Summer intern 2023 information session at 12:15-14:00

9. January 2023

The information session of MS Department's summer intern positionswill be organized on Wednesday, 11 January at 12.15-14.00 in the M wing coffee room of the Department on the 2^{nd} floor (close to the M2 and M3 lecture rooms). The research areas/groups of the Department will introduce themselves at the info session.

Summer internships at the department in 2023

20. December 2022

The summer intern positions are now open, deadline is 25 January.

Philine Schiewe uses algorithm to get new ideas for public transport planning

14. December 2022

What do you research and why?

From a background I’m a mathematician, but I do optimization research that is connected to mathematics, computer science and economics. The application areas are public transport planning and last mile logistics – the final step of the delivery process.

My research is mainly theoretical, but we also have a software project LinTim that moved to Aalto with me. The project was launched 15 years ago in Göttingen by Anita Schöbel and I started working in the project in 2013. In 2017, we made it open source.

In the project, we are collecting both algorithms and data sets related to public transport planning. This software project allows us to consider the planning process as a whole: if we change something in an early planning stage, how does that affect the outcome of later planning stages? For example, we can evaluate the impact of changing a line plan on the travel times of passengers by creating a corresponding timetable automatically. It is quite unique that one software is evaluating multiple planning stages in an integrated manner.

Since the project has been made available as open source, we gained multiple new collaborators and users. In Leipzig, a public transport company is testing the project and using algorithms to get new ideas for line planning. Usually, the lines that are operated by busses or trains are chosen from a predefined set, the so-called line pool. This pool is often created manually by planner. We introduced a new algorithm to generate these line pools automatically. Thus, we can get lines that differ from the ones planners would create manually and generate a larger solution space. Depending on the objective of the planner, this can either reduce the cost of the system or is beneficial for the customer by reducing the travel time or the number of transfers.

As LinTim is a research project, the algorithms contain some abstractions. To apply them to specific cities, they might need to be adapted slightly. This is currently tested in collaboration with researchers in Stuttgart and Winterthur.

The example of public transport shows that solving sequential problems in an integrated manner improves the solution quality but at the same time makes the problem more complicated to solve. As sequential problems are not limited to transportation and logistics applications, I am also considering the integration of sequential processes in a more general and abstract way. Here, it is important to understand when it is beneficial to look at the problem in an integrated manner.

How did you become a professor or a researcher?

I really liked the topic of my PhD; therefore, it was easy to stay with it. And when I found the call for this professorship, I really liked the idea of a department with both areas of research, mathematics, and operations research.

There are national operations research societies both in Finland and Germany. I got the opportunity to work as an assistant to the board in Germany for two years. There I was able to see more computer science and business-related operations research. It is a very large society in Germany and very nice networking opportunity. I am looking forward to getting to know the Finnish OR society as well and gain new perspectives this way.

What is the most important high point of your career?

I enjoy being at conferences. Recently I’ve presented my research for instance at the ALGO conference, the major European event for researchers, students, and practitioners in algorithms.

What is the most essential characteristic of a researcher?

I think being a researcher means being perseverant – even a bit stubborn – to look at problems and find solutions. One must also be able to collaborate with others especially from different fields to get new ideas and to get different viewpoints to the problems.

What do you expect from the future?

I’m very much looking forward to my own research group. There is an open call for PhD students at the Department of Mathematics and Systems analysis. I also wish to build connections to the system analysis lab and applied mathematics.

Further information:

LinTim, Integrated Optimization in Public Transportation

Call for applications for Crystal Flowers in Halls of Mirrors is now open

5. December 2022

MS-E1000 Crystal Flowers in Halls of Mirrors: Mathematics meets Art and Architecture is open to all students, from undergraduates to PhD students, from mathematics and engineering to art, design, architecture and chemistry to business.

A maximum of 50 students are expected at the course, and the application period ends 10 January 2023. The course is worth 15 credits and it is held once every two years.

An exhibition of the course will take place in spring 2023 at the Finnish Science Centre Heureka. A similar exhibition was also held at the Heureka in 2017, and as a result the exhibition received a lot of visibility for a period of six months.

‘It's great that after the pandemic we have this opportunity again. Remote teaching and learning were very challenging, although in the end, thanks to the perseverance of the students, we were able to create a great exhibition in the courtyard of the main lobby of the Undergraduate Centre. We are looking forward to positive interaction between students, teachers and Heureka's designers,’ says Kirsi Peltonen, Senior Lecturer in Mathematics and responsible for the course.

