Sofia Vallecorsa

EDBT 2026 Demo / reviewers in the wild / expert

dblp:182/5542 · DBLP profile ↗

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13ranked-venue papers

1as first author

8since 2021 · last 2024

0000-0002-7003-5765ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 7 · 1 first-author · 5 since 2021Artificial intelligence and machine learning · 2Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 2 · 2 since 2021

Year	Publication	Venue	Position
2024	Quantum-centric supercomputing for materials science: A perspective on challenges and future directions Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini, Torey Battelle, Daan Camps, David Casanova, Young Jay Choi, Fred Chong, Charles Chung, Christopher Codella, Antonio D. Córcoles, James Cruise, Alberto Di Meglio, Ivan Duran, Thomas Eckl, Sophia E. Economou, Stephan J. Eidenbenz, Bruce Elmegreen, Clyde Fare, Ismael Faro, Cristina Sanz Fernández, Rodrigo Neumann Barros Ferreira, Keisuke Fuji, Bryce Fuller, Laura Gagliardi, Giulia Galli, Jennifer R. Glick, Isacco Gobbi, Pranav Gokhale, Salvador de la Puente Gonzalez, Johannes Greiner, William Gropp, Michele Grossi, Emanuel Gull, Burns Healy, Matthew R. Hermes, Benchen Huang, Travis S. Humble, Nobuyasu Ito, Artur F. Izmaylov, Ali Javadi-Abhari, Douglas M. Jennewein, Shantenu Jha, Bert de Jong, Petar Jurcevic, William M. Kirby, Stefan Kister, Masahiro Kitagawa, Joel Klassen, Katherine Klymko, Kwangwon Koh, Masaaki Kondo, Doga Murat Kürkçüoglu, Krzysztof Kurowski, Teodoro Laino, Ryan Landfield, Matthew L. Leininger, Vicente Leyton-Ortega, Ang Li 0006, Meifeng Lin, Junyu Liu, Nicolás Lorente, André Luckow, Simon Martiel, Francisco Martín-Fernández, Margaret Martonosi, Claire Marvinney, Arcesio Castañeda Medina, Dirk Merten, Antonio Mezzacapo, Kristel Michielsen, Abhishek Mitra, Tushar Mittal, Kyungsun Moon, Joel Moore, Sarah Mostame, Mario Motta, Young-Hye Na, Yunseong Nam, Prineha Narang, Yu-ya Ohnishi, Daniele Ottaviani, Matthew Otten, Scott Pakin, Vincent R. Pascuzzi, Edwin Pednault, Tomasz Piontek, Jed W. Pitera, Patrick Rall, Gokul Subramanian Ravi, Niall Robertson, Matteo A. C. Rossi, Piotr Rydlichowski, Hoon Ryu, Georgy Samsonidze, Mitsuhisa Sato, Nishant Saurabh, Kunal Sharma, Soyoung Shin, George Slessman, Mathias Steiner, Iskandar Sitdikov, In-Saeng Suh, Eric D. Switzer, Joel Thompson, Synge Todo, Minh C. Tran, Dimitar Trenev, Christian Trott, Huan-Hsin Tseng, Norm M. Tubman, Esin Tureci, David García Valiñas, Sofia Vallecorsa, Christopher Wever, Konrad W. Wojciechowski, Xiaodi Wu 0001, Shinjae Yoo, Nobuyuki Yoshioka, Victor Wen-zhe Yu, Seiji Yunoki, Sergiy Zhuk, Dmitry Zubarev	Future Gener. Comput. Syst.	119
2022	Quantum Angle Generator for Image Generation abstract The Quantum Angle Generator (QAG) is a new generative model for quantum computers. It consists of a parameterized quantum circuit trained with an objective function. The QAG model utilizes angle encoding for the conversion between the generated quantum data and classical data. Therefore, it requires one qubit per feature or pixel, while the output resolution is adjusted by the number of shots performing the image generation. This approach allows the generation of highly precise images on recent quantum computers. In this paper, the model is optimised for a High Energy Physics (HEP) use case generating simplified one-dimensional images measured by a specific particle detector, a calorimeter. With a reasonable number of shots, the QAG model achieves an elevated level of accuracy. The advantages of the QAG model are lined out - such as simple and stable training, a reasonable amount of qubits, circuit calls, circuit size and computation time compared to other quantum generative models, e.g. quantum GANs (qGANs) and Quantum Circuit Born Machines. Florian Rehm, Sofia Vallecorsa, Michele Grossi, Kerstin Borras, Dirk Krücker, Schnake Simon, Alexis-Harilaos Verney-Provatas	SEC	2
