EVENTS

Departamento de Matemática da Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa (FCT NOVA)

The 11th European Conference on Mathematical and Theoretical Biology (ECMTB 2018) will be held in Lisbon, Portugal, from 23 to 27 July, 2018.

Departamento de Matemática da Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa (FCT NOVA)

Affiliation: University of Granada, Spain

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: Department of Mathematical Sciences, Durham University, Durham

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: Department of Philosophy, Université Paris 1 Panthéon-Sorbonne

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: University of Copenhagen

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: University of Tunis El Manar

Venue: office 18, 3rd floor of the Mathematics Department Building

Affiliation: Universidade Federal Fluminense

Venue: office 5, 3rd floor of the Mathematics Department Building

Affiliation: Komi Science Centre Ural Division, Russian Academy of Sciences, Department of Mathematics, Syktyvkar, Russian Federation and Syktyvkar State University

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: University of St Andrews, UK

Venue: office 18, 3rd floor of the Mathematics Department Building

Affiliation: Institute of Mathematics, Potsdam University, Germany

Venue: office 18, 3rd floor of the Mathematics Department Building

Affiliation: Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos (Cuernavaca, Morelos, México)

Venue: office 36, 3rd floor of the Mathematics Department Building

Affiliation:

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: Faculty of Mathematics and Natural Science, South-West University "Neot Rilski", Bulgaria

Venue: office 18, 3rd floor of the Mathematics Department Building

Affiliation: Università Degli Studi Di Salerno

Venue: office 35, 3rd floor of the Mathematics Department Building

Affiliation: King's College London

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos (Cuernavaca, Morelos, México)

Venue: Office 2, 3rd floor of the Mathematics Department Building

Departamento de Matemática da Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa (FCT NOVA)

Sala 1.A - Edifício VII

Affiliation: Departamento de Matemática, Federal University of Minas Gerais (UFMG), Brazil

Venue: Office 5, 3rd floor of the Mathematics Department Building

Affiliation: College of William & Mary, USA

Venue: office 27, 3rd floor of the Mathematics Department Building

The purpose of this first DCV - DM Meeting was to present the research of both groups seeking to create new synergies in the area of biomathematics. The discussion was very dynamic with 25 researchers present. New meetings are already scheduled, in the form of more specific seminars. CMA as several researchers working on Mathematical Biology  

Affiliation: Faculty of Mathematics and Natural Science, South-West University "Neofit Rilski", Bulgaria

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: Institute of Mathematics, University of Potsdam, Germany

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: Department of Statistics and Operations Research, Institute of Mathematics,
University of Sevilha, Sevilha, Spain

Venue: office 18, 3rd floor of the Mathematics Department Building

Affiliation: School of Mathematics and Physical Sciences, University of Hull, United Kingdom

Venue: office 6, 3rd floor of the Mathematics Department Building

Affiliation: School of Mathematics and Statistics, University of St Andrews, United Kingdom

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: UNIMIB - Department of Mathematics and its Applications,
Università degli Studi di Milano-Bicocca, Milan, Italy

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: Division of Mathematical Sciences, Nanyang Technological University, Singapore

Venue: office 2, 3rd floor of the Mathematics Department Building

Department of Mathematics of NOVA School of Science and Technology (FCT NOVA)

The conference will be held from Monday July 22nd through Friday July 26th, at Instituto Superior Técnico, University of Lisbon, Portugal.

1st WM2 - Women in Mathematics Meeting The Women in Mathematics Meeting will take place at Faculdade de Ciências e Tecnologia of the Universidade Nova de Lisboa, Portugal, from 22 to 24 July.

Affiliation: Federal University of Minas Gerais, Brazil

Venue: office 5, 3rd floor of the Mathematics Department Building

Affiliation: Institute of Mathematics, Potsdam University, Germany

Venue: office 18, 3rd floor of the Mathematics Department Building

Affiliation: Laboratoire G-SCOP, France

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: Faculty of Mathematics and Natural Science, South-West University "Neofit Rilski", Bulgaria

Venue: office 18, 3rd floor of the Mathematics Department Building

Department of Mathematics of NOVA School of Science and Technology (FCT NOVA)

Affiliation: Departamento de Biologia e CESAM, University of Aveiro

Venue: office 18, 3rd floor of the Mathematics Department Building

Workshop*
Sessões nos dias 9/10 e 10/10, das 14h às 15h30, no laboratório 2.2 do edifício 7

Modelação Estatística em Biologia: Como lidar com a não-normalidade de dados contínuos, com dados discretos ou dados qualitativos?

Prof.ª Regina Bispo, Departamento de Matemática & Centro de Matemática e Aplicações, FCT Nova

Resumo: Neste workshop pretende-se abordar alguns aspetos iniciais da Modelação Estatística, em duas situações frequentemente encontradas na Biologia: (1) quando a variável resposta, cuja variação se pretende explicar, apesar de contínua, não tem distribuição Normal e (2) quando a variável resposta não é contínua, podendo nesse caso ser quer uma quantidade discreta (contagem) que uma qualidade binária. Serão apresentados alguns casos reais que servirão de ponto de partida para uma abordagem prática, utilizando o software R.

* O workshop destina-se a investigadores e alunos de final do 1º ciclo ou 2º ciclo das áreas de Matemática e Biologia

[Entrada livre mediante inscrição prévia obrigatória AQUI ]

Workshop*
Sessões nos dias 9/10 e 10/10, das 14h às 15h30, no laboratório 2.2 do edifício 7

Modelação Estatística em Biologia: Como lidar com a não-normalidade de dados contínuos, com dados discretos ou dados qualitativos?

Prof.ª Regina Bispo, Departamento de Matemática & Centro de Matemática e Aplicações, FCT Nova

Resumo: Neste workshop pretende-se abordar alguns aspetos iniciais da Modelação Estatística, em duas situações frequentemente encontradas na Biologia: (1) quando a variável resposta, cuja variação se pretende explicar, apesar de contínua, não tem distribuição Normal e (2) quando a variável resposta não é contínua, podendo nesse caso ser quer uma quantidade discreta (contagem) que uma qualidade binária. Serão apresentados alguns casos reais que servirão de ponto de partida para uma abordagem prática, utilizando o software R.

* O workshop destina-se a investigadores e alunos de final do 1º ciclo ou 2º ciclo das áreas de Matemática e Biologia

[Entrada livre mediante inscrição prévia obrigatória AQUI ]

Mathematical Biology Day @ FCT NOVA
10/10/2019 Palestra
Dia 10/10, das 15h30 às 16h30, na sala de seminários do edifício 7

Título: Mathematical modeling: a fundamental tool in biology
Prof. Ruy Ribeiro, Laboratório de Biomatemática, Faculdade de Medicina da Universidade de Lisboa and Theoretical Biology and Biophysics, Los Alamos National Laboratory

[Entrada livre mediante inscrição prévia obrigatória AQUI ]
(https://eventos.fct.unl.pt/mathematicalbiologydayatfctnova)

Affiliation: Department of Mathematics, Denver University, USA

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: Alfréd Rényi Institute of Mathematics & Eötvös University, Budapest, Hungary

Venue: office 18, 3rd floor of the Mathematics Department Building

Affiliation: Fluminense Federal University, Brazil

Venue: office 5, 3rd floor of the Mathematics Department Building

Affiliation: Departamento de Matemática Aplicada, State University of Campinas, São Paulo, Brazil

Venue: office 18, 3rd floor of the Mathematics Department Building

Affiliation: Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Poland

Venue: CMA room, 2nd floor of the Mathematics Department Building

Affiliation: Moscow State University, Russia 

Venue: Office 18, 2nd floor of the Mathematics Department Building

Affiliation: University of Zielona Góra, Poland

Venue: CMA room, 2nd floor of the Mathematics Department Building

Affiliation: University of St Andrews, United Kingdom

Venue: Office 17, 3rd floor of the Mathematics Department Building

Faculdade de Ciências e Tecnologia – Universidade Nova de Lisboa (FCT NOVA), Seminar Room – Departamento de Matemática – Building VII

Affiliation: King's College London, London, United Kingdom

Venue: Office 2, 3rd floor of the Mathematics Department Building

Dates: 15/12/2019 to 21/12/2019

Departamento de Matemática da Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa (FCT NOVA)

Affiliation: Institute of Mathematics, Potsdam University, Germany

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: Department of Mathematics, Faculty of Science, Khon Kaen University, Thailand

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: School of Mathematics, University of Manchester, United Kingdom

Venue: office 18, 3rd floor of the Mathematics Department Building

Affiliation: Faculty of Mathematics and Natural Science, South-West University "Neofit Rilski", Bulgaria

