Prof. Dr. Ioana RADU
Senior Data Scientist & AI Systems Consultant. PhD in Artificial Intelligence
“Artificial Intelligence is not about replacing humans—it’s about amplifying human potential. My mission is to teach how to use AI responsibly, creatively, and effectively.”
About Me
I am a senior data scientist with over 10 years of experience in artificial intelligence, data analytics, and machine learning applications across finance, healthcare, and education. Currently working as Head of Data Science at a multinational technology firm in Berlin, I lead a team focused on developing AI-driven solutions that improve decision-making and automate complex workflows.
My passion for technology began with an interest in how data can tell stories. Over time, I transitioned from software engineering into advanced analytics, focusing on ethical AI and applied machine learning. I have designed predictive systems used by organizations across Europe and North America, and I now share that expertise with professionals and students eager to master AI tools.
I believe in teaching through experimentation. My courses emphasize hands-on projects, ethical awareness, and real-world applications—bridging the gap between academic knowledge and practical implementation.
Academic Credentials
PhD in Artificial Intelligence
Dissertation: “Explainable AI Models for Transparent Decision-Making in Healthcare Systems”
Master of Science in Data Analytics
(2012) Graduated with Distinction – University of Bucharest
Bachelor of Science in Computer Science
(2010) – Technical University of Cluj-Napoca
Professional Certifications
TensorFlow Developer Certificate (2023)
Google | Certification ID: TFD-2023-9821
Microsoft Certified: Azure AI Engineer Associate (2022)
Microsoft | Credential ID: AI-ENG-87654
IBM Data Science Professional Certificate (2021)
IBM | Coursera Credential
AWS Certified Machine Learning – Specialty (2023)
Amazon Web Services
Professional Experience
Head of Data Science
| 2020 – Present
- Leading a team of 10 data scientists and engineers in building scalable AI models
- Designed and implemented predictive analytics pipelines processing 2TB+ data daily
- Introduced ethical AI framework adopted company-wide
- Mentored over 20 junior analysts and AI engineers
- Technologies: Python, TensorFlow, PyTorch, Spark, Azure ML, PostgreSQL
Senior Data Scientist
| 2016 – 2020
- Built NLP models for multilingual sentiment analysis (90%+ accuracy)
- Developed recommendation systems increasing client engagement by 45%
- Designed model monitoring tools reducing drift detection time by 60%
- Technologies: Python, Scikit-learn, AWS SageMaker, Docker, SQL
Data Analyst / Research Fellow
| 2012 – 2016
- Conducted applied research on data-driven decision systems
- Published peer-reviewed studies on fairness in machine learning
- Collaborated on EU-funded AI research projects
- Technologies: R, Python, Tableau, Power BI
Publications & Speaking
Conference Presentations:
- “Building Fault-Tolerant Systems at Scale” – DevOps Summit Europe, Berlin (2022)
- “Microservices Best Practices from the Trenches” – France IT Conference, Paris (2021)
- “Cloud Architecture for Financial Services” – FinTech Innovation Forum (2020)
Technical Blog: 50+ articles on software architecture and development
Publications & Speaking
Conference Presentations:
- “Ethical AI in Practice” – European Data Science Summit, Amsterdam (2024)
- “Interpretable Machine Learning for Healthcare” – AI4Health Conference, Munich (2023)
- “The Future of Human-Centered AI” – Women in Tech Europe, Vienna (2022)
Technical Blog:
60+ articles on machine learning, ethics, and data visualization
Teaching Experience
Corporate Training Instructor
TechCorp| 2018 – Present
- Delivered 50+ training sessions on applied machine learning and data ethics
- Topics: Deep Learning, Data Visualization, Responsible AI
- Trained over 300 professionals globally
- Average rating: 4.9/5.0
- Courses: Data Science, AI Ethics, Applied Statistics
- Supervised 15 master’s theses in AI applications
- Member of the curriculum advisory board
Private Tutoring & Mentoring
Independent | 2014 – Present
- Mentored 40+ students transitioning to AI and data careers
- 90% of mentees employed within 6 months
- Focus: portfolio building, model evaluation, ethical considerations
What I Teach
1. Introduction to Data Science (Beginner to Intermediate)
- Data collection, cleaning, and visualization
- Python for data analysis (Pandas, NumPy, Matplotlib)
- Exploratory data analysis and basic statistics
2. Machine Learning Fundamentals (Intermediate)
- Supervised & unsupervised learning
- Model evaluation and optimization
- Real-world ML projects: prediction, clustering, classification
3. Deep Learning & Neural Networks (Advanced)
- Neural network architecture
- CNNs, RNNs, Transformers
- Practical projects: image recognition, NLP, time-series forecasting
4. AI Ethics & Responsible Innovation (All Levels)
- Bias detection and mitigation
- Explainability and transparency
- Legal and social implications of AI
5. Data Visualization & Storytelling (All Levels)
- Building dashboards with Tableau and Power BI
- Visual storytelling for decision-makers
- Communicating insights effectively
Teaching Approach & Methodology
Philosophy:
Learning AI means building with data, not just reading about it. Every session includes real datasets, live coding, and reflection on ethical use.
