Dr. Rosa M. Badia is the manager of the Workflows and Distributed Computing group at the Barcelona Supercomputing Center (BSC). Her current research interests include programming models for distributed computing platforms and its integration with novel storage technologies. Her group has been proposing the task-based StarSs programming model, and she is currently focused in the PyCOMPSs/COMPSs instance of this model for distributed computing platforms. This model can be used for the definition of medium-grain task-based applications of for the definition of coarse grain workflows, which can combine tasks written in other parallel programming models, such as OpenMP or MPI. The model also plays well with new concepts for persistent storage, and are integrated with the dataClay and Hecuba solutions. Her group has been very active in the participation on European funded projects. Dr Badia has published more than 200 articles in international journals and international conferences of the area.
Wu Feng is a Professor and Turner Fellow of Computer Science at Virginia Tech, where he directs the Synergy Lab as well as the Synergistic Environments for Experimental Computing (SEEC) Center. He also holds appointments in the Department of Electrical & Computer Engineering, Health Sciences, Biomedical Engineering and Mechanics, and the Biocomplexity Institute. His research interests in parallel and distributed computing sit at the synergistic intersection of architecture, systems software, algorithms, and applications, resulting in over 300 manuscripts, including “The Green Computing Book: Tackling Energy Efficiency at Large Scale,” and a worldwide commercial success on his biocomputing research in the Microsoft Cloud. He also founded and directs the Green500 List, a listing of the most energy-efficient supercomputers in the world. Dr. Feng holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. In addition to being a Distinguished Scientist of the ACM, Dr. Feng has been named to HPCwire’s Top People to Watch twice, once in 2004 and again in 2011.
Hervé Goëau is a computer scientist at CIRAD, the French agricultural research and international cooperation organization working for the sustainable development of tropical and Mediterranean regions, and member of the AMAP joint research unit at Montpellier in France, an interdisciplinary laboratory conducting primary and advanced applied research on plants and plant communities. His topics of interests include deep learning and large-scale fine-grained classification applied to living species identification, biodiversity informatics, ecological monitoring, scientific data management, crowdsourcing & citizen sciences. For the last ten years, he has been one of the leading researchers involved in the development of Pl@ntNet, the citizen science project renowned for its mobile plant identification apps daily used by dozens of thousands of users in the world. He annually co-organizes, within the international CLEF Initiative (Conference and Labs of the Evaluation Forum), the LifeCLEF Plant species Identification task, an ambitious challenge with several thousand species, based on the visual content and metadata analyses, that promotes machine learning and computer vision in the biodiversity informatics field. He currently works on advanced applications of machine learning to automated weed detection in cultivated fields, plant disease identification, organ detection and measurements in herbarium collections, and on a global flora plant species identification system based on millions of pictures that require distributed deep learning on GPU clusters and HPC infrastructures.
Dr. Leonardo Bautista Gomez is a Senior Research Scientist at the Barcelona Supercomputing Center where he works in multiple H2020 European projects related to resilience, energy efficiency and multilevel storage systems for high performance computing. In 2016 he was awarded with a European Marie Curie fellowship on Deep-memory Ubiquity, Resilience and Optimization. In addition, he was awarded the 2016 IEEE TCSC Award for Excellence in Scalable Computing (Early Career Researcher). Before moving to BSC he was a Postdoctoral researcher for three years at the Argonne National Laboratory, where he investigated data corruption detection techniques and error propagation. Prior to that, he did his PhD. in resilience for supercomputers at the Tokyo Institute of Technology. He developed a scalable multilevel checkpointing library called Fault Tolerance Interface (FTI) to guarantee application resilience at extreme scale. For this work, he was awarded the 2011 ACM/IEEE George Michael Memorial High Performance Computing Ph.D. Fellow at Supercomputing Conference 2011 (SC11), Honorable Mention; and a Special Certificate of Recognition for achieving a perfect score at the Supercomputing Conference 2011 (SC11) for the paper : FTI : High Performance Fault Tolerance Interface for Hybrid Systems. In Japan, he was awarded the Japanese Society for the Promotion of Science (JSPS), Research Fellowships for Young Scientists (Doctoral Course). Before moving to Tokyo Tech, he graduated in Master for Distributed Systems and Applications from the Paris 6 University, Pierre & Marie Curie. Prior to this, he obtained a Bachelor in Computer Science from the Paris 6 University, Pierre & Marie Curie.
Ignacio Laguna Peralta is a Computer Scientist at the Center for Applied Scientific Computing (CASC) at the Lawrence Livermore National Laboratory (LLNL), California. His main area of research is high-performance computing (HPC), and his interests include HPC programing models and systems.
He is particularly interested in fault tolerance, fault resilience, debugging, software correctness and general software reliability. He develops practical tools that allow scientific programmers improve the reliability and accuracy of their codes. To develop these tools, he usually relies on compiler instrumentation (with LLVM), low-level binary instrumentation, static analysis, dynamic analysis, and machine learning. He is an IEEE Senior Member and an R&D 100 Award Finalist (2017).
Paola Buitrago leads the Artificial Intelligence and Big Data group at the Pittsburgh Supercomputing Center, which is a joint effort of Carnegie Mellon University and the University of Pittsburgh. Her group is focused on advancing and supporting the convergence of High Performance Computing (HPC), Artificial Intelligence (AI) and Big Data. Paola initiated a new platform for AI research on emerging hardware and software technologies, enabling development of advanced algorithms and modeling approaches. She is also leading PSC’s Big Data-as-a-Service (BDaaS) initiative, through which Internet-scale datasets are being integrated with supercomputing resources for cross-cutting research communities.
Paola’s diverse background includes research in deep learning, large scale data, and workflow management for high energy physics experiments at the Fermi National Accelerator Laboratory. Paola developed courses in machine learning, simulation, and optimization at her university. She is passionate about education in technology and launched an education-focused start-up.
Paola’s academic background includes a Bachelors degree in Chemical Engineering and a Bachelors in Systems and Computing Engineering. She holds a Masters from Universidad de los Andes in Bogotá, Colombia.
Dr. Kihara is a full professor in the Department of Biological Sciences and the Department of Computer Science at Purdue University, West Lafayette, Indiana. He received the B.S. degree from the University of Tokyo, Japan in 1994, and the Ph.D. degree from Kyoto University, Japan in 1999. After studying as a postdoctoral researcher with Prof. Jeffrey Skolnick he joined Purdue University in 2003. He was promoted to full professor in 2014. From 2018, he holds an adjunct professor position at Department of Pediatrics, University of Cincinnati. He has been working in various topics in protein bioinformatics. His current research projects the developments of algorithms for protein-protein docking, protein tertiary structure prediction, structure modeling from low-resolution image data, structure- and sequence-based protein function prediction, and computational drug design. He has published over 150 research papers and book chapters. His research projects have been supported by funding from the National Institutes of Health, the National Science Foundation, the Office of the Director of National Intelligence, and industry. He has served on the program committee of various bioinformatics conferences including the Intelligent Systems for Molecular Biology (ISMB) where he is a track chair in 2019. In 2013, he was named a University Faculty Scholar by Purdue University.
Dr. Decencière is senior researcher at MINES ParisTech. His current research interests are focused on image segmentation using mathematical morphology and deep learning, with applications in retinal imaging, histology and astronomy, among others. Bridging the gap between theory and practice is one of his main motivations. Most of his research is financed by industrial partners. He has been for instance in charge since 2008 of a collaboration with L'Oréal, dealing with the characterization of human skin through different imaging modalities (e.g. multiphoton microscopy, whole-slide imaging, structured-light 3D scanner). The resulting software is routinely used by L'Oréal.