Venue: office 2, 3rd floor of the Mathematics Department Building

Affiliation: Laboratoire LMNO, Université de Caen Normandie, France

Venue: office 18, 3rd floor of the Mathematics Department Building

Affiliation: Instituto de Matemática e Estatística da Universidade de São Paulo

Venue: office 35, 3rd floor of the Mathematics Department Building

Affiliation: School of Mathematics and Statistics, University of St Andrews

Venue: office 2, 3rd floor of the Mathematics Department Building

09:45 - Opening

António Malheiro (CMA)

Luís Caires (NOVA LINCS)

Marcos Raydan (CMA)

Pedro Barahona (NOVA LINCS)

10:00 - 10:45

Statistics and Machine Learning in Biomedical and Environmental Sciences, Marta Lopes (CMA / NOVA LINCS)

10:45 - 11:15

How to test different block diagonal structures in one or several covariance matrices, Filipe Marques (CMA)  

11:15 - 11:30

SI-MORENA’s Data Driven Projects, Carlos Damásio and João Moura Pires (NOVA LINCS)

11:30 - 12:00

Classification with symbolic data, Paula Amaral (CMA)

12:00 - 12:30

Pot-pourri of Systems for Big Data and Data Science, Nuno Preguiça (NOVA LINCS)

12:30 - 14:00

Lunch

14:00 - 14:30

Semantic Similarity and Textual Entailment in the Portuguese language, Rui Rodrigues (CMA)

14:30 - 15:00

Title TBA, João Magalhães (NOVA LINCS)

15:00 - 15:30

Feature selection for marine species origin prediction, Regina Bispo (CMA)

15:30 - 16:00

Deep Models in Data Science: some problems and applications, Ludwig Krippahl (NOVA LINCS)

16:00 - 16:30

General discussion and closing

Addicional information here.

Addicional information here.

Título: Monitorização da transmissibilidade da COVID-19 em Portugal

Orador: Liliana Antunes, INSA

Título: Estimação de parâmetros epidemiológicos da COVID-19: internamentos e óbitos

Orador: João Pereira, INSA, UTAD (bolseiro do Projeto COVID-19 in-CTRL)

Título: Modelação matemática da dinâmica de transmissão de COVID-19 em Portugal

Orador: Constantino Caetano, INSA

More information is available here.

Addicional information here.

Website

Website

More information on the webpage

More information available here

More information available here

Venue: office 2, 3rd floor of the Mathematics Department Building

More information on the webpage

Venue: office 2, 3rd floor of the Mathematics Department Building

More information here

More information here

Venue: office 2, 3rd floor of the Mathematics Department Building

Venue: office 2, 3rd floor of the Mathematics Department Building

More information is available here

More information is available here

Venue: office 2, 3rd floor of the Mathematics Department Building

More information is available here

More information is available here

More information is available here

Venue: office 2, 3rd floor of the Mathematics Department Building

More information is available here

Venue: office 18, 3rd floor of the Mathematics Department Building

Venue: office 2, 3rd floor of the Mathematics Department Building

Venue: office 2, 3rd floor of the Mathematics Department Building

Venue: office 2, 3rd floor of the Mathematics Department Building

More information is available here

More information is available here

More information is available here

More information is available here

More information is available here

Venue: office 18, 3rd floor of the Mathematics Department Building

Venue: office 2, 3rd floor of the Mathematics Department Building

Abstract: "In this paper we consider constrained optimization problems where both the objective and constraint functions are of the black-box type. Furthermore, we assume that the nonlinear inequality constraints are non-relaxable, i.e. their values and that of the objective function cannot be computed outside of the feasible region. This situation happens frequently in practice especially in the black-box setting where function values are typically computed by means of complex simulation programs which may fail to execute if the considered point is outside of the feasible region. For such problems, we propose a new derivative-free optimization method which is based on the use of a merit function that handles inequality constraints by means of a log-barrier approach and equality constraints by means of a quadratic penalty approach. We prove convergence of the proposed method to KKT stationary points of the problem under quite mild assumptions. Furthermore, we also carry out a preliminary numerical experience on standard test problems and comparison with state-of-the-art solvers which shows efficiency of the proposed method."

Abstract: From minimizing construction costs to maximizing image quality, optimization arises naturally in virtually every field of modern research. In many of these applications, the objective function is provided by a blackbox or simulation. Derivative-free algorithms (DFA) provide powerful tools to solve such problems. In this workshop, we introduce the basics of DFA, including several examples of their usage. We expand further by including recent research results and
future directions of DFA.

Day 31 will cover:
- What is BBO and DFA?
- Basics of Direct-search methods
- Recent advancements in p-bases

Speaker Bio: Dr. Warren Hare is a Full Professor of Mathematics at the University of British Columbia, Canada. He serves as an Associate Editor with Set Valued and Variational Analysis and the Pacific Journal of Optimization. He is co-author of the book Derivative-Free and Blackbox Optimization and co-winner of the EURO excellence in practice prize.

Abstract: From minimizing construction costs to maximizing image quality, optimization arises naturally in virtually every field of modern research. In many of these applications, the objective function is provided by a blackbox or simulation. Derivative-free algorithms (DFA) provide powerful tools to solve such problems. In this workshop, we introduce the basics of DFA, including several examples of their usage. We expand further by including recent research results and
future directions of DFA.

Day 4 will cover:
     - Basics of Model-based methods
     - Constructing models
     - Future of DFA

Speaker Bio: Dr. Warren Hare is a Full Professor of Mathematics at the University of British Columbia, Canada. He serves as an Associate Editor with Set Valued and Variational Analysis and the Pacific Journal of Optimization. He is co-author of the book Derivative-Free and Blackbox Optimization and co-winner of the EURO excellence in practice prize.

Venue: office 2, 3rd floor of the Mathematics Department Building

More information available here

Title: EVT-Informed Statistical Inference

Speaker: Miguel de Carvalho, University of Edinburgh

Date | Time: December 7, 2022 | 14h00

Place: FCT NOVA, IX-2.25

Zoom: https://videoconf-colibri.zoom.us/j/88333359956  (For remote participants)

Abstract: The widely felt impact of record-breaking extreme events—such as stock market crashes, earthquakes, heatwaves, or widespread flooding—calls for an urgent need for a better understanding and quantification of their risk. In this talk, I will offer an overview on recent developments on the field of Extreme Value Theory (EVT) with a bias towards some of my own contributions.

Short Bio: Miguel de Carvalho is Reader in Statistics at the School of Mathematics, University of Edinburgh and the former Director of the Centre for Statistics of the same University. His research interests include, inter alia, Applied Statistics, Biostatistics, Econometrics, Risk Analysis, and Statistics of Extremes. He has been an AE for a variety of top tier journals in the field of Statistics, including the Journal of the American Statistical Association, the Annals of Applied Statistics as well as the American Statistician. He is currently the President of the Portuguese Statistical Society.

Será realizado em modelo híbrido presencial e online. O link para assistir online é o seguinte
https://videoconf-colibri.zoom.us/j/8088003611

Shor convexity, min-max QCQPs and application to min-max regret of
nonconvex QPs

Immanuel M. Bomze (Univ.Vienna), jointly with Paula Amaral (Univ.Nova
Lisboa, work in progress)

Under (finitely many) uncertain scenarios, min-max regret for (possibly
nonconvex) Standard QPs can be reduced to a min-max (fractional) QCQP.
En route to narrowing the gap between powerful conic lower bounds and
efficient upper bounds, i.e. good feasible values, we will study
the apparently novel notion of *Shor convexity* (not to be confused with
the well-known notion of Schur convexity) suggested by lifting
techniques, and discuss possibilities to use bundle methods for
tightening upper bounds. A generalization of the famous Jensen's
inequality will be proved as well.

Palestra Motivacional

Venue: office 2, 3rd floor of the Mathematics Department Building

Guillaume Cantin

Département Informatique, UMR_6004 Laboratoire des Sciences du Numérique de Nantes, Nantes Université

Title:

Modeling and analysis of complex epidemic events using hybrid dynamical systems

Abstract:

In this talk, I will present recent works realized in collaboration

with Cristiana J. Silva on the modeling and analysis of complex epidemic events.

In the first part, I will show how to model the spatial spreading of an epidemic in a heterogeneous geographical environment using complex networks of dynamical systems.

The influence of the topology of the underlying network

on the dynamics of the epidemic will be illustrated with a theoretical approach for small networks,

and with a computational approach for large networks.

