A5041169092- Computational Drug Discovery Methods
- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Parallel Computing and Optimization Techniques
- Cloud Computing and Resource Management
- Scientific Computing and Data Management
- Distributed and Parallel Computing Systems
- Graph Theory and Algorithms
- Interconnection Networks and Systems
- Cell Image Analysis Techniques
- Distributed systems and fault tolerance
- Machine Learning in Materials Science
- Service-Oriented Architecture and Web Services
- Usability and User Interface Design
- Computational Physics and Python Applications
- Context-Aware Activity Recognition Systems
- Semantic Web and Ontologies
- Quantum Mechanics and Applications
- Advanced Memory and Neural Computing
- Advanced Data Storage Technologies
Intel (United Kingdom)
2018
Vrije Universiteit Brussel
2009-2010
Transnational University Limburg
2008
Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, quality public might be different than industry due labs reporting measurements, measurement techniques, fewer samples less diverse specialized assays....
The volume of high throughput screening data has considerably increased since the beginning automated biochemical and cell-based assays era. This information-rich source provides tremendous repurposing opportunities for mining. It was recently shown that or assay results can be compiled into so-called high-throughput fingerprints (HTSFPs) as a new type descriptor describing molecular bioactivity profiles which applied in virtual screening, iterative target deconvolution. However, so far,...
In modern processors, prefetching is an essential component for hiding long-latency memory accesses. However, too aggressively can easily degrade performance by evicting useful data from cache, or saturating precious bandwidth. Tuning the prefetcher's activity thus important problem. Existing techniques tend to focus on detecting negative symptoms of aggressive prefetching, such as unused prefetches being evicted bandwidth saturation, and throttle prefetcher in response.
High performance large scale graph analytics is essential to timely analyze relationships in big data sets. Conventional processor architectures suffer from inefficient resource usage and bad scaling on workloads. To enable efficient scalable analysis, Intel developed the Programmable Integrated Unified Memory Architecture (PIUMA). PIUMA consists of many multi-threaded cores, fine-grained memory network accesses, a globally shared address space powerful offload engines. This paper presents...
High performance large scale graph analytics are essential to timely analyze relationships in big data sets. Conventional processor architectures suffer from inefficient resource usage and bad scaling on those workloads. To enable efficient scalable analysis, Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> developed the Programmable Integrated Unified Memory Architecture (PIUMA) as a part of DARPA Hierarchical Identify Verify...
<div>This article describes an application of high-throughput fingerprints (HTSFP) built upon industrial data accumulated over the years. </div><div>The fingerprint was used to build machine learning models (multi-task deep + SVM) for compound activity predictions towards a panel 131 targets. </div><div>Quality and scaffold hopping potential HTSFP were systematically compared traditional structural descriptors ECFP. </div><div><br></div>
This article describes an application of high-throughput fingerprints (HTSFP) built upon industrial data accumulated over the years. The fingerprint was used to build machine learning models (multi-task deep + SVM) for compound activity predictions towards a panel 131 targets. Quality and scaffold hopping potential HTSFP were systematically compared traditional structural descriptors ECFP.
In this paper we explore the structure and applicability of Distributed Measurement Calculus (DMC), an assembly language for distributed measurement-based quantum computations. We describe formal language's syntax semantics, both operational denotational, state several properties that are crucial to practical usability our language, such as equivalence well compositionality context-freeness DMC programs. show how put these use by constructing a composite program implements controlled...