LADE
A comprehensive infrastructure for data-driven scientific research
LADE’s state-of-the-art research infrastructure is dedicated to high-performance computing and artificial intelligence for scientific research. Working alongside experimental laboratories spanning from biology to material science, our researchers develop and implement algorithms integrating knowledge from heterogenous data.
The research lab includes research staff members, several postdocs and PhD students, and boasts a variety of backgrounds ranging from math, physics, bioinformatics, engineering, and computer science. We promote visiting programs for senior researchers and internships for young researchers.
The group enjoys close collaboration with two experimental facilities within Area Science Park, LAGE and LAME. Moreover, it has tight relations with several local institutions, such as UniTS, SISSA, ICTP, ICGEB, IRCCS Burlo Garofalo, CRO and has a wide network of collaborations across Europe, including Spain, Netherlands, UK and several other countries.
Research Lines
Research at LADE is organized into three primary themes: artificial intelligence, data ecosystems, and high-performance computing. By integrating these areas, we take a holistic approach to scientific data—extracting meaningful information and enabling comprehensive understanding and effective control of models built on this data.
AI research at LADE focuses on making machine learning models more interpretable, particularly by analyzing data representations. We use these analyses to enhance the applicability of AI across scientific domains such as biology and materials science.
- Foundations of Artificial Intelligence
Advancing the interpretability of transformers and multimodal models via geometrical and topological data analysis, and via mechanistic interpretability, to steer the underlying process which drive model’s decisions.
- AI for the Biological Macromolecules, Structural Biology and Genomics
Using the power of transformers to predict and model protein, RNA, and DNA properties, evolution, and interactions, with a focus on advancing structural biology, genomics, and cancer evolution studies.
- AI for Material Sciences
Enhancing material science experiments through automatic image calibration and denoising and background removal for spectroscopic data.
LADE’s work in scientific data management aims to achieve interoperability across experiments, emphasizing practical solutions and directly collaborating with experimental labs in materials science and life sciences.
Research Lines:
- Multi-omics FAIR data integration of life science laboratories
Developing standardized common data models and ontologies to achieve interoperability across multimodal life science data, from omics to imaging.
- Digital Ecosystems for research
Implementing proper data storage, complete data provenance, and data availability for fundamental research, for new discoveries and reproducibility purposes.
- HPC and AI pipelines
Integration and curation of scientific datasets to be used in highly automated HPC pipelines, especially with new AI techniques.
LADE runs and maintains the HPC and storage infrastructure ORFEO, a cutting-edge data center designed to adapt to rapidly evolving computing and storage workloads.
Research Lines:
- Infrastructure as a research platform
Making computational workflows for research projects, integrating the traditional HPC approach with emerging cloud technologies.
- Energy consumption monitoring and optimization
Developing methods to analyze energy usage of HPC workflows to create models to optimize computation, storage and data transfer.
- HPC for AI applications
Implementing environments and testing frameworks for small-medium enterprises in search for digitalizing and developing AI within their activities.
ORFEO (Open Research Facility for Epigenomics and Other) is our high-performance, modular data center built to power advanced scientific research and industrial innovation. It delivers millions of computing hours each year and supports flexible, cloud-ready HPC and AI workloads.
ORFEO provides:
- AI & Data Science computational infrastructure, supporting research performed at LADE and external collaborations
- Data hosting and repository management, aligned with FAIR principles
- Energy-aware infrastructure optimization
- Custom solutions for industrial R&D, including personalized consultancy, simulations, and SME support
Available as IaaS, PaaS, and SaaS, ORFEO is also tightly integrated with external institutions and supports data processing from the Open Labs for Life Sciences and Innovative Materials.
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