In the second part, I will present a novel approach for studying the impacts of human behaviors

on the spreading of an epidemic, using hybrid dynamical systems coupling a macroscopic approach relying on differential equations

with a microscopic individual-based approach.

Theoretical results on the well-posedness of such hybrid models and on the existence of particular

solutions exhibiting irregular oscillations will be exposed.

Guillaume Cantin

Département Informatique, UMR_6004 Laboratoire des Sciences du Numérique de Nantes, Nantes Université

Title:

Modeling and analysis of complex epidemic events using hybrid dynamical systems

Abstract:

In this talk, I will present recent works realized in collaboration

with Cristiana J. Silva on the modeling and analysis of complex epidemic events.

In the first part, I will show how to model the spatial spreading of an epidemic in a heterogeneous geographical environment using complex networks of dynamical systems.

The influence of the topology of the underlying network

on the dynamics of the epidemic will be illustrated with a theoretical approach for small networks,

and with a computational approach for large networks.

In the second part, I will present a novel approach for studying the impacts of human behaviors

on the spreading of an epidemic, using hybrid dynamical systems coupling a macroscopic approach relying on differential equations

with a microscopic individual-based approach.

Theoretical results on the well-posedness of such hybrid models and on the existence of particular

solutions exhibiting irregular oscillations will be exposed.

Zoom link: https://videoconf-colibri.zoom.us/j/95986880586?pwd=a2lHSWYwQnM2VGswRGdUWXFRUk5oUT09

Abstract: 

The Grassmann variety of k-dimensional subspaces of an n-dimensional vector space over a finite field with q-elements can be thought of as the ``moduli space'' of all linear q-ary (n,k)-codes. At the same time, each Grassmann variety naturally provides an algebraic geometry code via its Plucker embedding. The structure of Grassmann codes has been parsed by many researchers, most notably by Sudhir Ghorpade. In this talk, we will discuss a fruitful generalization of the Grassmann codes by using the embeddings of Grassmannians into higher-dimensional projective spaces. This new family of ``higher Grassmann codes'' has interesting connections with the modular representation theory of SL_n.

We discuss the process of averaging via Cesàro means, both for sequences and for functions. We start reviewing Hardy's results in the 1920s and arrive at some recent results in Functional Analysis and Operator Theory.

The talk is based on a joint work with Oleksiy Karlovych.

The lower estimate by Gohberg and Krupnik (1968) and the upper estimate

by Hollenbeck and Verbitsky (2000) for the norm of the Riesz projection

$P$ on the Lebesgue space $L^p$ lead to $\|P\|_{L^p\to L^p}=1/\sin(\pi/p)$

for every $p\in(1,\infty)$. Hence $L^2$ is the only space among all Lebesgue

spaces $L^p$ for which the norm of the Riesz projection $P$ is equal to one.

Banach function spaces $X$ are far reaching generalizations of Lebesgue spaces

$L^p$. We prove that the norm of $P$ is equal to one on the space $X$ if and

only if $X$ coincides with $L^2$ and there exists a constant $C\in(0,\infty)$

such that $\|f\|_X=C\|f\|_{L^2}$ for all functions $f\in X$. Independently

from this, we also show that the norm of $P$ on $X$ is equal to one if and

only if the norm of the backward shift operator $S$ on the abstract Hardy

space $H[X]$ built upon $X$ is equal to one.

Venue: office 2, 3rd floor of the Mathematics Department Building

Venue: office 2, 3rd floor of the Mathematics Department Building

Abstract

Robot Dance is a computational platform developed in response to the coronavirus
outbreak, to support the decision-making on public policies at a regional
level. The tool is suitable for understanding and suggesting levels of
intervention needed to contain the spread of diseases when the mobility of
inhabitants through a regional network is a concern. Such is the case for Covid-19
that is highly contagious and, therefore, the circulation of people must be
considered in the epidemiological compartmental models. Robot Dance anticipates
the spread of epidemics in a complex regional network, identifying fragile links
where applying differentiated measures of containment, testing, and vaccination
are the most effective. Based on stochastic optimization, the model determines
optimal strategies on the basis of the commuting of individuals and the situation
of hospitals in each district. Uncertainty in the capacity of intensive care beds
is handled by a chance-constraint approach. Some functionalities of Robot Dance
are illustrated on the state of São Paulo in Brazil, using real data for a region
with more than forty million inhabitants. We will also show extensions of the model
that allow to design vaccination campaigns that take into account multiple
sub-populations that present different responses to the virus.

Abstract

Robot Dance is a computational platform developed in response to the coronavirus
outbreak, to support the decision-making on public policies at a regional
level. The tool is suitable for understanding and suggesting levels of
intervention needed to contain the spread of diseases when the mobility of
inhabitants through a regional network is a concern. Such is the case for Covid-19
that is highly contagious and, therefore, the circulation of people must be
considered in the epidemiological compartmental models. Robot Dance anticipates
the spread of epidemics in a complex regional network, identifying fragile links
where applying differentiated measures of containment, testing, and vaccination
are the most effective. Based on stochastic optimization, the model determines
optimal strategies on the basis of the commuting of individuals and the situation
of hospitals in each district. Uncertainty in the capacity of intensive care beds
is handled by a chance-constraint approach. Some functionalities of Robot Dance
are illustrated on the state of São Paulo in Brazil, using real data for a region
with more than forty million inhabitants. We will also show extensions of the model
that allow to design vaccination campaigns that take into account multiple
sub-populations that present different responses to the virus.

Venue: office 36, 3rd floor of the Mathematics Department Building

Here  you will find  instructions on how to install Julia on Windows and
Linux. I can not provide instructions for Mac because I don't have
access to such machine at the moment.

Please try the instructions on your machine to see if they are clear.
I can help you with any problems tomorrow.

--- Windows ---

1) In your web browser go to

https://www.microsoft.com/store/productId/9NJNWW8PVKMN

and click on "Get in the store app"

This will open Windows Store in the Julia installer page. You can
install Julia installer from there.

2) Once the installer is installed, open up your favorite Windows
promp. I use PowerShell.

In the command line type:

juliaup add beta

This will download and install julia-1.9.0-beta3, the latest beta
version of Julia that I will use in the course.

3) Once Julia finish installing, you can open up Julias's prompt by
typing in the command line

julia

This will open up Julia's prompt.

4) Now in Julia prompt type:

import Pkg
Pkg.add(["Plots", "IJulia",  "MKL"])

This will download many packages and precompile them, so you need good
Internet connection and it will take a while. Grab yourself a cup of
coffee or tea.

You are done.

You may also want to download a good programming editor. I suggest you
to use Microsoft's VSCode (https://code.visualstudio.com/). After
installing it add its Julia extension
(https://marketplace.visualstudio.com/items?itemName=julialang.language-julia)

-------- Linux and Mac (I did not test it on Mac) -------

This installation method will install Julia and its version manager,
juliaup, for the local user only.

1) Open a shell and install juliaup (you will need curl installed, it
is usually already installed in many distributions):

curl -fsSL https://install.julialang.org | sh

After that, close the shell.

2) Now install Julia's beta version and make it the default version.
In a new shell type:

juliaup add beta
juliaup default beta

3) Once Julia finish installing, you can open up Julias's prompt by
typing in the command line

julia

This will open up Julia's prompt.

4) Now in Julia prompt type:

import Pkg
Pkg.add(["Plots", "IJulia",  "MKL"])

You are done.

This will download many packages and precompile them, so you need good
Internet connection and it will take a while. Grab yourself a cup of
coffee or tea.

You may also want to download a good programming editor. I suggest you
to use Microsoft's VSCode (https://code.visualstudio.com/). After
installing it add its Julia extension
(https://marketplace.visualstudio.com/items?itemName=julialang.language-julia)

Here  you will find  instructions on how to install Julia on Windows and
Linux. I can not provide instructions for Mac because I don't have
access to such machine at the moment.

Please try the instructions on your machine to see if they are clear.
I can help you with any problems tomorrow.

--- Windows ---

1) In your web browser go to

https://www.microsoft.com/store/productId/9NJNWW8PVKMN

and click on "Get in the store app"

This will open Windows Store in the Julia installer page. You can
install Julia installer from there.

2) Once the installer is installed, open up your favorite Windows
promp. I use PowerShell.

In the command line type:

juliaup add beta

This will download and install julia-1.9.0-beta3, the latest beta
version of Julia that I will use in the course.

3) Once Julia finish installing, you can open up Julias's prompt by
typing in the command line

julia

This will open up Julia's prompt.

