PBL
Novo Nordisk Project
AI and Computer Vision in BioTech
Leverage AI and computer vision to transform medical imaging and accelerate biotech innovations in clinical trials.
Project
Novo Nordisk Project
Location
Online
Duration
8 Weeks
Upcoming Sessions
Mar. 02 - Apr. 26, 2026
Outcomes
Interpreting AI results for clinical decision-making.
Training and validating machine learning models in healthcare.
Integrating AI-driven insights into biotech innovations
Utilizing Python and TensorFlow for AI model development.
Applying deep learning to image segmentation tasks.
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
Novo Nordisk, a global leader in healthcare and biotech, focuses on developing treatments for chronic diseases. This PBL aligns with their innovation strategy by using AI and computer vision to enhance medical imaging for clinical trials. Novo Nordisk could apply these technologies to automate patient recruitment, assess treatment efficacy, and improve research efficiency in drug development.
Popular Industry Positions
Medical Imaging Analyst
Apply AI for disease detection and diagnosis.
Clinical AI Engineer
Design AI solutions for clinical trials.
Data Scientist in Biotech
Analyze medical data and develop AI models.
Tracks
Track 1
Foundation Models for Image Classification
Students will explore the application of foundation models for image classification in clinical trials, particularly for automating patient recruitment.
Learn how image classification models automate the analysis of medical images like CT scans, MRIs, and ultrasounds.
Focus on identifying patients who meet clinical criteria, streamlining the recruitment process for clinical trials.
Use advanced tools like Python, TensorFlow, PyTorch, and other AI/ML frameworks for medical imaging.
Master techniques for data preprocessing, model training, and validation in real-world clinical trial settings.
Contribute to improving trial efficiency and patient outcome selection through AI-driven methods.
Track 2
Foundation Models for Image Segmentation
Students will focus on using foundation models for image segmentation, crucial for analyzing medical images in clinical trials to measure disease markers and assess treatment efficacy.
Gain hands-on experience in segmenting medical images like MRIs and CT scans to measure structures like tumors and organ volume.
Apply advanced segmentation techniques to enhance clinical trial assessments and primary endpoint evaluations.
Leverage tools like MONAI, Python, TensorFlow, and PyTorch for developing, training, and deploying segmentation models.
Learn about pre-processing, image annotation, and validating model performance.
Integrate segmentation results into clinical trial workflows to improve the accuracy and reliability of trial outcomes.
Track 3
Generative Models for Anomaly Detection
Students will develop generative models to detect anomalies in medical imaging, enhancing clinical trial safety and treatment monitoring.
Explore the use of generative models like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) for anomaly detection in medical images.
Learn how to identify deviations in images that could indicate disease, treatment responses, or unexpected side effects in clinical trials.
Use advanced frameworks like NVIDIA Clara along with Python, TensorFlow, and PyTorch for developing generative models.
Gain practical experience in training models and evaluating their performance in detecting medical anomalies.
Interpret the clinical significance of anomalies to enhance patient monitoring, safety, and efficacy assessments in trials.
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
Senior Imaging Scientist at Novartis and Researcher at MIT
He specializes in developing AI and machine learning algorithms for clinical trials. With a Ph.D. from MIT, his research focused on using ultrasound imaging and medical robotics for 3D organ mapping. He co-founded a medical robotics startup to enhance anesthesiologists’ interventional procedures and later co-founded a second startup creating augmented reality glasses for retinal diseases. He completed a postdoctoral fellowship at Massachusetts General Hospital, translating advanced medical imaging and AI research into commercial applications. At Novartis, he leads technical strategy efforts for integrating AI into clinical trials.
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.
