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NANO2/CH4/BIO1: Mechanically Interlocked Molecules: Properties and Applications

Theme: TBH
Time: 4. - 8.8.2025
Study mode: In person 
Max. number of participants:  30 
Lecturers: Prof. Stephen Goldup (University of Birmingham, the United Kingdom) and Dr. Fredrik Schaufelberger (University of Warwick, the United Kingdom)
Coordinator: Fabien Cougnon
Course code: 
Modes of study: Lectures and teamwork
Credits: 2 ECTS
Evaluation: Pass/Fail
Contents: Molecular machines are widespread in biology and involved in many of the processes essential to life. Inspired by nature, chemists have devised sophisticated artificial molecular devices that mimic the machinery of living systems. Mechanically interlocked molecules represent a large proportion of these synthetic machines. This course explains the design, synthesis and properties of mechanically interlocked molecules, notably in the context of molecular machines. 
Learning outcomes:    
After passing the course students should

• Recognize different types of mechanically interlocked molecules
• Understand the relationship between the structural features of mechanically interlocked molecules and their function
• Design mechanically interlocked molecules with specific properties

Prerequisites: Bachelor of science in Chemistry
- Basic knowledge of organic and supramolecular chemistry

CH1: Sus-Waste: Implementing the Sustainability in Waste Management 

Theme: TBD
Time:  4. - 8.8.2025 
Study mode:  In person
Max. number of participants: 30
Lecturers: Prof. Dr. Virginia Toy (Johannes Gutenberg-Universität Mainz, Germany), Adj. Prof. Dr. Ajay B. Patil (JYU), Professor Eugenia Ruiz (University of Valencia, Spain), Researcher Linards Klavins (University of Latvia, Latvia), Professor Dr. Ari Väisänen (JYU), Senior Lecturer Dr. Siiri Peramaki (JYU), Researcher Arttu Lehikoinen (JYU).
Coordinators: Siiri Perämäki and Jimi Siljanto
Course code: 
Modes of study: Lectures, exercises
Credits: 3 ECTS
Evaluation: Pass/fail
Contents: The Forthem course “Sus-Waste: Implementing the sustainability in waste management” aims to provide integrated data and examples-supported sustainable waste management issues critical in a transitional economy based on environmental and social governance pillars. Several challenges should be addressed in the waste management direction to sustainability: new technologies elaborated and implemented, issues of waste management logistics, and most importantly, attitudes regarding waste generation changes. Still, implementing sustainability in waste management is slow and fragmented, and the novelty of the course is to provide a holistic view on the problems related to mining, construction waste, biomass, electronics, sanitary and hazardous waste from environmental and social governance aspects simultaneously with strengthening of raw materials recovery and circular economy thinking.
Learning outcomes: After taking the course, students will be able to discuss integrated resource and waste management, and environmental legislation problems based on multinational experience based on tangible and intangible benefit valuation principles. The knowledge provided by the course is crucially essential for overall sustainability targets having an influential contribution to the primary study field of the students and further career opportunities. Within short span, the students can get the interdisciplinary overview of waste, resources and circular economy related concepts.
Prerequisites:  B.Sc in Chemistry, Biology, Environmental Science, Circular Economy, Management of Climate and Resources, or similar.

CH2: Chemistry/Circular Materials Chemistry (CIMACHEM)

Theme: TBD
Time: 11. - 15.8.2025
Study mode: In person
Max. number of participants: 70
Lecturers: Prof. Petri Pihko (JYU), Eero Kontturi (Aalto University)
Coordinators: Prof. Petri Pihko and Prof. Kaisa Helttunen
Course code: 
Modes of study: Pre-assignment, lectures, exercises
Credits:  3 ECTS + 2 ECTS for extra assignment
Evaluation: Pass/fail
Contents:  The course aims to give the participants an overview on circular materials chemistry, focusing on chemical and material products from plant resources. The course includes an overview on chemical composition and structure of plant biomass. The course will develop understanding in organic chemistry reaction mechanism concepts, lignocellulose chemistry in fractionation and lignocellulose chemistry in derivatization. Furthermore, the course aims for the students to be able to apply learned tools and skills in own research topic.
Learning outcomes: Have an overview on chemical composition and structure of plant biomass; Understand organic chemistry reaction mechanism concepts; Understand lignocellulose chemistry in fractionation; Understand lignocellulose chemistry in derivatization; Apply learned tools and skills in own research topic.
Prerequisites: MSc level studies in chemistry, passing pre-assignment 

CH3/NANO3: Magneto-Optical Properties of Lanthanide(III) Complexes 

Theme: TBD
Time: 4. - 8.8.2025
Study mode: In person
Max. number of participants: 30
Lecturer:  Dr. Diogo Alves Galico (University of Ottawa, Canada)
Coordinators: Essi Barkas and Jani Moilanen
Course code:
Modes of study: Lectures, case studies and problem-solving sessions
Credits: 2 ECTS
Evaluation: Pass/fail
Contents: This course will introduce basic concepts of magneto-optical properties of lanthanide(III) complexes. The course structure will address the following topics:

1. Basics of lanthanide(III) photophysics.
2. Lanthanides(III) ions in the presence of magnetic fields.
3. Polarization of light.
4. Magnetic circular dichroism (MCD).
5. Magnetic circularly polarized luminescence (MCPL).

Learning outcomes: After the course, the students will be able to understand the effects of an external magnetic field on the optical properties (absorption and luminescence) of lanthanide(III) complexes. The students will be introduced to the concept of magnetic induced circular polarization of light and two important techniques in which this effect is observed, MCD and MCPL.
Prerequisites: The course is mainly aimed for master and PhD students, but it is suitable also for bachelor students with some knowledge in the field.