4) Now in Julia prompt type:

import Pkg
Pkg.add(["Plots", "IJulia",  "MKL"])

This will download many packages and precompile them, so you need good
Internet connection and it will take a while. Grab yourself a cup of
coffee or tea.

You are done.

You may also want to download a good programming editor. I suggest you
to use Microsoft's VSCode (https://code.visualstudio.com/). After
installing it add its Julia extension
(https://marketplace.visualstudio.com/items?itemName=julialang.language-julia)

-------- Linux and Mac (I did not test it on Mac) -------

This installation method will install Julia and its version manager,
juliaup, for the local user only.

1) Open a shell and install juliaup (you will need curl installed, it
is usually already installed in many distributions):

curl -fsSL https://install.julialang.org | sh

After that, close the shell.

2) Now install Julia's beta version and make it the default version.
In a new shell type:

juliaup add beta
juliaup default beta

3) Once Julia finish installing, you can open up Julias's prompt by
typing in the command line

julia

This will open up Julia's prompt.

4) Now in Julia prompt type:

import Pkg
Pkg.add(["Plots", "IJulia",  "MKL"])

You are done.

This will download many packages and precompile them, so you need good
Internet connection and it will take a while. Grab yourself a cup of
coffee or tea.

You may also want to download a good programming editor. I suggest you
to use Microsoft's VSCode (https://code.visualstudio.com/). After
installing it add its Julia extension
(https://marketplace.visualstudio.com/items?itemName=julialang.language-julia)

Here  you will find  instructions on how to install Julia on Windows and
Linux. I can not provide instructions for Mac because I don't have
access to such machine at the moment.

Please try the instructions on your machine to see if they are clear.
I can help you with any problems tomorrow.

--- Windows ---

1) In your web browser go to

https://www.microsoft.com/store/productId/9NJNWW8PVKMN

and click on "Get in the store app"

This will open Windows Store in the Julia installer page. You can
install Julia installer from there.

2) Once the installer is installed, open up your favorite Windows
promp. I use PowerShell.

In the command line type:

juliaup add beta

This will download and install julia-1.9.0-beta3, the latest beta
version of Julia that I will use in the course.

3) Once Julia finish installing, you can open up Julias's prompt by
typing in the command line

julia

This will open up Julia's prompt.

4) Now in Julia prompt type:

import Pkg
Pkg.add(["Plots", "IJulia",  "MKL"])

This will download many packages and precompile them, so you need good
Internet connection and it will take a while. Grab yourself a cup of
coffee or tea.

You are done.

You may also want to download a good programming editor. I suggest you
to use Microsoft's VSCode (https://code.visualstudio.com/). After
installing it add its Julia extension
(https://marketplace.visualstudio.com/items?itemName=julialang.language-julia)

-------- Linux and Mac (I did not test it on Mac) -------

This installation method will install Julia and its version manager,
juliaup, for the local user only.

1) Open a shell and install juliaup (you will need curl installed, it
is usually already installed in many distributions):

curl -fsSL https://install.julialang.org | sh

After that, close the shell.

2) Now install Julia's beta version and make it the default version.
In a new shell type:

juliaup add beta
juliaup default beta

3) Once Julia finish installing, you can open up Julias's prompt by
typing in the command line

julia

This will open up Julia's prompt.

4) Now in Julia prompt type:

import Pkg
Pkg.add(["Plots", "IJulia",  "MKL"])

You are done.

This will download many packages and precompile them, so you need good
Internet connection and it will take a while. Grab yourself a cup of
coffee or tea.

You may also want to download a good programming editor. I suggest you
to use Microsoft's VSCode (https://code.visualstudio.com/). After
installing it add its Julia extension
(https://marketplace.visualstudio.com/items?itemName=julialang.language-julia)

---- Instructions for the course ---

1. Create a new folder for today's class
2. Download the attached files and place them in the folder.
3. Open Julia's prompt ant type the following and go grab a cup of
coffe or tea because this can take a while.

--- Commmands for Julia Prompt ---

import Pkg
Pkg.activate(".")
Pkg.instantiate()

--- End of commands ---

This will install the Packages we need for today's class.

Here  you will find  instructions on how to install Julia on Windows and
Linux. I can not provide instructions for Mac because I don't have
access to such machine at the moment.

Please try the instructions on your machine to see if they are clear.
I can help you with any problems tomorrow.

--- Windows ---

1) In your web browser go to

https://www.microsoft.com/store/productId/9NJNWW8PVKMN

and click on "Get in the store app"

This will open Windows Store in the Julia installer page. You can
install Julia installer from there.

2) Once the installer is installed, open up your favorite Windows
promp. I use PowerShell.

In the command line type:

juliaup add beta

This will download and install julia-1.9.0-beta3, the latest beta
version of Julia that I will use in the course.

3) Once Julia finish installing, you can open up Julias's prompt by
typing in the command line

julia

This will open up Julia's prompt.

4) Now in Julia prompt type:

import Pkg
Pkg.add(["Plots", "IJulia",  "MKL"])

This will download many packages and precompile them, so you need good
Internet connection and it will take a while. Grab yourself a cup of
coffee or tea.

You are done.

You may also want to download a good programming editor. I suggest you
to use Microsoft's VSCode (https://code.visualstudio.com/). After
installing it add its Julia extension
(https://marketplace.visualstudio.com/items?itemName=julialang.language-julia)

-------- Linux and Mac (I did not test it on Mac) -------

This installation method will install Julia and its version manager,
juliaup, for the local user only.

1) Open a shell and install juliaup (you will need curl installed, it
is usually already installed in many distributions):

curl -fsSL https://install.julialang.org | sh

After that, close the shell.

2) Now install Julia's beta version and make it the default version.
In a new shell type:

juliaup add beta
juliaup default beta

3) Once Julia finish installing, you can open up Julias's prompt by
typing in the command line

julia

This will open up Julia's prompt.

4) Now in Julia prompt type:

import Pkg
Pkg.add(["Plots", "IJulia",  "MKL"])

You are done.

This will download many packages and precompile them, so you need good
Internet connection and it will take a while. Grab yourself a cup of
coffee or tea.

You may also want to download a good programming editor. I suggest you
to use Microsoft's VSCode (https://code.visualstudio.com/). After
installing it add its Julia extension
(https://marketplace.visualstudio.com/items?itemName=julialang.language-julia)

---- Instructions for the course ---

1. Create a new folder for today's class
2. Download the attached files and place them in the folder.
3. Open Julia's prompt ant type the following and go grab a cup of
coffe or tea because this can take a while.

--- Commmands for Julia Prompt ---

import Pkg
Pkg.activate(".")
Pkg.instantiate()

--- End of commands ---

This will install the Packages we need for today's class.

Venue: office 2, 3rd floor of the Mathematics Department Building

Speaker: Gonçalo Araújo (DM FCT-NOVA)

Date/time: 22/02/2023 (*Wednesday*), at *13:30* (UTC)

Zoom link: https://videoconf-colibri.zoom.us/j/93296258325?pwd=Q2JtbjllWFNCVG9VdnN5aUN3YzBLQT09

Title:  Lean e um teorema de semigrupos inversos

Abstract:

Neste seminário vou explicar genericamente como se usa o Lean para formalizar matemática.
Vou depois exemplificar com um teorema de semigrupos inversos.

Abstract:

We view an idea as "powerful" if it is easy to understand and prove, but has strong implications and gives insight. Among the ideas we mention and discuss are Gersgorin's theorem, Schur's unitary triangularization and others as time permits.

Bio:

Charles Johnson received his Ph.D at Caltech, working with Olga Taussky and is now the Class of 1961 Professor of Mathematics at William and Mary. He loves the interplay between matrix analysis and combinatorics and has been the primary contributor to it for many years.

Venue: office 27, 3rd floor of the Mathematics Department Building

Title: The lot-sizing with remanufacturing problem

Resume:

In this presentation, we consider a production planning problem arising in the context of reverse logistics, namely the lot-sizing with remanufacturing (LSR).

In the LSR, deterministic demand for a single item over a finite time horizon has to be satisfied, which can be performed from either newly produced or remanufactured items. The goal consists in minimizing the total production costs.
 
Two versions of the problem will be considered, one with production capacity (CLSR) in each period and the other without (ULSR).

We will present formulations, valid inequalities, some computational results for the ULSR, and work proposals for the CLSR.

Speaker: Jesus Ossian da Cunha

Title: The lot-sizing with remanufacturing problem

Resume:

In this presentation, we consider a production planning problem arising in the context of reverse logistics, namely the lot-sizing with remanufacturing (LSR).

