Fernando Perez-Cruz
Deputy Executive Director & Chief Data Scientist
Academia
Leadership & Administration
(Alumni)
Fernando received a PhD. in Electrical Engineering from the Technical University of Madrid. He has been a member of the technical staff at Bell Labs and a Machine Learning Research Scientist at Amazon. Fernando has been a visiting professor at Princeton University under a Marie Curie Fellowship and an associate professor at University Carlos III in Madrid. He held positions at the Gatsby Unit (London), Max Planck Institute for Biological Cybernetics (Tuebingen), and BioWulf Technologies (New York). Since 2022, Fernando is the Deputy Executive Director of the SDSC.
Projects
SEMIRAMIS
Completed
AI-augmented architectural design
Energy, Climate & Environment
AURORA
In Progress
from Air pollUtion souRces tO moRtAlity
Biomedical Data Science
Energy, Climate & Environment
MLTox
In Progress
Enhancing toxicological testing through machine learning
Biomedical Data Science
SMARTAIR
In Progress
Self-guided machine learning algorithms for real-time assimilation, interpolation and rendering of flow data
Big Science Data
PolyNet
In Progress
Exploring disease trajectories and outcome prediction using novel methods in network analysis and machine learning
Biomedical Data Science
N2O-SSA
In Progress
Combining measurements, modeling and machine learning to improve N2O accounting for sustainable agricultural development in sub-Saharan Africa
Energy, Climate & Environment
NLP
In Progress
Narratives in Law and Politics: A Computational Linguistics Approach
Digital Administration
4Real
In Progress
Real-time urban pluvial flood forecasting
Energy, Climate & Environment
DEAPSnow
In Progress
Improving snow avalanche forecasting by data-driven automated predictions
Energy, Climate & Environment
PACMAN HIPA
In Progress
Particle Accelerators and Machine Learning
Big Science Data
MSEI
In Progress
Molecular structure elucidation by integrating different data mining strategies
Big Science Data
EconMultiplex
In Progress
Multiplex Networks in International Trade
Digital Administration
DATALAKES
Completed
Heterogeneous data platform for operational modelling and forecasting of Swiss lakes
Energy, Climate & Environment
Citizen-Controlled
Completed
Citizen-controlled Data Science for Multiple Sclerosis Research
Biomedical Data Science
BISTOM
Completed
Bayesian Parameter Inference for Stochastic Models
Big Science Data
AADS
In Progress
Data Science Enabled Acoustic Design
Energy, Climate & Environment
SPEEDMIND
In Progress
Improving species biodiversity analyses and citizen science feedback through machine learning
Energy, Climate & Environment
Publications
Mentioned in
October 25, 2023
Computerworld | AI predicts avalanche danger [In German]
Computerworld | AI predicts avalanche danger [In German]
The AI project "DEAPSnow" takes avalanche forecasting to a whole new level.
February 28, 2023
DLBIRHOUI | Deep Learning Based Image Reconstruction for Hybrid Optoacoustic and Ultrasound Imaging
DLBIRHOUI | Deep Learning Based Image Reconstruction for Hybrid Optoacoustic and Ultrasound Imaging
Optoacoustic imaging is a new, real-time feedback and non-invasive imaging tool with increasing application in clinical and pre-clinical settings. The DLBIRHOUI project tackles some of the major challenges in optoacoustic imaging to facilitate faster adoption of this technology for clinical use.
September 23, 2022
What you see is what you classify: black box attributions
What you see is what you classify: black box attributions
The lack of transparency of black-box models is a fundamental problem in modern Artificial Intelligence and Machine Learning. This work focuses on how to unbox deep learning models for image classification problems.
July 9, 2020
CarboSense4D | Modelling CO2 concentration across Switzerland
CarboSense4D | Modelling CO2 concentration across Switzerland
The goal of CarboSense4D is to produce an accurate map of the evolution of carbon dioxide over Switzerland by applying machine learning methods from a network of low-cost sensors.
November 7, 2019
Improving species biodiversity analyses and citizen science feedback through machine learning
Improving species biodiversity analyses and citizen science feedback through machine learning
The WSL and the SDSC are actively working towards the development and the study of the benefits of machine learning approaches for facilitating biodiversity assessments.
Case Studies
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