NANO2/CH4/BIO1: Mechanically Interlocked Molecules: Properties and Applications

Theme: TBH
Time: 4. - 8.8.2025
Study mode: In person 
Max. number of participants:  30 
Lecturers: Prof. Stephen Goldup (University of Birmingham, the United Kingdom) and Dr. Fredrik Schaufelberger (University of Warwick, the United Kingdom)
Coordinator: Fabien Cougnon
Course code: 
Modes of study: Lectures and teamwork
Credits: 2 ECTS
Evaluation: Pass/Fail
Contents: Molecular machines are widespread in biology and involved in many of the processes essential to life. Inspired by nature, chemists have devised sophisticated artificial molecular devices that mimic the machinery of living systems. Mechanically interlocked molecules represent a large proportion of these synthetic machines. This course explains the design, synthesis and properties of mechanically interlocked molecules, notably in the context of molecular machines. 
Learning outcomes:    
After passing the course students should

• Recognize different types of mechanically interlocked molecules
• Understand the relationship between the structural features of mechanically interlocked molecules and their function
• Design mechanically interlocked molecules with specific properties

Prerequisites: Bachelor of science in Chemistry
- Basic knowledge of organic and supramolecular chemistry

PH1: Impossible Quantum Machines and their Optimal Approximations

Theme: TBD
Time: 4. - 8.8.2025
Study mode: In person
Max. number of participants: 20
Lecturer: Claudio Carmeli (Universita di Genova, Italy)
Coordinator: Teiko Heinosaari 
Course code: 
Modes of study: Lectures + reading assignments + exercises
Credits: 2 ECTS
Evaluation : Pass/fail
Contents: It is well known that it is possible to clarify the conceptual basis of quantum mechanics and the well-known limitations it poses through a hierarchy of impossible machines. The aim of this mini-course is to present this approach and illustrate how it is possible to apply the theory of finite group representations to realise an approximate version of such impossible devices.
The lectures include:
- elements of group representation theory
- description of impossible machines (universal cloner, universal joint measurement, universal sequential measurement)
- quantum information theoretic description of possible machines
- impossibility proofs
- explanation of approximative scenarios
- group theoretic method to construct optimal devices
Learning outcomes: 
1) basic understanding of conceptual limitations imposed by quantum theory 
2) basic understanding of group theoretic strategies in the realization of approximate devices
Prerequisites: Analysis, linear algebra, basics of quantum theory

PH2: Nuclear Waste Management

Theme: TBD
Time: 4. - 8.8.2025
Study mode: In person
Max. number of participants: 20
Lecturer: Xavier Pintado (Mitta Engineering Oy, Finland)
Coordinator: Arttu Miettinen
Course code: 
Modes of study: Lectures + workshop
Credits: 2 ECTS
Evaluation: Pass/fail
Contents: The objective of the course is to present nuclear waste management (mainly the spent nuclear fuel produced in nuclear power plants) from an overall point of view. The course covers all nuclear waste issues in nuclear energy: the production of spent nuclear fuel, deep geological disposal, multi-barrier system concept, host rock, constitutive models for unsaturated soils to be able to carry out the performance assessment of the barriers from the emplacement, laboratory testing methods for materials characterization, in-situ tests in underground laboratories, and licensing processes.
Learning outcomes: After the course, the student is able to

• describe the prevalent issues in nuclear waste management,
• identify the key components of nuclear waste repositories and indicate their functions,
• discuss critically different constitutive models for the materials in the repository,
• suggest laboratory tests relevant for measuring the properties of the barrier components, and
• suggest the instrumentation in “in situ” tests performed in underground rock laboratories,
• recite the typical licensing process for a nuclear waste repository.

Prerequisites: Basic understanding of radiation physics. Experience in Continuum mechanics and Measurement techniques is considered an asset.

PH3: Numerical Methods in Geotechnical Engineering with CODE_BRIGHT Computer Code

Theme: TBH
Time:  11. - 14.8.2025 NB! The course lasts Mon-Thu
Study mode: In person
Max. number of participants: 15
Lecturer: Xavier Pintado (Mitta Engineering Oy, Finland)
Coordinator: Arttu Miettinen
Course code: 
Modes of study: Lectures + exercises
Credits: 2 ECTS
Evaluation: Pass/fail
Contents: The course involves the use of CODE_BRIGHT (COupled DEformation BRIne Gas Heat Transport) computer code to solve a wide range of geotechnical issues related to soil systems. The focus of the course is on cases related to the final deposition of spent nuclear fuel in deep geological repositories but classical examples in geotechnical engineering like excavations, foundations and ground water flow will also be presented.
Learning outcomes: After the course, the student is able to
-describe the basic principles of the CODE_BRIGHT simulation tool
-evaluate the suitability of some basic constitutive models for the simulation of selected soil systems
-apply CODE_BRIGHT in geometries related to civil engineering, and
-evaluate if simulation results are realistic.
Prerequisites: Basic conceptual understanding of material modeling in continuum mechanics or civil engineering.

NANO1/PH4: Quantum Inspired Algorithms Versus Quantum Computers: New Computational Routes for Solving Chemistry, Atomic Physics and Correlated Matter Problems

Time: 4. - 8.8.2025
Study mode: In person 
Max. number of participants: 30 
Lecturer: Prof. Xavier Waintal (CEA Grenoble, France), Dr. Christoph Groth (CEA Grenoble, France)
Coordinators: Tero Heikkilä and Stefan Ilic
Course code: 
Modes of study: Lectures and exercises. Exercises will require a laptop for each student as they will handle example codes written by the students.
Credits:  2 ECTS
Evaluation: Pass/Fail
Contents: Quantum computers have been envisioned as transformative tools that could help us solve exponentially difficult problems with early applications in chemistry (catalysis, drug discoveries…) and material science. In these lectures, we will go through some of the main algorithms that have been proposed for quantum computing, critically analyze them (the lecturer is a quantum skeptic) and propose alternative classical algorithms that run on classical computers. Building on the modern computational toolbox that involve tensor networks and neural networks we will build algorithms that can be exponentially efficient, depending on the situation, and beat the “curse of dimensionality”. We will go through classic material (such as the celebrated DMRG algorithm and some quantum Monte-Carlo approaches) as well as more modern algorithms that are reshaping the field (such as the Tensor Cross Interpolation). In the practical session you will build your own code for solving a mildly difficult many-body problem: simulated quantum annealing using Rydberg atoms. 
Learning outcomes:    
After passing the course students should