In the LSR, deterministic demand for a single item over a finite time horizon has to be satisfied, which can be performed from either newly produced or remanufactured items. The goal consists in minimizing the total production costs.
 
Two versions of the problem will be considered, one with production capacity (CLSR) in each period and the other without (ULSR).

We will present formulations, valid inequalities, some computational results for the ULSR, and work proposals for the CLSR.

Zoom link: https://videoconf-colibri.zoom.us/j/93296258325?pwd=Q2JtbjllWFNCVG9VdnN5aUN3YzBLQT09

Abstract:

Daniel Brown recently proposed a clever cryptographic key exchange
protocol based on the apparent difficulty of performing "division" in the
plactic monoid; that is, given elements c and b, find a q such that c=qb,
given that such a q exists. We will first give some background on public key cryptography,
and describe Brown's novel idea. After defining a natural metric on the plactic monoid,
we will show how it can be used by a probabilistic algorithm for efficiently performing
division (the efficiency is conjectured and empirically demonstrated, but not proven).
Finally, we will discuss some vague criteria for alternate monoids in which this division
problem may be more formidable, and hence be suitable replacements replacements
for the plactic monoid in Brown's protocol.

More information is available here.

More information is available here.

Note:  The speaker has some speech difficulties. The seminar may last slightly longer than usual, and there may be a short break in the middle.

Zoom link: https://videoconf-colibri.zoom.us/j/93296258325?pwd=Q2JtbjllWFNCVG9VdnN5aUN3YzBLQT09

Abstract:

Garside families have been introduced in order to abstract the properties which yield the greedy normal form observed in Artin-Tits monoids and in Garside monoids. A presentation is called coherent if it is enriched with a set of relations among relations "filling" any parallel pair of sequences of relations. Deligne constructed coherent presentations for spherical Artin-Tits monoids. Gaussent, Guiraud and Malbos extended this result in two distinct directions: to all Artin-Tits monoids and to Garside monoids. In this talk, we present a unifying generalisation of these two results in the setting of Garside families.

The classical umbral calculus studies Sheffer polynomial sequences on $\mathbb{R}$ (including polynomial sequences of binomial type and Appell sequences) and related operators. In this talk, we will present the foundations of an umbral calculus on the space $\mathcal D'(\mathbb{R}^d)$ of distributions on $\mathbb R^d$, $d\geq 1$, which leads to a general theory of Sheffer polynomial sequences on $\mathcalD'(\mathbb{R}^d)$. We  will construct a lifting of a Sheffer sequence on$\mathbb R$ to a Sheffer sequence on $\mathcal D'(\mathbb{R}^d)$. Examples of lifted polynomial sequences include the falling and rising factorials, Abel, Hermite, Charlier, and Laguerre polynomials on $\mathcal D'(\mathbb{R}^d)$. Some of these polynomials have already appeared in different branches of infinite dimensional (stochastic)analysis and played there a fundamental role. This talk is based on a joint work with D. Finkelshtein, Y. Kondratiev and E. Lytvynov(J. Funct. Anal. 2019).

Classical epidemic models are usually described by compartmental models that do not include spatial heterogeneity. However, it is known that heterogeneity and connectivity of the spatial environment can dramatically change the spread of the disease. The work by Medlock et al., (2003) analyses models that describe temporally and spatially the propagation of diseases, with two different approaches to introducing spatial dependency to the standard SI model. One of the approaches describes the space according to existing contacts in the population, thus considering that there is no movement of infected individuals - the Distributed-Contacts model (DC). The second approach describes the space according to the dispersion of infected individuals in the population, considering that there is movement of infected individuals - the Distributed-Infectives model (DI). The two resulting models are defined by integro-differential equations (IDE). We analysed how the speed of the travelling waves for each model, thus allowing us to compare them. Moreover, in this work, the authors develop two methods to obtain an approximate solution for the two models and compare the solution obtained by these methods with the solution obtained by the classical methods to solve IDE. One of the methods uses piecewise linearization, while the other uses a regular perturbation scheme. Finally, we reformulate the DI model presented by introducing the spatial dependency as in the discrete case, and we extend the DI and DC formulation for the case of the SIR model. To illustrate the results, we present several simulations.

More information is available here

Zoom link: https://videoconf-colibri.zoom.us/j/93296258325?pwd=Q2JtbjllWFNCVG9VdnN5aUN3YzBLQT09

Abstract:

We show that the 42-element monoid of all partial order preserving and extensive injections on the 4-element chain is not contained in any variety generated by a finitely based finite semigroup. This gives a first example of a strongly nonfinitely based monoid that is not inherently nonfinitely based monoid, thus solving a 20+ years old problem. The result has numerous implications for the finite basis problem for finite semigroups.

This a joint work with Sergey Gusev and Olga Sapir.

NOVA MATH – UNIDEMI Talks

Date | Time: April 26, 2023 | 13h00 – 14h00

Location: Building VII, Seminar room (2nd floor)

Program

Neural field equations were introduced in the 70 years of last century as a mathematical model for describing neural activity and studying certain processes that occur in the cerebral cortex. One of these processes is working memory, usually defined as the capacity of neurons to transiently hold sensory information to guide forthcoming action.

In this talk, we describe a neural field model which explains how a population of cortical neurons may encode in its firing pattern simultaneously the nature and time of sequential stimulus events Moreover, we investigate how noise-induced perturbations may af- fect the coding process. From a mathematical point of view, this is obtained by means of a two-dimensional neural field equation, where one dimension represents the nature of the event (for example, the color of a light signal) and the other represents the moment when the signal has occurred. Some numerical experiments are carried out using a computational algorithm for two-dimensional stochastic neural field equations. The numerical results are discussed and their physical interpretation is explained.

This talk is based on a joint work with G.Yu. Kulikov, M. V. Kulikova and W. Erlhagen.

Dear Colleagues and Students,

As you may have already noticed, we are honored to be hosting Professor Din-Chen  (Ph.D. Fellow of the American Statistical Association, Executive Director and Professor in Biostatistics, College of Health Solutions, Arizona State University,Phoenix, USA) in our department next week. I am excited to announce that he will be giving a mini-course on

HowTo Jointly Analyze Longitudinal Data & Time-To-Event Simultaneously: AnIntegrative Data Harmonization Approach

on Thursday May 4, 9h00-12h00 and 14h00-17h00 at Nova FCT, Building VII, Second Floor, Seminar room.


Time: Thursday May 4,  9h00-12h00 and 14h00-17h00

Location: NOVA FCT, Building VII, Second Floor, Seminar room


You can see the details, abstract, and syllabus of the course at the end of this email. Additionally, a short biography of the instructor is also provided for your reference.
I would like to express my gratitude to Professor Carlos Coelho for sharing this information with me, and I am thrilled to extend this invitation to students and professors at the university to join us for this mini-course. The course is open to all students and professors at the university who are interested in learning more about this topic.

I look forward to hearing from you and seeing you at the mini-course.

Kind regards,

Mina Norouzirad
responsible for organizing the seminar series of the Statistics and Risk Management group at NOVAMATH

Abstract:

Drawing on theory and prior research in clinical trials, evidence-based health research can generate data from different sources and phases of health interventions. A central feature of evidence-based public health intervention research is the sequential and longitudinal implementation with that participants may drop out or die or be censored during the time of the study. This mini-workshop is to discuss the integrative data harmonization and joint modeling of longitudinal data and time-to-event(such as, drop out and censored) data simultaneously, which has the potential to produce a more efficient and more powerful statistical analysis. Specifically,this workshop is to give an overview of statistical joint modeling to simultaneously analyze the data from longitudinal biomarkers and time-to-event with two publicly available real-world data. It is designed with two 3-hoursessions in a step-by-step implementation in the statistical software R for data from HIV/AIDS clinical trials and the Mayo Clinic Primary Biliary Cirrhosis study.  Participants are welcome to bring their own research data to be used in this workshop as examples.