2022	Quantum Convolutional Circuits for Earth Observation Image Classification abstract The amount of study on Quantum Machine Learning (QML) is increasing extensively due to its potential advantages in terms of representational power and computational resources. These advances suggest a possibility to extend its usage into the context of Earth Observations, where Machine Learning (ML) plays an important role due to its extensive amount of data to be manipulated. This paper presents our preliminary results of binary quantum classifiers, which consist of Quantum Convolutional Neural Networks (QCNNs), applied on Earth Observation datasets, EuroSAT and SAT4, with classically-reduced features. Especially, we compare the performance of different data embedding techniques and quantum circuits for binary classification tasks. Su Yeon Chang, Bertrand Le Saux, Sofia Vallecorsa, Michele Grossi	IGARSS	3
2022	Dual-Tasks Siamese Transformer Framework for Building Damage Assessment abstract Accurate and fine-grained information about the extent of damage to buildings is essential for humanitarian relief and disaster response. However, as the most commonly used architecture in remote sensing interpretation tasks, Convolutional Neural Networks (CNNs) have limited ability to model the non-local relationship between pixels. Recently, Transformer architecture first proposed for modeling long-range dependency in natural language processing has shown promising results in computer vision tasks. Considering the frontier advances of Transformer architecture in the computer vision field, in this paper, we present a Transformer-based damage assessment architecture (DamFormer). In DamFormer, a siamese Transformer encoder is first constructed to extract non-local and representative deep features from input multitemporal image-pairs. Then, a multitemporal fusion module is designed to fuse information for downstream tasks. Finally, a lightweight dual-tasks decoder aggregates multi-level features for final prediction. To the best of our knowledge, it is the first time that such a deep Transformer-based network is proposed for multitemporal remote sensing interpretation tasks. The experimental results on the large-scale damage assessment dataset xBD demonstrate the potential of the Transformer-based architecture. Hongruixuan Chen, Edoardo Nemni, Sofia Vallecorsa, Chen Wu 0003, Lars Bromley	IGARSS	3
2021	Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case abstract Deep learning is finding its way into high energy physics by replacing traditional Monte Carlo simulations. However, deep learning still requires an excessive amount of computational resources. A promising approach to make deep learning more efficient is to quantize the parameters of the neural networks to reduced precision. Reduced precision computing is extensively used in modern deep learning and results to lower execution inference time, smaller memory footprint and less memory bandwidth. In this paper we analyse the effects of low precision inference on a complex deep generative adversarial network model. The use case which we are addressing is calorimeter detector simulations of subatomic particle interactions in accelerator based high energy physics. We employ the novel Intel low precision optimization tool (iLoT) for quantization and compare the results to the quantized model from TensorFlow Lite. In the performance benchmark we gain a speed-up of 1.73x on Intel hardware for the quantized iLoT model compared to the initial, not quantized, model. With different physics-inspired self-developed metrics, we validate that the quantized iLoT model shows a lower loss of physical accuracy in comparison to the TensorFlow Lite model. Florian Rehm, Sofia Vallecorsa, Vikram A. Saletore, Hans Pabst, Adel Chaibi, Valeriu Codreanu, Kerstin Borras, Dirk Krücker	ICPRAM	2
