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Rodrigo González Alonso is a graduate of the Master’s programme in Applied Information and Data Science at the Lucerne University of Applied Sciences and Arts (HSLU). He worked on several international research projects aimed at identifying patterns in human and animal behaviour, with the collaboration at the Massachusetts Institute of Technology (MIT) marking the highlight of his research experience.

Introduction

First of all, tell us something about yourself: What hashtags best describe you?

#GlobalJourney #AlwaysLearning #Curious

Tell us more about the hashtags.

#GlobalJourney reflects my path so far – from growing up in Spain, studying in Finland, doing an internship in Italy and now building my career in Switzerland, as well as the time I spent at Massachusetts Institute of Technology (MIT) in Boston. Each step has been formative and given me new perspectives on how people live, work and collaborate across cultures.

At the same time, #AlwaysLearning and #Curious describe how I approach both work and life. I’m the kind of person who likes to ask questions and explore new things, whether it’s through my studies, projects at work or simply through my everyday experiences. This curiosity often leads me in an unexpected direction, and the constant process of learning keeps me motivated and moving forward.

About your job: What are you doing at SIX Group?

At SIX Group, the company that operated the Swiss Stock Exchange, I work in the Financial Information unit, where we acquire, process and deliver financial data to clients. My role in the Strategy team is to enhance productivity by building and improving tools with Python and Power BI.

I work with both client and internal data to help colleagues design strategies that guide business decisions. A big part of this involves pricing and financial planning, and data science methods help us to be more accurate and flexible. At the same time, I like to explore more creative uses of technology, such as computer vision for reading contracts or small AI agents that handle repetitive workflows.

The goal of all this is to turn data into something practical that helps people work more efficiently and make better decisions. My job combines data science, technology and strategy and gives me the chance to contribute to projects that directly affect operations.

What did you do before and why did you join SIX Group?

I began studying Business Administration in Spain, and completed my studies with a double degree from a university in Finland. However, my interest quickly shifted towards the field of data, specifically business intelligence. This led me to move to Lausanne to do an internship at Philip Morris International, where I worked in the Business Intelligence team for IQOS. There was no existing data infrastructure, so I had the opportunity to experience firsthand how to build the data processes from scratch over the course of a year.

Later on, I joined SIX Group, where I found the scale and impact of my work and knowing that I can influence many companies to be very motivating. Additionally, as someone deeply connected to Spain I have always valued being engaged with my home country through my professional work, as SIX owns the Spanish Stock Exchange.

The projects

Please tell us about your research projects.

The first project was a dual collaboration with Professor Peter Gloor, a researcher at MIT, professor at the University of Cologne, and lecturer at HSLU. During this work, I travelled to MIT in Boston, where I assisted with experiments to measure which combination of psychological factors improved group performance by identifying the personality types within the teams to enhance its effectiveness. This opportunity also allowed me to spend time in Boston, where I experienced MIT firsthand, particularly the Systems Design and MIT Sloan School of Management department. This led to the joint publication of a paper.

I continued to work with Professor Peter Gloor afterwards, contributed to publishing a paper as part of the course Collective Innovation Networks (COINs) at HSLU, and then attended the COINs conference in Gorizia. Additionally, we are about to collaborate and publish a paper on recognising “happy cows” at the Mediterranean Ruminant Congress, where we will be co-authoring with my father, a veterinarian and professor.

This collaboration has been highly rewarding and was made possible thanks to HSLU.

Module Collaborative Innovation Networks (COINs)

How do teamwork, technology and human dynamics interact across borders? The international module Collaborative Innovation Networks (COINs) in the MSc in Applied Information and Data Science programme at HSLU, brings together students from Germany, Switzerland, Italy, Poland, and Finland to explore these questions in depth.

The module is led by Prof. Peter Gloor, researcher, author and founder of the COINs concept at MIT. He is known for his pioneering work on collaborative innovation, swarm creativity, and social network analysis.

Using smartwatches, phones and environmental data, students analyse how hybrid work settings influence collaboration, performance and group creativity. Each project uses data science, social network analysis and machine learning to discover what makes teams truly effective in terms of topics ranging from human energy patterns all the way to digital communication flows.

Many student projects are later published in research journals or presented at international conferences, making this module a gateway into real-world, data-driven innovation.

