Background

I am an Associate Professor with interests spanning databases and systems to machine learning. I specialize in networks and graph analysis and enjoy the rich complexity of network data as a mean to represent relationships. Originally from Italy, I have moved to Germany, and then Denmark. I enjoy collaborative work, challenging problems, and puzzles. In my research I focus on all aspects of information from the management of data on storage, to extracting new insights. I am passionate about eduction and training students and I enjoy discussions with them.

Motivation

I enjoy spending time with students and hope to pass on what I’ve learned—however modest—throughout my journey to future generations. I find data science especially exciting, as it bridges rigorous mathematical foundations with real-world applications. As a mentor, I also see opportunities to grow personally and to engage in conversations about the broader challenges life presents, exploring possible solutions together. I strongly believe that education and knowledge are the pillars of a thriving society—but these cannot be separated from personal development and well-being.

Skills, expertise, and interests

• data science
• machine learning
• deep learning
• artificial intelligence
• data analytics
• career planning or change
• big data
• data management
• PhD advice
• data mining
• project management
• leadership
• time management
• mental health and well-being
• academic writing skills
• code review
• work-life balance

Background

As a globally experienced Mathematics and Biostatistics professional, I bring a unique perspective to my work in Data Science and Machine Learning, with a focus on addressing challenges in public health, with a growing interest in sustainability, climate change, and sustainable food systems. My educational journey took me from the University of Alberta to Université Paris-Saclay, and I defended my PhD at Université Paris Cité. Throughout my career, I have developed a strong command of R for data analysis and machine learning and Python for deep learning, continually updating my skillset through MOOCs and earning various Specializations and Professional Certificates in Data Science, Machine Learning, Deep Learning, MLOps, TinyML, GAN, and more. With three first-author publications in The Lancet Public Health, Nature Medicine, and Nature Communications, I strive to make meaningful contributions to a multidisciplinary field of study with data science and artificial intelligence.

Motivation 

Born in South Korea, I have gained valuable experience studying and working across South Korea, Canada, the United States, France, and Denmark. I like the idea of growing together and I belive that participating in this mentorship program would help expand my network in Denmark.

Skills, expertise, and interests 

• • data science
  • machine learning
  • deep learning
  • artificial intelligence
  • MLOps
  • data visualisation
  • career planning or change
  • problem solving
  • PhD advice
  • work-life balance
  • time management
  • data analytics
  • health and medical science
  • mental health and well-being
  • environmental sciences & sustainability
  • academic writing skills
  • complexity science

Background

I am a passionate Data Scientist with over 10 years of experience in building end-to-end Artificial Intelligence products in the Telecom, Healthcare, and Financial Services industries. With robust technical and leadership skills, I have applied AI to various business situations, helping organizations adopt data-driven solutions. Over the past decade, I have applied various machine learning methods such as classification, regression, time series analysis, and clustering, leveraging strong domain knowledge. I have deployed and maintained models in production on both Cloud and on-premise systems. I have collaborated with product managers, stakeholders, and business owners to effectively implement AI products across business units. I have worked with structured, semi-structured, and unstructured data, including near real-time and batch processing of data. I have also mentored Data Scientists and Machine Learning Engineers and helped them in their careers.

Motivation

As a seasoned Data Scientist, my motivation for joining the mentoring program stems from my desire to give back to the data science community, my thirst for continuous learning, and my enthusiasm for connecting with professionals from diverse backgrounds. I am deeply passionate about supporting young and aspiring data scientists. I believe that the field of data science is vast and constantly evolving, making it challenging for newcomers to navigate. By sharing my experiences and insights, I hope to provide guidance and support to those who are just embarking on their journey in this field. While I aim to impart knowledge, I also look forward to learning from the fresh perspectives and innovative ideas that mentees often bring to the table. This continuous learning process is what keeps me motivated and helps me stay abreast of the latest trends and technologies in data science. I am excited about the potential impact I can make and eagerly look forward to the journey ahead.

