Week 1 
Monday, Jan 19 
Tuesday, Jan 20 
Wendesday, Jan 21 
Thursday, Jan 22 
Friday, Jan 23 

8:30  10:00  Opening Session  Imecc 
On Codes and Lattices  Sueli I. R. Costa
On Codes and LatticesSueli I. R. Costa (State University of Campinas)It will be given an introduction to qary codes, their properties and examples. Lattices will be presented with the fundamental concepts of generator and Gram matrices, packing density, dual lattices, and theta series. Quantization, AWGN codes and associations between codes and lattices (Constructions A and B) considered in different metrics will be approached. Some applications to spherical codes and highsuelis to other applications to be developed in two following shortcourses will also be presented. Close 
On Codes and Lattices  Sueli I. R. Costa
On Codes and LatticesSueli I. R. Costa (State University of Campinas)It will be given an introduction to qary codes, their properties and examples. Lattices will be presented with the fundamental concepts of generator and Gram matrices, packing density, dual lattices, and theta series. Quantization, AWGN codes and associations between codes and lattices (Constructions A and B) considered in different metrics will be approached. Some applications to spherical codes and highsuelis to other applications to be developed in two following shortcourses will also be presented. Close 
Introduction to Network Coding  Danilo Silva
Introduction to Network CodingDanilo Silva (Federal University of Santa Catarina)This tutorial will review the basic principles of network coding, as well as some of its applications, challenges and related problems. Special attention will be given to two problems where algebraic codes (namely, rankmetric codes and lattice codes) have important applications: network error/erasure correction and wireless physicallayer network coding. Close 
Introduction to Network Coding  Danilo Silva
Introduction to Network CodingDanilo Silva (Federal University of Santa Catarina)This tutorial will review the basic principles of network coding, as well as some of its applications, challenges and related problems. Special attention will be given to two problems where algebraic codes (namely, rankmetric codes and lattice codes) have important applications: network error/erasure correction and wireless physicallayer network coding. Close 
Students' oral presentations (4 per session/day, 2 minutes for each poster)  
10:10  10:30  Coffee Break  
10:30  12:00 
Block Codes  Valdemar Cardoso da Rocha Jr.
Block CodesValdemar Cardoso da Rocha Jr. (Federal University of Pernambuco)Basic ConceptsIntroduction; Types of errors; Channel models; Linear codes and nonlinear codes; Block codes and convolutional codes. Block CodesIntroduction; Matrix representation; Minimum distance; Error syndrome and decoding; Simple codes; Lowdensity paritycheck codes. Cyclic CodesMatrix representation of a cyclic code; Encoder with nk shiftregister stages; Cyclic Hamming codes; Maximumlengthsequence codes; BoseChaudhuriHocquenghem codes; ReedSolomon codes; Golay codes; ReedMuller codes; Quadratic residue codes. Decoding Cyclic CodesMeggitt decoder; Errortrapping decoder; Information set decoding; Threshold decoding. Algebraic decoding; BerlekampMassey time domain decoding; Euclidean frequency domain decoding; Softdecision Decoding; Decoding LDPC Codes. Close 
Block Codes  Valdemar Cardoso da Rocha Jr.
Block CodesValdemar Cardoso da Rocha Jr. (Federal University of Pernambuco)Basic ConceptsIntroduction; Types of errors; Channel models; Linear codes and nonlinear codes; Block codes and convolutional codes. Block CodesIntroduction; Matrix representation; Minimum distance; Error syndrome and decoding; Simple codes; Lowdensity paritycheck codes. Cyclic CodesMatrix representation of a cyclic code; Encoder with nk shiftregister stages; Cyclic Hamming codes; Maximumlengthsequence codes; BoseChaudhuriHocquenghem codes; ReedSolomon codes; Golay codes; ReedMuller codes; Quadratic residue codes. Decoding Cyclic CodesMeggitt decoder; Errortrapping decoder; Information set decoding; Threshold decoding. Algebraic decoding; BerlekampMassey time domain decoding; Euclidean frequency domain decoding; Softdecision Decoding; Decoding LDPC Codes. Close 
Introduction to codebased cryptography  Paulo S. L. M. Barreto
Introduction to codebased cryptographyPaulo S. L. M. Barreto(University of São Paulo)Postquantum cryptosystems are those purely classical cryptosystems which are able, in principle, to resist attacks mounted with the help of quantum computers. Codebased cryptographic protocols are among the main proposed families of such systems, as certain constructions have withstood cryptanalysis largely unscathed since the concept was proposed by McEliece in 1978, and evidence has been found that quantum Fourier sampling, the strategy adopted in all quantum attacks known against more conventional cryptosystems, cannot possibly work against codebased alternatives. Recent engineering progress also show that codebased encryption is not only viable, but can be made fairly efficient even on the constrained platforms typical of the Internet of Things. This short course aims at introducing the essentials of codebased cryptography, from the basic protocols and supporting algorithms to the choice of parameters for actually deployable schemes, according to the state of the art. Close 