Session1 (3-hrs): Univariate Joint-Modeling

·       Introduction to integrative data harmonization (half hr)

·       Overview of statistical joint modeling (one hr)

·       Step-by-stepR implementations with R packages JM and JMBayes2 on HIV Clinical Trial data analysis (1 hr)

·       Group discussions (half hr)

Session2 (3-hrs): Latent-Class Joint Modeling and Multivariate Joint Modeling

• Overview of joint modeling to Latent-Class Joint Modeling with HIV clinical trial data (half hr)

·       Step-by-stepR implementations with R packages LCJM on HIV Clinical Trial data analysis (one hr)

·       Multivariate Joint Modeling with Mayo Clinic Primary Biliary Cirrhosis study (one hr)

·       Group discussions (half hr)

Brief Biography of Dr. Din Chen:

Dr. Din Chen received his Ph.D. in Statistics from the University of Guelph (Canada) in 1995 and is now the executive director and professor in Biostatistics at the College of Health Solutions, Arizona State University. He served as the Wallace H. Kuralt distinguished professor in biostatistics at the University of North Carolina-Chapel Hill, a biostatistics professor at the University of Rochester Medical Center, the Karl E. Peace endowed eminent scholar chair in biostatistics from the Jiann-Ping Hsu College of Public Health at the Georgia Southern University. Dr. Chen is an elected fellow of the American Statistical Association and a senior expert consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trial biostatistics. Dr. Chen has more than 200 scientific publications and co-authored/co-edited 38 books on clinical trials, survival data, meta-analysis, causal inference and structural equation modeling, Monte-Carlo simulation-based statistical modelling. His research has been funded as PI/Co-PI from NIH R01s and other governmental agencies.

Dear Colleagues and Students,

As you may have already noticed, we are honored to be hosting Professor Din-Chen  (Ph.D. Fellow of the American Statistical Association, Executive Director and Professor in Biostatistics, College of Health Solutions, Arizona State University,Phoenix, USA) in our department next week. I am excited to announce that he will be giving a mini-course on

HowTo Jointly Analyze Longitudinal Data & Time-To-Event Simultaneously: AnIntegrative Data Harmonization Approach

on Thursday May 4, 9h00-12h00 and 14h00-17h00 at Nova FCT, Building VII, Second Floor, Seminar room.


Time: Thursday May 4,  9h00-12h00 and 14h00-17h00

Location: NOVA FCT, Building VII, Second Floor, Seminar room


You can see the details, abstract, and syllabus of the course at the end of this email. Additionally, a short biography of the instructor is also provided for your reference.
I would like to express my gratitude to Professor Carlos Coelho for sharing this information with me, and I am thrilled to extend this invitation to students and professors at the university to join us for this mini-course. The course is open to all students and professors at the university who are interested in learning more about this topic. 

We have received several requests from individuals who are unable to attend the mini-course in-person, so we have decided to offer a hybrid format instead. You can now join us remotely by using the following link: https://videoconf-colibri.zoom.us/j/88333359956

I look forward to hearing from you and seeing you at the mini-course.

Kind regards,

Mina Norouzirad
responsible for organizing the seminar series of the Statistics and Risk Management group at NOVAMATH

Abstract:

Drawing on theory and prior research in clinical trials, evidence-based health research can generate data from different sources and phases of health interventions. A central feature of evidence-based public health intervention research is the sequential and longitudinal implementation with that participants may drop out or die or be censored during the time of the study. This mini-workshop is to discuss the integrative data harmonization and joint modeling of longitudinal data and time-to-event(such as, drop out and censored) data simultaneously, which has the potential to produce a more efficient and more powerful statistical analysis. Specifically,this workshop is to give an overview of statistical joint modeling to simultaneously analyze the data from longitudinal biomarkers and time-to-event with two publicly available real-world data. It is designed with two 3-hoursessions in a step-by-step implementation in the statistical software R for data from HIV/AIDS clinical trials and the Mayo Clinic Primary Biliary Cirrhosis study.  Participants are welcome to bring their own research data to be used in this workshop as examples.

Session1 (3-hrs): Univariate Joint-Modeling

·       Introduction to integrative data harmonization (half hr)

·       Overview of statistical joint modeling (one hr)

·       Step-by-stepR implementations with R packages JM and JMBayes2 on HIV Clinical Trial data analysis (1 hr)

·       Group discussions (half hr)

Session2 (3-hrs): Latent-Class Joint Modeling and Multivariate Joint Modeling

• Overview of joint modeling to Latent-Class Joint Modeling with HIV clinical trial data (half hr)

·       Step-by-stepR implementations with R packages LCJM on HIV Clinical Trial data analysis (one hr)

·       Multivariate Joint Modeling with Mayo Clinic Primary Biliary Cirrhosis study (one hr)

·       Group discussions (half hr)

Brief Biography of Dr. Din Chen:

Dr. Din Chen received his Ph.D. in Statistics from the University of Guelph (Canada) in 1995 and is now the executive director and professor in Biostatistics at the College of Health Solutions, Arizona State University. He served as the Wallace H. Kuralt distinguished professor in biostatistics at the University of North Carolina-Chapel Hill, a biostatistics professor at the University of Rochester Medical Center, the Karl E. Peace endowed eminent scholar chair in biostatistics from the Jiann-Ping Hsu College of Public Health at the Georgia Southern University. Dr. Chen is an elected fellow of the American Statistical Association and a senior expert consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trial biostatistics. Dr. Chen has more than 200 scientific publications and co-authored/co-edited 38 books on clinical trials, survival data, meta-analysis, causal inference and structural equation modeling, Monte-Carlo simulation-based statistical modelling. His research has been funded as PI/Co-PI from NIH R01s and other governmental agencies.

Abstract:

Let S be a non-commutative semigroup. Let Z(S) be the set of central elements of S, that is, the set of elements of S that commute with all elements of S. The commuting graph of S, denoted G(S), is the simple graph (that is, an undirected graph without loops and without multiple edges) whose vertices are the non-central elements of S and whose edges are the sets {a,b} where a and b are distinct elements of S that satisfy ab=ba.

The commuting graphs of some semigroups have already been studied. For instance, the diameter of the commuting graph G(T(X)), where T(X) is the semigroup of transformations on X, as well as the diameter of the commuting graph G(J), where J is a proper nonzero ideal of T(X), are known. The commuting graph of the symmetric inverse semigroup I(X) (the set of partial bijections on X) was also studied: the clique number of G(I(X)) is calculated, as well as the diameter of G(I(X)) when |X| is even or a power of an odd prime. The diameter of G(J), where J is a proper nonzero ideal of I(X), is also determined.

Venue: office 2, 3rd floor of the Mathematics Department Building

Statistics and Risk Management Seminar | May 10, 14h00 | NOVA MATH/FCT NOVA

Statistics and Risk Management Seminar

Department of Mathematics, NOVA MATH/FCT NOVA

Title: Big Data Inference and StatisticalMeta-Analysis

Speaker: Ding-Geng(Din) Chen, Executive Director and Professor in Biostatistics, College ofHealth Solutions, Arizona State University

Date | Time: May 10, 2023 | 14h00

Place: FCT NOVA, Building VII, First floor, meeting room of the department

Zoom: https://videoconf-colibri.zoom.us/j/88333359956

Abstract: Statisticalmeta-analysis (MA) is a common statistical approach in big data inference to combinemeta-data from diverse studies to reach a more reliable and efficientconclusion. It can be performed by either synthesizing study-level summarystatistics (MA-SS) or modeling individual participant-level data (MA-IPD), ifavailable. However, it remains not fully understood whether the use of MA-IPDindeed gains additional efficiency over MA-SS. In this talk, we review the classicalfixed-effects and random-effects meta-analyses, and further discuss therelative efficiency between MA-SS and MA-IPD under a general likelihoodinference setting. We show theoretically that there is no gain of efficiencyasymptotically by analyzing MA-IPD. Our findings are further confirmed by extensiveMonte-Carlo simulation studies and real data analyses.  

Short Bio:  Dr. Din Chen received his Ph.D. in Statistics from University of Guelph(Canada) in 1995 and is now the executive directorand professor in Biostatistics at the College of Health Solutions, ArizonaState University. He served as a professor in biostatistics at the Universityof North Carolina-Chapel Hill, biostatistics professor at the University ofRochester Medical Center, the Karl E. Peace endowed eminent scholar chair inbiostatistics from the Jiann-Ping Hsu College of Public Health at the GeorgiaSouthern University. Dr. Chen is an elected fellow of the American Statistical Associationand a senior expert consultant for biopharmaceuticals and government agencieswith extensive expertise in clinical trial biostatistics. Dr. Chen has morethan 200 scientific publications and co-authored/co-edited 38 books on clinicaltrials, survival data, meta-analysis, causal inference and structural equationmodeling, Monte-Carlo simulation-based statistical modelling. Hisresearch has been funded as PI/Co-PI from NIH R01s and other governmentalagencies.

Organizers: Mina Norouzirad & Isabel Natário

Abstract: Stationary vectors of stochastic matrices describe the asymptotic distribution of finite state Markov chains and have abundant applications in big data analysis. Being solutions of linear fixed point equations they can be computed by Gauss elimination and more efficiently by the iterative method of the classical Perron Frobenius Theorem. The limitations of this method become clear when the matrices are large and the spectral gap is small. We will discuss a different approach based on a recent Isospectral Theorey of Benjamim Webb and Leonid Bunimovich.
 