As in previous years, the course will feature several guest lecturers in addition to the participating teachers Taneli Luotoniemi and Laura Isoniemi. For example, origami artist Paul Jackson from Tel Aviv will give a folding workshop for the students. Professor Marcelo Dias from the University of Edinburgh will shed light on the physics of the subject. Pirjo Kääriäinen, Professor of Design and Material Science, will bring her strong expertise in multidisciplinary collaboration to the course. University lecturer Luka Piškorec will bring an architectural perspective to the course and the exhibition will be produced by Markus Holste and Marco Rodriguez.

‘This course is group work, and it requires more commitment than participating in a theoretical course,’ says Peltonen.

The students can have a background in intermediate mathematics, but the course opens new perspectives also for example for the students with a major in physics or mathematics. Each group will be made up of students with as many different talents as possible. The course is therefore a unique opportunity to see the reality of people from other disciplines and to get hands-on. Previous courses have included students from all of Aalto's schools, from freshmen to graduate students.

Public defence in Mathematics, M.Sc. (Tech) / M.Soc.Sc. Emma-Karoliina Kurki, 25.11.2022

15. November 2022

Title of the doctoral thesis: Weight theory on bounded domains and metric measure spaces

Public defence announcement:

A weight function describes an unequal distribution of mass. Muckenhoupt weights are a class of weight functions that are well-behaved in the sense that their oscillation is limited. Muckenhoupt weights are an indispensable part of the toolkit of modern harmonic analysis, and have important applications to neighboring fields of mathematics. One example is studying the regularity of solutions to partial differential equations, which in turn are the quasi-universal language of physics and mathematical modelling.

The present thesis develops the theory of locally defined weight functions on bounded domains of the Euclidean space, as well as weights on more general metric spaces where the facts of classical geometry are not necessarily true. A cohesive treatment of these cases has been lacking since the introduction of Muckenhoupt weights 50 years ago. One benefit of working in abstract metric spaces is that the structure of the problem is laid bare, ideally allowing us to determine the minimal conditions for a statement to hold true. Furthermore, the methods developed are useful in mathematical analysis in nonlinear environments such as groups and graphs.

On certain domains of the Euclidean space, we show a Poincaré inequality involving a different weight on each side. Poincaré inequalities are essential in the regularity theory of partial differential equations. The proof applies dyadic techniques that have lately been influential in the field of harmonic analysis. On metric measure spaces, we show that a Muckenhoupt weight defined on a measurable subset can be extended into the whole space under certain conditions. Furthermore, we investigate other possible ways to characterize Muckenhoupt-type weights.

Contact details of the doctoral student: emma-karoliina.kurki@aalto.fi

Opponent: ProfessorSheldy Ombrosi, Universidad Nacional der Sur, Argentina Custos: Professori Juha Kinnunen, Aalto University School of Science, Department of Mathematics and Systems Analysis

The public defence will be organised on campus.

The doctoral thesis is publicly displayed 10 days before the defence in the publication archive Aaltodoc of Aalto University.

Award-winning decision analysis researcher Ahti Salo: In crises, old data does not tell the whole story of the future

28. October 2022

Kuva: Anni Hanén.

The Decision Analysis Society (DAS) of the Institute for Operations Research and the Management Sciences (INFORMS) has awarded Professor Ahti Salo the 2022 Frank P. Ramsey Medal.

‘It may be that my work as a member of the Covid scientific panel of the Prime Minister Office and as a producer of research reviews also contributed to my selection,’ says Salo.

As a member of the Covid scientific panel during spring 2020, Salo was responsible for resilience, the ability to anticipate, withstand and recover from shocks.

‘These types of crises show that the rearview mirror, or old data, does not tell us everything about the future. In the midst of a major crisis, the world is a different place from the one that the data came from. Model-based analyses can be of great help here, as long as we are humble and understand the extent to which the assumptions of mathematical models correspond to reality,’ says Salo.

Salo's research has developed a portfolio decision analysis that selects many options from a large set of alternatives.

‘For example, a portfolio can be a set of large projects. They can be investment projects that need to be evaluated on various criteria, such as cost, profitability, safety, and use of resources. Decision analysis supports the rational use of these resources.’

Application areas in Salo's work have included the selection of infrastructure for rehabilitation, decisions on the siting of medical helicopters and the development of risk management plans.

In the summer of 2022, the EURO 2022 major conference on operations research was held at Aalto University, attended by almost 2000 researchers from around the world, with several Salo’s former students on the programme committee.

‘We rarely get this main conference in Finland, maybe once in a generation only. It was last time organised here in 1992. I'm glad to have grown up together with the graduate students and to have seen their careers move on nicely.’

