My research career is at a good pace. Since 2019: Nine peer-reviewed articles, one completed and defended dissertation (includes six articles out of nine). I have worked for two months as a postdoctoral researcher, and so far, rour articles are in progress to be published before September.

I am supervising three master’s theses and the work of two research assistants, and in April 2023 I will start supervising a PhD student which is to be hired into my team in FireMan project. I am slowly building a machine vision laboratory infrastructure, which allows us to offer students and other research groups the possibility to use special machine vision systems and other sensors with Arduino and Raspberry Pi platforms in their studies and research. Therefore, the next career change should enable both, research and teaching. I wish for a tenure position, which allows me to use my management, leadership and marketing skills in developing curriculum and teaching besides working as a researcher. Then, based on my research, I could teach courses on a topic such as machine vision, tiny ML etc., where I am already bringing hands-on education with the machine vision laboratory.

Planning

1. Find a short time from students’ schedules. It can be 1 to 5 days.
2. Ask the student to form a goal for the sprint. A goal finishes the sentence: “I wish to accomplish –A– When it is done, I can concentrate to –B–.”
3. Ask the student to break goal A into smaller goals: 1, 2, …n. The result is an algorithm that tells which smaller goals accomplishes together the sprint goal A.
4. Ask the student to break into tickets as small as possible. It does not matter if there are more tickets than time; those all will benefit the work.
5. Ask students to classify tickets. In case of getting stuck, it is wise to have small tasks that require nothing demanding. Those are the way of keeping the work on the move. Instruct students to do those whenever they don’t know what to do. The other tickets are the ones the student will plan for the next day and perform one by one.

Sprint!

1. First morning and each morning after that: A small daily meeting with the supervisor. The student will show today’s tickets, and the supervisor will advise if needed. Supervisor shares his/her daily schedules, so the student will know how to get immediate short advice if needed.
2. During the day, student works for the tickets. The supervisors’ role is sparring, helping if needed and keeping up positive energy.
3. Daily short retrospective – where we are now, acknowledge the done tickets and re-organise + select tickets for the next days.
4. Repeat 1-3 until the last day.
5. Last retrospective. Here is the place to say good work! Discussion over the remaining tickets, planning the thesis’s next steps (goal B) and how to achieve it. The ultimate goal is to clarify the rest of the steps needed for finalising the thesis and coach and encourage the student to work it through.

Lessons learned from Thesis Sprints

1. It is easy to lose focus on another thing/idea/problem that arises in the middle of performing a ticket. Solution: Each new thing/idea/problem must be recognised, written down and added to the ticket/task list. After documentation, return immediately to the ongoing ticket.
2. It is easy to lose focus and try to find the missing reference. Solution: Write your thoughts down and mark the missing citation. Do a ticket that says: find the missing reference.
3. It is easy to lose focus and spend hours on figures. Solution: Draw as bad versions as possible (pen and paper, take a photo) and add it to a placeholder. Make a ticket to draw it better later on, and go back to your current ticket.
4. If the student is completely stuck, a supervisor can help with the process and teach the student how to break the thesis into goals, tasks, tickets, etc. It helps, even if there is no time for an office sprint.
5. Handwriting and drawing. There is no greater force than releasing the brain to think differently while the hand is drawing or writing. Use it!
6. Remember to be positive and see the small accomplished tasks. Pay attention also to workload – we don’t want to see overworking.
7. This method is easy and effective, it does not require much from the supervisor, (usually a short comment carries a long way), and the students work independently.

2022

• Master’s degree in Information and Software Engineering; participating in the planning and piloting of the own-teacher model, which aims to support and integrate new students into the university community and studies, providing “own teacher” that interviews students and is easy to approach in any study-related questions during the first periods.
  
  
• Curriculum work has started (computer science and data science

2023

• Continuation: Master’s degree in Information and Software Engineering; participating in the planning and piloting of the own-teacher model, which aims to support and integrate new students into the university community and studies, providing “own teacher” that interviews students and is easy to approach in any study-related questions during the first periods.
  
  
• Continuation: Curriculum work continues (computer science and data science)

TIES411: Konenäkö ja kuva-analyysi

Target: Master-level students.

