The NOVA Distinguished Lecture Series in Mathematics is a joint initiative of NOVA Math (https://novamath.fct.unl.pt/), the Center for Mathematics and Applications, and the Department of Mathematics of NOVA School of Science and Technology.

A leading researcher in the area of Mathematics is invited to give a lecture to students of the Department of Mathematics, but also to the general public of the NOVA School of Science and Technology, presenting a relevant area of research in Mathematics and his/her main contributions on the field.

By bringing together outstanding researchers and students, these lectures aim to broaden students´mathematical education, present new challenges, and spark their scientific curiosity regarding research in Mathematics.

About the speaker:  Afonso Bandeira is a mathematician and professor at ETH Zurich, specializing in High-Dimensional Probability, Random Matrix Theory, Mathematical Statistics, Theoretical Computer Science, Combinatorics, and Mathematical Optimization. He received his Ph.D. in Mathematics from Princeton University in 2015.

His academic career began in 2009 at the Universidade de Coimbra and has included teaching and research positions at Princeton University, MIT, and New York University. He joined ETH Zurich in 2020, where he is also a member of the Max Planck ETH Center for Learning Systems and the ETH Foundations of Data Science (ETH-FDS).

Professor Bandeira’s contributions have been recognized with several international awards, including the 2022 Prémio António Aniceto Monteiro from the Portuguese Mathematical Society, the 2020 Stephen Smale Prize from the Foundation of Computational Mathematics (FoCM), and the 2019 ISAAC Award for Young Scientists from the International Society for Analysis, its Applications and Computation (ISAAC).

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Abstract: The community detection problem is a central problem in network theory and algorithms to solve this problem are used in a variety of networks/graphs, including social networks, protein-protein interaction networks, and many others. In this talk we will see how algorithms to detect communities, and their performance, are connected to the phenomenon of spontaneous synchronization: how pendulum clocks swing in synch spontaneously when left in the same table. This talk will be very light on pre-requisites and is aimed at undergraduate students. It will discuss Graph/Network theory, Probability theory, Statistical inference, Theoretical Computer Science, Optimization, and Dynamical Systems, without assuming knowledge in any of these areas.

About the speaker:  Susan Holmes is a statistician and professor at Stanford University. She is noted for her work in applying nonparametric multivariate statistics, bootstrapping methods, and data visualization to biology. She received her PhD in 1985 from Université Montpellier II. She served as a tenured research scientist at INRA for ten years. She then taught at MIT, Harvard and was an associate professor of biometry at Cornell before moving to Stanford in 1998. She is a Fellow of the Fields Institute and also of the Institute of Mathematical Statistics.

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Abstract: Modern data science uses geometry to compare and contrast heterogeneous objects that are non-uniformly distributed in non-Euclidean spaces. I will show examples of how we can predict multivariate heteroegeneous response variables when the data combine trees, networks, binary and real valued variables. Examples of applications include image analysis, bootstrapped data and the analysis of the human microbiome.

About the speaker:  Albrecht Beutelspacher studied math, physics and philosophy at the University of Tübingen and received his PhD from the University of Mainz. Was an associate professor at the University of Mainz, worked for a research department of Siemens and was a tenured professor of geometry and discrete mathematics at the University of Giessen. He became a well-known popularizer of mathematics in Germany by authoring several books in the field of popular science and recreational math and by founding Germany's first math museum, the Mathematikum. He received several awards for his contributions to popularizing mathematics. He has a math column in the German popular science magazine Bild der Wissenschaft and moderates a popular math series for the TV-Channel BR- alpha (educational TV).

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Abstract: Using only simple material, such as paper and scissors, many mathematical experiments are presented. While making these experiments, quasi automatically mathematical insight is stimulated. A lecture which provides simultaneously knowledge and entertainment.  

To have a hands-on participation, please bring with you an A4 sheet of paper.

About the speaker:  Charles R. Johnson is an American mathematician specialized in Linear Algebra. He is a Class of 1961 Professor of Mathematics at College of William and Mary. He has authored or co-authored more than a dozen books, and three hundred papers in prestigious specialized journals. The books Matrix Analysis and Topics in Matrix Analysis, co-written with Roger Horn, are standard texts in advanced Linear Algebra.

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Abstract: As a practical matter, the very difficult nonnegative inverse eigenvalue Problem  (NIEP) has become a bundle of more particular problems. We report on two of these: 1) The doubly stochastic single eigenvalue problem asks which individual complex numbers occur as an eigenvalue of a doubly stochastic matrix. This problem, first discussed in the 1960's, remains open, though its row stochastic analog enjoyed its first "solution" about 70 years ago and has received refinements since. We report on the intriguing progress that is partly empirical. 2) Spectra with repeated eigenvalues may be nonnegatively realizable with some Jordan structures and not others. We sort out what is currently known and what is likely true about the Jordan NIEP.     

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About the speaker: Persi Diaconis is an American mathematician of Greek descent and former professional magician. He is the Mary V. Sunseri Professor of Statistics and Mathematics at Stanford University.

He is particularly known for tackling mathematical problems involving randomness and randomization, such as coin flipping and shuffling playing cards.

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Abstract: 'Everybody knows' that a vigorously flipped coin is equally likely to come up heads or tails AND that different flips are independent. But is it true? I will report work with Susan Holmes and Richard Montgommery showing that vigorously flipped coins are slightly biased (it's about .51) to come up on the same side they started. The math involves classical mechanics and 'the method of arbitrary functions as well as image analysis to look at what real coins do when flipped by real people. Similar analysis applies to most of our basic notions of random phenomena; rolling dice or roulette balls. This thinking hard about the underlying sources of randomness sheds some light on the variety of 'made up models' prevalent in much of applied mathematics(in particular, my field of statistics).