What data and methods did you use, and what insights did you gain or do you hope to gain?

My projects with professor Gloor used a wide range of data sources, which made them both challenging and exciting. In my thesis, I worked with tens of millions of Reddit comments and analysed how online discussions reflect emotions, values and group decision making. In another project in the COINs seminar, we studied WhatsApp group chats from students at the Polytechnic University of Madrid, which gave us a very different view of communication styles and group dynamics in an academic context. And more recently, we even looked at video recordings of cows that I collected during hikes in Switzerland, exploring how computer vision can help us identify indicators of animal welfare.

Results and Findings

How can your insights help our society?

My insights can help society by showing how we can make better use of the enormous amount of data we generate daily. The shared theme in the MIT experiments on finding the best team compositions and in professor Gloor’s Happy Cows project is that we don’t need to produce more data because it already exists. What matters is that we use the right methods to discover patterns that would otherwise remain invisible.

In that sense, the value lies in saving time and exploring new perspectives. Whether it involves improving how teams collaborate, understanding wellbeing in unexpected ways or making better decisions in business, the techniques I’ve learned about help us to see connections and opportunities that might get overlooked with traditional approaches.

What are your goals for your projects in future?

In the future, I’d like to apply what I’ve learned in these projects to new areas, especially in finance and business, where decisions are made quickly and often with incomplete information. The mix of the data and methods I used, from text analysis to computer vision, can be useful in many fields other than research.

At the same time, I’d like to stay connected to interdisciplinary projects. Working on topics like team performance and animal wellbeing gave me fresh perspectives, and I think that striking a good balance between occupational activities and non-occupational research produces mutually beneficial results.

How did your studies in the Applied Information and Data Science programme influence the projects?

My Master’s studies in Applied Information and Data Science strongly influenced these projects – inspiring me to explore new ideas and connect them to real-world problems as well as providing me with the methods and technical foundation, from advanced analytics to project work with classmates.

Most importantly, the Master’s programme created opportunities to work with real data and applied research, which allowed me to move beyond theory and test my ideas in practice.

What advice would you give to others starting on similar projects?

The most important advice I would give to someone starting on a similar project is to take enough time to plan it properly. It’s tempting to dive straight into the analysis, but understanding the limitations of the data early on can save a lot of frustration down the road.

I would also recommend looking for similar projects and learning from them. Exploring how others approached their methods can be a great source of inspiration and help you avoid common mistakes, save time and ultimately improve the quality of your work.

And finally: What new hashtag are you aiming for in future?

#Learning: I’m never done learning. In many ways, I’m only just getting started.

#Impacting: Data will continue to be the engine of growth for companies and society.

#ThinkingCritically: Being aware of where data and AI are heading – including the moral challenges, and preparing to face them responsibly.

We want to thank Rodrigo González Alonso for his dedication and for sharing these valuable insights.

Publications

Rodrigo González Alonso’s contribution has been published as a book chapter in an edited scientific volume by Springer Nature.

 → Read the chapter: Artificial Intelligence and Networks for a Sustainable Future

Start your career with the MSc in Applied Information and Data Science now!
Register and join us for a free online Info-Event:

Monday, 26 May 2026 (Online, German)
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In her Master’s thesis, she developed a decision support map that ranks homes by their energy efficiency, helping policymakers and homeowners prioritise sustainable building retrofit, cut emissions, and make better energy investments.

In this interview, Ezgi explains her reasons, gives details of her methods and shares her passion for sustainability, all of which helped her to develop a data-driven solution for a greener future.

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Ezgi Köker Gökgöl, a graduate of the MSc in Applied Information and Data Science programme at Lucerne University of Applied Sciences and Arts, developed a decision support map to identify buildings that most urgently need retrofitting.

Introduction

First of all, tell us something about yourself: What hashtags best describe you?

#AnalyticalThinking #PuzzleSolver #DataforFuture #KindtoNature

Tell us more about the hashtags.

For me, every problem or task is like a puzzle in life, and my brain automatically creates algorithms that sometimes even take them to unpredictable advanced stages. As I get older, I realise that you can achieve the best results when you dream big and shape those dreams to see what steps you can take without losing sight of the actual circumstances. The most significant threat we currently face is global warming, and my biggest dream is to help sustain life for ourselves and future generations. I believe that the most productive way to live this dream is to use the available data to recognise the severity of the situation and find the most feasible solution.

