Wancheng Lin

1st-year PhD student · Probability & Stochastic Processes

Hi, thanks for visiting! I go by Walden, and I am currently studying Stochastic Analysis and Probabilistic Foundation for Generative Models

Topic Interests

🎲

Stochastic Analysis

Broadly interested in the theoretical study of large stochastic systems, especially on rare events and heavy tails

🤖

Generative Models

Understanding the probabilistic underpinnings of generative models and their theoretical guarantees.

🔗

Blockchain

Building decentralized systems, aligning consensus and incentives with the transformative potential of DeFi, InfoFi, and AI agents.

Contact

wancheng@bu.edu

Office 338

Duan Family Center for Computing & Data Sciences

Department of Mathematics and Statistics

Boston University

665 Commonwealth Ave

Boston, MA 02215

Education & Training

Now, I am a first-year Ph.D. student in the Department of Mathematics and Statistics at Boston University, and I received my Bachelor's degree from the University of North Carolina at Chapel Hill in 2025.

My educational path has been unconventional yet deeply rewarding. I began as an undergrad exploring Actuarial Science/Accounting/Data Science at Nankai University for 2 years during the global pandemic, while also joining short-term programs on basic computer science at Peking University, Fudan University, and University of Oxford. Before coming to the U.S., I completed a Globalink Mitacs internship at the Université de Montréal, Canada. Later, I transferred to UNC, where I studied @MATH and @STOR for 3 years, also taking a course at Duke University. Alongside formal study, much of my growth has been shaped by open platforms such as YouTube, Bilibili, 得到 (Dedao), Coursera, and edX. Now, as a Ph.D. student in the Department of Mathematics and Statistics at Boston University, I remain sincerely grateful for the high-quality education I have received throughout this journey—and above all, for the privilege of learning from truly exceptional mentors and teachers along the way.

Probability Theory Prof. Konstantinos Spiliopoulos

Advanced Stochastic Processes Prof. Solesne Bourguin

Mathematical Foundations of DL Prof. Konstantinos Spiliopoulos

Probability Theory II Prof. Solesne Bourguin

Statistical Machine Learning Prof. Luis Carvalho

Deep Visual Generative Models Prof. Boqing Gong

Numerical PDE* Prof. Samuel Isaacson

Advanced Calculus I Prof. Hans Christianson

Advanced Calculus II Prof. Michael Taylor

Topology Prof. Jiuzu Hong

Algebraic Structure Prof. Alexander Varchenko

Measure Theory & Integration Prof. Mark Williams

Ordinary Differential Equations (H) Dr. Jian Wang

Partial Differential Equations Prof. Jeremy Marzuola

Functional Analysis Prof. Idris Assani

Probability II Prof. Sayan Banerjee

Mathematical Statistics Prof. Jan Hannig

Theoretical Statistics II Prof. Andrew Nobel

Methods of Data Analysis Dr. Souvik Ray

Stochastic Models I Prof. Nilay Argon

Stochastic Models II Prof. Serhan Ziya

Simulation Modeling Prof. Mariana Olvera-Cravioto

Markov Decision Process & RL Prof. Guanting Chen

Auction and Learning Theory Mentor: Prof. Guanting Chen

Optimization for ML & DS Prof. Michael O'Neil

Data Driven Decision Models Prof. Vidyadhar Kulkarni

Advanced Probability for Applications Prof. Shankar Bhamidi

Weak convergence and local weak convergence Prof. Zoe Huang

Dynamic Programming & Stochastic Control (Duke) Prof. Peng Sun

Practical Web Design & Development for Everyone Prof. Tessa Nicholas

Rare Events and Heavy-Tailed Phenomena: from Queuing and Insurance Ruin to Random Graph and Complex Network Advisor: Prof. Mariana Olvera-Cravioto

Bayesian Robust Statistics with Applications in Actuarial Science Globalink Mitacs Research Intern Mentor: Philippe Gagnon

Introductory Microeconomics

Intermediate Microeconomics

Introductory Macroeconomics

Intermediate Macroeconomics

Investment

Theory of Interest

Financial Accounting Prof. Le Zhao

Financial English Prof. Yifang Chu

Finance Prof. Xue Wang

Insurance and Risk Management

Corporate Finance

Data Structures and Algorithms

Econometrics Prof. Tingting Cheng

Adv Algebra and Analytic Geometry Prof. Dongxiao Han

Probability Prof. Lidan Wang

Actuarial Modelling Prof. Lianzeng Zhang

Machine Learning and R Prof. Lianzeng Zhang

Dynamic Decision Making Prof. Xiaowei Chen

Python, Summer School Prof. Bin Chen — Peking University

Internet Data Analysis & Research University of Oxford

Summer School, School of Economics Fudan University

Measure Theory Prof. Claudio Landim

Mathematics of Deep Learning 22 Prof. Joan Bruna and Dr. Carles Domingo

Machine Learning Specialization Andrew Ng et al. - Coursera

Deep Learning Specialization Andrew Ng et al. - Coursera

Generative AI with Large Language Models Chris Fregly et al. - Coursera

Neural Networks: Zero to Hero Andrej Karpathy

Introduction to Flow Matching and Diffusion Models Peter Holderrieth

Experience

Teaching

I have served as a Teaching Fellow for CASMA 116 Statistics II in Fall 2025 and Spring 2026, and I enjoy working with students to make classical and essential statistics clearer, more intuitive, and less intimidating — and perhaps spark curiosity about how these ideas actually connect to modern AI :)

Serendipity

I'm incredibly grateful to have crossed paths with so many brilliant minds and kindred spirits throughout my journey. Each encounter has been a gift. These experiences have not only deepened my love for probability theory and stochastic analysis, but also shown me how to connect the dots between theory and real-world impact.

ISBA World Meeting 2022 Montreal, Canada

STORFest 2023 Chapel Hill, NC

INFORMS Annual Meeting 2024 Seattle, WA

Extreme Value Analysis 2025 Chapel Hill, NC

Boston Diffusion Day 2025 Cambridge, MA

极客公园创新大会 2019 Beijing, China

ETHDenver 2025 Colorado, USA

Solana Scale or Die & Ship or Die 2025 NYC, USA

SolanaBoston & ETHBoston 2025 Boston, USA