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Optimization

Finding optimal solution for AI tasks ↓
Optimization

Research on the intersection of mathematics, computer science, and electrical engineering with a clear mission: find the optimal solution using advanced computational methods.

Mathematical optimization is a field of study on the intersection of mathematics, computer science, and electrical engineering that deals with the selection of a best element out of a set with respect to some criterion. The elements of the set are known as feasible solutions and the criterion is known as the objective function. Over the past couple of centuries, much of the work in mathematical optimization has focussed on the case of a convex, time-invariant set of feasible solutions and convex, time-invariant objective functions. This special case has become the work horse of machine learning, artificial intelligence, and most fields of engineering.

Research Focus

In our basic research, we focus study extensions towards (1) certain smooth, non-convex feasible sets and objective functions and (2) time-varying feasible sets and objective functions. The smooth non-convex problems, known as commutative and non-commutative polynomial optimization, have extensive applications in power systems, control theory, and machine learning, among others. The same applications can often benefit from the time-varying extensions.

Particular examples of this include our papers at AAAI 2019 (https://arxiv.org/abs/1809.05870) and AAAI 2020 (https://arxiv.org/abs/1809.03550), which deal with time-varying optimization.

Our papers at AAAI 2021 (https://arxiv.org/abs/2006.07315) and in the Journal of AI Research (https://arxiv.org/abs/2209.05274), which deal with non-commutative polynomial optimization. Notably, we can get the present best results on the COMPAS dataset.

In a recent paper in Automatica (https://arxiv.org/abs/2110.03001), we are working on the control on non-linear systems under uncertainty.

CoDiet

CoDiet

Horizon Europe project aiming to combat diet-related diseases through innovative diet-monitoring technologies, AI-assisted data analysis and personalized nutrition.

TENORS

TENORS

Several Ph.D. positions are available within Marie Skłodowska-Curie Doctoral Network for tensor modeling, geometry and optimization.

AutoFair (Human-Compatible AI with Guarantees)

AutoFair (Human-Compatible AI with Guarantees)

Horizon Europe project for fair AI algorithms supported by the EU with 3,8 million Euros. Imperial College London, the Israeli Institute of Technology Technion and the National and Capodistrian University of Athens as well as partners from the industry collaborate on developing explainable and transparent algorithms.

Research Results

Predictability and Fairness in Social Sensing

Predictability and Fairness in Social Sensing

The manner in which agents contribute to a social-computing platform (e.g., social sensing, social media) is often governed by distributed algorithms. We explore the guarantees available in situations, where fairness among the agents contributing to the platform is needed.

Hacking
human future

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