• Know some basic quantum computing algorithms for studying correlated electron systems
• Identify efficient classical simulation techniques of quantum algorithms
• Know tensor network and neural network approaches for simulation
• Be able to solve the ground state of Rydberg atoms using a Density Matrix Renormalization Group (DMRG) algorithm), a Variational Monte Carlo (VMC) and a Green’s Function Monte Carlo (GFMC) method

Prerequisites: Bachelor of science in Physics
- Basic knowledge of quantum mechanics.
- Some limited experience with one programming language for computation (any language will do, we recommend Python or Julia for beginners, Rust or C++ for more advanced programmers).
 

NANO1/PH4: Quantum Inspired Algorithms Versus Quantum Computers: New Computational Routes for Solving Chemistry, Atomic Physics and Correlated Matter Problems

Time: 4. - 8.8.2025
Study mode: In person 
Max. number of participants: 30 
Lecturer: Prof. Xavier Waintal (CEA Grenoble, France), Dr. Christoph Groth (CEA Grenoble, France)
Coordinators: Tero Heikkilä and Stefan Ilic
Course code: 
Modes of study: Lectures and exercises. Exercises will require a laptop for each student as they will handle example codes written by the students.
Credits:  2 ECTS
Evaluation: Pass/Fail
Contents: Quantum computers have been envisioned as transformative tools that could help us solve exponentially difficult problems with early applications in chemistry (catalysis, drug discoveries…) and material science. In these lectures, we will go through some of the main algorithms that have been proposed for quantum computing, critically analyze them (the lecturer is a quantum skeptic) and propose alternative classical algorithms that run on classical computers. Building on the modern computational toolbox that involve tensor networks and neural networks we will build algorithms that can be exponentially efficient, depending on the situation, and beat the “curse of dimensionality”. We will go through classic material (such as the celebrated DMRG algorithm and some quantum Monte-Carlo approaches) as well as more modern algorithms that are reshaping the field (such as the Tensor Cross Interpolation). In the practical session you will build your own code for solving a mildly difficult many-body problem: simulated quantum annealing using Rydberg atoms. 
Learning outcomes:    
After passing the course students should

• Know some basic quantum computing algorithms for studying correlated electron systems
• Identify efficient classical simulation techniques of quantum algorithms
• Know tensor network and neural network approaches for simulation
• Be able to solve the ground state of Rydberg atoms using a Density Matrix Renormalization Group (DMRG) algorithm), a Variational Monte Carlo (VMC) and a Green’s Function Monte Carlo (GFMC) method.

Prerequisites: Bachelor of science in Physics
- Basic knowledge of quantum mechanics.
- Some limited experience with one programming language for computation (any language will do, we recommend Python or Julia for beginners, Rust or C++ for more advanced programmers).

NANO2/CH4/BIO1: Mechanically Interlocked Molecules: Properties and Applications

Theme: TBH
Time: 4. - 8.8.2025
Study mode: In person 
Max. number of participants:  30 
Lecturers: Prof. Stephen Goldup (University of Birmingham, the United Kingdom) and Dr. Fredrik Schaufelberger (University of Warwick, the United Kingdom)
Coordinator: Fabien Cougnon
Course code: 
Modes of study: Lectures and teamwork
Credits: 2 ECTS
Evaluation: Pass/Fail
Contents: Molecular machines are widespread in biology and involved in many of the processes essential to life. Inspired by nature, chemists have devised sophisticated artificial molecular devices that mimic the machinery of living systems. Mechanically interlocked molecules represent a large proportion of these synthetic machines. This course explains the design, synthesis and properties of mechanically interlocked molecules, notably in the context of molecular machines. 
Learning outcomes:    
After passing the course students should

• Recognize different types of mechanically interlocked molecules
• Understand the relationship between the structural features of mechanically interlocked molecules and their function
• Design mechanically interlocked molecules with specific properties

Prerequisites: Bachelor of science in Chemistry
- Basic knowledge of organic and supramolecular chemistry.

CH3/NANO3: Magneto-Optical Properties of Lanthanide(III) Complexes

Theme: TBD
Time: 4. - 8.8.2025
Study mode: In person
Max. number of participants: 30
Lecturer:  Dr. Diogo Alves Galico (University of Ottawa, Canada)
Coordinators: Essi Barkas and Jani Moilanen
Course code:
Modes of study: Lectures, case studies and problem-solving sessions
Credits: 2 ECTS
Evaluation: Pass/fail
Contents: This course will introduce basic concepts of magneto-optical properties of lanthanide(III) complexes. The course structure will address the following topics:

1. Basics of lanthanide(III) photophysics.
2. Lanthanides(III) ions in the presence of magnetic fields.
3. Polarization of light.
4. Magnetic circular dichroism (MCD).
5. Magnetic circularly polarized luminescence (MCPL).

Learning outcomes: After the course, the students will be able to understand the effects of an external magnetic field on the optical properties (absorption and luminescence) of lanthanide(III) complexes. The students will be introduced to the concept of magnetic induced circular polarization of light and two important techniques in which this effect is observed, MCD and MCPL.
Prerequisites: The course is mainly aimed for master and PhD students, but it is suitable also for bachelor students with some knowledge in the field.