Joint work with Longmei Shu, Alexandre Baraviera and M. Joana Torres.

Venue: office 1, 3rd floor of the Mathematics Department Building

Venue: office 2, 3rd floor of the Mathematics Department Building

Venue: office 2, 3rd floor of the Mathematics Department Building

Venue: office 2, 3rd floor of the Mathematics Department Building

Zoom link:  https://videoconf-colibri.zoom.us/j/93296258325?pwd=Q2JtbjllWFNCVG9VdnN5aUN3YzBLQT09

Abstract:

Cryptography aims to efficiently secure communication and computation against powerful adversaries. The past 50 years have seen great efforts to place cryptography on solid mathematical ground through rigorous security models and proofs of security. This "provable security" perspective has become the cornerstone of modern cryptography. Various mathematical objects (which found their original motivation elsewhere) have been fundamental tools in the design of secure cryptographic protocols. Of particular note, computational problems on point lattices and linear error-correcting codes form the basis of "post-quantum" cryptography -- protocols which are believed to resist attacks by quantum computers -- and linear codes are used to design cryptographic protocols with unconditional security (a.k.a. information-theoretic security) for a plethora of important tasks.

The main goal of this mini-course is to give an overview of applications of point lattices and linear codes to post-quantum and information-theoretic cryptography. Another goal is to entice more people with strong mathematical background to join the cryptography community in analyzing the mathematical problems that power most of the cryptography that we use (or will use in the near future) and in identifying ways in which other mathematical objects can be exploited to develop improved cryptographic protocols.

We will start by introducing basic concepts in cryptography. Then, we will discuss some of the most important mathematical problems underlying post-quantum cryptography, including why we care about them and how they are related to lattices and codes. Finally, we will see applications of linear codes to information-theoretic cryptography. A more detailed tentative program follows below.

No background is required beyond basic algebra, combinatorics, and probability. Some mathematical maturity is a plus.

Tentative program:

May 23: Basics of "provable security". The one-time pad and perfect secrecy. Computationally-bounded adversaries. Definitions of basic cryptographic concepts (pseudorandom generators, one-way functions, symmetric- and public-key encryption schemes).

May 25: The "Learning With Errors" (LWE) and "Short Integer Solution" (SIS) problems. Constructions of cryptographic protocols based on the hardness of solving LWE and SIS.

May 30 + part of Jun 1: Why do we care about LWE and SIS? Worst-case to average-case reductions. NP-hardness of the Closest Vector Problem and the Shortest Vector Problem on lattices.

part of Jun 1 + Jun 6: Information-theoretic cryptography. Secret sharing schemes and privacy amplification protocols from linear codes.

Bio:

João Ribeiro is an Assistant Professor in the Department of Computer Science of Universidade Nova de Lisboa and a researcher at NOVA LINCS. Previously, he was a Post Doctoral Fellow in the Computer Science Department of Carnegie Mellon University, hosted jointly by Vipul Goyal and Venkatesan Guruswami. João received his PhD from the Department of Computing of Imperial College London, where he was advised by Mahdi Cheraghchi. Before that, he received an MSc in Computer Science from ETH Zurich and a BSc in Applied Mathematics and Computation from Instituto Superior Técnico.

He has broad interests within theoretical computer science, with special emphasis on pseudorandomness, coding theory, and cryptography.

More details can be found at https://sites.google.com/site/joaorib94/.

Abstract:

Cryptography aims to efficiently secure communication and computation against powerful adversaries. The past 50 years have seen great efforts to place cryptography on solid mathematical ground through rigorous security models and proofs of security. This "provable security" perspective has become the cornerstone of modern cryptography. Various mathematical objects (which found their original motivation elsewhere) have been fundamental tools in the design of secure cryptographic protocols. Of particular note, computational problems on point lattices and linear error-correcting codes form the basis of "post-quantum" cryptography -- protocols which are believed to resist attacks by quantum computers -- and linear codes are used to design cryptographic protocols with unconditional security (a.k.a. information-theoretic security) for a plethora of important tasks.

The main goal of this mini-course is to give an overview of applications of point lattices and linear codes to post-quantum and information-theoretic cryptography. Another goal is to entice more people with strong mathematical background to join the cryptography community in analyzing the mathematical problems that power most of the cryptography that we use (or will use in the near future) and in identifying ways in which other mathematical objects can be exploited to develop improved cryptographic protocols.

We will start by introducing basic concepts in cryptography. Then, we will discuss some of the most important mathematical problems underlying post-quantum cryptography, including why we care about them and how they are related to lattices and codes. Finally, we will see applications of linear codes to information-theoretic cryptography. A more detailed tentative program follows below.

No background is required beyond basic algebra, combinatorics, and probability. Some mathematical maturity is a plus.

Tentative program:

May 23: Basics of "provable security". The one-time pad and perfect secrecy. Computationally-bounded adversaries. Definitions of basic cryptographic concepts (pseudorandom generators, one-way functions, symmetric- and public-key encryption schemes).

May 25: The "Learning With Errors" (LWE) and "Short Integer Solution" (SIS) problems. Constructions of cryptographic protocols based on the hardness of solving LWE and SIS.

May 30 + part of Jun 1: Why do we care about LWE and SIS? Worst-case to average-case reductions. NP-hardness of the Closest Vector Problem and the Shortest Vector Problem on lattices.

part of Jun 1 + Jun 6: Information-theoretic cryptography. Secret sharing schemes and privacy amplification protocols from linear codes.

Bio:

João Ribeiro is an Assistant Professor in the Department of Computer Science of Universidade Nova de Lisboa and a researcher at NOVA LINCS. Previously, he was a Post Doctoral Fellow in the Computer Science Department of Carnegie Mellon University, hosted jointly by Vipul Goyal and Venkatesan Guruswami. João received his PhD from the Department of Computing of Imperial College London, where he was advised by Mahdi Cheraghchi. Before that, he received an MSc in Computer Science from ETH Zurich and a BSc in Applied Mathematics and Computation from Instituto Superior Técnico.

He has broad interests within theoretical computer science, with special emphasis on pseudorandomness, coding theory, and cryptography.

More details can be found at https://sites.google.com/site/joaorib94/.

Speaker: Agostinho Agra, Universidade de Aveiro

Title:

Distributionally robust Optimization: the cases of berth allocation and wind farms layout

Abstract:

In this talk we present two optimization problems that are modelled using  a two-stage distributtionally robust model: a berth allocation problem with uncertain handling times and a wind farm layout design problem where wind direction and speed are uncertain. In both cases, the ambiguity set used to model the probability distribution of the uncertain parameters is the well-known Kantorovich ambiguity set, which can be seen as the feasibility set of a transportation problem.

First, we review the basic concepts of distributtionally robust optimization for two-stage models and discuss an exact decomposition algorithm. Then we address the two applications giving particular emphasis to the issues raised on the implementation of the algorithm. Computational tests are reported.

Speaker: Agostinho Agra, Universidade de Aveiro

Title:

Distributionally robust Optimization: the cases of berth allocation and wind farms layout

Abstract:

In this talk we present two optimization problems that are modelled using  a two-stage distributtionally robust model: a berth allocation problem with uncertain handling times and a wind farm layout design problem where wind direction and speed are uncertain. In both cases, the ambiguity set used to model the probability distribution of the uncertain parameters is the well-known Kantorovich ambiguity set, which can be seen as the feasibility set of a transportation problem.

First, we review the basic concepts of distributtionally robust optimization for two-stage models and discuss an exact decomposition algorithm. Then we address the two applications giving particular emphasis to the issues raised on the implementation of the algorithm. Computational tests are reported.

Abstract:

Cryptography aims to efficiently secure communication and computation against powerful adversaries. The past 50 years have seen great efforts to place cryptography on solid mathematical ground through rigorous security models and proofs of security. This "provable security" perspective has become the cornerstone of modern cryptography. Various mathematical objects (which found their original motivation elsewhere) have been fundamental tools in the design of secure cryptographic protocols. Of particular note, computational problems on point lattices and linear error-correcting codes form the basis of "post-quantum" cryptography -- protocols which are believed to resist attacks by quantum computers -- and linear codes are used to design cryptographic protocols with unconditional security (a.k.a. information-theoretic security) for a plethora of important tasks.

