machine Learning 2018/2019. Undergraduate term-long introductory Machine Learning course offered at the University of Genova.
CBMM Summer School: Machine Learning Classes. One day introduction to the essential concepts and algorithms at the core of modern Machine Learning.
RegML master page. Previous editions of RegML.

The course started in 2008 has seen an increasing national and international attendance over the years, with a peak of over 90 participants in 2014.


Important dates:

application deadline: March 20
notification of acceptance: March 27
registration fee deadline: April 17

Registration fee:

students and postdocs: EUR 50
professors: EUR 100
professionals: EUR 150
UNIGE students and IIT affiliates: no fee
Once accepted, each candidate has to follow the instructions in the acceptance email and proceed with the payment. The registration fee is non-refundable.

Genova Italy 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.

Paris France Go
28 Jun 2020 04 Jul 2020 Swedish Summer School in Computer Science 2020

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The Swedish Summer School in Computer Science (S3CS) 2020 is held June 28 to July 4 in Stockholm.

The summer school runs for a full week Monday-Friday in early July when Sweden is at its best, with arrival on Sunday evening and departure Saturday morning. S3CS consists of mini-courses on The Method of Moments in Computer Science and Beyond by Ankur Moitra and Polyhedral Techniques in Combinatorial Optimization by Ola Svensson.

Timeline

March 6 Application deadline
March 27 Notification of acceptance to the summer school (or placement in waiting list).
April 24 Deadline for confirming participation and paying registration fee.
Sun Jun 28 - Sat Jul 4 The summer school

Stockholm Sweden Go
28 Jun 2020 10 Jul 2020 The Machine Learning Summer School

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The Machine Learning Summer School (MLSS) Series
The machine learning summer school (MLSS) series was started in 2002 with the motivation to promulgate modern methods of statistical machine learning and inference. It was motivated by the observation that while many students are keen to learn about machine learning, and an increasing number of researchers want to apply machine learning methods to their research problems, only few machine learning courses are taught at universities. Machine learning summer schools present topics which are at the core of modern Machine Learning, from fundamentals to state-of-the-art practice. The speakers are leading experts in their field who talk with enthusiasm about their subjects.
MLSS 2020 in Tübingen, Germany
In the summer of 2020, the MLSS will make its fifth appearance in the beautiful medieval university town of Tübingen, in southwestern Germany. It will be hosted by the Department of Empirical Inference at Max Planck Institute for Intelligent Systems between 28 June - 10 July 2020.
All past and future MLSSs can be found here. For general inquiries about MLSS 2020 in Tuebingen, please write to mlss2020-team@tuebingen.mpg.de. If you have questions regarding application to the MLSS 2020, please check FAQ first before contacting us.
Key Dates
25 December 2019
Application starts.
11 February 2020
Application deadline. Application portal may close earlier than the deadline if the number of applications exceeds our capacity to review. How to apply
18 February 2020
Deadline for supervisors to complete the recommendation form for student applicants.
Mid March 2020
Notification of acceptance
End of March 2020
Deadline for registration to attend
28 June 2020
First day of MLSS 2020
10 July 2020
Last day of MLSS 2020
Updates
25 Dec 2019
Application portal is open.
19 Dec 2019
Add registration fees. See "participate".
18 Dec 2019
Add details on target audience. See "participate".
27 Nov 2019
Add a list of required documents when applying to the MLSS. See this page.
19 Nov 2019
Added key dates. The dates are tentative at the moment. Application will open in mid December 2019.
07 Nov 2019
Added the list of confirmed speakers. This list is growing.
16 Oct 2019
MLSS 2020 web site is online.

Max Planck Institute for Intelligent Systems, Tübingen Germany Go
25 Jun 2020 26 Jun 2020 2nd EUROYoung Workshop

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The 2nd EUROYoung Workshop, which will take place at INESC-TEC in Porto, Portugal during June, 25-26, 2020.

The enrolment form is already available at the following link: https://forms.gle/KqWvvQRUxWg16Vtd8

To know more visit the event's website: https://euroyoung.github.io/workshop20/

Highlights:
No enrolment fees.
Free accommodation on a first-come first-serve basis.
Free meals (2 lunches and 1 dinner).
Social activities.
Invited lectures by Anita Schöbel and Pedro Amorim.
Contributed sessions to present your research in a young, collaborative, and relaxed environment.
Tutorial sessions to learn about useful techniques and tools.
Opportunities for networking and tutoring with our invited speakers.