MA1: Machine Learning and Stochastic Control

Theme: TBH
Time: 4. - 8.8.2025
Study mode: In person. If there is some demand on a hybrid form, we could reconsider the hybrid mode.  
Max. number of participants: Not applicable
Lecturer: Prof. Huyên Pham (Ecole Polytechnique, Centre de Mathématiques Appliquées, France)
Coordinator: Stefan Geiss 
Course Code:
Modes of study: Participation in lectures required. Solving of problems after the course and sending them to the lecturer.
Credits: 2 ECTS   
Evaluation:  Pass/fail
Contents: This course explores the interplay between machine learning and stochastic control, addressing some challenges in decision-making under uncertainty.
Plan of the course:
I.      Foundations of Machine learning and stochastic control
                             1.     Introduction/motivation
                             2.      Basics of Markov decision process (MDP) and Reinforcement learning (RL)
 
II.    Deep learning for stochastic control and PDEs
                             1.      Neural networks algorithms for MDP
                             2.      Deep Galerkin, Physics-Informed neural networks
                             3.      Deep backward SDE
                             4.      Deep backward dynamic programming

III.   Reinforcement learning methods in continuous time
                              1. Exploratory formulation of RL
                             2. Policy gradient methods and actor/critic algorithms
                             3. q-learning and approximation in continuous time   

Learning outcomes: By the end of the course, participants will be able to:

• Understand Key concepts of stochastic control, deep learning and reinforcement learning, including Bellman equations, value functions, and policy optimization.
• Describe the mathematical and computational connections between ML and stochastic control.
• Apply Machine Learning to Stochastic Control by
  • Formulating real-world problems (e.g., in finance or robotics) as stochastic control or RL tasks.
  • Using machine learning techniques to solve stochastic control problems, including: the resolution of PDEs and BSDEs with neural networks, and the implementation of RL algorithms in continuous and discrete time.  

Prerequisites: Usual notions on measures, integration and probability theory, stochastic calculus:  Brownian motion, Itô’s formula, Feynman-Kac formula.   
 

MA2: Introduction to Logarithmically Correlated Fields and Multiplicative Chaos Measures

Theme: TBD
Time: 4. - 8.8.2025
Study mode: In person
Participants: 
Lecturer: Christian Webb (University of Helsinki, Finland)
Coordinator: Elefterios Soultanis
Course code: 
Modes of study: The course consists of 10 hours of lectures and it can be passed by solving exercise problems, writing an essay, or through a simulation project.
Credits: 2 ECTS
Evaluation: Pass/fail
Contents: Logarithmically correlated random fields are stochastic processes that play an important role in various branches of modern probability theory such as probabilistic number theory, random matrix theory and euclidean quantum field theory. Many of their properties are best studied through associated random multi fractal measures known as multiplicative chaos measures. In this course, we will study the definitions and basic properties of logarithmically correlated random fields and multiplicative chaos measures as well as briefly discuss applications of the theory.
Learning outcomes: After the course, the student will
- Understand what kind of phenomena logarithmically correlated random fields are typically associated with.
- Be able to apply the theory of Gaussian multiplicative chaos to study fractal properties of logarithmically correlated Gaussian fields.
Prerequisites: Prerequisites for the course are a course on measure-theoretic probability and a basic course on functional analysis.

MA3: Random Geometry and Embeddability

Theme: TBD
Time: 4. - 8.8.2025
Study mode: In person
Participants:
Lecturer: Sasha Troscheit (Uppsala University, Sweden)
Coordinator: Elefterios Soultanis
Course code: 
Modes of study: Lectures 10 hours + exercises
Credits: 2 ECTS
Evaluation: Pass/fail
Contents: This course aims to convey the basic structure of two important objects in random geometry: the Brownian continuum random tree and the Brownian map. We will take several approaches to defining the objects and study their local (metric) properties. To do this we will also delve into dimension theory and consider embeddability and their repercussions.

The lecture course will be (mostly) self-contained and no specific background in random geometry or dimension theory is assumed. There will be several exercises throughout the week and pass/fail will be based on successful completion of the exercises.
Learning outcomes: After this mini-course, the participants are expected to be familiar with the definitions and basic properties of the Brownian continuum random tree, the Brownian map, as well as their relations to other objects in random geometry. Participants will be able to use methodology from probability, dynamical systems, and geometric analysis to analyse local and global scaling behaviour of these objects.
Prerequisites: Undergraduate level probability, real analysis and properties of metric spaces, basic point set topology will also be helpful.

MA4: Gaussian Multiplicative Chaos, Quasiconformal Techniques and Discrete Models

Theme: TBD
Time: 11. - 15.8.2025
Study mode: In person
Max. number of participants: Max. 16 participants
Lecturer: Janne Junnila, Kalle Kytölä (Aalto University, Finland), Sylvester Eriksson-Bique (JYU) and Elefterios Soultanis (JYU)
Coordinator:  Sylvester Eriksson-Bique
Course code: 
Modes of study: Oral presentation and written summary of read literature. Before the in person course, students can meet remotely with their mentor to discuss their assigned paper.
Credits: 2 ECTS
Evaluation:  Pass/Fail
Contents: This course will investigate recent developments in three interlinked fields: Gaussian multiplicative chaos, conformal weldings and discrete models including discrete complex analysis. Their study brings together a rich set of techniques from probability, harmonic analysis, quasiconformal analysis and fractal geometry. The goal of this course is to showcase some of the most exciting recent developments, focusing on the interplay of different techniques and allow the participants a chance to learn a new topic at the forefront of research. The participants will be assigned a paper to read before the course, will write a short summary and then give a 2x45 minutes lectures on the topic. There will be opportunities to receive remote individual guidance before the in person course. The course participants present 15 papers on gaussian multiplicative chaos, quasiconformal techniques and discrete models.
Learning outcomes: Students learn a new topic from reading and discussing contemporary research papers, and an opportunity to network with early career researchers.
Prerequisites:  Students from a variety of backgrounds are invited, but the following are helpful: Probability theory, Complex Analysis.