Introduction to codebased cryptography  Paulo S. L. M. Barreto
Introduction to codebased cryptographyPaulo S. L. M. Barreto(University of São Paulo)Postquantum cryptosystems are those purely classical cryptosystems which are able, in principle, to resist attacks mounted with the help of quantum computers. Codebased cryptographic protocols are among the main proposed families of such systems, as certain constructions have withstood cryptanalysis largely unscathed since the concept was proposed by McEliece in 1978, and evidence has been found that quantum Fourier sampling, the strategy adopted in all quantum attacks known against more conventional cryptosystems, cannot possibly work against codebased alternatives. Recent engineering progress also show that codebased encryption is not only viable, but can be made fairly efficient even on the constrained platforms typical of the Internet of Things. This short course aims at introducing the essentials of codebased cryptography, from the basic protocols and supporting algorithms to the choice of parameters for actually deployable schemes, according to the state of the art. Close 
Structure of the capacity region and its use in multiuser systems  Ninoslav Marina
Structure of the capacity region and its use in multiuser systemsNinoslav Marina (Haute Ecole ARC)In this tutorial we will describe the structure of the capacity region of the asynchronous memoryless multiple access channel. The asynchronous capacity region of memoryless multipleaccess channels is the union of certain polytopes. It is wellknown that vertices of such polytopes may be approached via a technique called successive decoding. It is also known that an extension of successive decoding applies to the dominant face of such polytopes. The extension consists of forming groups of users in such a way that users within a group are decoded jointly whereas groups are decoded successively. We show that successive decoding extends to every face of the above mentioned polytopes. The group composition as well as the decoding order for all rates on a face of interest are obtained from a label assigned to that face. From this label one can extract a number of geometrical properties, such as the dimension of the corresponding face and whether or not two faces intersect. Expressions for the number of faces of any given dimension are also derived from the labels. We explain also the derivation of the total number of vertices (faces of dimension zero) in the capacity region, that equals floor(eM!). Close 
12:00  12:10  Students' oral presentations (4 per session/day, 2 minutes for each poster)  
12:10  14:00  Lunch Time  Churrasco (Meat & vegetables BBQ)  
14:00  15:30 
Introduction to Information Theory (aka IT in a nutshell)  Max Costa
Introduction to Information Theory (aka IT in a nutshell)Max Costa (University of Campinas)This is intended to be a brief introduction to Information Theory (IT), one of the jewels of 20th Century Applied Mathematics, with rich transdisciplinary implications. We start with some definitions: entropy, relative entropy (Kullback Leibler distance), mutual information, and differential entropy. Then we see a simple and intuitive relation that has surprisingly strong consequences, the data processing inequality (DPI). Next we visit what I consider to be the DNA of IT, the asymptotic equipartition property (AEP). We then look at some applications in source coding and channel coding, with highlight to some simple codes of both kinds. We plan to close with some multiple user variations of the theme, such as distributed source coding (SlepianWolf), multiple access channels, broadcast channels and interference channels. Close 
Introduction to Information Theory (aka IT in a nutshell)  Max Costa
Introduction to Information Theory (aka IT in a nutshell)Max Costa (University of Campinas)This is intended to be a brief introduction to Information Theory (IT), one of the jewels of 20th Century Applied Mathematics, with rich transdisciplinary implications. We start with some definitions: entropy, relative entropy (Kullback Leibler distance), mutual information, and differential entropy. Then we see a simple and intuitive relation that has surprisingly strong consequences, the data processing inequality (DPI). Next we visit what I consider to be the DNA of IT, the asymptotic equipartition property (AEP). We then look at some applications in source coding and channel coding, with highlight to some simple codes of both kinds. We plan to close with some multiple user variations of the theme, such as distributed source coding (SlepianWolf), multiple access channels, broadcast channels and interference channels. Close 
Introduction to Convolutional Codes  Reginaldo Palazzo Jr
Introduction to Convolutional CodesReginaldo Palazzo Jr (University of Campinas)