A passion for writing has been a driving force in Salo's career.

‘The most important quality of a researcher is independent thinking. A researcher must be able to read between the lines. What is missing from previous research, or under what conditions the results could be different.’

Ahti Salo did his doctoral thesis on decision analysis under the supervision of Professor Emeritus Raimo P. Hämäläinen who is a Ramsey medalist as well. In his dissertation, Salo examined how to produce decision recommendations based on incomplete information about alternatives and their evaluation criteria.

Salo worked at VTT and Nokia in the 1990s. At Nokia, the portfolio decision analysis was about what new features to develop for the mobile phones.

Salo has supervised 26 doctoral theses and 185 Master’s theses. Throughout his career, Salo has strived to produce solid research which is of as high quality as possible.

‘Immediately after my PhD, I spent a couple of years doing research in London and Mannheim under the supervision of international professors. They encouraged me to look beyond the scope of my dissertation. I didn't get stuck in its topics.’

Salo keeps moving anyway. To balance his research, he does endurance running. In the marathon, he has clocked under 2h44min and won the Finnish championships for masters athletes.

The Frank P. Ramsey Medal is the Decision Analysis Society's highest award, given for outstanding contributions to the development of decision analysis theory, methods and applications, education and social impact. The medal has been awarded since 1985 and has been awarded to only four European researchers before Salo.

Public defence in Mathematics, M.Sc. Julian Weigt, 21 October 2022

10. October 2022

Opponent: DoctorEmanuel Carneiro, ICTP, Italy

Custos: ProfessorJuha Kinnunen, Aalto University School of Science, Department of Mathematics and Systems Analysis

The public defence will be organised on campus (Otakaari 1, lecture hall U147 U5).

Title of the doctoral thesis: Endpoint regularity of maximal functions in higher dimensions

The variation of a function is a quantity that measures how strongly that function oscillates. It is one of many ways to describe the regularity of a function, or how smooth and well-behaved the function looks. Studying the regularity of functions is one of the main goals in the area of partial differential equations. Almost all systems in physics can be modeled by differential equations, and also quantities in other fields such as chemistry, biology or economics satisfy differential equations. This is why we are interested in the regularity of functions, it can be important to know if the path of a particle, or certain substance concentrations or market indicators can behave erratically or always follow a smooth curve.

In this thesis we investigate one particular type of regularity of so-called maximal functions: We prove that some maximal functions have bounded variation. Maximal functions are classically used as tools in the study of partial differential equations and in related mathematical areas. A maximal function is defined using maxima over averages of a function. The average is of course a very important mathematical notion, and thus features crucially not only in partial differential equations and but also in many other fields of mathematics. This is why the maximal function is often used to estimate functions in mathematical analysis.

The regularity of maximal functions has now been studied for about twenty-five years. It has been shown that maximal functions in one dimensional space have bounded variation, and it has been shown that they satisfy several notions of regularity in all dimensions. This thesis proves the natural conjecture that maximal functions in higher dimensions also have bounded variation, at least for some maximal functions, and opens the field to investigate this conjecture for further classical maximal functions.

Contact information of doctoral candidate: julian.weigt@aalto.fi, +358504754400

New hourly paid teachers of mathematics and systems analysis for spring 2023

7. October 2022

The Department of Mathematics and Systems Analysis is seeking

New hourly-paid teachers in Mathematics and Systems Analysis for spring term 2023.

Your tasks include teaching in exercise groups and grading exercises and exams.

Regarding teaching in mathematics, we expect the applicants to have completed at least 20 credits of mathematical studies at university level with good grades. Regarding teaching in systems analysis (courses MS-C/E2xxx), we expect the applicants to have completed the course they would like to teach. If you have previous experience in teaching, it is considered as an advantage, but is not necessary. This is a part-time job (2-4 hours/week). The salary is 30-40 euros/teaching hour based on your education level.

Grading exercises and exams will be (typically) compensated separately (300-400 euros depending on your education and the course level).

Read carefully! If you are not working for Aalto at the moment you apply, fill in the application form here. If you are working for Aalto at the moment you apply, you have to apply as an internal candidate via Workday, see instructions Sisäisen työpaikan hakeminen | Aalto-yliopisto.

Attach an open motivation letter, a cv and a transcript of records as one PDF file.

Deadline for the applications is Monday 7 November 2022.

Based on the applications, we will invite some of the applicants for a web interview.

More information: johanna.glader@aalto.fi

Note: if you have previously worked as an hourly-based teacher at the MS Department, you have received a separate link from johanna.glader(at)aalto.fi.