IP1: Mathematics of X-ray Computed Tomography

Theme: TBD
Time: 11. - 15.8.2025
Study mode: In person
Max. number of participants: Not restricted
Lecturer: Tatiana Bubba (University of Ferrara, Italy)
Coordinator: Janne Nurminen
Course code: 
Modes of study: Lectures and exercises
Credits: 2 ECTS
Evaluation: Exercises pass/fail
Contents: In this course we learn about the mathematics of X-ray computed tomography, what is the regularization theory of inverse problems, and how some signal processing ideas are relevant in the context of inverse problems.
Learning outcomes: Basics of X-ray tomography, regularization theory and signal processing.
Prerequisites: Basics of linear algebra, numerical and functional analysis.
 

IP2: Introduction to Uncertainty Quantification for Inverse Problems

Theme: TBD
Time: 11.-15.8.2025
Study mode: In person
Max. number of participants: Not restricted 
Lecturer: Babak Maboudi Afkham (University of Oulu)
Coordinator: Janne Nurminen
Code:
Modes of study: Lectures and exercises
Credits: 2 ECTS
Evaluation: Exercises pass/fail
Contents: In this course, we will explore how to formulate inverse problems within a Bayesian framework. This involves representing both noise and unknowns using probability distributions. We will then define the solution to the inverse problem as the conditional probability distribution of the unknown given the measurements, commonly known as the posterior distribution. Finally, we will examine how to interpret the posterior to quantify the uncertainty in our predictions and reconstructions.
Learning outcomes:
•    Formulate an inverse problem with additive noise using a Bayesian framework.
•    Identify appropriate prior distributions based on the problem context.
•    Perform point estimation using maximum a posteriori (MAP) and conditional mean estimates.
•    Implement the Metropolis-Hastings algorithm to explore the posterior distribution.
•    Conduct uncertainty quantification to assess prediction reliability.
Prerequisites: Basics of numerical and computational skills, coding in Python is mandatory; Basic knowledge of probability theory and statistics.
 

COG1: Fundamentals of Inclusive and Accessible Design of Technology  

Theme: TBD
Time: 4. - 8.8.2025 (5 days, 4h per day) 
Study mode: Hybrid  
Max. number of participants: 30
Lecturers: Markku T. Häkkinen, PhD (Educational Testing Service ETS, USA) and Helen T. Sullivan, PhD (Rider University, USA)  
Coordinator(s): Laura Mononen
Code:  
Modes of study: Lectures, demonstrations, readings.  
Credits: 3 ECTS  
Evaluation: Pass/Fail. Obligatory attendance at all lectures and lab sessions. Active participation is required. In addition, participants will present a problem in Inclusive Design and a proposed, evidence-based solution in a 7 - 10 minute oral presentation prepared beforehand. Each participant filling the above-stated requirements will receive a diploma of participation to the workshop, but to receive a course diploma with credit statement (3 ECTS) the student must also return a written project report.  
Contents: This course bridges the fundamentals of sensory, perceptual, cognitive and physical capabilities with a growing technological toolbox to create devices and services that work for individuals with and without disabilities. This topic becomes even more important with the implementation of the EU Web Accessibility Directive and the European Accessibility Act coming into effect in 2025, and similar requirements in a growing number of countries. To build inclusive and accessible technologies that work for a broad range of human abilities and disabilities requires understanding of how people sense and perceive information, how information design (and complexity) impacts the ability to understand information, and how physical (or virtual) interface design impacts a user’s ability to operate it. Emerging technologies, such as multi-modal generative AI, multi-modal interfaces, sensors, and IoT provide a rich set of tools that can augment, or offer new modes of, interaction with our environment, devices, systems, and services. This course will include lecture, hands on demonstrations, and exercises to understand the challenges and new opportunities for inclusive and accessible design.  
Learning outcomes: Students who successfully complete the course will be able to understand how to apply fundamental principles in inclusive and accessible design to guide creation of new applications or systems, and how they can begin to apply this knowledge in their work and research. Crucial to this is understanding the role guidelines and technical standards play in defining legal requirements for accessibility. Knowledge of these fundamentals will increase the probability of creating highly usable and accessible products for a broad audience, including those with disabilities. Motivated students can use successful completion as a basis for further study or research in the field of inclusive design and accessibility.  
Prerequisites: Students should have a background in cognitive science, information systems, computer science or related discipline; or approval of instructors. 

COG2: Accessible Visualizations: Conveying Information Across Sensory Modalities Hands-on Lab 

Theme: TBD
Time: 11.  - 15.8.2025
Study mode: In person  
Max. number of participants: 15  
Lecturers: Markku T. Häkkinen, PhD (Educational Testing Service ETS, USA) and Helen T. Sullivan, PhD (Rider University, USA)  
Coordinator: Laura Mononen  
Code:
Modes of study: Lecture/Lab  
Credits: 3 ECTS  
Evaluation: Pass/Fail. Obligatory attendance at all lab sessions. Active participation is required. In addition, participants may work individually or in small groups. The class will examine the topic of data visualization from the context of accessibility and explore how sensory transformation and adaptation can be used to address specific sensory or cognitive needs. Ideally, the students are expected to bring their own data to be used in this course, whether it is research data to be presented in a poster or presentation, or data representative of their field of work or research. Students will develop an empirically based approach to supporting one or more accessibility solutions, and implement a demonstration using a toolbox of technologies made available in the lab. The resulting solution will be presented in the final day by an oral presentation and demonstration. Each participant filling the above-stated requirements will receive a certificate of participation in the course, but to receive a course diploma with credit statement (3 ECTS) the student must also return a project report describing the basis of their approach.  
Contents: Data visualization is a key component of how we communicate research findings, real time economic data, or climate measurements, to name a few. In the context of the EU Accessibility Act, and similar legislation in other countries, ensuring the accessibility of data visualization is becoming a requirement.  A key aspect of digital accessibility is ensuring that information can be perceived and understood irrespective of the cognitive and sensory capabilities of the individual, for example, persons with visual, auditory or cognitive disabilities. The principles of accessible design also have relevance for data presentation where environmental or situational factors limit usefulness of what might seem preferred modalities, for example, industrial workers engaged in high workload tasks receiving critical life safety data. By understanding the foundational concepts of our sensory/perceptual systems, and the requirements of accessible design, we will explore how data can be adapted and transformed to suit a variety of individual needs. Modalities examined will include speech and non-speech audio, tactile displays, haptics, and visual adaptation 
Learning outcomes: Through a combination of lecture and laboratory projects, students will uncover the foundational principles of designing alternative representations for traditionally visual or auditory information. These principles will be aligned with requirements defined by EU and international Accessibility Standards. Students will also understand how attention to accessible design of information can be of broader benefit to users in a variety of contexts.  
Prerequisites: This course is open to graduate students interested in learning how to make their data to accessible to the widest possible audience, including for those with disabilities.  Prior coursework in accessibility or cognitive science is welcome, but not required. 

