Fernando Perez-Cruz
Former Deputy Executive Director & Chief Data Scientist
Academia
Leadership & Administration
(Alumni)
Fernando Perez-Cruz received a PhD. in Electrical Engineering from the Technical University of Madrid. He is Titular Professor in the Computer Science Department at ETH Zurich and Head of Machine Learning Research and AI at Spiden. 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). Fernando Perez-Cruz has served as Chief Data Scientist at the SDSC from 2018 to 2023, and Deputy Executive Director of the SDSC from 2022 to 2023
Projects
IRMA
In Progress
Interpretable and Robust Machine Learning for Mobility Analysis
Engineering
FLBI
In Progress
Feature Learning for Bayesian Inference
LEAP
Completed
LEArning to Print – towards data-driven real-time predictions for additive manufacturing
Engineering
DUPLET
In Progress
DUal Positron Lifetime Emission Tomography
Biomedical Data Science
DAAAD_Bridges
Completed
Domain-aware-AI Augmented Design of Bridge Structures
Engineering
SEMIRAMIS
Completed
AI-augmented Architectural Design
Engineering
MLTox
Completed
Enhancing toxicological testing through machine learning
Energy, Climate & Environment
SMARTAIR
Completed
Self-guided Machine Learning Algorithms for Real-Time Assimilation, Interpolation and Rendering of Flow Data
Engineering
PolyNet
Completed
Exploring disease trajectories and outcome prediction using novel methods in network analysis and machine learning
Biomedical Data Science
NLP
Completed
Narratives in Law and Politics: A Computational Linguistics Approach
Digital Administration
4Real
Real-time urban pluvial flood forecasting
Energy, Climate & Environment
DEAPSnow
Completed
Improving snow avalanche forecasting by data-driven automated predictions
Energy, Climate & Environment
PACMAN HIPA
Completed
Particle Accelerators and Machine Learning
Big Science Data
MSEI
Completed
Molecular structure elucidation by integrating different data mining strategies
Chemistry
EconMultiplex
Completed
Multiplex Networks in International Trade
Digital Administration
DATALAKES
Completed
Heterogeneous data platform for operational modelling and forecasting of Swiss lakes
Energy, Climate & Environment
DemocraSci
Completed
A research platform for Data-Driven Democracy Studies in Switzerland
Digital Administration
Citizen-Controlled
Completed
Citizen-controlled Data Science for Multiple Sclerosis Research
Biomedical Data Science
BISTOM
Completed
Bayesian Parameter Inference for Stochastic Models
Physics
AADS
Completed
Data Science Enabled Acoustic Design
Engineering
SPEEDMIND
Completed
Improving species biodiversity analyses and citizen science feedback through machine learning
Energy, Climate & Environment
Publications
Mentioned in
May 1, 2024
Smartair | An active learning algorithm for real-time acquisition and regression of flow field data
Smartair | An active learning algorithm for real-time acquisition and regression of flow field data
We’ve developed a smart solution for wind tunnel testing that learns as it works, providing accurate results faster. It provides an accurate mean flow field and turbulence field reconstruction while shortening the sampling time.
February 6, 2024
PassGPT | Using language models to enhance password security
PassGPT | Using language models to enhance password security
PassGPT is a Large Language Model for password generation trained on leaked passwords, which can outperform existing methods based on generative adversarial networks by guessing twice as many unseen passwords.
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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