Matteo Manica

Title

Senior Research Scientist

Bio

Matteo is a Senior Research Scientist and Manager in Algorithms and Applications at IBM Research Europe–Zurich. His research interests focus on AI-powered algorithms and their applications to enterprise challenges. He earned an MSc in Mathematical Engineering from Politecnico di Milano in 2013. After graduating, he worked at a startup as a software engineer and scientific-computing analyst. In 2019, he completed a joint PhD program between IBM Research and ETH Zurich with a thesis on multimodal learning approaches for computational biology.

Matteo's full publication record is available on Google Scholar.

Publications

Multiscale Byte Language Models -- A Hierarchical Architecture for Causal Million-Length Sequence Modeling
- - Eric Egli
  - Matteo Manica
  - et al.
- 2025
- ICML 2025
Aligning foundation models on encoded synthetic omic data for patient stratification
- - Nikita Janakarajan
  - Antonio Foncubierta
  - et al.
- 2025
- ICDH 2025
Foundation models for materials discovery – current state and future directions
- - Edward Pyzer-knapp
  - Matteo Manica
  - et al.
- 2025
- npj Computational Materials
Activity Recognition in Scientific Experimentation using Multimodal Visual Encoding
- - Gianmarco Gabrieli
  - Irina Espejo Morales
  - et al.
- 2024
- Digital Discovery
Unified Lookup Tables: Privacy-Preserving Foundation Models
- - Nikita Janakarajan
  - Irina Espejo Morales
  - et al.
- 2024
- NeurIPS 2024
Language models can identify enzymatic binding sites in protein sequences
- - Yves Gaetan Nana Teukam
  - Loïc Kwate Dassi
  - et al.
- 2024
- Computational And Structural Biotechnology Journal
Transforming The Hybrid Cloud for Emerging AI Workloads
- - Deming Chen
  - Alaa Youssef
  - et al.
- 2024
- arXiv
Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi-Sensor System with Foundation Models
- - Gianmarco Gabrieli
  - Matteo Manica
  - et al.
- 2024
- Advanced Science
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

Visit Google Scholar

Projects

AI for Scientific Discovery
Creating the AI-enabled lab for a new era of reproducible and collaborative experimentation
Accelerator Technologies
We're developing technological solutions to assist subject matter experts with their scientific workflows by enabling the Human-AI co-creation process.
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.