My status: I am one of three responsible teachers, developing course materials, lecturing and supervising tasks, and responsible for the hands-on demos.

Course level: Advanced
Course actions: This course has a dual approach. Mathematical foundations are introduced and applied into practice using Python. At the same time, students are learning how to work with machine vision sensors, and control devices, to capture and modify the captured images. The demos with Basler sensors and Raspberry Pi cameras deepen the learning experience. The demos will be live.

Exam or demos (5 ECTS)

Learning outcomes: Students understand how to design and build machine vision systems. A student can capture and modify the data and understands the mathematical-physical limitations of machine vision. Students will learn to extract features, work with 3D data, and apply multiple mathematical functions to real-life image-analysis tasks.

Teaching challenges: The course format is new, rolling in period 4 / 2023. The course material is currently under work. This course will be developed within the next years.

Goal – in future, this course could be flipped and materials enlightened with videos

KYBS3050: Koneoppimismenetelmiä kyberturvallisuuteen

Target: Master-level cyber security students.

My status: I am one of two responsible teachers, developing course materials, lecturing and supervising tasks.

Course level: Advanced
Course actions: At first: Python, Jypyter notebook & Sckit learn: Building the environment, testing some classification, clustering and anomaly detection methods for warming up.

We introduce basic machine learning methods and learn how to use them in cyber security issues: Curse of dimensions, SVM, Time series, NLP, MLP, CNN, Autoencoder, CNN autoencoder, Reinforcement learning, transfer learning, GAN, adversial attacks, Minimal Learning Machine.

Demo + essay (5 credits)

Learning outcomes: A student understands machine learning principles and can apply machine learning to various issues related to information security.

Teaching challenges: The students have varying backgrounds; for example, maths is not required. Therefore the course has been traditionally lecture-based, and we provide personal guidance for exercises.

Goal – in future, this course could be flipped and materials enlightened with videos

KYBS7041: Anomalian havaitseminen

Target: Master-level cyber security students.

My status: I am one of two responsible teachers, developing course materials, lecturing and supervising tasks.

Course level: Advanced
Course actions: First: Python, Jypyter notebook & Sckit learn: Building the environment.

Introduction to anomalies and categories. Review of required linear algebra and math skills. Eigenvalue decomposition and singular value decomposition. Data types and data pre-processing and processing. Method evaluation and validation, thresholds, clusters, and neighbour-based methods.

Exam (3 ECTS) or demo + exam (5 ECTS)

Learning outcomes: Students will understand the concept of anomaly and the different categories, concepts of data point, data set and type and different categories. Students will learn the operating principles of different anomaly detection methods and can apply anomaly detection methods in different situations. Can evaluate the performance of methods.

Teaching challenges: The students have varying backgrounds; for example, maths is not required. Therefore the course has been traditionally lecture-based, and we provide personal guidance for exercises.

Goal – in future, this course could be flipped and materials enlightened with videos

My teaching goals

1. Make the above teaching strategy fluent and rolling. Once the materials are high-quality and the schedules are rolling, I have time to face the students truly, coaching time!
2. The aim is to test and develop teaching methods in order to find and share good practices.
3. Renew the materials and methods for the two machine-learning courses
4. Supervise the thesis by paying attention to removing obstacles from students’ work using new tools.
5. Supervise PhD students encouraging them to work in a team that has a sparring atmosphere. One of my goals is to mentor them to explore the nature of an Academic career and enable them to effectively learn skills of selling research (funding), communication and building a versatile CV.
6. Work as a sparring and inspiring colleague, maintaining an open and good working atmosphere and helping others if needed
7. Continue ongoing work by bringing more sensors and hands-on hardware experiences to students. I am building a small machine vision laboratory in the corner of the office, which offers students Raspberry Pi4 platforms with Raspberry Pi cameras, Basler sensors, four PCs and optical components. This work will continue: for example, with marketing so that students will find devices for their thesis and other projects.
8. Communication. Besides scientific research (Postdoctoral researcher in FireMan project), I will continue developing araita.com pages. The science I publish will transform into a form that is easy to find and easier to approach.
9. Pay attention and respond to the different needs of adult students.