🎉 My new study is now published!

Everyday integration in Nordic welfare societies often hinges on something deceptively simple: access to clear, reliable, and usable information. From understanding healthcare systems to navigating the labour market, education, and social life, immigrants’ sense of belonging is shaped through everyday information encounters. This insight motivated my newly published article in Library & Information Science Research:

“Belonging through information: Mapping immigrant integration needs in Nordic societies”

🔗 https://doi.org/10.1016/j.lisr.2026.101400

The article is part of Mobile Futures - Research Consortium and was carried out in collaboration with Migration Institute of Finland & Åbo Akademi University.

📊 Conceptual focus of the study

The article maps immigrant integration through five interconnected dimensions, placing belonging at the centre and showing how information needs cut across all areas of everyday life:

1. Social integration – information about social networks, community activities, family support, childcare services, volunteering, and peer support.

2. Health integration – information on how healthcare systems work in practice, public vs. private services, entitlements, interpreters, mental health support, and medical communication.

3. Labour market integration – information about recognition of foreign qualifications, job-search practices, labour laws, workplace rights, and professional networks.

4. Cultural integration – information on local norms and customs, cultural events, community centres, media, and opportunities to express and maintain one’s own cultural traditions.

5. Educational integration – information about language learning, the education system, student services, scholarships, and educational and career pathways for both adults and children.

Based on semi-structured interviews with 54 first-generation immigrants in Finland, Sweden, and Norway, the findings show that integration challenges are not only about policies or services, but about whether information is understandable, accessible in familiar languages, and actionable in everyday situations. When institutional information is fragmented or unclear, people rely heavily on informal networks—often at the cost of confidence, wellbeing, and a sense of belonging.

📄 Read the article:

#ImmigrantIntegration #InformationBehaviour #Belonging #NordicCountries #LibraryAndInformationScience #QualitativeResearch #MigrationStudies #MobileFutures #ÅboAkademi #MigrationInstituteOfFinland #Finland #Sweden #Norway #HamedAhmadinia

I'm excited to share that I have officially completed the Practical Teacher Training (IPTE, 60 ECTS) program at Häme University of Applied Sciences (HAMK), Finland! 🎉
As part of this journey, I designed and delivered an 8-week university course titled
"Python for Data Analytics & Statistics" at Metropolia University of Applied Sciences.
This experience allowed me to merge educational theory with hands-on teaching, while applying student-centred, inclusive, and feedback-rich learning approaches in a real classroom setting.

📘 You can now read my full Learning Diary, documenting the full process from planning to outcomes:
🔗 https://lnkd.in/dkAwXYiH

💬 Special Thanks
This achievement wouldn’t have been possible without the support of:
Janne Salonen, my supervising teacher at Metropolia University of Applied Sciences, whose guidance, feedback, and encouragement were instrumental throughout my teaching journey.
Vesa Parkkonen, my tutor teacher at HAMK, for his thoughtful mentoring and for bridging the connection between pedagogical theory and practical implementation.
Your belief in my work made this learning experience not only successful but deeply meaningful. 🙏

👨‍🏫 What This Means
With this milestone, I am now a certified teacher in the Finnish higher education system. I’ve gained first-hand experience in course design, student engagement, and digital pedagogy—with practical tools like Jupyter, GitHub, and Kahoot integrated into my curriculum.
Whether in academia or industry education, I’m now even more prepared to contribute to impactful, learner-focused programs.

#Education #CertifiedTeacher #IPTE #HAMK#Metropolia #PythonTeaching #Pedagogy #DataAnalytics #TeachingJourney #OpenEducation #JupyterNotebooks #LearningDiary #StudentCentredLearning #DigitalPedagogy #GitHubEducation #FinnishEducation #EdTech

I’ll be honest—doing multidisciplinary research isn’t always smooth sailing. It’s exciting, yes, but it also comes with its fair share of challenges. During my time in the Mobile Futures project, I found myself constantly juggling different perspectives, methodologies, and even ways of thinking. Bringing together data science, sociology, psychology, and economics into one cohesive study felt like solving multiple puzzles at once—each with its own rules and missing pieces.

But that’s the beauty of it, right? The challenge is also the reward.

One of the key tools that made this research possible was Python—and I can’t imagine doing this kind of work without it. Here’s why. 👇

🌍 Bridging Disciplines with Python

Multidisciplinary research means working with different types of data—from structured survey datasets to unstructured behavioral data. The beauty of Python is that it lets me seamlessly integrate diverse methodologies, whether I’m running statistical models, performing data cleaning, or visualizing behavioral trends.

