Fernando Perez-Cruz

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

DAAAD_Bridges

Completed
Domain-aware-AI Augmented Design of Bridge Structures
Energy, Climate & Environment

SEMIRAMIS

Completed
AI-augmented architectural design
Energy, Climate & Environment

MLTox

In Progress
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
Energy, Climate & Environment

PolyNet

Exploring disease trajectories and outcome prediction using novel methods in network analysis and machine learning
Biomedical Data Science

NLP

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
Energy, Climate & Environment

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
Big Science Data

AADS

Completed
Data Science Enabled Acoustic Design
Energy, Climate & Environment

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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