2021	On protocols for increasing the uniformity of random bits generated with noisy quantum computers Elías F. Combarro, Federico Carminati, Sofia Vallecorsa, José Ranilla, Ignacio F. Rúa	J. Supercomput.	3
2021	A report on teaching a series of online lectures on quantum computing from CERN Elías F. Combarro, Sofia Vallecorsa, Luis J. Rodríguez-Muñiz, Álvaro Aguilar-González, José Ranilla, Alberto Di Meglio	J. Supercomput.	2
2021	Correction to: A report on teaching a series of online lectures on quantum computing from CERN Elías F. Combarro, Sofia Vallecorsa, Luis J. Rodríguez-Muñiz, Álvaro Aguilar-González, José Ranilla, Alberto Di Meglio	J. Supercomput.	2
2020	Evaluating Mixed-Precision Arithmetic for 3D Generative Adversarial Networks to Simulate High Energy Physics Detectors abstract Several hardware companies are proposing native Brain Float 16-bit (BF16) support for neural network training. The usage of Mixed Precision (MP) arithmetic with floating-point 32-bit (FP32) and 16-bit half-precision aims at improving memory and floating-point operations throughput, allowing faster training of bigger models. This paper proposes a binary analysis tool enabling the emulation of lower precision numerical formats in Neural Network implementation without the need for hardware support. This tool is used to analyze BF16 usage in the training phase of a 3D Generative Adversarial Network (3DGAN) simulating High Energy Physics detectors. The binary tool allows us to confirm that BF16 can provide results with similar accuracy as the full-precision 3DGAN version and the costly reference numerical simulation using double precision arithmetic. John Osorio Ríos, Adrià Armejach, Gul Rukhkhattak, Eric Petit 0002, Sofia Vallecorsa, Marc Casas	ICMLA	5
2019	Particle Detector Simulation using Generative Adversarial Networks with Domain Related Constraints abstract Generative Adversarial Networks have been used extensively to generate high resolution images in different fields. The present work explores the adversarial paradigm to train a deep neural network as a tool for scientific simulation, more specifically, simulation of High Energy Physics detectors. At the core of this approach lies the possibility to interpret the detector response as an image: the main challenges being represented by the large pixel intensity dynamic range (spanning over more than ten orders of magnitude) together with a high level of image sparsity. An initial prototype [1] simulated electrons travelling through an example electromagnetic calorimeter and depositing energy in its volume. It achieved very good results using an adversarial approach to reproduce a simplified physics use case. The present work extends the model to a more complex and realistic scenario: the conditioning approach is modified to include additional physics variables, the network architecture is adjusted to take into account larger image size and more complex pixel dynamic features, the cost function is re-designed in order to include physics based constraints. We ntroduce a multi-step training process based on transfer learning, by initially optimizing the network performance on a restricted particle energy range (therefore, a simplified pixel intensity distribution). Further training the network on the full energy range yields very good results, without the need of additional optimization. Beyond a qualitative visual inspection, the validation of GAN performance is based on a detailed comparison to standard Monte Carlo simulation in terms of several physics quantities: results show a remarkable agreement, ranging from a few percent up to 10% across a large particle energy range. Gul Rukhkhattak, Sofia Vallecorsa, Federico Carminati, Gul Muhammad Khan	ICMLA	2
