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Understanding this hidden structure could help us visualize data, remove noise, compare examples, and build machine-learning systems that are faster, more reliable, and easier to understand.

In this project, we will try to answer: When can we discover the hidden shape of data accurately and efficiently?

This is a difficult problem. In the most general setting, learning the full shape may require a very large amount of data and computation. Real data are also noisy, so observations may not lie exactly on a clean surface. Even deciding how many underlying dimensions the data have can be challenging.

Tags: Python, Basic Programming, Linear Algebra, Calculus, Statistics, Machine Learning, Optimization, All Years

This project wants to answer: Why does sparse regression often work well in practice, even when the usual theoretical assumptions do not clearly apply?

We will study this question using ideas from geometry, statistics, and optimization. Here, geometry means thinking about variables as directions or points in space. For example, two variables that contain almost the same information can be viewed as pointing in nearly the same direction. This viewpoint may help us understand when sparse regression makes reliable predictions, when it selects meaningful variables, and when its answer is unstable.

Tags: Python, Basic Programming, Linear Algebra, Statistics, Calculus, Optimization, Machine Learning, All Years

In this project, we will explore how machine learning can help astronomers find and study interesting objects or events. For example, a model might be used to classify astronomical objects, identify unusual observations, detect rare events, study populations of galaxies or galaxy clusters, or uncover patterns in the shape and organization of these systems. It may also help researchers understand the different stages or components of events such as gamma-ray bursts. The exact scientific question will depend on the available datasets and discussions with collaborators in astronomy and cosmology. There are opportunities to collaborate with astrophysicists and cosmologists in institutes like Perimeter Institute and Vera Rubin Observatory in medium and/or longer term.

Tags: Python, Basic Programming, Data Structures, Algorithms, Statistics, Linear Algebra, Calculus, Machine Learning, Astronomy, All Years

This project explores how AI can help understand bilingual doctor–patient conversations and automatically generate accurate medical documentation. It has the potential to improve healthcare accessibility and reduce documentation workload for clinicians serving multilingual populations. We have already build 280 hours speech corpus containing code-switched Kazakh-Russian medical data.  We now collecting an additional 100 hours of simulated doctor and patient conversations to improve model performance.

Tags: Basic Programming, Python, Artificial Intelligence, Machine Learning, Natural Language Processing, Data Science, All Years

Modern AI and machine learning systems are increasingly trained and deployed on distributed infrastructures consisting of multiple servers working together. While distributed computing enables larger models and faster processing, it also introduces new security challenges. Communication between nodes, shared resources, and distributed coordination mechanisms can create vulnerabilities that may not exist in single-machine systems. The goal of this project is to understand and evaluate security risks that arise when training or running AI/ML models in distributed environments. By identifying and studying these vulnerabilities, we can help build more secure and trustworthy AI systems.

Tags: Networks, Operating Systems, Artificial Intelligence, Machine Learning, Security, Systems, All Years