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About

LU Shan

I come from a background shaped by physics, statistics, and I am gradually moving toward a clearer research direction in theoretical corporate finance.

My undergraduate training at The Hong Kong Polytechnic University, began in physics, but it never stayed inside a single boundary. Over time, I found myself drawn toward probability, statistics, optimisation, and the kinds of questions that reward both structure and patience.

My final-year project in electron ptychography taught me something I still value: difficult problems rarely yield to speed. They require time, repeated framing, careful writing, and the willingness to stay close to uncertainty until the logic begins to show itself.

I am now increasingly drawn to theoretical corporate finance. What attracts me is the balance between rigorous modelling, uncertainty, and economic meaning. I do not think of this as abandoning my earlier training; I think of it as giving that training a new centre.

Outside formal research, I keep notes and write steadily. For me, writing is not separate from thinking — it is one of the ways thought becomes precise enough to keep. This page is the fuller outline behind the homepage: background, direction, method, and a few ways to reach me.

What Shapes My Work

The broader pattern behind the homepage.

Foundation
Cross-disciplinary grounding
Physics taught me patience with formal structure. Statistics gave me a language for uncertainty. Mathematics keeps me honest whenever ideas begin to drift into vagueness.
Direction
Research in transition
My present interest is increasingly centred on theoretical corporate finance — especially questions that deserve rigorous models rather than surface-level commentary.
Method
Notes as working method
I rely on notes, re-derivation, and careful writing as working tools. They slow me down in the right way and help difficult ideas become durable.
Academic Path

The fuller background behind the transition.

The path has not been linear, but it has been cumulative: each stage left a way of thinking I still carry.

2022 — Present
PolyU · Physics and quantitative coursework
My undergraduate training began in physics, then widened into statistics, probability, real analysis, optimisation, and machine learning. What stayed constant was a preference for rigorous structure.
2025 — 2026
Final-year project · Electron ptychography
My project focused on understanding and optimising ePIE for electron microscopy. It deepened my relationship with difficult problems and taught me how much careful writing can sharpen thought.
Now
Direction in formation
I am moving toward a more clearly defined research future in theoretical corporate finance, while trying to retain the breadth and discipline that came from my earlier training.
Coursework
Quantitative threads
A few years of study across physics, statistics, mathematics, and computation. The later years increasingly reveal the transition toward probability, inference, finance, and modelling.
25/26 Academic Year Current year · exchange + final semester
Semester 1 · Exchange study at SUSTech
  • MA401 Dynamical Systems
  • MAT7102 Topics in Probability and Statistics
  • MAT7092 Stochastic Processes
  • MAT8030 Advanced Probability
  • PHY5001 Advanced Quantum Mechanics
  • PHY5003 Advanced Statistical Mechanics
These were taken during my exchange semester at SUSTech in Semester 1 of the 25/26 academic year.
Semester 2 · Current semester
  • AMA3201 Computational Methods
  • AMA4325 Derivative Pricing
24/25 Academic Year
  • AMA3304 Theory of Interest and Portfolio Analysis
  • AMA3602 Applied Linear Models
  • AMA3640 Statistical Inference
  • AMA3658 Stochastic Processes for Investment
  • AMA3707 Real Analysis
  • AMA4380 Algorithmic and High Frequency Trading
  • AMA4688 Simulation
  • AMA4840 Decision Analysis
  • AP30010 Radiation Physics
  • AP30011 Solid State Physics
  • AP30019 Data Analysis Techniques for Scientists
  • AP30020 From Semiconductor to Intelligent Devices
  • AP30022 Scientific Instrumentation and Automation
  • AP30023 Designing Sensing Systems for Internet of Things in Smart Cities
  • COMP4431 Artificial Intelligence
  • COMP4432 Machine Learning
23/24 Academic Year
  • AMA1501 Introduction to Statistics for Business
  • AMA1611 Data Analytics Fundamentals
  • AMA2111 Mathematics I
  • AMA2691 Probability & Distributions
  • AMA3724 Further Mathematical Methods
  • AP20002 Materials Science
  • AP20006 Quantum Mechanics for Scientists and Engineers
  • AP20016 Electromagnetism and Waves
  • AP20017 Mechanics and Robotic Motion
  • COMP1012 Programming Fundamentals and Applications
  • COMP2013 Data Structures and Algorithms
22/23 Academic Year
  • ABCT1101 Introductory Life Science
  • ABCT1700 Introduction to Chemistry
  • AMA1110 Basic Mathematics I — Calculus and Probability & Statistics
  • AMA1120 Basic Mathematics II — Calculus and Linear Algebra
  • AMA1600 Fundamentals of AI and Data Analytics
  • AMA1D08 The Mathematics Behind Music
  • AP10005 Physics I
  • AP10006 Physics II
  • AP10007 Applied Physics Laboratory
  • AP30012 Thermal and Statistical Physics
  • AP40017 Experiment X
The list is long because the transition was real. I did not arrive at my present direction from one discipline alone.