Before ITU, she was a research staff member at IBM Almaden Research Center. Prior to joining IBM, she received her PhD from EPFL. Her research focuses on performance characterization of data-intensive workloads, and scalability and efficiency of data-intensive systems on modern hardware.
Before joining ITU, he studied at the Radboud University Nijmegen (NL), where he graduated with a Masters degree in Data Science with a traineeship focussed on Massively-Parallel acceleration of Machine Learning (via graphics cards) at the University of Copenhagen.
Ehsan holds a masters degree in Computer Engineering - Computer Architecture from the Sharif University of Technology, Tehran, Iran. His primary field of interest is Computer Architecture, narrowly parallel computing systems and energy-efficient designs and their application in Heterogeneous Systems.
A data scientist interested in the intersection of modern hardware and data science. More specifically, he is interested in efficiently utilizing hardware accelerators (GPUs, FPGAs) for machine learning workloads.
Neil graduated from ITU with a masters degree in Computer Science and conducts research with a focus on data management aspects of machine learning (ML). His research experience spans modern hardware in the context of machine learning, hardware accelerators, and optimizing end-to-end ML training pipelines.
A data scientist gone computer scientist with a special interest in machine learning pipelines and how hardware accelerators can be used across the entire pipeline. Especially, he is interested in the trade-off between energy consumption and throughput. Currently working on SoCs and embedded systems.
Collaborators and Helpers
The DAPHNE project aims to define and build an open and extensible system infrastructure for integrated data analysis pipelines, including data management and processing, high-performance computing (HPC), and machine learning (ML) training and scoring. The acronym DAPHNE relates to the title "integrated Data Analysis Pipelines for large-scale data management, High-performance computing, and machiNE learning". To develop a comprehensive framework, the project is organized in system architecture, hardware, scheduling, benchmarks, use cases, open source.
Sebastian Sebastian holds a Ph.D. in physics from the Technical University of Berlin in Germany, with a focus on optics, radio spectroscopy, photovoltaic systems and scientific programming. He loves and plays music, is fascinated and engaged with text, language and poetry in many forms. He is responsible for managing the DASYA lab, coordinating tasks between researchers and the IT department, enabling experimental digital technology (from acquisition to maintenance), developing system and application, compiling and managing documentations, managing student programs. His focus ares include IoT, sensors, physical computing, (wireless) networks, IT and sustainable energy, and open and free software.
Julian Priest is a researcher in the DASYA Lab and project scientist for the Discosat project. He is responsible for representing ITU in the Discorat project, coordinating and mentoring Discosat students, developing satellite, managing ground station, coordinating DASYA space activities. His research areas include satellite IT, culturization of space, and art science. Aside from his work at ITU, Julian has a background in community wireless networking and a long running international media arts practice that includes the recent launch of a satellite as an artwork. Julian holds a Masters from Victoria University of Wellington ‘New Directions in Space Art’. http://julianpriest.org
Lottie manages ITU's High-Performance Computing (HPC) systems.