COG3: Tools for Interaction Design 

Theme: TBD
�վ�����: 13. - 15.8.2025 NB! The course lasts Wed-Fri
Study mode: In person 
Max. number of participants: 20
���𳦳ٳܰ����(��): Antti Salovaara (Aalto University, Finland)
Coordinator: Laura Mononen
Course code: 
Modes of study: In-class exercises, Readings, Individual design exercise
Credits: 2����ճ� &�Բ�����;
���������ܲ��پ��Dz�: Pass/fail
��DzԳٱ�Գٲ�: This course introduces participants to the basics of user interface design, mostly for screen-based systems such as desktop and mobile applications. The focus is on methods, theories and concepts that are commonly used in UX/UI profession. After the course, the student has readiness to critically evaluate and discuss the benefits and drawbacks of different design choices on interaction level when they participate in digital product design projects. 
Learning outcomes: After successful completion of this course, students will:

• Be able to sketch and design interaction sequences and user flows, considering UI design beyond individual screen layouts only;
• Apply design patterns and other interface concepts in interaction design, informed by theories from psychology, human factors;
• Be aware of digital tools in interaction design;
• Carry out a heuristic expert-based usability evaluation on a UI;
• Has an initial understanding on how graphical design can be integrated to UI design via design systems.

�ʰ�������ܾ������ٱ��: Students should have a background in cognitive science, information systems, computer science or related discipline.

COM1: Graph Neural Networks 

Time: 4. - 8.8.2025
Study mode:  In person
Max. number of participants: 20
���𳦳ٳܰ����: �įAmauri Souza (Federal Institute of Ceara (IFCE), Brazil)
Coordinator: Joonas Hämäläinen
Course code: 
Modes of study: Lectures and demonstrations. Each student is required to give a presentation on the final day.
Credits: 1 ECTS
Evaluation: Pass/fail
��DzԳٱ�Գٲ�:  
Relational learning

• Overview
• Graph neural networks (GNNs)
• Spectral perspective
• Spatial perspective
• Main architectures
• Dealing with oversmoothing and oversquashing
• Theory of GNNs
• Expressivity
• Generalization
• Extensions
• High-order GNNs
• GNNs for knowledge graphs
• Graph Transformers
• Hypergraph neural networks
• Topological neural networks
• Generative models for graphs

Materials:

• William L. Hamilton, Graph Representation Learning. Synthesis Lectures on AI and ML, Vol. 14, No. 3. 2020.
• Michael M. Bronstein and Joan Bruna and Taco Cohen and Petar Veličković, Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. ArXiv, 2021.
• L. Wu, P. Cui, J. Pei, and L. Zhao. Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore, 2022
• M. Hajij et. al., Topological Deep Learning: Going Beyond Graph Data. ArXiv, 2023.
• Stefanie Jegelka. Theory of Graph Neural Networks: Representation and Learning. ArXiv, 2022.

Learning outcomes: Understanding main concepts and methods in relational learning.
Prerequisites: Minimum: basic level machine learning courses. Recommended: advanced courses on machine learning and deep learning.

COM2: Julia for Scientific Computing  

Theme: TBD
Time: 4. - 8.8.2025 
Study mode: In person 
Max. number of participants: 25 
Lecturer(s): Dr. Luca Ferranti (Aalto University, Finland) 
Coordinator(s): Frankie Robertson
Course code:
Modes of study: Lectures and exercises 
�����徱�ٲ�: �į2 ECTS 
���������ܲ��پ��Dz�: Pass/fail
��DzԳٱ�Գٲ�: Julia programming language has recently gained great popularity among the open-source scientific community, having been increasingly adopted both in industry and academia. The Julia programming language aims to solve the so-called two-language problem, where you prototype in a language with easy syntax and, after validation, reimplement your code into a performance-focused language. In particular, the Julia programming language has a pleasant and expressive syntax, while still achieving C-like performance.

The Julia ecosystem has also rapidly developing, having now state-of-the-art libraries for differential equations, machine learning, mathematical optimization, data analysis and so on. These features make the Julia programming language an appealing alternative to MatLab or Python for research and development. During this course, Julia features and workflow will be given, focusing on machine learning and scientific computing for with examples from the natural sciences including energy applications.

The course is taught with sessions that blend lectures and practical sessions. Each session has a lecture in the first half and a practical component in the second designed to be completed in the session. Please bring a laptop on which you can install Julia.

Learning outcomes: By the end of the course participants will have gained familiarity with Julia and a variety of packages:
* Able to efficiently author and run Julia scripts and packages 
* Able to make use of high level numeric and scientific libraries 
* Able to train and use machine learning models using the Flux package 
* Able to solve systems of differential equations using the SciML ecosystem 
* Able to perform model-based optimization with JuMP 
�ʰ�������ܾ������ٱ��: Basic programming skills; Prior exposure to scientific computing concepts would be helpful.