Skills, expertise, and interests

• data science
• machine learning
• deep learning
• artificial intelligence
• statistical and data modelling
• data analytics
• MLOps
• big data
• data visualisation
• natural language processing (NLP)
• problem solving
• statistics and mathematics
• data management
• causality and explainability
• time series analysis
• data mining
• computer vision
• cloud computing
• leadership
• high-performance computing
• engineering
• version control

Background 

Background

As a Mechatronic Engineer with a PhD in Automatic Control, I excel in mathematical modeling, nonlinear control, and signal processing. Proficient in Python, MATLAB/Simulink, Julia, and C, I have a strong grasp of machine learning and optimization techniques. Passionate about innovating AI applications to enhance business productivity, my interdisciplinary project experience has refined my skills in algorithm development and large data analysis, enabling me to tackle complex problems effectively.

Motivation

I want to join the mentoring program because I want to share what I’ve learned over the years and help others grow in their careers. I love the idea of inspiring others, especially when it comes to AI and advanced control systems.

Skills, expertise, and interests

• machine learning
• statistics and mathematics
• problem solving
• engineering

Background

After completing my studies in Geoinformatics at Aalborg University in Copenhagen, I gained experience as a GIS/IT consultant, where I worked on satellite image processing, scripting, and full-stack development using technologies such as Python, C#, Java, and Angular. I later transitioned into a backend development role, where I became involved with Building Information Modeling (BIM) and developed solutions for data management through both scripting and low-code platforms. Currently, I’m working as a Solutions Architect in the IT department at Ørsted.

Motivation

I’m passionate about sharing knowledge and supporting individuals who are looking to build a career in IT. Starting out in this field can feel overwhelming—there’s so much to learn, and it’s hard to know where to begin. I’ve been fortunate to have great mentors who helped guide me early on, and that support made a huge difference in shaping my path. Now, I’m eager to pay it forward by helping others navigate their own journeys, sharing the insights and experiences I’ve gained along the way.

Skills, expertise, and interests

• data science
• data analytics
• big data
• software engineering
• project management
• consulting
• leadership
• work-life balance
• business innovation
• strategy
• digitalisation
• software project management
• version control
• geospatial data science
• cloud computing
• data visualisation
• data management

Background

Juan Manuel Rodriguez is an Assistant Professor at Aalborg University. He earned his Ph.D. in Computer Science from Universidad Nacional del Centro de la Provincia de Buenos Aires in 2012. His research focuses on applied machine learning, deep learning, including multi-modal data. Notable publications include studies on applied image, and text analysis, and recommender systems. He is also an NVIDIA DLI Certified Instructor and University Ambassador.

Motivation

​As a mentor, I aim to enhance collaboration between academia and the private sector by guiding mentees in applying data science and machine learning techniques to real-world challenges. This engagement is intended to foster complex partnerships and drive applied research initiatives that benefit both industry and academia.

Skills, expertise, and interests

• • data science
  • machine learning
  • deep learning
  • artificial intelligence
  • PhD advice
  • software engineering
  • image analysis
  • natural language processing (NLP)

Background

I have a BSc in Software Engineering and a MSc in Human-Centered AI. I have worked as a data scientist and consultant at Todai since graduating in the beginning of 2024. A few months ago I also became the technical advisor for Hello Ada, a Danish startup building a learning platform for kids powered by AI. Additionally, I worked as a student research engineer on the AI team at Uizard (now acquired by Miro), and collaborated with the Pioneer Centre of AI and Vivino on a multimodal project that created a flavor space for wine, resulting in a public dataset and a NeurIPS paper.

Motivation 

I want to support aspiring data scientists from diverse backgrounds and specifically those that might be underrepresented in the field thrive within the data science community in Denmark. My goal is to help remove obstacles and create more opportunities for underrepresented groups to succeed in Danish data science roles.

Skills, expertise, and interests 

• • data science
  • machine learning
  • deep learning
  • artificial intelligence
  • natural language processing (NLP)
  • problem solving
  • computer vision
  • software engineering
  • consulting
  • social (data) science
  • image analysis
  • image processing

Background

My research interests focus on using genetic, bioinformatic, and epidemiological tools to better understand neurological and psychiatric disease.

Motivation

I love giving advice and support to younger scientists. I was able to get to this position because of support I was given as a young scientist.

Skills, expertise, and interests

• bioinformatics
• big data
• statistics and mathematics
• PhD advice
• health and medical science
• mental health and well-being
• academic writing skills

Background

I have my bachelor’s degree in Robotics, from which I transitioned to Data Science during my masters, focusing on specifically computer vision and MLOps. I had to luck to work throughout my studies and rotate through three different R&D departments before landing my full-time position at Novo Nordisk, which allowed me to be exposed to a high variety of projects ranging from academic to technical. Some of the most recent ones were predictive maintenance, fine-grain image analysis, multi-task learning for mobile robotics and productionizing ML solutions within the highly restricted pharmaceutical industry.

Motivation

During my studies and worklife, I have been enjoying great guidance and insights from professors and industry mentors, which shaped my career direction significantly and let me meet a lot of great people. I think it is very relevant to create these relationship for growing one’s network, getting exposed to new topics or perspectives and to be able to pose questions to someone outside one’s peer group. I am still relatively early in my career with only 4 years of industry experience, which means that the experience of transitioning from studies to worklife is still fresh in my memory and I would be happy to pay forward all the guidance I got along the way!

Skills, expertise, and interests

• data science
• machine learning
• deep learning
• artificial intelligence
• MLOps
• computer vision
• cloud computing
• academic writing skills
• code review
• version control

Background

I am a political science researcher and educator with a strong interest in social data science methods. I have a PhD from the Centre for Doctoral Training in Digital Civics at Newcastle University, and most recently I have worked as a research associate at the Technical University of Munich’s Hochshule für Politik. Among other things, I have developed a masters level course on Computational Methods for political science students, and I have supervised many masters theses that utilised social data science methods. My primary research interests revolve around the intersection of digital technologies and democracy. In particular, I do research on political participation, democratic innovations, and civic participation with an emphasis on the role of digital media in this context.

Motivation

I decided to join the DDSA mentoring programme because I have recently relocated to the Copenhagen area and would like to build connections with people here, who have an interest in social data science. One of the best parts of my job as a university researcher has been to supervise and teach talented students. I enjoy seeing the academic interests of my students develop and find great pleasure in being able to contribute to their career ambitions. So I am very interested in sharing my personal experiences and getting to know any mentees with an interest in social data science, especially if they are also interested in politics and democracy.

Skills, expertise, and interests

• PhD advice
• natural language processing (NLP)
• career planning or change
• data analytics
• statistical and data modelling
• data mining
• social (data) science
• text analysis
• academic writing skills

Background

I’ve had a special focus on social data science, and am now a data scientist and consultant working with information retrieval, a bit of NLP and demand forecasting. I have experience on the technical side and project management / client relations side.

Motivation

I had a great mentor in the same program who helped me a lot. I’d like to pass it on.

Skills, expertise, and interests

• data science
• machine learning
• artificial intelligence
• data analytics
• consulting
• project management
• social (data) science
• network science
• sustainable transportation
• geospatial data science

Background

Xin Sui received the M.Sc. degree in from Chinese Academy of Sciences, Beijing, China, in 2018 in electrical engineering. In 2022, she received the Ph.D. degree in machine learning for battery state of health estimation from Aalborg University, Aalborg, Denmark. She was a visiting researcher with the Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, in 2023, and with the Department of Mechanical Engineering, Imperial College London, London, UK, in 2024. She is currently an Assistant Professor in the Department of Energy, Aalborg University, Denmark. Her research expertise lies in smart battery management, covering 1) Lithium-ion battery modeling; 2) Battery diagnostics and prognostics including state estimation, health assessment, and lifetime prediction; 3) Advanced battery management techniques, including pulse charging strategies, and balancing; 4) Feature engineering, machine learning, and AI application in battery system; 5) Smart battery technology.

Motivation

My motivation stems from both a sense of responsibility and a passion for supporting early-stage researchers. Having faced the challenges of initiating research topic myself, I understand how difficult it can be for young data scientists to find a clear direction amidst concerns about innovation, and time management, etc. Over the past few years, I have had the opportunity to supervise both PhD and Master’s students and observed that many have strong capabilities, but often struggle to identify the key focus of their work. I believe my experience can guide them more efficiently toward impactful outcomes. I also see this as a mutually beneficial opportunity. Mentoring helps me improve my own leadership and pedagogical skills through reflection and feedback. Moreover, I believe that collaboration often sparks creativity and fresh perspective. I hope to engage in meaningful discussions that could lead to new research ideas. I am excited about the potential of building a supportive, collaborative environment where knowledge flows both ways and new insights emerge naturally.

Skills, expertise, and interests

• • data science
  • machine learning
  • deep learning
  • data analytics
  • statistical and data modelling
  • PhD advice
  • time series analysis
  • data mining
  • digitalisation

Background 

I studied Physics for my Bachelor and Master Degree and proceeded to do a PhD in Observational Astrophysics. After doing a postdoc at KU, I decided to make a career transition to Data Science and the private sector. I’ve been a data scientist for a few years now, working as a consultant in the last two, implementing technical solutions and managing takeholder engagement. On the technical side, I would put myself in the ‘classical machine learning’ field: random forests, parametric models, and sprinkle of statistics are what excite me!

Motivation 

I had a couple of informal mentors during my career, and they really helped me understand my own preferences and strengths better, so I would love to be able to give back and have that kind of discussion with a mentee. And it is also a great opportunity to expand my data science network in Denmark!

Skills, expertise, and interests

• • • data science
    • machine learning
    • data analytics
    • statistical and data modelling
    • MLOps
    • career planning or change
    • problem solving
    • statistics and mathematics
    • consulting
    • project management
    • work-life balance
    • data visualisation
    • teamwork
    • mental health and well-being

Background

Iannis is a Data Science professional with nearly two decades of experience driving digital transformation by leveraging Artificial Intelligence and Cloud solutions to revolutionize Research & Development in Life Sciences. He has successfully led AI integration efforts across pharmaceutical, biotech, and public institutions, adeptly navigating complex change management processes during periods of high uncertainty. Iannis has built multiple high-performing teams and programmes, operationalized data and analytics at scale, and delivered first-in-class AI products. His work consistently translates into improved patient outcomes and measurable business growth. Coming from academia, Iannis has developed deep expertise in the pharmaceutical industry, having served as Head of Data Engineering (Novo Nordisk), Head of Data Science (Lundbeck), member of EFPIA, EMA, and FDA working groups, and Public-Private Partnership (PPP) lead with Pfizer, J&J, Bayer, and AZ. Iannis’ work also includes collaborations with biotech companies (Illumina, Beckman Coulter, BD Biosciences) on software solutions, advancement of Data Science and AI in public institutions (the Danish National Vision for Personalized Medicine, European Society for Clinical Cell Analysis, Sorbonne University, University of Patras), and recognized contributions to open-source communities (EHDEN, OHDSI, i2b2–tranSMART, FHIR, OBO). In his current role as Chief AI Officer at GENerX.life, Iannis continues to provide strategic advisory to pharmaceutical, biotech, and public organizations on data, AI, and digital transformation initiatives, gaining unique insights from Europe, the USA, and Asia. Iannis holds a PhD in Artificial Intelligence in Medicine and has more than 20 peer-reviewed publications and book chapters.

Motivation

The Danish Data Science Academy is one of the networks that makes Denmark, and Data Science in Denmark, unique. DDSA brings together academia, the public sector, industry, and individuals to exchange ideas, share knowledge, discuss trends, and collaboratively shape the future of Data Science. Participating in DDSA activities is both rewarding and enjoyable.

Skills, expertise, and interests

• • forming sustainable habits
  • entrepreneurship and startups
  • digitalisation
  • life science
  • bioinformatics
  • academic writing skills
  • end-to-end implementation
  • mental health and well-being
  • teamwork
  • health and medical science
  • strategy
  • high-performance computing
  • computational science
  • leadership
  • cloud computing
  • project management
  • PhD advice
  • data management
  • big data
  • MLOps
  • artificial intelligence
  • deep learning
  • machine learning
  • data science

Background

Andreas has 10+ years experience researching and working with AI & data in academia, as a specialist in the biomedical industry and as a consultant. Andreas has a MSc in biomedical engineering and holds a PhD in machine learning and cognitive neuroscience.

Motivation

I’ve been fortunate to have been given some great advice and support from competent people in my career in AI and data. I see the DDSA mentorship program as a good opportunity for me to pay some of it forward.

Skills, expertise, and interests

• • data science
  • machine learning
  • deep learning
  • artificial intelligence
  • data analytics
  • statistical and data modelling
  • MLOps
  • career planning or change
  • big data
  • data visualisation
  • natural language processing (NLP)
  • problem solving
  • statistics and mathematics
  • data management
  • causality and explainability
  • time series analysis
  • PhD advice
  • consulting
  • project management
  • cloud computing
  • computational science
  • work-life balance
  • time management
  • social (data) science
  • health and medical science
  • mental health and well-being
  • end-to-end implementation
  • engineering
  • life science
  • forming sustainable habits

Background

Motivation

I am excited to join the mentoring programme as a mentor to support and inspire the next generation of data scientists and AI researchers. Having navigated the challenges of academia and research, I understand the value of guidance and community. I hope to share my experience in computer vision, self-supervised learning, and foundation models to help mentees grow. Mentoring is also a chance for me to reflect, improve my communication skills, and expand my professional network through meaningful connections.

Skills, expertise, and interests

• deep learning
• machine learning
• artificial intelligence
• PhD advice
• computer vision
• image analysis

Background

I’m a geographer with passion for geospatial data and the interactions between data, space, people and environment. I did my education in Geography and Geoinformatics, whereI first specialized in urban planning and more recently in geoinformatics and geospatial data, where I was able to publish some work during my studies. During my work experience, I’ve been able to work on many different projects, under a large diversity of themes, which I think is one of the coolest parts of working with geospatial data. Besides the processing and manipulation of data, my daily work is focused on the analysis of geospatial data, as well as building systems and workflows that facilitate the production of geospatial solutions. What I am passionate about is the ability to combine both the technology and the analytics framed by different disciplines and geographies, where different sciences can influence the approach to take. As examples, at my current work I’ve been part of mapping historical stonewalls in Denmark using Deep Learning, mapping rural agents for financial inclusion in Ghana, network analysis on local mobility for the future Light Rail train in Copenhagen, or mapping invasive species using satellite imagery and machine learning in Denmark.

Motivation

At my work I’ve had the opportunity to supervise students that did their master thesis or projects at our department. This experience was quite enriching, and also motivates me to apply for the mentorship program. I saw this as an exchange of knowledge and experiences, as well as a way to support aspiring professionals, with the great addition of witnessing their exciting contributions to the field. Furthermore, I believe that this will also be an important contribution for my personal growth and a learning experience, where I also hope to contribute in any way can to the mentees development path.

Skills, expertise, and interests

• machine learning
• deep learning
• data analytics
• data management
• computer vision
• consulting
• image analysis
• image processing
• urban planning
• geospatial data science
• spatial optimizations

Background

Dr. Fariba Ahmadizar is an epidemiologist with a strong background in genetic and molecular (pharmaco)epidemiology. She holds a PhD in Epidemiology and a Master’s in Pharmacoepidemiology. Her research focuses on Multi-Omics studies (including cardiometabolic disorders, mainly diabetes), interaction between genetic and environmental factors and the effectiveness and safety of medications, with an interest in prediction algorithms. She has published over 100 papers in leading journals. Dr. Ahmadizar has also applied advanced methodologies, including Mendelian Randomization, and is an expert in meta-analysis. With a proven track record in mentoring, she has supervised more than 10 PhD students and more than 25 master’s, bachelor’s, and intern students. Her in-depth research expertise and dedication to student development make her an excellent mentor for those seeking to advance their knowledge and skills in the epidemiology of chronic disorders and public health

Motivation 

My motivation for joining the mentoring program stems from a commitment to supporting the next generation of data scientists and researchers. I am passionate about sharing my knowledge in epidemiology and public health to help young and aspiring scientists navigate their academic and professional paths. Mentoring also provides an opportunity for personal growth through learning from fresh perspectives and staying connected to emerging trends. Additionally, I see this program as an excellent platform for shaping future leaders in data science and research.

Skills, expertise, and interests 

• data science
• machine learning
• data analytics
• health and medical science
• life science

Background

Originally trained in mathematics and philosophy, I initially did PhD and postdoc within logic/good-old-fashion-AI, but then left academia to become a data scientist in industry. After 4 years in industry, I returned to academia as assistant professor at Roskilde University. In addition to projects applying data and machine learning to various applied problems, I am also doing research on ethical and societal aspect of data science and AI, as well as philosophy of science aspects of data science and AI. Finally, I am also interested in social network analysis and information dynamics on social media, such as polarization and echo chambers.

Motivation

I hope to support young aspiring data scientists especially in their choice between academia and industry, as I have tried both. Moreover, I hope to expand my network within data science in Denmark across academia and industry.

Skills, expertise, and interests

• • • data science
    • machine learning
    • artificial intelligence
    • deep learning
    • data analytics
    • statistical and data modelling
    • career planning or change
    • data visualisation
    • problem solving
    • statistics and mathematics
    • PhD advice
    • causality and explainability
    • social (data) science
    • teamwork
    • end-to-end implementation
    • network science
    • complex systems and chaos
    • complexity science
    • digitalisation

Background

With a PhD in ML and 12+ years of experience in the industry, I have developed and delivered many ML solution to various business. It’s all around taking ML research (eg. papers and open-source code) and apply to a real-world problems and ensuring it actually runs in production. In practice I have built recommender systems, textual embeddings, user behaviour models, Bayesian bandits, Bayesian AB-test systems to name a few 🙂

Motivation

I have always been in the industry and enjoyed both supervising and lead young talent within ML and Data Science. This has lead to many skilled professional today both in startups and commercial companies. Thats’ my motivation – to support and help young talent make an impact for themselves and evolve in their career.

Skills, expertise, and interests

• data science
• machine learning
• deep learning
• artificial intelligence
• data analytics
• statistical and data modelling
• natural language processing (NLP)
• data visualisation
• statistics and mathematics
• PhD advice
• big data
• data management
• time series analysis
• data mining
• project management
• cloud computing
• leadership
• computational science
• work-life balance
• time management
• business innovation
• text analysis
• strategy
• teamwork
• end-to-end implementation
• mathematical optimisation
• academic writing skills
• engineering
• digitalisation
• code review
• version control
• software project management

Background

I have a background in time series analysis as well as computer vision and in general data science. I have work in defense where data is a sparse resource and in cloud where data was coming in form of 24 hours video surveillance. I have supervised multiple master thesis in the company I have worked in and I am experienced in a team lead role. I am used to help my team both as a more academic both also as MLops and software development with in data science. I have also developed and end to end machine learning platform which is a hybrid between cloud and local setup

Motivation

I join this program as I love to help other and that is part of why I took a PhD. I thrive by having the team lead role where I help others especially as a mentor role. I am focused on details in work but have also been in multiple companies so I can also give career advice and where to focus depending of what a person wants to achieve

Skills, expertise, and interests

• data science
• machine learning
• deep learning
• artificial intelligence
• data analytics
• statistical and data modelling
• MLOps
• data visualisation
• problem solving
• time series analysis
• computer vision
• image analysis
• teamwork
• image processing
• data mining

Background

I am an assistant professor in energy with a profound commitment to addressing climate change through the lens of AI. With extensive experience at prestigious institutions like IIT, NUS, MIT, and University of Edinburgh, my research focuses on developing energy-efficient algorithms to meet the soaring computing demands of our energy systems, while also exploring the transformative potential of neuromorphic computing in enhancing the security and reliability of critical infrastructure. Passionate about education and innovation, my vision is to drive sustainable solutions with significant contribution to science for a greener future.

Motivation

Excited to embark on mentoring in neuromorphic computing. It’s a cutting-edge field pushing the boundaries of conventional computing. With its potential to revolutionize AI and computing, raising awareness and providing training is crucial. Joining as a mentor allows me to share insights, nurture young talent, and contribute to shaping the future of computing architecture in Denmark.

Skills, expertise, and interests

• machine learning
• deep learning
• artificial intelligence
• computational science
• end-to-end implementation
• digitalisation
• neuromorphic computing

Background

I am an assistant professor at Aalborg University working in the intersection of machine learning, and graph theory. My academic journey began with a Bachelor’s degree in Mathematics from Bogazici University, followed by a Master’s degree at the same university. I obtained my Ph.D. degree from Paris-Saclay University, and during my doctoral studies, I was interested in the representation learning methods for predictive graph-structured data and published several papers in high-impact journals and conferences. At Aalborg University, I have been working on the metagenomics binning problem and on the integration of heterogeneous environmental factors with bacterial genome data. I also teach a course including various topics of machine learning and data science, and I supervise both undergraduate and graduate students.

Motivation

I would like to join the DDSA Mentoring Program as a mentor because I believe in the value of guidance and knowledge sharing. I am passionate about helping young and aspiring data scientists achieve their personal and professional growth. Mentoring not only benefits the mentees but also offers me the opportunity to grow. Engaging with mentees brings fresh perspectives, encourages the exchange of ideas, and helps expand my professional network both within and beyond the academic community.

Skills, expertise, and interests

• data science
• machine learning
• artificial intelligence
• statistical and data modelling
• bioinformatics
• network science

Background

I am a last year PhD student at Aalborg University. I have been studying machine learning and AI both on my free time and as a part of my research. I have also supervised several student projects regarding theoretical and practical aspects of ML/DS. Seeing how theoretical constructs are translated into actual working software is something in am strongly interested in. My research focuses on finding methods to reduce dynamical models. Before, I studied theoretical mathematics and engineering. So I would be happy to discuss either the theory or engineering applications of AI.

Motivation

My main motivation is to share what I have learned with younger researchers/aspiring data scientists. I know the field can be intimidating at parts but I can ensure it is rewarding. Moreover, I had an awesome experience as a mentee during the last edition of the program. Finally, one of the best exercises is to answer questions from people that are new to the field. It is satisfying to find new ways to understand concepts and share them with others.

Skills, expertise, and interests

• artificial intelligence
• deep learning
• machine learning
• data science
• MLOps
• statistics and mathematics
• PhD advice
• time series analysis
• data mining
• software engineering
• work-life balance
• time management
• high-performance computing
• mathematical optimisation
• academic writing skills
• bioinformatics
• engineering
• version control

Background

Motivation

Skills, expertise, and interests

• data science
• data analytics
• statistical and data modelling
• career planning or change
• data visualisation
• statistics and mathematics
• data management
• time series analysis
• consulting
• PhD advice
• social (data) science
• leadership
• work-life balance
• academic writing skills

Background

My main research interest lies in the management of graph data, including data analytics, learning and data privacy. I am interested in studying new methods and algorithms, as well as developing system prototypes and proof of concepts. On the teaching side, I am involved in the Data Science and Machine Learning education at Aalborg University, where I teach Data Privacy and Graph Analytics and Learning, and I supervise semester projects. I hold a PhD in Information Technology from Politecnico di Milano. Before joining Aalborg University, I was a postdoc at the University of Zurich, and a research fellow at the University of Aberdeen. During my PhD, I also spent a summer at IBM Research Ireland as a research intern and a trimester at WU Vienna as a visiting student. Before getting enrolled in the PhD program, I worked for a few years as a consultant and researcher at CEFRIEL, a digital innovation centre in Milano.

