Join the Upcoming Free PBL Session
PBL
Spotify Project
AI in Marketing Personalization
Build and evaluate personalized text and image generation systems using cutting-edge generative AI techniques for real-world marketing use cases.
Project
Spotify Project
Location
Online
Duration
8 Weeks
Upcoming Sessions
Winter 2026
Outcomes
Understand scalability challenges in personalized AI systems
Conduct A/B testing for generative content output
Evaluate personalization effectiveness with user metrics
Explore LLM prompting techniques for better control
Build multimodal models integrating text and images
You Will Get
Industry Guidance
Work directly with our project leads—experts and top researchers—who bring their real-world insights and expertise straight to your learning experience.
Research Experience
Collaborate with teammates and the project lead in a multi-week project to pursue novel questions in your research field.
Peer Networks
Engage with our PBL participants from all over the world. Collaborate with new peers and learn about their own research endeavours.
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A Strong Portfolio
Put your best foot forward in the PBL with a standout project and receive a PBL Evaluation Report that can be used as a recommendation letter for employers and grad schools.
Expert Guidance
Get personalized feedback to grow your research and innovation skills.
Deliverables
Real projects, lasting connections, and new opportunities beyond your program.
Project Deliverables
The final presentation of your 8 weeks could be a poster, written report, or a slide deck, all of which can be expanded on.
Research Extension
Utilize up to 5 additional meeting times with the project lead after the project’s conclusion to build your work out for publication or conference presentation.
Industry Network
Meet peers in your projects and participate in a global talent community both online and in-person.
Industry Application
Spotify is a leader in personalized content delivery, using AI to tailor music, podcast, and ad experiences to billions of users. This PBL reflects real-world challenges in content recommendation, user modeling, and generative personalization—key areas of innovation at Spotify and other tech platforms. Students will gain practical exposure to text and image generation, multi-modal modeling, and preference-aware learning, aligning directly with AI use cases in streaming, e-commerce, and digital advertising.
Popular Industry Positions
AI Product Analyst
Evaluate performance of GenAI-driven recommendation systems.
Data Scientist (NLP/Multimodal)
Analyze and model user behavior from text and visual signals.
Machine Learning Engineer
Build and deploy personalization pipelines for user content.
Tracks
Track 1
Scaling Named Entity Recognition (NER) with Generative AI
Students will build and compare NER systems using both fine-tuned encoder models and prompt-based LLM approaches to explore efficiency and accuracy tradeoffs.
Implement NER with fine-tuned encoder models (e.g., BERT)
Apply few-shot prompting using large language models
Benchmark BERT vs. LLM-based NER on performance metrics
Design hybrid pipelines that combine classification and generation
Optimize for accuracy, latency, and inference cost
Track 2
Context-Aware Chatbot with Retrieval-Augmented Generation (RAG)
Students will develop a chatbot system that uses knowledge graphs and RAG to enhance contextual understanding and response accuracy.
Extract semantic triples using dependency parsing
Build and query a Neo4j graph database for context storage
Use SPARQL and LLMs for context-aware retrieval
Vectorize knowledge using Graph Neural Networks (GNNs)
Compare chatbot outputs with vs. without RAG integration
Track 3
Text-to-Image Personalization for Advertising
Students will create a personalized advertising system that uses multimodal AI models to generate and select high-impact visual content.
Pretrain or fine-tune ViT + BERT for image captioning
Generate visuals using Stable Diffusion tailored to user profiles
Rank images using LLMs for click-through prediction
Benchmark against conventional deep learning models
Analyze personalization impact using CTR and engagement metrics
PBL Journey
Online PBL Projects meet once a week for 8 weeks, and follow the research project format. Participants will meet the project lead, learn the conventions of the field and familiarize themselves with the tracks, then spend the middle portion of their time collaborating to develop their research.
At the end, participants will present their final project and receive feedback, with the opportunity to extend their timeline and develop the project in greater depth.
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Project Team
Our Academic Team plays a vital role in your PBL journey at Blended Learning. We are dedicated to enhancing your learning experience and ensuring your academic success. Our team consists of three distinct roles, each with a specific focus to support your Research Guidance, Project Progress, and Personal Growth.
Project Lead
Providing Industry and Research Guidance
Staff Research Scientist, Epsilon
He is a Staff Research Scientist at Epsilon, where he has led the development of machine learning models for advertising personalization at scale—impacting billions of users and processing petabytes of data. He earned his Ph.D. in Computer Science from the University of Illinois at Chicago in 2022, with a research focus on multimodal conversational agents. His academic and industry work has centered on state-of-the-art generative AI, personalization, and scalable deployment of LLM-powered applications. Through internships and thesis research, he has contributed to advancing AI-driven user engagement and natural interaction systems.
Academic Advisor
Tracking Your Project Development
The Academic Advisor is dedicated to your project completion success. They manage the progress of your PBL, guiding team formation, facilitating group discussions, and resolving conflicts. Additionally, the Academic Advisor ensures team member contributions are on track and provides logistical support, including attendance tracking, hosting recitation sessions, managing research support requests, and conducting student evaluations at the end of the PBL.
From Our Students
"After a night spent debugging, I suddenly discovered the program running perfectly. In that triumphant moment, you realize your true capability and success. The exhaustion fades, replaced by the thrill of knowing your skills and persistence led to this achievement, reaffirming your potential."
Nicole Y.
National University of Singapore
B.S. Economics
FAQs
What is the learning format of a PBL?
All PBLs are offered in an 8-week online format that begins with an orientation followed by subject setup overview of the different tracks. The majority of the session time is dedicated to project development, with a final presentation at the culmination of the 8 weeks. Many PBLs are also offered bi-annually in an on-campus format that consists of daily in-person meetings.
How long does each PBL cohort last？
One round of the Online PBL cohort lasts 8 weeks, preceded bys a pre-PBL orientation week. Each On-Campus PBL usually has 8 in person meetings, with intensive classroom education and collaboration. This means the biggest difference between online and on-campus PBLs is time participants have in between meetings.
How can I be more academically prepared before the PBL starts?
Review the Blended Learning Insights sent by the Academic Advisor and familiarize yourself with the project topic and pre-learning materials. Ensure you have all necessary softwares and other resources needed for the PBL.
For each PBL cohort, will I work in teams? Are PBL team members self-selected or assigned?
Yes, you will work in teams for each round of the PBL Cohort. Each team has 3 to 6 participants, organized by the Academic Team. The Academic Advisor will organize groupings based on students' backgrounds, preferred track, and skills.
Can I work with the Project Lead on my project after the PBL ends?
Yes, with your AI + X Research Plan, you may request up to five PBL Research Extension meetings, where you work with the project lead to develop your project into a working manuscript. To schedule a PBL Research Extension meeting, talk to your Academic Advisor at the conclusion of your PBL.
What do I receive at the end of the PBL?
At the conclusion of the PBL cohort, you can request a PBL Evaluation Report which summarizes the PBL content, the hours you spent, the track you chose, and includes a recommendation letter from the Project Lead (for eligible participants who completed the project successfully).
Is attendance mandatory for PBL Live Sessions and Recitation Sessions?
Yes, attendance is mandatory for both PBL Live Sessions and Recitation Sessions. Participants with three or more unexcused absences forfeit their eligibility for a PBL Evaluation Report.
Do I need to have my camera on during online PBL Live Sessions?
Yes, you must have your camera on during online PBL Live Sessions. Participants with cameras off will be marked as absent. This is meant to encourage active engagement and participation in meetings.