Learning Framework:
- Concept Overview → Live Demonstration → Guided Practice → Project Application → Feedback & Revision
Key Strengths:
- Project-Based Learning: Build AI solutions from day one
- Real-World Relevance: Tools and practices used in 2025’s data industry
- Personalized Mentorship: Adapts to your pace and goals
- Ethical Focus: Understand not just how, but why AI matters
Tools Used:
Zoom | Google Colab | GitHub | Notion | Slack
Student Success Stories
Testimonial 1:
“Alexandra helped me move from basic data analysis to machine learning confidently. Her guidance was both technical and strategic—now I work as a data scientist at a major fintech company.”
— Laura P., Data Scientist, Warsaw
Testimonial 2:
“Her mentorship is transformative. I learned not just algorithms but how to think critically about data ethics and impact. Every session was incredibly engaging.”
— Tomasz K., AI Engineer, Berlin
Testimonial 3:
“Alexandra’s sessions were structured, motivating, and focused on real projects. I built a complete NLP model as part of our lessons—now I use it in my daily work.”
— Julia N., Machine Learning Specialist
Rates & Scheduling
Hourly Rates:
- Individual Sessions: 270 PLN/hour (€60/hour)
- 10-Session Package: 2,400 PLN (10% off)
- 20-Session Package: 4,500 PLN (17% off)
Group Sessions (2–4 students):
- 420 PLN/hour total (split among participants)
Availability:
- Weekdays: 17:00 – 21:00 CET
- Saturdays: 09:00 – 15:00 CET
- Sundays: By appointment
Booking:
- Send an email with your background and goals
- Schedule a free 20-minute introduction call
- First session setup and customized learning plan
Cancellation Policy:
- 24h notice: No charge
- <24h: 50% fee
- No-show: Full fee
Languages
Romanian – Native
English – Fluent (C2)
German – Intermediate (B1)
Professional Memberships
- Association for the Advancement of Artificial Intelligence (AAAI) – Member since 2015
- IEEE Computer Society – Senior Member since 2018
- Women in Machine Learning (WiML) – Active Mentor
Contact & Connect
Email: prof.dr.ioana_radu@tychy-university.edu.pl
LinkedIn: [linkedin.com/in/]
GitHub: – See my open-source contributions
Schedule Consultation: [calendly.com/]
Additional Information
Work Environment: Dedicated office with high-speed internet, dual 4K monitors, and professional audio-video setup.
Student Requirements: Laptop with Python installed; internet connection; enthusiasm to learn.
Preferred Student Profile: Curious minds interested in data-driven innovation.
Current Projects: Leading a cross-European AI initiative on medical data transparency and open-source fairness libraries.
I appreciate the insights shared in this article regarding the role of AI in enhancing human potential rather than replacing it. It’s refreshing to see a professional with a strong ethical focus on AI development and deployment. The emphasis on responsible innovation is crucial, especially as we continue to integrate AI into various sectors like finance and healthcare. It makes me curious about how these principles can be applied in practical scenarios.
‘Learning through experimentation’ stands out as an effective philosophy from this article that can encourage innovation among students pursuing careers related to artificial intelligence and data analytics. Practical experience seems essential for grasping complex theoretical concepts, particularly when it comes to topics such as deep learning or neural networks where trial and error can lead to valuable insights over time.
‘Interpretable Machine Learning’ is an area that interests me significantly because transparency within algorithms is key for trust-building among end-users of any technology based solution developed today! I’m intrigued by Dr.Radu’s work focusing on explainability frameworks; it’s crucial we create systems where decisions made by machines are understandable by humans—not just technical experts but society at large.
‘Artificial Intelligence amplifying human potential’ is a powerful statement made by Dr. Radu that resonates deeply within current technological discussions today. With increasing concerns around job displacement due to automation, it’s vital that experts advocate for using technology as a tool for enhancement rather than replacement. I’m curious if she has any predictions regarding future trends in AI ethics as technology continues evolving.
I found Dr. Radu’s insights into ethical AI particularly noteworthy, especially given the rapid advancements in this field. The idea that teaching should incorporate ethical awareness alongside technical skills is critical for developing responsible professionals in data science and AI engineering. It raises questions about how educational institutions can adapt their curricula to reflect these needs better.
The balance between technical expertise and ethical considerations presented by Dr. Ioana Radu is commendable. As someone who is exploring a career in data science, I find her approach to teaching through experimentation very appealing. It seems that hands-on projects really help in understanding complex concepts better. I would love to know more about her views on how ethical AI can be implemented effectively in real-world situations.
The approach outlined by Dr. Radu regarding project-based learning is something that could greatly benefit students entering fields like data science or machine learning. By bridging academic knowledge with practical implementation, learners are better prepared for industry challenges ahead of them. Her focus on mentorship also signifies the importance of guidance during one’s educational journey, which often makes all the difference.
Dr. Radu’s extensive background in both academia and industry gives her a unique perspective on the challenges faced in AI today. Her dedication to mentoring aspiring professionals is inspiring and speaks volumes about her commitment to fostering the next generation of data scientists. I am particularly interested in her training sessions focused on applied machine learning, as they seem well-structured for real-world application.
This article highlights important aspects of Dr. Ioana Radu’s career, showcasing not just her qualifications but also her vision for the future of AI education. The mention of designing predictive systems that are used widely across Europe and North America illustrates the impact of effective AI solutions on decision-making processes. It would be interesting to learn more about specific case studies she might have encountered during her work.