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 Deep Learning

Topic Interests

🎲

Stochastic Analysis

Broadly interested in the theoretical study of large stochastic systems, especially on Rare Events and Heavy Tails

🧠

Learning Theory

Currently studying the convergence theory of deep reinforcement learning algorithms.

🎨

Generative
Modeling
& Sampling

Understanding the probabilistic underpinnings of both generative models and sampling algorithms.

Me in Colorado

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. During the global pandemic, I began as an undergrad exploring Actuarial Science/Accounting/Data Science at Nankai University for 2 years, while also joining short-term programs on basic computer science at Peking University, Fudan University, and Oxford University. 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
Algorithms for Machine Learning Prof. Alina Ene
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
Rare Events and Heavy-Tailed Phenomena: from Queuing and Insurance Ruin to Random Graph and Complex Network Advisor: Prof. Mariana Olvera-Cravioto
Non-stationary RL and Auctions in Real-time Bidding Environment Mentor: Prof. Guanting Chen
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
Markov Decision Process & RL Prof. Guanting Chen
Simulation Modeling Prof. Mariana Olvera-Cravioto
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
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. Libin Huang, 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 Oxford Internet Institut at Oxford University
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 Dr. Andrew Ng et al. - Coursera
Deep Learning Specialization Dr. Andrew Ng et al. - Coursera
Generative AI with Large Language Models Dr. Chris Fregly et al. - Coursera
Neural Networks: Zero to Hero Dr. Andrej Karpathy
Introduction to Flow Matching and Diffusion Models Peter Holderrieth
LMS Lectures 2026: Extreme Value Theory for Heavy-Tailed Time Series Prof. Thomas Mikosch

Experience

Teaching

I will be teaching CAS MA 581 Probability in Summer 2026. 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

Let's Connect!

Get in Touch

I'm always glad to connect—whether it's sharing ideas in mathematics, exploring collaborations, making friends and learning together, or enjoying tennis, basketball, poker, biking, photography, road trips, and music theory. I'd be delighted to meet and grow with people wherever we are.
🎾 🏀 🃏 🚴 📸 ⛰️ 🎵

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