What is EUROYoung?
It's an initiative sponsored by EURO, with the following objectives:
Fostering collaboration among students and early-career researchers in O.R.
Providing young O.R. scholars and practitioners with tools to advance their careers, mainly through training.
Creating networks both among young researchers and with more senior leaders in the field of O.R.
Connecting demand and offer in the O.R. job market, both in academia and the industry.
We are looking forward to seeing you soon in Porto!

The organising committee,
Maria João Santos, INESC TEC and University of Porto
Sara Martins, INESC TEC and University of Porto
Alberto Santini, Universitat Pompeu Fabra

INESC-TEC, Porto Portugal Go
22 Jun 2020 26 Jun 2020 Third International Summer School on Artificial Intelligence and Games

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Summer School Description

The third International Summer School on Artificial Intelligence and Games will be held in Copenhagen, Denmark, from June 22 to 26, 2020. The school is organized by modl.ai in partnership with Unity, DeepMind, and Creative Assembly (more partners will be announced soon).

The summer school is dedicated to the uses of artificial intelligence (AI) techniques in and for games. After introductory lectures that explain the background and key techniques in AI and games, the school will introduce participants the uses of AI for playing games, for generating content for games, and for modeling players.

This school is suitable for industrial game developers, designers, programmers and practitioners, but also for graduate students in games, artificial intelligence, design, human-computer interaction, and computational intelligence.

The main lecturers are Georgios N. Yannakakis and Julian Togelius, co-authors of the AI and Games textbook (http://www.gameaibook.org), the first comprehensive textbook on the use of AI in games. During the first phase of the school theoretical lectures will be complemented by guest lectures on special topics in game AI and by hands-on workshops given by world-leading practitioners. For the second phase of the school, we plan a game AI jam on the taught material.

Copenhagen Denmark Go
21 Jun 2020 26 Jun 2020 DTU CEE Summer School 2020 Advanced Optimization, Learning, and Game‐Theoretic Models in Energy Systems

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Sessions/tutorials

During the 5-day course distinguished speakers, excelling both in research and teaching, give talks about the latest developments in optimization for energy systems. The topics that the DTU CEE Summer School 2020 will cover are:

Electricity Market Design
Electricity Markets: Challenges and Solutions
Machine Learning: State-of-the-art and Applications to Renewable Energy
Stochastic Programming Applications to Power System Operation and Investment
Aggregative and Network Games
Model Predictive Control
Learning and Optimization for Power Distribution Grids
Equilibrium Models in Power Systems
Forecasting in Power Systems
Machine Learning Applications in Power Systems
Poster session

Please prepare a poster (please bring the printed poster on A0 format, 1189 x 841 mm) on your current, past or future research! The two best posters selected by all participants on day 1 will have a chance to be presented orally on day 4.

Certificate for 2.5 ECTS

The participants who will send an extensive summary of lectures by mid August will receive 2.5 ECTS.

Social events for networking and fun

Every year we organize a whole afternoon dedicated to social events.
Don’t miss the opportunity to socialize with fellow colleagues from all over the world!

DTU Lyngby Denmark Go
15 Jun 2020 17 Jun 2020 CTW2020: 18th Cologne-Twente Workshop on Graphs and Combinatorial Optimization

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The Cologne-Twente Workshop on Graphs and Combinatorial Optimization 2020 welcomes contributions on theory and applications of discrete algorithms, graphs and combinatorial optimization in the wider sense.

The series of Cologne-Twente Workshops on Graphs and Combinatorial Optimization started with meetings organized every two years by the universities of Cologne and Twente. The organizational base was later expanded by other universities. Since 2003 the workshop is held (almost) every year.

CTWs are especially intended to let doctoral students and young researchers present the results of their research activities in a friendly and highly interactive atmosphere.

PLENARY SPEAKERS:
Dan Bienstock (Columbia University, USA)
Marco Sciandrone (Università di Firenze, Italia)

PAPER SUBMISSION
There will be two types of submissions:
Standard papers (from 8 to 12 pages) that will be selected for publication in a volume of the AIRO Springer series (a copyright transfer will be necessary).
Traditional CTW Extended abstracts (up to 4 pages) that will be published on the workshop webpage (subject to author’s approval).
See http://ctw2020.iasi.cnr.it submission page for information.