Structure of the capacity region and its use in multiuser systems  Ninoslav Marina
Structure of the capacity region and its use in multiuser systemsNinoslav Marina (Haute Ecole ARC)In this tutorial we will describe the structure of the capacity region of the asynchronous memoryless multiple access channel. The asynchronous capacity region of memoryless multipleaccess channels is the union of certain polytopes. It is wellknown that vertices of such polytopes may be approached via a technique called successive decoding. It is also known that an extension of successive decoding applies to the dominant face of such polytopes. The extension consists of forming groups of users in such a way that users within a group are decoded jointly whereas groups are decoded successively. We show that successive decoding extends to every face of the above mentioned polytopes. The group composition as well as the decoding order for all rates on a face of interest are obtained from a label assigned to that face. From this label one can extract a number of geometrical properties, such as the dimension of the corresponding face and whether or not two faces intersect. Expressions for the number of faces of any given dimension are also derived from the labels. We explain also the derivation of the total number of vertices (faces of dimension zero) in the capacity region, that equals floor(eM!). Close 

15:30  15:40  Students' oral presentations (4 per session/day, 2 minutes for each poster)  
15:40  16:15  Coffee Break & "Posters of the day" session  
16:15  17:45 or forever  Impromptu Problem Session 
Introduction to Convolutional Codes  Reginaldo Palazzo Jr
Introduction to Convolutional CodesReginaldo Palazzo Jr (University of Campinas)

Impromptu Problem Session  Research Opportunities in São Paulo  Brazil 
Week 2 
Monday, Jan 26 
Tuesday, Jan 27 
Wendesday, Jan 28 
Thursday, Jan 29 
Friday, Jan 30 

8:30  10:00 
Wireless Network Coding: Algorithms and Applications  Alex Sprintson
Wireless Network Coding: Algorithms and ApplicationsAlex Sprintson (Texas A&M)This minicourse will provide basic and indepth knowledge of the rapidly evolving area of wireless network coding. It will cover concepts, theories, and solutions for a broad range of wireless network coding problems as well as a comprehensive survey of practical applications of networking coding in various areas of wireless networking. We will cover fundamental wireless network coding problems such as Index Coding and Coded Cooperative Data Exchange problems. We will emphasize the security aspects of the problems. The minicourse will emphasize deep connections between network coding and other areas of information theory, complexity theory, graph theory, matroid theory, and coding theory. We will provide a comprehensive survey of discoveries and insights gained from years of intensive research and discuss open problems and present new exciting opportunities in wireless coding research and applications. The mini course will enable the students to get familiar with the recent developments and pursue research in this exciting area. Close 
Wireless Network Coding: Algorithms and Applications  Alex Sprintson
Wireless Network Coding: Algorithms and ApplicationsAlex Sprintson (Texas A&M)This minicourse will provide basic and indepth knowledge of the rapidly evolving area of wireless network coding. It will cover concepts, theories, and solutions for a broad range of wireless network coding problems as well as a comprehensive survey of practical applications of networking coding in various areas of wireless networking. We will cover fundamental wireless network coding problems such as Index Coding and Coded Cooperative Data Exchange problems. We will emphasize the security aspects of the problems. The minicourse will emphasize deep connections between network coding and other areas of information theory, complexity theory, graph theory, matroid theory, and coding theory. We will provide a comprehensive survey of discoveries and insights gained from years of intensive research and discuss open problems and present new exciting opportunities in wireless coding research and applications. The mini course will enable the students to get familiar with the recent developments and pursue research in this exciting area. Close 
Explicit Lattice Constructions: From Codes to Number Fields  Frédérique Oggier and JeanClaude
Explicit Lattice Constructions: From Codes to Number FieldsFrédérique Oggier (Nanyang Technological University) and JeanClaude Belfiore (Télécom Paris Tech)We start this course by presenting wiretap lattice codes for Gaussian channels. To analyze and design such codes, we will explain:

Lattice Coding for Signals and Networks  Ram Zamir
Lattice Coding for Signals and NetworksRam Zamir (Tel Aviv University)It will cover some of the following topics:

Lattice Coding for Signals and Networks  Ram Zamir
Lattice Coding for Signals and NetworksRam Zamir (Tel Aviv University)It will cover some of the following topics:

10:00  10:10  Students' oral presentations (4 per session/day, 2 minutes for each poster)  
10:10  10:30  Coffee Break  
10:30  12:00 
Chordal Codes for ChiptoChip Communication  Amin Shokrollahi
Chordal Codes for ChiptoChip CommunicationAmin Shokrollahi (Kandou Bus and EPFL  Switzerland)Modern electronic devices consist of a multitude of IC components: the processor, the memory, the RF modem and the baseband chip (in wireless devices), and the graphics processor, are only some examples of components scattered throughout a device. The increase of the volume of digital data that needs to be accessed and processed by such devices calls for ever faster communication between these IC's. Faster communication, however, often translates to higher susceptibility to various types of noise, and inevitably to a higher power consumption in order to combat the noise. This increase in power consumption is, for the most part, far from linear, and cannot be easily compensated for by Moore's Law. In this talk I will give a short overview of problems encountered in chiptochip communication, and will advocate the use of novel coding techniques to solve those problems. I will also briefly talk about Kandou Bus, and some of the approaches the company is taking to design, implement, and market such solutions. Close 
Coding problems for memory and storage applications  Alexander Barg
Coding problems for memory and storage applicationsAlexander Barg (Maryland University)Recent interest in coding problems for memory and storage applications gave rise to some new questions of coding theory, expanding the traditional problems to new metric spaces as well as placing new restrictions on decoding algorithms of codes. We explore some of these problems, focusing on coding in the space of permutations (this problem is motivated by a coding scheme for flash memories) and codes with the local recovery property, motivated by largescale storage applications. Close 
PhysicalLayer Security: Bounds, Codes and Protocols  João Barros
PhysicalLayer Security: Bounds, Codes and ProtocolsJoão Barros (Universidade do Porto)The goal of this course is to give a general overview of the basic building blocks of information theoretic security and how they can be used to provide authentication, confidentiality and integrity at the physical layer of modern communication systems. Starting from first principles and fundamental performance bounds, we shall elaborate on the code constructions needed to achieve reliability and secrecy simultaneously and how to build secure communication protocols that leverage the additional secrecy provided by these code constructions. Finally, we will discuss how to integrate these techniques in existing standards and provide a basic costbenefit analysis. Close 
Coding problems for memory and storage applications  Alexander Barg
Coding problems for memory and storage applicationsAlexander Barg (Maryland University)Recent interest in coding problems for memory and storage applications gave rise to some new questions of coding theory, expanding the traditional problems to new metric spaces as well as placing new restrictions on decoding algorithms of codes. We explore some of these problems, focusing on coding in the space of permutations (this problem is motivated by a coding scheme for flash memories) and codes with the local recovery property, motivated by largescale storage applications. Close 
Explicit Lattice Constructions: From Codes to Number Fields  Frédérique Oggier and JeanClaude
Explicit Lattice Constructions: From Codes to Number FieldsFrédérique Oggier (Nanyang Technological University) and JeanClaude Belfiore (Télécom Paris Tech)We start this course by presenting wiretap lattice codes for Gaussian channels. To analyze and design such codes, we will explain:

12:00  12:10  Students' oral presentations (4 per session/day, 2 minutes for each poster)  
12:10  14:00  Lunch at Imecc  Lunch Time  
14:00  15:30 
Simple Tools for an Information Theory for Large Networks  Michelle Effros
Simple Tools for an Information Theory for Large NetworksMichelle Effros (Caltech)The goal of expanding information theory from the study of very small networks to the understanding of extremely large networks is understood to be both critically important and insurmountably difficult. This talk considers the challenges faced and some simple tools for tackling them. Close 
Spatial Coupling  A Primer  Rudiger Urbanke
Spatial Coupling  A PrimerRudiger Urbanke (EPFL)
Designing good sparsegraph codes is the problem of finding graphical structures that interact
well with the messagepassing decoder. Over the past 20 years many such structures have
been proposed and have been found to be useful. Prominent examples of structures that
work well are turbo codes, lowdensity paritycheck codes with properly chosen degree
distributions, or multiedge ensembles. 
Optimality of Gaussian auxiliaries via singleletterization arguments  Chandra Nair
Optimality of Gaussian auxiliaries via singleletterization argumentsChandra Nair (Chinese University of Hong Kong)In this tutorial we will visit a recently developed technique that establishes the optimality of Gaussian (auxiliary) random variables. This technique combines a characterization of Gaussian random variables (known since 1940s) and singleletterization arguments that form the central body of converses to coding theorems or outer bounds. The technique will be introduced via wellknown examples and the power will be illustrated by using it to obtain the capacity region of vector Gaussian broadcast channels with common information; a scenario that had resisted attempts using traditional techniques. Close 
Spatial Coupling  A Primer  Rudiger Urbanke
Spatial Coupling  A PrimerRudiger Urbanke (EPFL)
Designing good sparsegraph codes is the problem of finding graphical structures that interact
well with the messagepassing decoder. Over the past 20 years many such structures have
been proposed and have been found to be useful. Prominent examples of structures that
work well are turbo codes, lowdensity paritycheck codes with properly chosen degree
distributions, or multiedge ensembles. 
Optimality of Gaussian auxiliaries via singleletterization arguments  Chandra Nair
Optimality of Gaussian auxiliaries via singleletterization argumentsChandra Nair (Chinese University of Hong Kong)In this tutorial we will visit a recently developed technique that establishes the optimality of Gaussian (auxiliary) random variables. This technique combines a characterization of Gaussian random variables (known since 1940s) and singleletterization arguments that form the central body of converses to coding theorems or outer bounds. The technique will be introduced via wellknown examples and the power will be illustrated by using it to obtain the capacity region of vector Gaussian broadcast channels with common information; a scenario that had resisted attempts using traditional techniques. Close 
15:30  15:40  Students' oral presentations (4 per session/day, 2 minutes for each poster)  
15:40  16:15  Coffee Break & "Posters of the day" session  
16:15  17:45 
Bursterror correcting codes & Topics Where Coding Meets Crypto  Henk Van Tilborg
Bursterror correcting codes & Topics Where Coding Meets CryptoHenk Van Tilborg (Eindhoven University of Technology)
In some communication or storage applications errors tend to cluster in so called bursts.
Codes that can correct a burst of length b need less redundancy than errorcorrecting codes
that correct b random errors. Authentication codes
The name authentication code was introduced by G. Simmons in 1992. The goal is to
authenticate messages in an unconditionally secure way. An authentication code is designed to
protect a message against an impersonation attack or a substitution attack and typically can be
used only once. Sometimes also privacy needs to be guaranteed. Other connections between coding theory and cryptography
The BerlekampMassey algorithm is based on the decoding algorithm for BCH codes. 
Simple Tools for an Information Theory for Large Networks  Michelle Effros
Simple Tools for an Information Theory for Large NetworksMichelle Effros (Caltech)The goal of expanding information theory from the study of very small networks to the understanding of extremely large networks is understood to be both critically important and insurmountably difficult. This talk considers the challenges faced and some simple tools for tackling them. Close 
Bursterror correcting codes & Topics Where Coding Meets Crypto  Henk Van Tilborg
Bursterror correcting codes & Topics Where Coding Meets CryptoHenk Van Tilborg (Eindhoven University of Technology)
In some communication or storage applications errors tend to cluster in so called bursts.
Codes that can correct a burst of length b need less redundancy than errorcorrecting codes
that correct b random errors. Authentication codes
The name authentication code was introduced by G. Simmons in 1992. The goal is to
authenticate messages in an unconditionally secure way. An authentication code is designed to
protect a message against an impersonation attack or a substitution attack and typically can be
used only once. Sometimes also privacy needs to be guaranteed. Other connections between coding theory and cryptography
The BerlekampMassey algorithm is based on the decoding algorithm for BCH codes. 
PhysicalLayer Security: Bounds, Codes and Protocols  João Barros
PhysicalLayer Security: Bounds, Codes and ProtocolsJoão Barros (Universidade do Porto)The goal of this course is to give a general overview of the basic building blocks of information theoretic security and how they can be used to provide authentication, confidentiality and integrity at the physical layer of modern communication systems. Starting from first principles and fundamental performance bounds, we shall elaborate on the code constructions needed to achieve reliability and secrecy simultaneously and how to build secure communication protocols that leverage the additional secrecy provided by these code constructions. Finally, we will discuss how to integrate these techniques in existing standards and provide a basic costbenefit analysis. Close 
Closing Session & Celebration 
17:45  18:30 or forever  (18  19:30) Panel Session: Information Theory, Coding Theory and the Real World  Chair: Vinay Vaishampayan  IEEE Information Theory Society  Michelle Effros  President  Impromptu Problem Session  Impromptu Problem Session 