COM3: Interactive Multiobjective Optimization: Applications and Tools to Support Decision Making  

Theme: TBD
Time: 11. - 15.8.2025
Study mode: In person 
Max. number of participants: 30   
Lecturers: Dr.Giovanni Misitano, Dr. Bhupinder Saini, Dr. Giomara Lárraga,  
Dr. Juho Roponen, and Juuso Pajasma (all JYU)  
Coordinator: Dr.Giovanni Misitano 
Course code:
Modes of study: Attendance and exercises, optional final project 
Credits: 2/4 ECTS  
The course will award 2-4 credits depending which option participants choose:
- Option 1: 2 credits for attending lectures and exercise sessions;
- Option 2: 2 additional credits will be awarded to participants who, on top of attending lectures and practical sessions, also complete a final project.
Evaluation: Pass/Fail. The minimum requirement for passing the course is to take part in the daily lectures and exercise sessions.
Contents: Real-life optimization problems rarely involve only a single objective. Instead, meaningful decision-making requires optimizing multiple conflicting objectives simultaneously. To navigate these conflicts and find a satisfactory solution, preference information from a domain expert, the decision maker, is essential. Based on these preferences, we can search for solutions that best align with their goals. However, exploring such problems is often computationally and cognitively demanding, making decision support crucial.

Understanding how a decision maker expresses their preferences and how these preferences are utilized is crucial. This course introduces interactive multiobjective optimization methods, which address these key questions. Given the increasing role of data in real-world decision-making, we will also explore techniques for modeling data-driven multiobjective optimization problems. Through practical examples, we will examine and solve various real-world multiobjective problems, including both data-driven and simulator-based cases.

We will explore various interactive multiobjective optimization methods, including scalarization-based approaches and evolutionary algorithms. Additionally, we will examine how different methods can be combined and guide participants in developing their own approaches. The course will also cover graphical interfaces for interactive methods.. Hands-on experience with these concepts will be facilitated through the 2.0 version of the DESDEO framework [1], which provides the necessary tools for practical application.

Each day will focus on a central theme in interactive multiobjective optimization. The morning sessions will introduce key concepts related to the day's theme, while the afternoon sessions will provide hands-on experience applying these ideas using the DESDEO framework. To reinforce learning, an optional final project will be available, allowing participants to deepen their understanding and even contribute to the open-source DESDEO framework.

The final project is required for those seeking 4 ECTS credits. Participants who attend daily lectures and exercise sessions without the final project will receive 2 ECTS credits.

[1] G. Misitano, B. S. Saini, B. Afsar, B. Shavazipour and K. Miettinen, 'DESDEO: The Modular and Open Source Framework for Interactive Multiobjective Optimization,' IEEE Access, vol. 9, pp. 148277-148295, 2021, doi: 10.1109/ACCESS.2021.3123825.

Learning outcomes: After completing the course, participants will have a solid understanding of multiobjective optimization and the interactive methods that support decision-making. They will be familiar with both scalarization-based and evolutionary interactive methods, as well as various visualization and GUI techniques for assisting decision makers. Additionally, students will gain the necessary prerequisites to apply the DESDEO framework in modeling and solving their own data-driven optimization problems.

Participants are encouraged to bring their own laptops and optimization problems (if any) to the course!
Prerequisites: Participants are expected to have prior knowledge of the following concepts:
- Python (basic programming skills) 
- Fundamentals of calculus 
- Optimization and mathematical programming 
- Basics of single-objective optimization

CYB1: Understanding and Mitigating Malware Campaigns and their Underlying Cybercriminal Operations, Complemented with a Preliminary Dive into Digital Forensics and IoT Device Firmware Hardening against Exploitation 

Theme: TBD
Time:  4. - 13.8.2025 NB! The course lasts 1,5 weeks
Study mode: In person
Max. number of participants: 25
Lecturers: Guillermo Suarez-Tangil (IMDEA Networks and Kings College London, the United Kingdom), Javier Carrillo-Mondéjar (Universidad de Zaragoza, Spain), Ricardo J. Rodríguez (Universidad de Zaragoza, Spain), (optionally/if-necessary-replacement) Andrei Costin (JYU)
Coordinator: Andrei Costin
Course code: 
Modes of study: Obligatory attendance at lectures and completing the exercises
Credits: 4 ECTS
Evaluation: P������/�󲹾���
Contents: Malware is increasingly becoming a key problem for organizations and Internet users. Cybercriminals infect computers with malware and use them for their own gain, for example by stealing sensitive financial information or corporate data. This ecosystem has become so profitable that an entire underground economy has emerged around it, in which specialized actors provide services to each other and collaborate towards the success of these criminal endeavors. In this course, we will cover malware and cybercriminal operations in detail, focusing on both the engineering and the social and economic aspects of malware operations. We will then introduce mitigation techniques against malware operations and illustrate what an effective mitigation strategy against malware operations looks like.  Additionally, we will delve deeper into the field of memory forensics, exploring how to detect and analyze malware artifacts in system memory. We will also cover using the Volatility tool to perform memory analysis, including mining and analyzing volatile data to uncover hidden malware and its operational footprints.
Learning outcomes: The module aims to provide students with the skills needed to understand cybercrime in a global context and the role of a malware analyst. During this course, students will learn how cybercriminals operate, and how to develop better mitigations against this threat. Students will learn advanced methods used by malware developers to produce stealthy malware and how cybersecurity professionals analyze malware. Specifically, the module has the following learning objectives: 

1. Understand core concepts and nomenclature of malware and cybercriminal operations.
2. Understand the technical, economic, and social aspects of malware operations. These aspects will allow the participant to understand how a single malware infection factors into the complex cybercriminal ecosystem.
3. Understand the process carried out by malware analysts to reverse malware. Be able to analyze malware analysis traces to understand the nature of a malware infection on an affected computer.
4. Understand the first line of defense against malware and strategic mitigations. Devise effective mitigation techniques against malware operations. These mitigation will be not only technical, but will also factor in economic, social, and legal aspects.
5. Gain both theoretical foundations and, increasingly importantly, hands-on experience in memory/malware/digital forensics. Learn how to use tools like Volatility to analyze system memory, uncover traces of malware, and understand its behavior in volatile memory.

Prerequisites: Bachelor-level degree in Computer Science, Information technology, or comparable.