2018	Data-Parallel Training of Generative Adversarial Networks on HPC Systems for HEP Simulations abstract In the field of High Energy Physics (HEP), simulating the interaction of particle detector materials is a compute-intensive task, that currently uses 50% of the computing resources globally available as part of the Worldwide LCH Computing Grid (WLCG). Since some level of approximation is acceptable, it is possible to implement fast simulation simplified models that have the advantage of being less computationally intensive. In this work, we present a fast simulation approach based on Generative Adversarial Networks (GANs). The model consists of a conditional generative network that describes the detector response and a discriminative network; both networks are trained in adversarial manner. The adversarial training process is computationally intensive and the application of a distributed approach is not straightforward. We rely on the MPI-based Cray Machine Learning Plugin to efficiently train the GAN over multiple nodes and GPGPUs. We report preliminary results on the accuracy of the generated samples and on the scaling of the time to solution. We demonstrate how HPC systems could be utilized to optimize this kind of models, on account of their large computational power and highly efficient interconnect. Sofia Vallecorsa, Diana Moise, Federico Carminati, Gul Rukhkhattak	HiPC	1
2018	Three Dimensional Energy Parametrized Generative Adversarial Networks for Electromagnetic Shower Simulation abstract High Energy Physics (HEP) simulations are traditionally based on the Monte Carlo approach and generally rely on time consuming calculations. The present work investigates the use of Generative Adversarial Networks (GANs) as a fast alternative. Our approach treats the energy deposited by a particle inside a calorimeter detector as a three-dimensional image. True three-dimensional convolutions can be employed to capture the spatio-temporal correlation of shower energy depositions. Three-dimensional images are generated, conditioned on the energy of the incoming particle and validated against Monte Carlo simulation. The results show an agreement to full Mote Carlo simulations well within 10% thus proving that GAN can be used as a fast alternative for simulation of HEP detector response. Gul Rukhkhattak, Sofia Vallecorsa, Federico Carminati	ICIP	2
2015	The IceProd framework: Distributed data processing for the IceCube neutrino observatory Mark G. Aartsen, Rasha U. Abbasi, Markus Ackermann 0003, Jenni Adams, Juan Antonio Aguilar Sánchez, Markus Ahlers, David Altmann, Carlos A. Argüelles Delgado, Jan Auffenberg, Xinhua Bai, Michael F. Baker, Steven W. Barwick, Volker Baum, Ryan Bay, James J. Beatty, Julia K. Becker Tjus, Karl-Heinz Becker, Segev BenZvi, Patrick Berghaus, David Berley, Elisa Bernardini, Anna Bernhard, David Z. Besson, G. Binder, Daniel Bindig, Martin Bissok, Erik Blaufuss, Jan Blumenthal, David J. Boersma, Christian Bohm, Debanjan Bose, Sebastian Böser, Olga Botner, Lionel Brayeur, Hans-Peter Bretz, Anthony M. Brown, Ronald Bruijn, James Casey, Martin Casier, Dmitry Chirkin, Asen Christov, Brian John Christy, Ken Clark, Lew Classen, Fabian Clevermann, Stefan Coenders, Shirit Cohen, Doug F. Cowen, Angel H. Cruz Silva, Matthias Danninger, Jacob Daughhetee, James C. Davis 0002, Melanie Day, Catherine De Clercq, Sam De Ridder, Paolo Desiati, Krijn D. de Vries, Meike de With, Tyce DeYoung, Juan Carlos Díaz-Vélez, Matthew Dunkman, Ryan Eagan, Benjamin Eberhardt, Björn Eichmann, Jonathan Eisch, Sebastian Euler, Paul