About your job: What do you do?

I am not working at the moment, but I am looking for an opportunity to live my dream. 

What did you do before? 

Before coming to Switzerland, I had an academic career in Turkey. I focused on analysing water systems so as to improve water quality and reduce leaks – as a contribution to sustaining life. At the same time, I worked as a teaching and research assistant at the Middle East Technical University. After moving to Switzerland, I decided to learn more about data science and hone my technical skills. That’s why I enrolled in the Applied Information and Data Science MSc programme at Lucerne University of Applied Sciences and Arts.

The project

Please tell us about your research project.

Before explaining what we actually did, I would like to explain the reasons behind the study. The energy sector is the source of nearly three-quarters of the global greenhouse gas emissions, and buildings make up around one-third of them. The largest share of the energy consumed in buildings comes from their heating systems, which mostly run on fossil fuels. To meet the demands of increasing populations, we must switch to clean energy immediately.

Given the necessity of the renovation projects and the large investments they require, choosing the right buildings that will maximise the improvement is of the utmost importance for decision-makers. Moreover, knowing how energy efficient their buildings are will help homeowners decide how to retrofit them, and households will thus be able to save significantly through reduced energy consumption. 

With this in mind, my Master’s thesis aims to develop a decision support map based on a rating system that classifies buildings within a municipality from “good” to “bad” depending on their energy use intensity, their heating system type, and their age. The specific heating energy requirement is the main indicator here, but only a few countries make this available nationwide. I therefore chose a small Swiss municipality of Wittenbach in the canton of St. Gallen for my case study. 

Based on the physical properties and heating energy demands of the buildings, I developed a ranking system that uses an analytic hierarchy process (AHP) and evaluates each building by considering its energy usage, heating system, and construction year. The subsequent rankings on the energy efficiency scale are then shown on the map of the municipality and serve as a decision support for finding the most suitable properties to retrofit.

What data and method did you use, and what insights did you gain or do you hope to gain?

For the case study, we chose the municipality of Wittenbach, a town near the city of St. Gallen. We learned about the characteristics of the buildings from the Federal Register of Buildings and Dwellings for the canton of St. Gallen, which is available from the Federal Statistical Office.

The second dataset required for the ranking system was the energy demands of the households in Wittenbach. These are available from the Swiss Federal Office of Energy as a heatmap of energy demands by residential and commercial buildings in Switzerland. The underlying data for the map is provided by the Swiss District Heating Association and is based on information from the Federal Register of Buildings and Dwellings.

Studying the physical properties and the heating energy demands of the buildings made it possible to develop a ranking system through an analytic hierarchy process, a widely used method for multicriteria analysis. This method helps to select the best option in a structured manner by applying several weighted criteria – similar to the way we select the best candidate for a job. Each household within the municipality is evaluated by considering its energy usage, existing heating system and age so as to produce a ranking based on the energy efficiency scale. Finally, these rankings are represented visually on the map of the municipality to help with the decision of finding the most suitable candidates to retrofit.

Results and Findings

How can your insights help our society?

The multi-layered decision support map with interactive details developed in this study adds value for both homeowners and decision-makers when evaluating the potential of a particular renovation.
The map enables homeowners to assess the energy efficiency of their buildings easily and to compare them with those of others in the municipality. The findings clearly show that energy-efficient renovations not only have ecological benefits but can also positively influence household budgets, for example, through significantly lower energy consumption.

The map provides policymakers with an overview of buildings in particular need of renovation and of spatial clusters of low-efficiency values. This information then helps with planning investments more effectively, developing regional support programmes and designing municipal strategies based on efficiency classes and their distribution.

What are your goals for your project in future?

The biggest dream I had while working on this project was to be able to make the outcomes become part of a solution that can be applied in real life. For me, this would be the best indication that the work was worthwhile. In fact the results of my study were later used as the initial step of an energy replacement systems recommender tool by the Energy Demand Governance project (EDGE https://www.sweet-edge.ch/en/home) that my advisors are working on. That gave me the satisfaction of being a part of the solution. 

How did your studies in the Applied Information and Data Science programme influence the project?