The main goal of this mini-course is to give an overview of applications of point lattices and linear codes to post-quantum and information-theoretic cryptography. Another goal is to entice more people with strong mathematical background to join the cryptography community in analyzing the mathematical problems that power most of the cryptography that we use (or will use in the near future) and in identifying ways in which other mathematical objects can be exploited to develop improved cryptographic protocols.

We will start by introducing basic concepts in cryptography. Then, we will discuss some of the most important mathematical problems underlying post-quantum cryptography, including why we care about them and how they are related to lattices and codes. Finally, we will see applications of linear codes to information-theoretic cryptography. A more detailed tentative program follows below.

No background is required beyond basic algebra, combinatorics, and probability. Some mathematical maturity is a plus.

Tentative program:

May 23: Basics of "provable security". The one-time pad and perfect secrecy. Computationally-bounded adversaries. Definitions of basic cryptographic concepts (pseudorandom generators, one-way functions, symmetric- and public-key encryption schemes).

May 25: The "Learning With Errors" (LWE) and "Short Integer Solution" (SIS) problems. Constructions of cryptographic protocols based on the hardness of solving LWE and SIS.

May 30 + part of Jun 1: Why do we care about LWE and SIS? Worst-case to average-case reductions. NP-hardness of the Closest Vector Problem and the Shortest Vector Problem on lattices.

part of Jun 1 + Jun 6: Information-theoretic cryptography. Secret sharing schemes and privacy amplification protocols from linear codes.

Bio:

João Ribeiro is an Assistant Professor in the Department of Computer Science of Universidade Nova de Lisboa and a researcher at NOVA LINCS. Previously, he was a Post Doctoral Fellow in the Computer Science Department of Carnegie Mellon University, hosted jointly by Vipul Goyal and Venkatesan Guruswami. João received his PhD from the Department of Computing of Imperial College London, where he was advised by Mahdi Cheraghchi. Before that, he received an MSc in Computer Science from ETH Zurich and a BSc in Applied Mathematics and Computation from Instituto Superior Técnico.

He has broad interests within theoretical computer science, with special emphasis on pseudorandomness, coding theory, and cryptography.

More details can be found at https://sites.google.com/site/joaorib94/.

NOVA MATH – UNIDEMI Talks

Date | Time: May 31, 2023 | 14h00 – 15h00

Location: Building VII, Seminar room (2nd floor)

Program

Abstract:

Cryptography aims to efficiently secure communication and computation against powerful adversaries. The past 50 years have seen great efforts to place cryptography on solid mathematical ground through rigorous security models and proofs of security. This "provable security" perspective has become the cornerstone of modern cryptography. Various mathematical objects (which found their original motivation elsewhere) have been fundamental tools in the design of secure cryptographic protocols. Of particular note, computational problems on point lattices and linear error-correcting codes form the basis of "post-quantum" cryptography -- protocols which are believed to resist attacks by quantum computers -- and linear codes are used to design cryptographic protocols with unconditional security (a.k.a. information-theoretic security) for a plethora of important tasks.

The main goal of this mini-course is to give an overview of applications of point lattices and linear codes to post-quantum and information-theoretic cryptography. Another goal is to entice more people with strong mathematical background to join the cryptography community in analyzing the mathematical problems that power most of the cryptography that we use (or will use in the near future) and in identifying ways in which other mathematical objects can be exploited to develop improved cryptographic protocols.

We will start by introducing basic concepts in cryptography. Then, we will discuss some of the most important mathematical problems underlying post-quantum cryptography, including why we care about them and how they are related to lattices and codes. Finally, we will see applications of linear codes to information-theoretic cryptography. A more detailed tentative program follows below.

No background is required beyond basic algebra, combinatorics, and probability. Some mathematical maturity is a plus.

Tentative program:

May 23: Basics of "provable security". The one-time pad and perfect secrecy. Computationally-bounded adversaries. Definitions of basic cryptographic concepts (pseudorandom generators, one-way functions, symmetric- and public-key encryption schemes).

May 25: The "Learning With Errors" (LWE) and "Short Integer Solution" (SIS) problems. Constructions of cryptographic protocols based on the hardness of solving LWE and SIS.

May 30 + part of Jun 1: Why do we care about LWE and SIS? Worst-case to average-case reductions. NP-hardness of the Closest Vector Problem and the Shortest Vector Problem on lattices.

part of Jun 1 + Jun 6: Information-theoretic cryptography. Secret sharing schemes and privacy amplification protocols from linear codes.

Bio:

João Ribeiro is an Assistant Professor in the Department of Computer Science of Universidade Nova de Lisboa and a researcher at NOVA LINCS. Previously, he was a Post Doctoral Fellow in the Computer Science Department of Carnegie Mellon University, hosted jointly by Vipul Goyal and Venkatesan Guruswami. João received his PhD from the Department of Computing of Imperial College London, where he was advised by Mahdi Cheraghchi. Before that, he received an MSc in Computer Science from ETH Zurich and a BSc in Applied Mathematics and Computation from Instituto Superior Técnico.

He has broad interests within theoretical computer science, with special emphasis on pseudorandomness, coding theory, and cryptography.

More details can be found at https://sites.google.com/site/joaorib94/.

Abstract:

The commutator of congruences can be defined quite generally, giving rise to the notions of nilpotence and solvability. In groups, the general theory happens to specialize to the classical theory we all know, but in many varieties the theories differ. I will introduce the general notions carefully and discuss recent results on solvability, nilpotence and supernilpotence in varieties close to groups, particularly in Moufang loops (think octonions).

Abstract:

Cryptography aims to efficiently secure communication and computation against powerful adversaries. The past 50 years have seen great efforts to place cryptography on solid mathematical ground through rigorous security models and proofs of security. This "provable security" perspective has become the cornerstone of modern cryptography. Various mathematical objects (which found their original motivation elsewhere) have been fundamental tools in the design of secure cryptographic protocols. Of particular note, computational problems on point lattices and linear error-correcting codes form the basis of "post-quantum" cryptography -- protocols which are believed to resist attacks by quantum computers -- and linear codes are used to design cryptographic protocols with unconditional security (a.k.a. information-theoretic security) for a plethora of important tasks.

The main goal of this mini-course is to give an overview of applications of point lattices and linear codes to post-quantum and information-theoretic cryptography. Another goal is to entice more people with strong mathematical background to join the cryptography community in analyzing the mathematical problems that power most of the cryptography that we use (or will use in the near future) and in identifying ways in which other mathematical objects can be exploited to develop improved cryptographic protocols.

We will start by introducing basic concepts in cryptography. Then, we will discuss some of the most important mathematical problems underlying post-quantum cryptography, including why we care about them and how they are related to lattices and codes. Finally, we will see applications of linear codes to information-theoretic cryptography. A more detailed tentative program follows below.

No background is required beyond basic algebra, combinatorics, and probability. Some mathematical maturity is a plus.

Tentative program:

May 23: Basics of "provable security". The one-time pad and perfect secrecy. Computationally-bounded adversaries. Definitions of basic cryptographic concepts (pseudorandom generators, one-way functions, symmetric- and public-key encryption schemes).

May 25: The "Learning With Errors" (LWE) and "Short Integer Solution" (SIS) problems. Constructions of cryptographic protocols based on the hardness of solving LWE and SIS.

May 30 + part of Jun 1: Why do we care about LWE and SIS? Worst-case to average-case reductions. NP-hardness of the Closest Vector Problem and the Shortest Vector Problem on lattices.

part of Jun 1 + Jun 6: Information-theoretic cryptography. Secret sharing schemes and privacy amplification protocols from linear codes.

Bio:

João Ribeiro is an Assistant Professor in the Department of Computer Science of Universidade Nova de Lisboa and a researcher at NOVA LINCS. Previously, he was a Post Doctoral Fellow in the Computer Science Department of Carnegie Mellon University, hosted jointly by Vipul Goyal and Venkatesan Guruswami. João received his PhD from the Department of Computing of Imperial College London, where he was advised by Mahdi Cheraghchi. Before that, he received an MSc in Computer Science from ETH Zurich and a BSc in Applied Mathematics and Computation from Instituto Superior Técnico.

He has broad interests within theoretical computer science, with special emphasis on pseudorandomness, coding theory, and cryptography.

More details can be found at https://sites.google.com/site/joaorib94/.

Venue: office 2, 3rd floor of the Mathematics Department Building

Venue: office 2, 3rd floor of the Mathematics Department Building

Venue: office 2, 3rd floor of the Mathematics Department Building

Venue: office 40, 3rd floor of the Mathematics Department Building

Venue: office 2, 3rd floor of the Mathematics Department Building

More information is available here.

Venue: office 36, 3rd floor of the Mathematics Department Building