Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi-Sensor System with Foundation Models
- 2024
- Advanced Science
Matteo Manica
Pronouns
He/Him/His
Title
Senior Research Scientist
Bio
Matteo is a Senior Research Scientist at IBM Research Europe - Zürich. His research focuses on AI applications to science and engineering: from AI-driven autonomous labs and multimodal generative models for material science to modelling energy storage devices. He received his degree in Mathematical Engineering from Politecnico di Milano in 2013. After getting his MSc, he worked in a startup, Moxoff spa, as a software engineer and analyst for scientific computing. In 2019 he obtained his doctoral degree at the end of a joint Ph.D. program between IBM Research and the Institute of Molecular Systems Biology, ETH Zürich, with a thesis on multimodal learning approaches for precision medicine.
Matteo's full publication record is available on Google Scholar.
Publications
Predicting polymerization reactions via transfer learning using chemical language models
- Ronaldo Giro
- Brenda S. Ferrari
- et al.
- 2024
- ACS Fall 2024
Catalysts Synthesis Procedures Extraction from Synthesis Paragraphs using Large Language Models
- Daniel Pereira Costa
- Matteo Manica
- et al.
- 2024
- ICCatalysts 2024
Multimodal foundation models for more reproducible scientific experimentation and data capture
- 2024
- Future Labs Live 2024
Growing strings in a chemical reaction space for searching retrosynthesis pathways
- 2024
- npj Computational Materials
Language models can identify enzymatic binding sites in protein sequences
- Yves Gaetan Nana Teukam
- Loic Kwate Dassi
- et al.
- 2024
- Computational And Structural Biotechnology Journal
Using Domain Specific Languages to Enable Artificial Intelligence for Polymer and Catalyst Design
- 2024
- MRS Spring Meeting 2024
Using Foundation Models to Promote Digitization and Reproducibility in Scientific Experimentation
- Amol Thakkar
- Andrea Giovannini
- et al.
- 2023
- NeurIPS 2023
Zero-Shot-BERT-Adapters: a Zero-Shot Pipeline for Unknown Intent Detection
- Daniele Comi
- Dimitrios Christofidellis
- et al.
- 2023
- EMNLP 2023
Enzeptional: enzyme optimization via a generative language modeling-based evolutionary algorithm
- 2023
- MRS Fall Meeting 2023
Patents
- US
- 12124958
- US
- 11942189
- JP
- 7358005
- US
- 11651860
- US
- 11651841
- US
- 11515005
- GB
- 2595994
- US
- 11150926
- US
- 10593422
Projects
Lab that Learns
Leveraging AI foundation models and multi-cloud computing to usher in a new era of reproducible and collaborative experimentation for scientific discovery.
Accelerator Technologies
We're developing technological solutions to assist subject matter experts with their scientific workflows by enabling the Human-AI co-creation process.
AI for Scientific Discovery
Creating the AI-enabled lab for a new era of reproducible and collaborative experimentation
Blog posts
How generative AI models can fuel scientific discovery
ExplainerJohn R Smith and Matteo Manica6 minute readNeuNetS: Automating Neural Network Model Synthesis for Broader Adoption of AI
ResearchCristiano Malossi
Top collaborators
TL
Teodoro Laino
Distinguished RSM, Manager
GG
Gianmarco Gabrieli
Pre-Doctoral Researcher
JB
Jannis Born
Research Scientist in AI for Scientific Discovery
FZ
Federico Zipoli
Research Scientist