Benjamín Béjar Haro

Lead Data Scientist & group leader SDSC Hub at PSI

Research

Leadership & Administration

(Alumni)

Benjamín Béjar received a PhD in Electrical Engineering from Universidad Politécnica de Madrid in 2012. He served as a postdoctoral fellow at École Polytechnique Fédérale de Lausanne until 2017, and then he moved to Johns Hopkins University where he held a Research Faculty position until Dec. 2019. His research interests lie at the intersection of signal processing and machine learning methods, and he has worked on topics such as sparse signal recovery, time-series analysis, and computer vision methods with special emphasis on biomedical applications. Since 2021, Benjamin leads the SDSC office at the Paul Scherrer Institute in Villigen.

Projects

MAGNIFY

In Progress

Machine learning Assisted larGe scale quaNtIfication of building energy FlexibilitY

Academia

SDATE

In Progress

Smart Data Acquisition for Tomoscopy Experiments

Academia

DataXAS

In Progress

Data science for X-ray Absorption Spectroscopy

Academia

EUTHERMO

Completed

Scalable deep reinforcement learning algorithms for building climate control and energy management

Academia

ML-L3DNDT

Completed

Robust and scalable Machine Learning algorithms for Laue 3-Dimensional Neutron Diffraction Tomography

Academia

LAMP

Completed

Lensless Actinic Metrology for EUV Photomasks

Academia

DS4MS

Completed

Data Science for Multiplexing Spectrometers

Academia

DUPLET

In Progress

DUal Positron Lifetime Emission Tomography

Academia

ML-ED

Completed

Increased spatial resolution in electron detectors through machine learning

Academia

CHIP

In Progress

MaCHIne-Learning-assisted Ptychographic nanotomography

Academia

RED-ML

Completed

Reduction of high volume experimental data using machine learning

Academia

COVID-19

Completed

Epidemic Forecasting

Academia

Neuro-choice

Completed

Extracting Neural Activity Signals from Large-scale Calcium Imaging Data

Academia

Publications

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Poghosyan, E.; Xie, X.; Reuteler, J.; Paton, K. A.; Flores, L. B.; Haro, B. B.; Fröjdh, E.; Bergamaschi, A.; Müller, E. "Sub-Pixel Electron Beam Alignment for Machine Learning Characterization of Hybrid Pixel Detectors" Preprint 2026 View publication 

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Saha, S.; Ansuinelli, P.; Barba, L.; Mochi, I.; Haro, B. B. "Ptycho-LDM: A Hybrid Framework for Efficient Phase Retrieval of EUV Photomasks Using Conditional Latent Diffusion Models" Photonics 12 9 900 2025 View publication 

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Ansuinelli, P.; Saha, S.; Flores, L. F. B.; Haro, B. B.; Ekinci, Y.; Mochi, I. "Prior–primed deep neural network based EUV mask inspection" Optics Express 33 6 12572 2025 View publication 

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Xie, X.; Barba Flores, L.; Bejar Haro, B.; Bergamaschi, A.; Fröjdh, E.; Müller, E.; Paton, K.; Poghosyan, E.; Remlinger, C. "Enhancing spatial resolution in MÖNCH for electron microscopy via deep learning" Journal of Instrumentation 19 1 C01020 2024 View publication 

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Cramer, E. Y.; Huang, Y.; Wang, Y.; Ray, E. L.; Cornell, M.; Bracher, J.; Brennen, A.; Rivadeneira, A. J. C.; Gerding, A.; House, K.; et al. "The United States COVID-19 Forecast Hub dataset" Scientific Data 9 1 462 2022 View publication 



Bracher, J.; Wolffram, D.; Deuschel, J.; Görgen, K.; Ketterer, J. L.; Ullrich, A.; Abbott, S.; Barbarossa, M. V.; Bertsimas, D.; Bhatia, S.; et al. "A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave" Nature Communications 12 1 5173 2021 View publication 

Mentioned in

July 8, 2026

SDSC and PSI teams jointly explore the AI potential of large-scale infrastructures

Tailored community event at PSI in Villigen, Aargau, reveals machine learning and AI opportunities in accelerating scientific discovery

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Case Studies

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