2026

Energy disaggregation, is a technique used to analyze and break down the energy consumption in a building or household into individual appliance-level energy usages. The goal is to identify and monitor the energy consumption of specific “appliances” without the need for additional metering or sensors on each device. This project will look at the insights available for energy distribution through the application of energy disaggregation.

This project aims to compare and evaluate the performance of various optimization protocols applied to the training of Shallow Water Equations and study the effect of optimization approaches on long lead time spectral fidelity.

Mathematical auto-formalization is emerging as a game-changing outcome of LLMs and modern AI tools. This is the process of turning mathematical statements in natural language into fully machine checked proofs. Our problem will investigate how well these tools actually work by exploring advanced mathematics and its formalization.

This project examines how re-identification risk varies within datasets (particularly for individuals with unique or complex diagnostic profiles) and identifies factors beyond average sample-level risk that contribute to vulnerability. It aims to assess how data type, dataset structure, and existing safeguards influence re-identification risk, with a focus on informing responsible data sharing practices for research involving children with complex diagnoses.

The purpose of this project is to identify, through numerical optimization, fusion reactor shapes which maximize the average pressure of the fusion fuel.

We have controlled vocabularies and topic structures that are used to index and understand large bodies of text. We want to better understand how text is clustered around known structured topics so that unknown topics can be identified in texts and added to our controlled vocabularies and topic structures.

Qavis Technologies seeks a rigorous mathematical formulation of the Job-Shop Scheduling Problem (JSSP) as a binary quadratic optimization model (QUBO/CQM-compatible), including penalty coefficient analysis and a decomposition strategy for large-scale instances, to extend our hybrid quantum-AI optimization platform from logistics into manufacturing.

In real organizations, each role (CEO, CFO, VP Sales…) sees different information and applies different decision criteria - yet every multi-agent simulation framework today gives all agents the same shared context. This project seeks to mathematically test whether modeling information compartmentalization and role-specific decision processes produces measurably better collective outcomes than the standard shared-context approach.

The goal of the project is to develop an interface between quantum and classical- binary computing devices for the purpose of climate modelling.