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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.

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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!

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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

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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.

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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

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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

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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.

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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.

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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

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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

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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.

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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

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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.

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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)

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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.

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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.

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ABOUT THE WINTER SCHOOL
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.

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

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SCOPE
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.

ADDRESSED TO
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.

VENUE
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
STRUCTURE
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.

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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

Acknowledgements:
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.

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Organisation:
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.

Registration:
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.

Program:
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.

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Objectives:
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

Scope:
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.

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

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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.

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Description
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.

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Event
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.

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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)