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

Guiding multistep retrosynthesis planning with continuous pathway representations
- - Jannis Born
  - Matteo Manica
  - et al.
- 2022
- ACS Fall 2022
MPEGO: A toolkit for multi-level performance evaluation of generative models for material discovery domains
- - Girmaw Abebe Tadesse
  - Jannis Born
  - et al.
- 2022
- KDD 2022
A Fully Differentiable Set Autoencoder
- - Nikita Janakarajan
  - Jannis Born
  - et al.
- 2022
- KDD 2022
PGT: a prompt based generative transformer for the patent domain
- - Dimitrios Christofidellis
  - Antonio Berrios Torres
  - et al.
- 2022
- ICML 2022
PCfun: a hybrid computational framework for systematic characterization of protein complex function
- - Varun S Sharma
  - Andrea Fossati
  - et al.
- 2022
- Briefings in Bioinformatics
Identification of Enzymatic Active Sites with Unsupervised Language Modeling
- - Matteo Manica
  - Loic Kwate Dassi
  - et al.
- 2022
- ISGC 2022
Regression Transformer - Blending Numerical and Textual Tokens for Concurrent Property Predicition and Conditional Generation
- - Jannis Born
  - Matteo Manica
- 2022
- MRS Spring Meeting 2022
Identification of enzymatic active sites with unsupervised language modelling
- - Loic Kwate Dassi
  - Matteo Manica
  - et al.
- 2022
- MRS Spring Meeting 2022
Regression Transformer: Concurrent Conditional Generation and Regression by Blending Numerical and Textual Tokens
- - Jannis Born
  - Matteo Manica
- 2022
- ICLR 2022
Multitask Prompted Training Enables Zero-Shot Task Generalization
- - Victor Sanh
  - Albert Webson
  - et al.
- 2022
- ICLR 2022

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.