The Applied Information and Data Science Master’s programme at HSLU has a broad range of courses that cover various topics such as healthcare, energy systems or computational language technologies. This range helped me to see which field I would enjoy working on before I chose my thesis topic and how I could contribute more effectively to making our planet more sustainable. Moreover, the programming skills and new statistical tools I acquired through the programme helped me to maximise the outcomes of my thesis. Finally, and maybe not directly related to my project, I thought it was a nice way to learn about different fields, such as business administration or data ideation – especially for me because of my background as an engineer.

What advice would you give to others starting on similar projects?

I believe that all research projects are driven by curiosity and interest. The advice I would like to give to others would be to find the subject that catches their interest during their courses and to develop it in their thesis, which is more like a marathon than a 100-meter sprint. While writing the thesis, there will invariably be easy times as well as setbacks. During those tough moments, I think it’s important to remind yourself that you love what you’re working on and to believe that it will make a difference.

And finally: What new hashtag are you aiming for in future?

#SustainableFuture: For a future where people are more conscious and responsible towards the planet. 
#WorkWithLove: To find my place in life where I can contribute to this.

We want to thank Ezgi Köker Gökgöl for her dedication and for sharing these valuable insights.

Publications

Ezgi Köker Gökgöl contributed to a peer-reviewed article based on her Master’s thesis, which was published in the scientific journal Energy and Buildings (Elsevier).

→ Read the article: A community-based decision support map for building retrofit towards a more sustainable future

Start your career with the MSc in Applied Information and Data Science now!
Register and join us for a free online Info-Event:

Monday, 26 May 2026 (Online, German)
Monday, 8 June 2026 (Online, German)
Monday, 10 August 2026 (Online, English)
Monday, 7 September 2026 (Online, German)
Monday, 5 October 2026 (Online, English)

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Morteza Kiani Haftlang, a graduate of the MSc in Applied Information and Data Science at Lucerne University of Applied Sciences and Arts, conducted his thesis at IMAI MedTec on AI-driven medical imaging for cancer detection.

Introduction

First of all, tell us something about yourself: What hashtags best describe you?

#Learner #Multidisciplinarity #Cook #AGIEnthusiast

Tell us more about the hashtags.

Each of the hashtags reflects an essential part of my personality and professional journey:

• #Learner: Learning constantly is essential in an era of emerging technologies. I strive to keep up with mainstream AI trends, continuously learning and exploring new concepts in AI, healthcare and other fields.
• #Multidisciplinarity: My goal is to connect multiple fields, from AI and medical imaging all the way to engineering.
• #Cook: Cooking is my passion. Just like in AI, combining the right ingredients – data, models, and algorithms – leads to the best outcomes.
• #AGIEnthusiast: I am obsessed with the future of artificial general intelligence and its potential to transform industries, particularly healthcare.

About your job: What did you do at IMAI MedTec?

At IMAI MedTec, my thesis focused on a comparative study of various models, particularly self-supervised AI models, to improve cancer detection from 3D histopathological images. In essence, the goal is to identify and label cancerous cells accurately. My work involves researching, training, and fine-tuning deep learning models that help pathologists to analyse tissue samples more accurately and efficiently. By reducing the need for manual annotations, we can make cancer screening faster and more precise.

What did you do before? 

Before my thesis, I did an internship at Roche, where I was involved in data engineering and data analysis, working with data from production lines and blood sensors. Prior to Roche, I was an electrical engineer. So, you can see how my career has changed. I switched to healthcare AI because I wanted to apply my skills to a field where technology can save lives. The opportunity to work on innovative medical imaging at IMAI MedTec was too exciting to pass up – so that’s how my project started.

The project

Please tell us about your research project.

My research focused on self-supervised deep learning models for detecting cancer in 3D light sheet microscopy (LSM) images. Traditional histological analysis often overlooks cancerous cells due to limited tissue sampling, which can result in false negatives – up to 20% of cases may miss cancer cells. By leveraging AI, we aim to analyse entire tissue samples in 3D, thus reducing the risk of missed diagnoses.

We applied the models considered in this study – U-Net, BTUNet, YOLOv8x-seg, YOLOv8x+SAM, and HoverNet – to a dataset that IMAI provided. The project compared multiple models to find the optimal balance between accuracy and efficiency in segmenting cancerous cells.