💡 Why Python?
✔ Flexibility: Python works across disciplines—great for both statistical analysis and machine learning.
✔ Efficiency: Automating repetitive tasks (like data wrangling) saves hours of manual work.
✔ Powerful Libraries: Pandas, NumPy, and Scikit-learn make handling complex data much easier.

Instead of struggling with manual data processing, I was able to focus on making sense of the findings—which is what research should really be about.

🖥️ Python for Data Analysis: Debugging is Half the Battle

If you’ve ever spent hours debugging code, you’ll understand why writing clean, efficient Python scripts is crucial. Early in my research, I realized that messy code = messy analysis.

📷 Below is a snapshot from my Jupyter Notebook, showing the essential Python libraries I used for data processing and visualization.

I relied heavily on Pandas for data manipulation, Matplotlib & Seaborn for visualization, and Scikit-learn for statistical modeling. But even with these great tools, I ran into issues—data inconsistencies, missing values, and errors that took hours to debug.

🚀 What helped?
✔ Writing reusable functions instead of copy-pasting code.
✔ Version-controlling my scripts with Git to track changes.
✔ Using Jupyter Notebooks to document my workflow and visualize results interactively.

These small tweaks saved me so much time in the long run and made my workflow more efficient.

📊 Choosing the Right Data: Python to the Rescue

One of the biggest challenges I faced was handling missing and inconsistent survey responses in the European Social Survey (ESS) dataset. If not properly addressed, missing values like "Refusal" or "Don't know" could introduce bias and distort the results.

📷 Here’s an example from the ESS dataset builder, showing how survey responses include missing values that need careful handling.

🧐 How Python helped:
✔ Pandas allowed me to quickly filter, clean, and structure survey data.
✔ Missingno (a Python library) helped visualize missing patterns.
✔ Multiple Imputation in Scikit-learn provided a robust way to estimate missing values.

Without Python, this would have been an exhausting manual process. Instead, I could automate data cleaning, ensuring that my analysis was accurate and reliable.

📈 Python for Behavioral Insights: Analyzing Internet Use Trends

One of my research questions focused on how different demographic groups engage with the internet. But behavioral data is messy—patterns are influenced by external factors like cultural norms, technological adoption, and accessibility gaps.

📷 Here’s a visualization of the frequency distribution of internet use from different ESS rounds. These graphs illustrate how internet habits shift over time.

📱 How Python made analysis easier:
✔ Seaborn & Matplotlib helped me visualize usage trends over time.
✔ Groupby functions in Pandas allowed me to break data down by demographics.
✔ Scikit-learn helped identify correlations between internet use and attitudes toward migration.

The biggest takeaway? Numbers alone don’t tell the whole story. Python gave me the tools to explore not just the what, but the why behind behavioral shifts.

🔍 Survey Data & Missing Values: A Python-Powered Solution

Working with survey data means dealing with ambiguous and missing responses. Ignoring them wasn’t an option, but incorrectly handling them could skew my results.

📌 Python’s role in fixing this:
✔ Pandas & NumPy helped detect and clean missing data efficiently.
✔ Scikit-learn’s imputation techniques ensured my dataset remained robust.
✔ Sensitivity analysis scripts let me test how different approaches impacted findings.

The result? A dataset I could trust—one that didn’t just fill gaps but preserved the integrity of the analysis.

🔎 Final Thoughts: Why Python is a Researcher's Best Friend

Multidisciplinary research isn’t easy, but Python made it manageable. It allowed me to:
✅ Automate tedious tasks (instead of getting lost in spreadsheets).
✅ Analyze large datasets quickly (without endless manual cleaning).
✅ Visualize trends in ways that made insights clear and compelling.

Looking back, I can’t imagine tackling this research without Python’s flexibility, efficiency, and powerful libraries. The biggest lesson? The right tools don’t just make research easier—they make better research possible.

💡 What about you? Have you used Python for research? What challenges did you face? Drop a comment—I’d love to hear your experiences! 👇

I am honored to receive a postdoctoral research grant from the Finnish Cultural Foundation for the first year of my two-year project on AI-driven labor market integration of international students in Finland.

This research, to be conducted at the Migration Institute of Finland, will explore how artificial intelligence-based services can support international students in overcoming key employment barriers such as language challenges, lack of labor market knowledge, and discrimination. By leveraging AI-driven job-matching platforms, automated translation tools, and personalized career support, I aim to develop practical solutions that foster equitable employment opportunities and strengthen Finland’s labor market inclusivity.

A big thank you to the Finnish Cultural Foundation for supporting this important work! 🙌 I also want to express my gratitude to my colleagues at the Migration Institute of Finland for their invaluable comments, support, and feedback on my project.

I’m excited to collaborate with researchers, policymakers, and industry experts working at the intersection of AI, migration, and labor market policy. If you are interested in discussing potential partnerships, feel free to reach out!