PBL
J.P. Morgan Project
Machine Learning in Quantitative Finance
Master machine learning for financial analysis, modeling, and risk management through practical, industry-relevant projects.
Project
J.P. Morgan Project
Location
Online
Duration
8 Weeks
Upcoming Sessions
Feb. 23 - Apr. 19, 2026
Outcomes
Machine Learning techniques for financial predictions
Financial Modeling for asset price forecasting
Advanced risk management strategies
Quantitative Analysis of market data
Time Series Analysis for asset returns
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.
.png)
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
J.P. Morgan employs advanced machine learning and financial modeling techniques in wealth management and structured note development. This PBL program mirrors these industry practices, preparing students to meet real-world demands by providing hands-on experience in predicting asset prices, calibrating financial models, and optimizing portfolios.
Popular Industry Positions
Quantitative Analyst
Uses statistical and machine learning models to analyze financial data.
Risk Manager
Identifies and mitigates financial risks using advanced modeling techniques.
Financial Engineer
Develops and implements models for pricing complex financial instruments.
Tracks
Track 1
Chronological or Horizontal Prediction of Asset Prices/Returns
Students will apply machine learning to predict asset prices and returns, a core component of financial analysis influencing profit and loss directly.
Time Series Analysis: Gather and analyze historical price or return data.
Predictive Modeling: Employ models to forecast future trends using techniques like ensemble learning, linear regression, or dimensionality reduction.
Market Analytics: Focus on predicting movements of stocks, volatility indices, and cryptocurrencies.
Risk Assessment: Evaluate performance based on forecast accuracy.
Track 2
Advanced Model Calibration on Financial Instrument Prices
Students will calibrate models for pricing complex financial instruments, crucial for assessing risks and setting premiums accurately.
Model Calibration: Adjust parameters of sophisticated valuation models using market data from standard European-style options.
Risk Quantification: Enhance calibration precision and efficiency with supervised learning techniques.
Stochastic Modeling: Develop algorithms to fit models on real-time data.
Financial Engineering: Evaluate models based on accuracy and computational efficiency.
Track 3
Consumption-Investment Problems in Incomplete Markets
Students will navigate consumption-investment challenges in incomplete markets, focusing on optimal investment strategies and risk management.
Portfolio Optimization: Formulate investment strategies considering market constraints like illiquidity and transaction costs.
Risk Management: Understand clients' financial goals and constraints to propose suitable investment solutions.
Market Simulation: Use neural network-based algorithms to simulate asset price trajectories.
Dynamic Programming: Optimize portfolios aiming to minimize specific loss functions in real-world financial scenarios.
Track 4
Efficient Valuation of Multi-Asset Financial Contracts - The PIDE Approach
Students will focus on the advanced valuation of multi-asset financial contracts using the PIDE method, enhanced by neural networks.
Numerical Analysis: Apply neural networks to solve PIDEs for complex derivatives like rainbow options.
Complex Valuation: Improve valuation accuracy and reduce computational effort.
Neural Networks: Utilize advanced methodologies for financial engineering in multi-asset pricing.
Computational Finance: Explore the impact of increasing dimensionality on computational effort.
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.
.png)
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
Assistant Professor of Mathematics, University of Southern California (USC)
Dr. Xia is a distinguished researcher in mathematical finance, with affiliations to the Finance Group at MIT Sloan School of Management and Boston University's Questrom School of Business. His current research spans a wide array of topics, including investment strategies under incomplete preferences, pricing exotic derivatives, and exploring the dynamics of cryptocurrency markets. Additionally, Dr. Xia focuses on the optimal control of set-valued stochastic processes, applications of Lévy functionals, measures of market illiquidity, and the development of neural network architectures designed for these complex financial 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.