What data and method did you use, and what insights did you gain or do you hope to gain?

We worked with high-resolution 3D LSM images of histopathological tissue samples. Because of their size (sometimes up to 100 GB) and complex content, these images have to be pre-processed as well as normalised, augmented and adapted to formats that are friendly to deep learning.

We evaluated five key models:

• U-Net: A classic segmentation model widely used in biomedical imaging.
• BTUNet: A self-supervised learning version of U-Net utilising Barlow Twins.
• YOLOv8x-seg: A real-time segmentation model optimised for speed.
• YOLOv8x+SAM: A hybrid model incorporating the Segment Anything Model (SAM).
• HoverNet: A powerful dual-task model designed for histopathology.

Results and Findings

How can your insights help our society?

Our research provides valuable insights that can significantly enhance cancer detection and diagnosis. Among the models we evaluated, HoverNet demonstrated the highest segmentation accuracy, making it the most reliable choice for precise cancer detection. BTUNet excelled in handling limited labelled data, proving the effectiveness of self-supervised learning while delivering more stable prediction results. Meanwhile, YOLOv8x-seg stood out for its speed, making it a strong candidate for real-time applications, though with a slight trade-off in segmentation accuracy.

Early cancer detection saves lives. By improving segmentation accuracy, enabling better auto-labelling, and reducing reliance on manual annotation, our research contributes to:

• Increased diagnostic precision, minimising the risk of false negatives.
• Automation of tedious tasks, allowing pathologists to focus on more complex cases.
• Enhanced accessibility, making advanced diagnostics feasible even in low-resource settings.

What are your goals for your project in future?

As I look ahead, I see several key directions in which to develop this project further. One major focus is to make the model more robust by training on a more diverse dataset to ensure better generalisation across different tissue types and conditions. Additionally, optimising model architectures for real-time deployment will help reduce processing times, making AI-assisted diagnostics faster and more efficient. Another exciting approach is integrating multi-modal imaging data; for example, by combining MRI with histopathology to provide a more comprehensive analysis of cancerous tissues. Ultimately, the goal is to apply AI-assisted diagnostic tools in real-world clinical settings and to bridge the gap between research and practical medical applications and thus improve patient care.

How did your studies in the Applied Information and Data Science programme influence the project?

My background in engineering, AI and deep learning provided the technical foundation for this research. Additionally, my studies at HSLU helped me develop a more structured problem-solving approach, which was crucial when working with large-scale medical datasets and conducting deep-learning experiments. More importantly, though, the invaluable support from my supervisor, Dr Umberto Michelucci, pointed me in the right direction and played a key role in shaping this project.

What advice would you give to others starting on similar projects?

Understanding the domain is crucial – working closely with medical experts ensures that AI models align with real-world needs and address practical challenges. At the same time, data quality is just as important as the model architecture. After all, preparing data from scratch can be as demanding as extracting oil from an offshore field, requiring significant effort and precision. Experimentation and iteration play a key role in improving performance, making it essential to try different models, loss functions, and augmentation techniques while also learning from existing research and best practices in the field. Lastly, patience is vital, as data-driven projects in med-tech are often time-consuming due to complex data structures and ethical considerations. But persistence and careful refinement will ultimately help you make meaningful progress.

And finally: What new hashtag are you aiming for in future?

#CollectiveLearning: I truly believe that progress in AI isn’t an individual pursuit – it’s a collective journey. By sharing research, collaborating across disciplines and keeping an open mind, we can learn from each other and move forward together. AI has the power to transform our world, but only if we build it transparently, inclusively and ethically. I want to be part of a future where knowledge is shared rather than hoarded and will improve everyone’s lives through the power of AI. Only by working hand in hand can we create an AI-powered world that benefits everyone.

We want to thank Morteza Kiani Haftlang for his dedication and for sharing these valuable insights.

Start your career with the MSc in Applied Information and Data Science now!
Register and join us for a free online Info-Event:

Monday, 26 May 2026 (Online, German)
Monday, 8 June 2026 (Online, German)
Monday, 10 August 2026 (Online, English)
Monday, 7 September 2026 (Online, German)
Monday, 5 October 2026 (Online, English)