IMPORTANT DATES
December 31st, 2019: Submission deadline for Standard papers.
February 15th, 2020: Notification of acceptance for standard papers.
March 1st, 2020: Submission deadline for extended abstracts.
April 15th, 2020: Notification of acceptance for extended abstracts.
May 1st, 2020: Deadline for Early Registration payment.
June 15th, 2020: Beginning of Conference.

ORGANIZING COMMITTEE
Claudio Gentile, IASI-CNR
Leo Liberti, CNRS-LIX
Gaia Nicosia, U. RomaTre
Andrea Pacifici, U. TorVergata Roma
Giovanni Rinaldi, IASI-CNR
Giuseppe Stecca, IASI-CNR
Paolo Ventura, IASI-CNR

Ischia Italia Go
08 Jun 2020 19 Jun 2020 PhD Summer Academy in Logistics and Supply Chain Management

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Methodology

In addition to being introduced to different topics in the field by a group of distinguished professors, it is a great opportunity to meet doctoral students from different institutions and exchange ideas. Although we expect applicants to come from different institutions, countries and backgrounds, the one common denominator is excellence. Applicants are selected to be part of a discussion forum made up of outstanding scholars in the area of logistics and supply chain management.

Certificate

The PhD Summer Academy 2020 program is administered under the MIT-Zaragoza International Logistics Program, one of the select MIT educational and research partnerships. Upon completion of all courses to which you have enrolled, you will be awarded a certificate stating that you have completed a PhD summer course under the MIT- Zaragoza Program.

Courses
Operations and Logistics in Fragmented Grocery Retail
Behavioral Operations Management
Health Care Logistics
Circular Economy Models and Applications

Zaragoza Spain Go
08 Jun 2020 12 Jun 2020 ICAPS-ICRA Summer School on Plan-Based Control for Robotic Agents

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The 2020 ICAPS-ICRA Summer School on Plan-Based Control for Robotic Agents will provide students with the opportunity to learn about both the foundations and recent developments in exciting research areas in AI Planning and Scheduling and Robotics. There are planned lectures as well as hands-on sessions. The courses will cover planning basics, task and motion planning, planning with robotic uncertainty, time-constrained planning, and complete robot planning systems.

Following the success of the previous editions of the summer school, the aim of this edition is to bring together the fields of AI and Robotics. Lectures will be given by top subject experts from the Planning, Scheduling, and Robotics research communities.

The summer school will start on June 8, around 10:00 CET and last until June 12, around 16:30 CET.

Paris France Go
07 Jun 2020 12 Jun 2020 Summer School in Graph Theory (SGT 2020)

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Dear all,

A Summer School in Graph Theory (SGT'20) will take place on June 7-12 2020 near Clermont-Ferrand (in Murol, France).

Lectures will be given by:
- Paul Seymour (Princeton University) : Excluding induced subgraphs
- Piotr Micek (Jagiellonian University, Krakow): Coloring geometric graphs
- Paul Wollan (University of Rome) : Graphs minors, structure and algorithms (initially planned to be shared with Jim Geelen, University of Waterloo - canceled)

Registration is now open: https://sgt2020.limos.fr/
Please note that the overall capacity is limited, so we advise to pre-register soon!

This summer school is part of a series, organized by members of the French Graph Theory community. Former editions were SGT'13 on the Ol?ron island, SGT'15 on the Porquerolles island, and SGT'18 in Séte.

Best wishes,

Aurélie Lagoutte
For the organizing committee

Murol France Go
07 Jun 2020 10 Jun 2020 IPCO 2020 LSE

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The 21st Conference on Integer Programming and Combinatorial Optimization June 8-10, 2020, London, UK
The 21st Conference on Integer Programming and Combinatorial Optimization (IPCO XXI) will take place from June 8-10 at the London School of Economics, in London UK. It will be organised by the Department of Mathematics. The conference will be preceded by a Summer School (June 6-7).

IPCO conference is under the auspices of the Mathematical Optimization Society. It is held every year. The conference is a forum for researchers and practitioners working on various aspects of integer programming and combinatorial optimization. The aim is to present recent developments in theory, computation, and applications in these areas.

