Teaching

Applied AI, Data Science, and Responsible Digital Systems Education

Prof. Dr. Jalali teaches courses at the intersection of artificial intelligence, data science, recommender systems, machine learning, data management, and social computing, with a focus on practical implementation, responsible use, and interdisciplinary applications.

01 Recommendation Systems and Media Personalization 02 Statistics and Machine Learning 03 Data Management 04 Social Media Analytics and Sentiment Analysis

Current courses

Courses with applied, research-informed practice

Current teaching combines foundations, hands-on implementation, reproducible workflows, and critical discussion of how digital systems shape scientific, professional, and social contexts.

01

Recommendation Systems and Media Personalization

Foundations and modern directions in recommender systems for personalized media, platforms, and digital services.

Key topics

Content-based filteringCollaborative filteringHybrid recommendersEvaluation metricsEthics and filter bubblesLLM-based, multimodal, graph-based, reinforcement-learning, and responsible recommenders

Methods and tools

  • Python
  • Jupyter/Colab notebooks
  • GitHub exercises
  • Real-world recommendation datasets
  • Group projects

Learning outcomes

  • Design and evaluate recommender pipelines
  • Compare personalization strategies
  • Reflect on platform influence, fairness, and responsible deployment

02

Statistics and Machine Learning

Statistical foundations and machine-learning concepts for data-driven reasoning, modeling, evaluation, and reporting.

Key topics

Descriptive statisticsProbability and distributionsSampling and central limit theoremInference and hypothesis testingCorrelation and regressionClassification, regularization, unsupervised learning, time series, and reporting

Methods and tools

  • Python
  • Jupyter/Colab
  • Hands-on notebooks
  • Model evaluation exercises
  • Applied datasets

Learning outcomes

  • Apply statistical reasoning
  • Build and evaluate machine-learning models
  • Report findings clearly and reproducibly

03

Data Management

Principles and practical workflows for managing, documenting, cleaning, and sharing data in research and professional settings.

Key topics

FAIR principlesMetadataData qualityCleaning and profilingResearch data managementData management plansRepositories, Dublin Core, DataCite, schema.org, and graph data management

Methods and tools

  • Data profiling exercises
  • Metadata templates
  • Repository workflows
  • Graph data examples
  • Research-data case studies

Learning outcomes

  • Create data management plans
  • Improve data quality and documentation
  • Use metadata standards for reusable and findable data

04

Social Media Analytics and Sentiment Analysis

Analytical methods for social media data, text signals, public discourse, network behavior, and responsible interpretation.

Key topics

Social media data collectionText preprocessingSentiment analysisOpinion miningSocial network analysisEngagement analyticsMisinformation and bias awareness

Methods and tools

  • Python notebooks
  • Text analytics pipelines
  • Network analysis examples
  • Real-world social platform datasets
  • Ethical reflection

Learning outcomes

  • Analyze social media datasets
  • Build sentiment and opinion-mining workflows
  • Interpret engagement and network patterns responsibly

Previous teaching areas

Established teaching foundations

Earlier teaching activities connect semantic technologies, web intelligence, data mining, artificial intelligence, and responsible AI.

Semantic Web

Web Mining

Data Mining

Artificial Intelligence

Responsible AI

Teaching philosophy

Learning by doing, with critical reflection

Teaching bridges theory and practice through reproducible notebooks, GitHub-based exercises, real-world datasets, group projects, and active discussion. Students are encouraged to build working systems while reflecting on reliability, bias, explainability, privacy, and social impact.

Learning by doing through notebooks, code, and applied exercises

Bridging theory and practice with real-world datasets

Responsible and trustworthy AI as a recurring design principle

Interdisciplinary examples from media, health, materials science, business, and social platforms

GitHub/Jupyter-based reproducible learning

Active discussion, peer exchange, and critical reflection

Supervision record

Graduate research mentoring

15+

Primary PhD supervisions

100+

Primary Master's supervisions

5+

Secondary PhD supervisions

50+

Secondary Master's supervisions

Supervision and collaboration

Student and thesis supervision is especially aligned with applied AI, materials informatics, graph learning, LLM-enabled research workflows, optimization algorithms, semantic technologies, and responsible data-centric decision support.

Course resources

Selected learning infrastructure

Public materials are shared only when appropriate. Private Moodle spaces and internal course repositories are not exposed publicly.

Moodle

Access through official university course spaces

Public course materials

Selected materials may be added when available