Shen-Ning Tung

Group

Mon, 01 Jan 0001 00:00:00 +0000

I work with students at the intersection of digital finance, mathematical modeling, and AI. My goal is to help students build both the theoretical foundations and practical skills needed for research or industry in quantitative finance and data science — through problem-driven projects with real-world relevance.

I am looking for students and collaborators with interests in stochastic control / mathematical finance, machine learning / deep learning, quantitative research or algorithmic trading, or decentralized finance (DeFi). Most projects are in active early development, so there is genuine room to shape the direction. Feel free to reach out if any of the areas below interest you.

MATH 570: Quantitative Finance I (S26)

Mon, 01 Jan 0001 00:00:00 +0000

Turning Financial Theory into Computational Reality. As the foundational course in the Quantitative Data Science sequence, MATH 570 builds the rigorous mathematical, probabilistic, and statistical frameworks necessary for modern Quantitative Finance. We move beyond abstract theory to equip you with the tools needed for real-world financial modeling and data-driven decision-making.

Course Information

Instructor: Shen-Ning Tung (tung@math.nthu.edu.tw)
Lecture Time: Wednesdays, 14:20 PM – 15:10 PM and Fridays, 10:10 AM – 12:00 AM
Office Hours: By appointment
Target Audience: Upper-level Undergraduate and Graduate students in Quantitative Finance, Mathematics, and Data Science.

Course Description

Students will explore the core concepts of probabilistic modeling (including conditional independence and copulas) and mean-covariance statistics. The course bridges theory and practice through a balanced curriculum:

MATH 594: Numerical Computation with JAX (S26)

Mon, 01 Jan 0001 00:00:00 +0000

Turning theoretical math into high-performance code. This course provides a deep dive into JAX — a cutting-edge, high-performance numerical computing library in Python. We explore the potential for implementing scalable algorithms for large-scale data in fields such as probabilistic machine learning, scientific ML, and AI.

Course Information

Instructor: Shen-Ning Tung (tung@math.nthu.edu.tw)
Lecture Time: Wednesdays, 10:10 AM – 12:00 PM and Fridays, 2:20 PM – 3:10 PM
Office Hours: By appointment
Target Audience: Students interested in Differentiable Programming, Scientific ML, and Scalable AI.

A Cooperative Learning Environment

This course is designed as a cooperative learning community. Success in MATH 594 depends on students actively contributing to the collective mastery of JAX. Beyond standard lectures, students are expected to collaborate, share implementation strategies, and lead discussions on specialized tools. We function as a research-and-development cohort where peer feedback and collaborative troubleshooting are central to the experience.

MATH 597: Mathematical Foundations of Data Science (F24)

Mon, 01 Jan 0001 00:00:00 +0000

Unlock the power of data with mathematical precision.

Course Information

Instructor: Shen-Ning Tung (tung@math.nthu.edu.tw)
Lecture Time: Wednesdays, 3:30 PM – 6:00 PM
Office Hours: Available for questions until 7:00 PM after each lecture (may leave early if no one is in the classroom).

Evaluation

Weekly Problem Sets (20%)
Midterm Report (30%)
Final Project (50%)

Communication

Primary Platform: All course communication and announcements will be made on the course Discord channel.
Questions and Discussions: Please use public Discord posts for questions about course material. This fosters collaborative learning and allows instructors to address queries efficiently.
Private Matters: For private discussions or questions, send a direct message to the instructors on Discord.
Submissions: All notes and reports should be submitted via HackMD notes.

Course Project

The course project is an opportunity to delve deeper into the mathematical foundations of data science. You can work individually or in a group. Choose one of the following project types:

Research

Mon, 01 Jan 0001 00:00:00 +0000

I am a researcher working at the intersection of digital finance, mathematical modeling, and AI. My work combines rigorous mathematical tools with modern machine learning to understand and improve how digital financial markets are designed and operated. The central question is: can we replace hand-crafted rules and assumptions in market design with systems that learn directly from data?

This work spans two connected areas, both grounded in representation learning — systems that learn structure from data.

Talks

Mon, 01 Jan 0001 00:00:00 +0000

Invited Lectures

DeFi Under the Microscope: A Data Science Approach — Taiwanese Society of Industrial and Applied Mathematics (TWSIAM), Taipei, Taiwan (May 2024)

Seminar Talks

Replacing the Banker with a Function — NCCU Mathematics Colloquium, Taipei, Taiwan (March 2026) (Slides)

Decentralized Finance: Foundations and Innovations — NSYSU Mathematics Colloquium, Kaohsiung, Taiwan (March 2025) (Slides)

Automated Market Makers: A Mathematical Finance Perspective — University of Calgary Mathematical Finance Seminar, Calgary, Canada (February 2025) (Slides)

Teaching

Mon, 01 Jan 0001 00:00:00 +0000

National Tsing Hua University (2022–)

I primarily teach advanced courses focusing on the intersection of mathematics, finance, and data science.

Current Courses

MATH 570: Quantitative Finance I · MATH 594: Numerical Computation with JAX

Past Graduate Courses

MATH 597: Math Foundations of Data Science (Fall 2024) — Linear algebra, optimization, and probability for advanced ML and AI. (Link)

MATH 570: Financial Mathematics II (Spring 2024) — Data-driven quantitative methods: statistical learning, RL, and deep learning applied to financial problems. (Link)