AI

“My recommendation was… to take the maximum number of courses on foundations (e.g. math, physics) rather than take courses on the trendy technology du jour… [We should] learn things with a long shelf life… basic things in mathematics, in modeling, mathematics that can be connected with reality.”

— Yann LeCun (Chief AI Scientist at Meta, Turing award winner for his work on AI)

Interact with the map below to explore our AI related curriculum.

The Foundation Math & Stats The Engine Discrete Math & Algorithms The Applications Env. Science & Physics AI IN EDUCATION EARTH MODELING

The Wisdom of Knowing “When Not To”

True expertise isn’t just about knowing how to build and deploy a model—it’s about knowing when not to use one. At EMME, we cultivate the critical thinking and mathematical logic required to recognize the fundamental limitations of current AI, such as its struggles with symbolic reasoning. We empower you with the judgment to discern the best tool for the job,
whether that is a neural network, statistical modelling or symbolic computation.

×

Mathematical Foundations

∇f(x) · v
Calculus & Linear Algebra

At EMME, we build the theoretical intuition required to create new AI models, not just use existing ones.

1. Linear Algebra (The Representation)

Every dataset is a high-dimensional vector space. We teach you to manipulate these structures to “see” patterns in complex environmental data.

2. Calculus (The Learning)

Learning is optimization. Through Backpropagation and Gradient Descent, models navigate error landscapes. We teach the multivariable calculus essential for understanding how machines actually “learn” from data.

3. Probability and Statistics (Interpretation)

AI outputs are probabilities, not certainties. We provide the statistical training necessary to rigorously interpret the reliability of models, which is essential for scientific decision-making.
×

The Computational Engine

Discrete Math & Algorithms

Understanding the math isn’t enough; you must implement it efficiently.

Discrete Mathematics

From graph theory to combinatorics, we teach the discrete structures that underpin neural network architectures and decision trees.

Programming & Algorithms

We focus on the efficient implementation of learning algorithms and data manipulation. Through the study of automata theory and algorithm analysis, students learn to write code that is optimized for heavy computational tasks.
×

Real World Applications

FACULTY RESEARCH
1. Geochemical Data Science

Applying statistical machine learning to trace elemental cycling and monitor environmental changes.
Lab: Keita Itano

2. Generative AI for Education

Developing AI systems that use Generative AI to create adaptive exercises, customizing learning pathways based on student performance.
Lab: Khaing Hsu Wai