CYB2: Cyber Security Management 

Theme: TBD
Time: 4. - 8.8.2024
Study mode: In person
Max. number of participants: 35
Lecturers: The CYB2 course brings together lecturers from different universities, including Dr. Research director Martti Lehto (Ģ��ֱ��), Dr. Mika Kerttunen (Aalto University), Dr. Eneken Tikk (Tampere University) and Prof. Tero Vartiainen (University of Vaasa).
Coordinators: Martti Lehto and Piia Perälä
Course code:
Modes of study: Obligatory attendance at lectures and completing the exercises
�����徱�ٲ�:  3 ECTS
Evaluation: �ʲ�����/�ڲ����� �į�į�į&�Բ�����;
Contents: The summer course is organized in cooperation with Finnish universities working in the project funded by the Ministry of Education and Culture. The course will enhance understanding of rapidly changing cyber security environment. During the course, the students will get familiar with cyber security phenomena and elements. Themes covered include cybersecurity management, information influence, cybersecurity regulation, cyber risk management, and human cybersecurity. Through lectures and workshop case studies, the students will learn to identify how to build cyber resilience. The course will be interactive, encouraging the students in critical thinking concerning cyber security building. The lectures and workshops are produced by several visiting lecturers from organizing universities.
Learning outcomes: Basic common understanding about Cyber Security Management.
Prerequisites: Candidate level degree in Computer Science, Information technology, or comparable.

CYB3: Cyber Security Technology 

Theme: TBD
Time: 11. - 15.8.2025
Study mode: In person
Max. number of participants: 35, of which at least 10 from outside the organizing universities
���𳦳ٳܰ������: The CYB3 course brings together lecturers from different universities, including Prof. Kimmo Halunen (University of Oulu), Prof. Valtteri Niemi (University of Helsinki), Assistant Professor Irfan Khan (Texas A&M), Dr. Marko Helenius (Tampere University) and Dr. Gunn Lachlan (Aalto University).
Coordinators: Martti Lehto and Piia Perälä
Course code: 
Modes of study: Obligatory attendance at lectures and completing the exercises
Credits:  3 ECTS
Evaluation: �ʲ�����/�ڲ����� �į�į�į&�Բ�����;
Contents: The summer course is organized in cooperation with Finnish universities working in the project funded by the Ministry of Education and Culture. The course will enhance understanding of rapidly changing cyber security environment. During the course, the students will get familiar with cyber security phenomena and elements. Through lectures and workshop case studies, the students will learn to identify vulnerabilities, threats and how to build cyber resilience. Topics covered include, for example, critical infrastructure protection, artificial intelligence and cybersecurity, OT/IT cyber security, crypto and post-quantum security, and secure programming (SecDevOps). The course will be interactive, encourage the students in critical thinking concerning cyber security building. The lectures and workshops are produced by several visiting lecturers from organizing universities.
Learning outcomes: Basic common understanding about Cyber Security Technology.
Prerequisites: Candidate level degree in Computer Science, Information technology, or comparable sufficient technological expertise, sufficient knowledge of programming. 

STEM1: Creative Approaches, Methods, and Practices in STEM Research, Innovation and Education

Theme: TBD
Time: 4. - 8.8.2025
Study mode: In person
Maximum number of participants: 30
Lecturers: Prof. Arto Lahti (Aalto University, Finland), Prof. Leonid Chechurin (LUT University, Finland), Prof. Pekka Neittaanmäki (JYU), Dr. Kati Clements (JYU), Dr. Kristof Fenyvesi (JYU)
Coordinators: Dr. Kristof Fenyvesi, Dr. Kati Clements and Prof. Pekka Neittaanmäki
Course code: 
Modes of study: Lectures, hands-on workshops, seminars, reflections and case study discussions
Credits: 1 ECTS
Evaluation: Pass/Fail (based on active participation and final project presentation)
Contents: 
- Creative Research Methodologies in STEM – Exploring innovative research approaches across science, technology, engineering, and mathematics. 
- Systematic Creativity and Problem-Solving – Applying structured creativity techniques to foster innovation in STEM disciplines. 
- Multidisciplinary Integration in STEM Education – Bridging technology, engineering, natural sciences, and pedagogy for effective learning experiences.
- Technology-Driven Innovation in Research and Teaching – Utilizing emerging technologies and digital tools to enhance research and STEM education.
-Collaborative Strategies for STEM Advancement – Developing interdisciplinary teamwork skills to generate innovative solutions in research and education.

The course, Creative Approaches, Methods, and Practices in STEM Research, Innovation and Education, explores innovative methodologies and practical techniques to drive impactful research across STEM disciplines (Science, Technology, Engineering, and Mathematics), with strong connections to IT, natural sciences, and STEM pedagogy. Emphasizing inter-, multi-, and transdisciplinary collaboration, participants will engage in systematic creativity, problem-solving, and technology-driven innovation through lectures, hands-on workshops, and case studies. The course integrates engineering methodologies and scientific inquiry with effective teaching strategies, equipping Master's and Doctoral students with the skills to foster innovation, tackle complex STEM challenges, and create engaging STEM learning experiences. This course brings together a distinguished team of instructors whose expertise spans interdisciplinary research, innovation, creativity, and STEM education. Together, this team of internationally recognized scholars and practitioners provides a rich and diverse learning experience, equipping participants with the creative and innovative skills needed to excel in multidisciplinary research.
Learning outcomes: Upon successful completion, participants will:
-Gain a comprehensive understanding of creative methodologies in research and innovation.
-Apply systematic creative methods to solve research challenges.
-Integrate multidisciplinary insights from IT, science, technology, and the arts into their research projects.
-Collaborate effectively on innovation-focused projects within diverse teams.
-Develop actionable solutions for real-world problems using creative and innovative approaches.
Prerequisites: Basic knowledge in IT, engineering, or natural sciences. An interest in multidisciplinary approaches, creativity, and innovation.