A. Evenson, Oladipo O. Fadiran, Ali R. Fazely, Anatoli Fedynitch, Jacob Feintzeig, Tom Feusels, Kirill Filimonov, Chad Finley, Tobias Fischer-Wasels, Samuel Flis, Anna Franckowiak, Katharina Frantzen, Tomasz Fuchs, Thomas K. Gaisser, Joseph S. Gallagher, Lisa Gerhardt, Laura E. Gladstone, Thorsten Glüsenkamp, Azriel Goldschmidt, Geraldina Golup, Javier G. González, Jordan A. Goodman, Dariusz Góra, Dylan T. Grandmont, Darren Grant, Pavel Gretskov, John C. Groh, Andreas Groß, Chang Hyon Ha, Abd Al Karim Haj Ismail, Patrick Hallen, Allan Hallgren, Francis Halzen, Kael D. Hanson, Dustin Hebecker, David Heereman, Dirk Heinen, Klaus Helbing, Robert Eugene Hellauer III, Stephanie Virginia Hickford, Gary C. Hill, Kara D. Hoffman, Ruth Hoffmann, Andreas Homeier, Kotoyo Hoshina, Feifei Huang, Warren Huelsnitz, Per Olof Hulth, Klas Hultqvist, Aya Ishihara, Emanuel Jacobi, John E. Jacobsen, Kai Jagielski, George S. Japaridze, Kyle Jero, Ola Jlelati, Basho Kaminsky, Alexander Kappes, Timo Karg, Albrecht Karle, Matthew Kauer, John Lawrence Kelley, Joanna Kiryluk, J. Kläs, Spencer R. Klein, Jan-Hendrik Köhne, Georges Kohnen, Hermann Kolanoski, Lutz Köpke, Claudio Kopper, Sandro Kopper, D. Jason Koskinen, Marek Kowalski, Mark Krasberg, Anna Kriesten, Kai Michael Krings, Gösta Kroll, Jan Kunnen, Naoko Kurahashi, Takao Kuwabara, Mathieu L. M. Labare, Hagar Landsman, Michael James Larson, Mariola Lesiak-Bzdak, Martin Leuermann, Julia Leute, Jan Lünemann, Oscar A. Macías-Ramírez, James Madsen, Giuliano Maggi, Reina Maruyama, Keiichi Mase, Howard S. Matis, Frank McNally, Kevin James Meagher, Martin Merck, Gonzalo Merino, Thomas Meures, Sandra Miarecki, Eike Middell, Natalie Milke, John Lester Miller, Lars Mohrmann, Teresa Montaruli, Robert M. Morse, Rolf Nahnhauer, Uwe Naumann, Hans Niederhausen, Sarah C. Nowicki, David R. Nygren, Anna Pollmann, Sirin Odrowski, Alex Olivas, Ahmad Omairat, Aongus Starbuck Ó Murchadha, Larissa Paul, Joshua A. Pepper, Carlos Pérez de los Heros, Carl Pfendner, Damian Pieloth, Elisa Pinat, Jonas Posselt, P. Buford Price, Gerald T. Przybylski, Melissa Quinnan, Leif Rädel, Ian Rae, Mohamed Rameez, Katherine Rawlins, Peter Christian Redl, René Reimann, Elisa Resconi, Wolfgang Rhode, Mathieu Ribordy, Michael Richman, Benedikt Riedel, J. P. Rodrigues, Carsten Rott, Tim Ruhe, Bakhtiyar Ruzybayev, Dirk Ryckbosch, Sabine M. Saba, Heinz-Georg Sander, Juan Marcos Santander, Subir Sarkar 0002, Kai Schatto, Florian Scheriau, Torsten Schmidt, Martin Schmitz 0004, Sebastian Schoenen, Sebastian Schöneberg, Arne Schönwald, Anne Schukraft, Lukas Schulte, David Schultz, Olaf Schulz, David Seckel, Yolanda Sestayo de la Cerra, Surujhdeo Seunarine, Rezo Shanidze, Chris Sheremata, Miles W. E. Smith, Dennis Soldin, Glenn M. Spiczak, Christian Spiering, Michael Stamatikos, Todor Stanev, Nick A. Stanisha, Alexander Stasik, Thorsten Stezelberger, Robert G. Stokstad, Achim Stößl, Erik A. Strahler, Rickard Ström, Nora Linn Strotjohann, Gregory W. Sullivan, Henric Taavola, Ignacio J. Taboada, Alessio Tamburro, Andreas Tepe, Samvel Ter-Antonyan, Gordana Tesic, Serap Tilav, Patrick A. Toale, Moriah Natasha Tobin, Simona Toscano, Maria Tselengidou, Elisabeth Unger, Marcel Usner, Sofia Vallecorsa, Nick van Eijndhoven, Arne Van Overloop, Jakob van Santen, Markus Vehring, Markus Voge, Matthias Vraeghe, Christian Walck, Tilo Waldenmaier, Marius Wallraff, Christopher Weaver 0001, Mark T. Wellons, Christopher H. Wendt, Stefan Westerhoff, Nathan Whitehorn, Klaus Wiebe, Christopher H. Wiebusch, Dawn R. Williams, Henrike Wissing, Martin Wolf 0007, Terri R. Wood, Kurt Woschnagg, Donglian Xu, Xianwu Xu, Juan Pablo Yáñez, Gaurang B. Yodh, Shigeru Yoshida, Pavel Zarzhitsky, Jan Ziemann, Simon Zierke, Marcel Zoll	J. Parallel Distributed Comput.	257