London UK Go
01 Jun 2020 05 Jun 2020 NATCOR Course on Convex Optimization

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PRE-REQUISITES:

1. Undergraduate level knowledge of linear algebra (e.g. the relevant chapter in Winston’s textbook on OR) and calculus (e.g. basic notions of continuity and differentiability).
2. The students are expected to have familiarized themselves with the material marked with an asterisk in the teaching schedule and with other directed reading.

AIMS OF THE COURSE

1. To develop knowledge of different aspects of convex optimization and its applications.
2. To develop an ability to model real life problems as mathematical programming problems and an ability to adapt industry standard solvers to process them.
3. To develop an ability to analyze optimization algorithms for their merits and shortcomings.
4. To develop an ability to work independently as well as in a peer group with limited supervision.

LEARNING OUTCOMES FOR THE COURSE

The course provides opportunities for students to develop and demonstrate knowledge and understanding, qualities, skills and other attributes in the following areas:

(A) Knowledge and Understanding

On successful completion of this course, the students will have

1. knowledge of theoretical underpinning of convexity in optimization and of general nonlinear programming methods. This knowledge will act as a foundation to understand an advanced graduate textbook or a research paper without significant help.
2. understanding of linear programming methods and related theoretical issues.
3. knowledge of semi-definite programming.
4. ability to use an industry standard optimization software system for processing optimization models.

(B) Cognitive Skills

On successful completion of this course, the students will be able to

5. formulate realistic industrial problems as mathematical programming problems.
6. analyze critically the choice of algorithms for solving different classes of a particular optimization model regarding their computational effectiveness.
7. construct elementary proofs related to the properties of optimization methods.

(C) Other Skills and Attributes (Practical/Professional/Transferable)

On successful completion of this course, the students will be able to

8, plan and execute a solution to an optimization problem as a group and will be able to present the results to peers and tutors.

PRINCIPAL TOPICS OF STUDY:

Foundations of Convexity: affine and convex sets, convex functions, composition of convex functions.

General Convex Optimization: examples of convex optimization problems, duality, unconstrained minimization, steepest descent method, first and second order optimality conditions in unconstrained minimization, Newton’s method and convergence analysis, norm approximation problems.

Linear Programming: simplex method, duality for LP, interior point methods for LP.

Convex Quadratic Programming: simplex method for quadratic programming, application in finance, KKT conditions for convex QP.

Semi-definite Programming: formulation, extension of interior point methods to SDP, quadratically constrained convex quadratic programs.

A case study of convex optimization in industry.

Numerical Linear Algebra: algorithm complexity, Cholesky factorization, sparsity.

The latter part of this course will be run in two parallel streams: an application stream (A stream) and a theory stream (T stream). The lectures and workshops for the two streams will differ for the two streams for a part of the course, and the students will need to choose beforehand which stream they prefer to follow. Most of the topics of study are the same for both the streams, apart from the topics mentioned below:

A stream: Use of Industry Strength Solver Systems for LP/QP to process industrial problems.

T stream: advanced topics in optimization including interior point methods for convex quadratic optimisation, complexity analysis via self-concordance, interior point methods for second order cone programming, Nesterov’s method for smooth and non-smooth programming.

REFERENCES

[1] S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004. As of June 2009, this text is freely available to download at http://www.stanford.edu/~boyd/cvxbook/ . The chapters relevant to this course are 2-5 and 9-11.
[2] D.G. Luenberger, Linear and Nonlinear Programming, Kluwer, 2003. The chapters relevant to this course are 2-10.
[3] R. Fourer, .M. Gay, B.W. Kernighan, Ampl: A Modeling Language for Mathematical Programming, Brooks Cole, 2002.
[4] Hillier and Lieberman, Introduction to Operations Research, McGraw Hill, 2002. The chapter relevant to this course is 13.

Edinburgh UK Go
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.

Lecturers

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)

Organization

Organizers: Frédéric Meunier and Axel Parmentier (firstname.lastname@enpc.fr)

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 cg2020@gerad.ca

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.
Dates:
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.

PRE-REQUISITES
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.

AIM
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.

LEARNING OUTCOMES
– 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.

TOPICS COVERED
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.

ASSESSMENT
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