Public defence in Mathematics, M.Sc. Cintia Pacchiano Camacho, 23.9.2022

15. September 2022

Opponent is Assistant Professor Cristiana De Filippis, University of Parma, Italy

Custos is Professor Juha Kinnunen, Aalto University School of Science, Department of Mathematics and Systems Analysis

Contact details of the doctoral student: cintia.pacchiano@aalto.fi, +358 (50) 4149900

The public defence will be organised on campus.

The doctoral thesis is publicly displayed 10 days before the defence in the publication archive Aaltodoc of Aalto University.

This dissertation studies existence and regularity properties of functions related to the calculus of variations on metric measure spaces that support a weak Poincaré inequality and doubling measure. We concentrate especially on variational solutions to the total variation flow, and quasiminimizers to the (p,q)-Dirichlet integral. The main interest in this work is to extend some classical results of the calculus of variations to metric measure spaces.

Variational methods appeared as an answer to the problem of finding minima of functionals. It is about giving a necessary and sufficient condition for the existence of the minimum, as well as conditions that allow its calculation and algorithms that let us compute it. Variational calculus is intimately linked with the theory of partial differential equations since the conditions for existence of a solution to the minimization problem normally depend on the fact that said solution satisfies a certain differential equation.

This dissertation focuses on various classes of functions related to a Dirichlet type integral. We first define variational solutions to the total variation flow (TVF) in metric measure spaces. We establish their existence and, using energy estimates and the properties of the underlying metric, we give necessary and sufficient conditions for a variational solution to be continuous at a given point. As far as we know, this is the first time that existence and regularity questions are discussed for parabolic problems with linear growth on metric measure spaces. We then take a purely variational approach to a (p,q)-Dirichlet integral. We define its quasiminimizers, and using the concept of upper gradients together with Newtonian spaces, we develop interior regularity and regularity up to the boundary. Lastly, we prove higher integrability together with stability results in the context of general metric measure spaces.

Analysis on metric spaces is nowadays an active and independent field, bringing together researchers from different parts of the mathematical spectrum. It has applications to disciplines as diverse as geometric group theory, nonlinear PDEs, and even theoretical computer science. This can offer us a better understanding of the phenomena and also lead to new results, even in the classical Euclidean case.

Public defence in Mathematics and Statistics, M.Sc. (Tech) Sami Helander, 25.8.2022

12. August 2022

With the recent developments in the precision of measurement technology and storage capacity, massively large and high dimensional data sets have become commonplace over nearly all fields of science. Functional data – data arising from measuring a generating process of continuous nature over its continuum – has emerged as a prominent type of such big data due to the richness of its structural features. One does not have to search far to find an abundance of great examples of functional data sets: the growth curves of children, measurements of meteorological events such as temperature or precipitation and hourly electricity consumption over a day, are all examples of processes within the realm of functional data.

Detailed analysis of the shape features of functional data is often the key to revealing important modes of variance in functional data. For instance, recognizing structural deviancies from the typical in the growth pattern of school aged children can be one of the earliest markers warning of potential underlying problems in health and well-being. Accurately predicting the hourly electricity consumption is crucial for an electricity company to be able to match the production to the demand. Distinguishing between the silhouettes of a child and a dog can be crucial in computer vision applications for self-driving cars. In short, sensitivity of the developed methodology to variations in shape has become an important topic in the literature. However, precisely defining typicality or atypicality in shape has proven to be a difficult problem. In how fine detail should the variations in local features be considered? What precisely makes a curve ‘too curvy’ in comparison to a set of other observations? Clearly, it is time to leave the classical, location-based considerations in the offside and shift our focus towards the intricacies in shape and structure.

In the dissertation, we develop methods for assessing the shape typicality and similarity of observations and study their properties in theory and in practice. Furthermore, we study the practical implementations of the methods in some prominent, common applications such as supervised learning and outlier detection, and evaluate their performance compared to some popular modern competitors. In particular, we demonstrate the excellent properties of the proposed methods and show that in many commonly encountered settings, they are able to match or even outperform many of the leading competitors.

Opponent is Professor Thomas Verdebout, Université Libre de Bruxelles, Belgium

Custos is Professor Pauliina Ilmonen, Aalto University School of Science, Department of Mathematics and Systems Analysis

Contact details of the doctoral student: sami.helander@aalto.fi, +358 50 5186136

The public defence will be organised on campus (Otakaari 1, lecture hall H304).

The doctoral thesis is publicly displayed 10 days before the defence in the publication archive Aaltodoc of Aalto University.