My career goals and teaching

Since I have education and skills in marketing, productising, sales, management and leadership, I wish for the future the chance to use these skills in my teaching, but to be the teacher that creates materials and teaches courses is not of the essence.

After the career change to Computer science and PhD studies, I feel that just becoming a teacher could be something I could have done without the research experience and five years of work. On the other hand, I have a good background, and besides the promising start as a scientist, I wish to combine these both.

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.

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.

Thesis sprint – a new tool for speeding up theses work

As a supervisor (and former adult student), I have noticed that a certain group of students that study and work at the same time benefit from certain tools. Time is one of the most valuable things when trying to complete a thesis, work and handle your daily life simultaneously. Therefore, it might be valuable to use tools for raising the effectiveness and for deleting time-related barriers between the started and finished thesis.

Since my master’s thesis was about joining scrum and design thinking, I started to think about whether we could use sprints, tasks and dailies and offer students possibilities to work efficiently close to supervisors.

A scrum framework is familiar to many students working in software engineering. I took the liberty to join elements of scrum, coaching, and sparring, and as a result, we are now piloting Thesis Sprints at the Spectral Imaging Laboratory. The method offers tools for fluent thesis work.

In our sprints, we invite students to work in the office for a short period. Before joining the office, the student will plan the sprint’s goals and tasks and divide them into tickets. Each morning, we will meet and discuss the planned daily tasks. The student can ask for help at any time (since we spend time in the same office, and have lunch and coffee breaks at the same time; it is easy to ask small things that might take time in traditional email-based supervision). After each day, we spend a few minutes to retrospect and pay attention to what is accomplished and select the tickets for the next day. After the pre-agreed office time, we have the final retrospective, where we can discuss face-to-face the next steps. The last retrospective aims to help students to continue using tasks and tickets while performing tests and writing the thesis.

Summary of the Thesis Sprints. Feel free to test!

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.

Strategic teaching development

Here are examples of strategic teaching development in which I have been participating

Working for the JYU

My positions in the University collegium and the faculty council offer me a larger strategic view and understanding of teaching and research policies and their development in our University.

1.1.2022-31.12.2025 Member of the faculty council (elected)

1.1.2022-31.12.2025 Member of University collegium (deputy, elected)

Besides the strategic council and collegium work, I have been actively participating in strategic teaching development in our faculty. The main topics for the years 2022-2023:

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)

Visiting lectures

Besides conferences, I have been presenting our research as a visiting lecturer and conference presenter.

Feel free to contact me, if you wish to have a lecture, speech or presentation. Example topics: hyperspectral imaging, machine vision, computer vision, machine learning etc.

Click here to see my current contact information at the University of Jyväskylä.

Visiting lectures:

13.04.2022 the University of Vaasa, target audience: Advanced level students in spectral imaging course

Lecture: FPI-based Hyperspectral Imager for the Complex Surfaces
– Toward Optical Biopsy

Conference presentations

2022 ISPRS International Society For Photogrammetry ad Remote sensing: Updating strategies for distance-based classification model with recursive least squares. Awarded with a live presentation, pre-recorded presentation and poster presentation.

DIME21 Eccomas Thematic Conference, JAMK University of Applied Sciences, Jyväskylä, Finland. FPI-based hyperspectral imager for the complex surfaces -toward optical biopsy

2021, ISPRS International Society For Photogrammetry ad Remote sensing: Piecewise anomaly detection using minimal learning machine for hyperspectral images. Remote participant, pre-recorded presentation

2020, SPIE Image and Signal Processing for Remote Sensing XXVI: Minimal learning machine in hyperspectral imaging classification. Remote participant, pre-recorded presentation

Current teaching

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

Teaching goals for the next five years

The teaching strategy arises from my own experiences as an adult Master’s student without previous computer science background (and with a full-time job, big house, two dogs, two kids, three lambs and so on…). When participating the basic teaching with young co-students, there were definetly many situations to learn from the pedagogical point of view. Similarly, my eight years of business experience with leadership education (MBA) completed with management courses and coaching experience gives me perspective to season my teaching.

I have been teaching now for two years as a shared principal lecturer in two courses (3-5 ECTS and 5 ECTS, listed below). Currently, I am preparing a third course, which enables me the role of one of three principal lecturers and the possibility to design the course materials and utilise flipping. I studied my pedagogical studies in 2022, and since then, I have been actively developing the following goals and tools for teaching.

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.

Teaching portfolio

As a teacher, my personal teaching philosophy is simple. I believe in open communication, good materials and easy to approach atmosphere. In every course, instead of filling the students’ minds with mountains of information, the aim is to deliver 5 to 6 main outcomes, the ones they will remember when woken up at 3 AM. These will be covered with details, but the aim is to deliver deeper learning experiences for the main topics and broaden the view over important things to know but not essential to spend hours with -they can later on go find the sources and deepen the understanding if necessary.

My way of working is visual. As a student, I need frames to fill with small details. Therefore, I often use figures to deliver information and deepen the understanding through the theories, which can be written, and lectured in front of students in the classroom or via videos.

Figure 1. This is an example of a visualisation that can be used to explain the essence of 3D imaging. 3D imaging can be addressed through mathematical formulas, or we can ease the learning a little by giving an educative visualisation before diving deeper to the algorithm.
Figure 2. A highly dimensional hyperspectral (HS) data cube can be difficult to understand. The challenge arises when we add 3D to HS data cube. This visualisation can ease the students understanding over the dimensionalities.

Above is an example of the visualisations I have drawn for the students (Figures 1 and 2). It reflects my teaching statement, which is built from the strategy:

My teaching portfolio contains six topics, which are introduced as follows: