Background

Motivation

Skills, expertise, and interests

• • machine learning
  • MLOps
  • software engineering
  • computer vision
  • cloud computing
  • end-to-end implementation
  • deep learning
  • data science
  • artificial intelligence
  • DevOps
  • engineering
  • pharmaceutics
  • code review
  • version control
  • problem solving

Background

Motivation

Skills, expertise, and interests

• artificial intelligence
• natural language processing (NLP)
• consulting
• work-life balance

Background 

Motivation 

Skills, expertise and interests 

• data science
• statistical and data modelling
• big data
• data analytics
• statistics and mathematics
• PhD advice
• software project management
• version control
• code review
• digitalisation
• life science
• engineering
• bioinformatics
• mathematical optimisation
• academic writing skills
• DevOps
• time management
• software engineering
• data management
• problem solving

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

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 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 

Background 

Background

Motivation

Skills, expertise, and interests

• • data science
  • machine learning
  • deep learning
  • artificial intelligence
  • data analytics
  • PhD advice
  • computer vision
  • image analysis
  • image processing
  • speech processing
  • pharmaceutics
  • audio processing

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 

I am a newly hired Numerical Specialist at Ørsted, trying to use my expertise in machine learning and statistics to innovate in the green transision. I was before a postdoctoral researcher and lecturer at the University of Helsinki working on sample-effiencient Bayesian inference with Luigi Acerbi. Prior to that, I was a postdoc at the University of Oxford and a Junior Research Fellow at Linacre College. There I worked with Michael Osborne’s Bayesian Exploration Lab. I obtained my PhD degree under Søren Hauberg’s supervision at the Technical University of Denmark. My thesis is available here. For all publications please visit my Google Scholar and for short news you can follow me on Twitter. Research Interests Gaussian Processes, Scalable Bayesian Inference, Bayesian Quadrature and Optimisation, Sample-efficienct methods, Probabilistic Numerics.

Motivation 

Skills, expertise and interests 

• data science
• machine learning
• artificial intelligence
• data analytics
• statistical and data modelling
• statistics and mathematics
• time series analysis
• mathematical optimisation
• environmental sciences & sustainability

Background

Background 

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

In 2019, I moved to Denmark as a postdoc to research the genetic factors responsible for brain disorders using the large publicly available biobanks and registers. In 2022, I joined Roskilde University as an assistant professor. My research interests focus on using genetic, bioinformatic, and epidemiological tools to better understand neurological and and endocrine disorders. I am also interested in personalized medicine and genetic prediction of diseases.

Motivation

As a first-generation college graduate, my initial years in college were quite challenging. Fortunately, I had the privilege of being mentored by a professor whose lab I joined for a research project. Her exceptional guidance introduced me to the world of scientific research and PhD programs. Inspired by her support, I am passionate about helping students navigate the research community, just as I was supported during my journey as a young scientist.

Skills, expertise, and interests

• • data science
  • data visualisation
  • big data
  • PhD advice
  • health and medical science
  • bioinformatics

Background 

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

Background

Motivation 

Skills, expertise and interests 

• data science
• machine learning
• deep learning
• artificial intelligence
• data analytics
• statistical and data modelling
• MLOps
• PhD advice
• causality and explainability
• computer vision
• work-life balance
• academic writing skills
• big data
• data visualisation
• mental health and well-being
• data mining
• career planning or change
• problem solving
• leadership
• time management
• image analysis
• image processing
• engineering

Background

I have an academic background in data analysis from CERN and Yale. I spent a period in the private sector, working in the AI Solutions department at Grundfos. I currently conduct research in AI and sustainability, and I’m passionate about applying my data expertise to drive positive impact.

Motivation

Given the rapid pace of data science, it’s crucial to uphold ethical principles. I’d be thrilled to contribute to shaping a new generation of data professionals who understand the importance of these values

Skills, expertise and interests

• data science
• machine learning
• artificial intelligence
• data analytics
• big data
• data management
• PhD advice
• digitalisation
• environmental sciences & sustainability

Background

Experienced analytics leader with a broad profile. Chartered Financial Analyst with additional post-graduate degrees in Bioinformatics and Economics. Avid reader and believer in the value of strong processes and continued education. Strong inter-personal and collaboration skills, extensive stakeholder management experience as well as a glass-half full attitude. 10+ years of professional experience with quantitative methods and data science, including 3+ years of data science leadership experience with HR responsibility.

Motivation

Skills, expertise, and interests

• • machine learning
  • deep learning
  • data science
  • artificial intelligence
  • data analytics
  • career planning or change
  • data visualisation
  • leadership
  • time management
  • economics, finance, and business
  • bioinformatics

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 

Motivation 

Skills, expertise and interests 

• data science
• machine learning
• deep learning
• artificial intelligence
• data analytics
• statistical and data modelling
• career planning or change
• problem solving
• time series analysis
• mental health and well-being

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

I earned my Ph.D. in Computer Science from the University of Malaya in 2014 and subsequently embarked on a professional journey that led me to Denmark in 2015. Before joining the Technical University of Denmark (DTU), I gained valuable experience in healthcare informatics at the University of Southern Denmark (SDU). My expertise spans across various domains including data analytics, data visualization, software technologies, and social network analysis, with a primary focus on data-driven health technology. Since joining DTU in April 2022, I have shifted my focus to bioengineering. My research at DTU Bioengineering revolves around life science automation and digitalization, where I leverage data science and artificial intelligence to drive advancements in biology and biotechnology. Specifically, I am dedicated to enhancing bioengineering processes through synergistic approaches with data science, digitalization, and automation.

Motivation

As a seasoned data scientist, my motivation for joining the DDSA Mentoring Programme stems from a deep-seated commitment to fostering the growth and development of aspiring data scientists. By participating in this program, I aim to provide guidance, support, and practical insights to the next generation of data scientists, helping them navigate the complexities of the industry and accelerate their learning curve.Beyond individual growth, I’m also excited about the opportunity to contribute to the broader data science community and cultivate a culture of collaboration and knowledge-sharing.

Skills, expertise, and interests

• data science
• machine learning
• deep learning
• artificial intelligence
• data analytics
• big data
• data visualisation
• data management
• data mining
• computer vision
• image analysis
• bioinformatics

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

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

Motivation

Skills, expertise, and interests

• • data science
  • career planning or change
  • PhD advice
  • project management
  • leadership
  • social (data) science
  • teamwork
  • mental health and well-being
  • environmental sciences & sustainability
  • academic writing skills
  • network science
  • complex systems and chaos
  • complexity science
  • sustainable transportation
  • urban planning
  • geospatial data science
  • transcultural communication
  • forming sustainable habits
  • DEI

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 

Background 

Background 

Background 

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 

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

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

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

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 

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