Organic gunshot residue (OGSR) is a trace residue released during firearm discharge. It includes unburned, partially burned, and pyrolyzed components of the propellant (smokeless powder) and other organic substances (stabilizers, plasticizers, flame suppressants). Recently, the forensic community has shown interest in complementing standard SEM-EDS methods for the detection of inorganic gunshot residue (IGSR), with analytical […]

This project aims to uncover how Cfp1 PHD domain, a chromatin reader domain traditionally known for coordinating zinc might use a special iron–sulfur (Fe–S) cluster to sense and respond to changes in the cell’s environment. By studying this protein under oxygen-free conditions and determining its 3D structure, we hope to reveal how redox chemistry influences […]

This project focuses on developing environmentally friendly electronic materials by combining biodegradable polymers with semiconducting materials to develop degradable, flexible, and high-performance organic electronic devices. The work aims to improve device stability and sustainability, contributing to the advancement of green electronics. Through collaboration between participating institutions, the project will promote knowledge exchange in materials design, […]

Considerable scientific research is oriented towards providing answers to the disturbing effects of the impacts of global change. There is a growing need to introduce new methodologies to explore the often very complex and interconnected chemistry of the atmosphere. This research program aims to advance our current mass spectrometry-based studies on halogenated atmospheric pollutants with […]

In supply-chain management, organizations rely on a wide variety of structured and semi-structured analytical resources such as task flows and key performance indicators (KPIs) to plan, monitor, and adapt to changes. These resources undergo frequent review and adaptation, often necessitating queries and manipulations in numerous ways. Utilizing Large Language Models (LLMs), this project aims to […]

The integration of machine learning models and the extraction of meaningful data into industrial workflows continues to accelerate, driving innovation and new business opportunities. Leveraging our expertise in mathematical and statistical modeling, we aim to apply these technologies to address critical challenges in agriculture, where up to 40% of crop yields are lost due to […]

Standing requires every claimant to establish a connection between his situation and the basis of the lawsuit which justifies his participation in the legal proceedings. It is often characterized as context sensitive, particularly in administrative law, given the diversity of decisions likely to be subject to judicial review of administrative action. The objective of my […]

Our research concerns a group of lipid trafficking proteins, scientifically known as ABC transporters, that only work when submerged in the sea of lipids, which we call membranes. These proteins can “pick up” lipid molecules, especially sterols, and move the lipids out or into the cells. We know that different members of these lipid trafficking […]

The project focuses on Variational Mean-Field Games: it will provide theoretical guarantees for the convergence of numerical algorithms currently used and possibly develop also novel machine learning methods for it.

This project address the so-called Quantum Marginal Problem from a mathematical, physics and computational perspective. It develops the mathematical theory of optimal transport adapt to quantum states in order to make fundamental advances in physical aspects and computational algorithms solving the quantum marginal problem.

When Kinaxis trains its machine learning models, it does so on two time scales. Every week or so, it looks at around 10 billion sales records and tries to learn the rapidly-changing “parameters” that best describe this data. Every 3-6 months, it updates 10 thousand collections of “hyperparameters” that govern the parameter-learning process. Kinaxis would […]

This research project aims to develop a comprehensive predictive framework for heat treatment furnaces to enhance operational efficiency, product quality, and environmental sustainability. The project focuses on creating a data preprocessing pipeline tailored for big machinery sensor data, innovative AI-enabled anomaly detection models, real-time furnace operation analysis, and quality prediction models. Key objectives include obtaining […]