Motivation

Throughout my career, I have met various mentors that have guided me and contributed to letting me achieve my objectives. I feel extremely lucky for these opportunities. Following my mentors example, I aim to help young and aspiring data scientists pursue their goals. I am interested in discussing various aspects of the data scientist career, including career planning and PhD life.

Skills, expertise, and interests

• • data science
  • machine learning
  • deep learning
  • artificial intelligence
  • data analytics
  • career planning or change
  • big data
  • data management
  • PhD advice
  • project management
  • time management
  • academic writing skills
  • forming sustainable habits
  • bioinformatics
  • mental health and well-being

Background

Ramtin is a senior research engineer and data scientist with a multidisciplinary background in biomedical engineering, artificial intelligence, and machine learning. He holds a PhD in Biomedical Science and Engineering from Aalborg University and has over a decade of experience spanning academia, industry, and clinical research. His recent work focuses on AI applications in healthcare, with emphasis on explainable and trustworthy machine learning for clinical decision support. He has developed open-source tools such as the Medical AI Toolbox and the ExplaineR R package, and has contributed to research on precision medicine, biomedical signal processing, and healthcare technologies. He has collaborated with medical doctors, data scientists, and engineers in both academic and industry settings and has mentored students and professionals from various disciplines.

Motivation

Through this mentoring program, He hopes to raise awareness within AI communities about real-world challenges and promote discussions around best practices. By sharing insights from applied research, he aims to help the next generation of data scientists recognize often-overlooked issues in AI development and implementation, encouraging responsible and impactful solutions.

Skills, Expertise, and Interests

• • data science
  • machine learning
  • deep learning
  • artificial intelligence
  • MLOps
  • statistics and mathematics
  • PhD advice
  • causality and explainability
  • bioinformatics
  • engineering
  • digitalisation
  • computational science
  • cloud computing
  • statistical and data modelling

Background

I’m passionate about the power of data science and AI to solve real-world challenges, and I’m equally passionate about helping aspiring professionals navigate this exciting and complex field. My journey has taken me through diverse roles – from deep technical application to strategic leadership – and I’m eager to share the lessons learned along the way. While I’ve recently founded my own venture, C7intelligence, focused on accelerating business value through data and AI, my primary goal here is to connect with and support emerging talent.

Motivation

My diverse background, spanning strategic leadership, AI architecture, hands-on ML/DL/RL application, generative AI, IoT, and team building, provides a broad perspective. I believe I can help you by: Bridging Theory and Practice: Sharing insights on how theoretical concepts translate into real-world projects and business impact. Navigating Career Paths: Discussing the different roles within data science (technical specialist, architect, strategist, leader) and how to develop the necessary skills. Tackling Complex Problems: Offering frameworks and perspectives for breaking down challenging technical or strategic issues. Understanding Industry Applications: Providing context on how AI/ML is used across different sectors (like smart home tech and industrial applications). Developing Strategic Thinking: Helping you see the bigger picture beyond the code or algorithm. Fostering a Growth Mindset: Encouraging continuous learning and adaptability in this rapidly evolving field.

Skills, expertise, and interests

• • data science
  • machine learning
  • deep learning
  • artificial intelligence
  • data analytics
  • statistical and data modelling
  • MLOps
  • career planning or change
  • big data
  • data visualisation
  • natural language processing (NLP)
  • problem solving
  • statistics and mathematics
  • data management
  • PhD advice
  • causality and explainability
  • time series analysis
  • data mining
  • computer vision
  • software engineering
  • consulting
  • project management
  • cloud computing
  • social (data) science
  • leadership
  • work-life balance
  • time management
  • image analysis
  • high-performance computing
  • business innovation
  • economics, finance, and business
  • text analysis
  • strategy
  • teamwork
  • DevOps
  • image processing
  • end-to-end implementation
  • health and medical science
  • mathematical optimisation
  • environmental sciences & sustainability
  • speech processing
  • academic writing skills
  • engineering
  • network science
  • digitalisation
  • code review
  • version control
  • freelancing
  • software project management