| Page 1626 | Kisaco Research
 

John Parkison

Computational Biologist
University of Toronto

Dr John Parkinson is a computational biologist whose research interests focus on the impact of microbiota on human health. After completing his PhD at the University of Manchester, studying molecular self-assembly, John spent a year at the University of Manitoba investigating diatom morphogenesis. In 1997, John moved to Edinburgh where he applied computer models to study the evolution of complement control proteins with Dr Paul Barlow.

John Parkison

Computational Biologist
University of Toronto

John Parkison

Computational Biologist
University of Toronto

Dr John Parkinson is a computational biologist whose research interests focus on the impact of microbiota on human health. After completing his PhD at the University of Manchester, studying molecular self-assembly, John spent a year at the University of Manitoba investigating diatom morphogenesis. In 1997, John moved to Edinburgh where he applied computer models to study the evolution of complement control proteins with Dr Paul Barlow. With the emergence of high throughput sequencing, John then led the bioinformatics efforts associated with the parasitic nematode expressed sequence tag project, responsible for the processing and curation of sequence data from 30 species of parasitic nematodes. John was recruited to the Hospital for Sick Children in 2003 and was promoted to Senior Scientist in 2009. He holds cross-appointments in both the departments of Biochemistry and Molecular Genetics at the University of Toronto. Current lab interests center on the role of the microbiome in health and disease as well as the mechanisms that allow  pathogens and parasites to survive and persist in their human hosts.  Key to this research is the integration of computational systems biology analyses with comparative genomics to explore the evolution and operation of microbial pathways driving pathogenesis. Findings from our research programs are helping guide new strategies for therapeutic intervention.

 

Fiona Walsh

Assistant Professor
University of Maynooth

Fiona Walsh

Assistant Professor
University of Maynooth

Fiona Walsh

Assistant Professor
University of Maynooth
 

Chris Reynolds

University of Reading

Chris Reynolds

University of Reading

Chris Reynolds

University of Reading
 

Chad Hastat

New Fashion Pork

Chad Hastat

New Fashion Pork

Chad Hastat

New Fashion Pork
 

Arnaud Bouxin

Deputy Secretary General
FEFAC

Arnaud Bouxin is agronomist by education and graduated from the Institut National Agronomique Paris-Grignon. He started his carrier as policy advisor in the French Association of feed manufacturers, SNIA, in 1990 and joined FEFAC as Deputy Secretary General in 1998. He is busy primarily with feed legislation and the drafting of tools to support its implementation, for example the FEFAC Guide to Good Hygiene Practice for compound feed and premixture manufacturing (EFMC), or the Code of practice for compound feed labeling drafted in cooperation with Copa-Cogeca.

Arnaud Bouxin

Deputy Secretary General
FEFAC

Arnaud Bouxin

Deputy Secretary General
FEFAC

Arnaud Bouxin is agronomist by education and graduated from the Institut National Agronomique Paris-Grignon. He started his carrier as policy advisor in the French Association of feed manufacturers, SNIA, in 1990 and joined FEFAC as Deputy Secretary General in 1998. He is busy primarily with feed legislation and the drafting of tools to support its implementation, for example the FEFAC Guide to Good Hygiene Practice for compound feed and premixture manufacturing (EFMC), or the Code of practice for compound feed labeling drafted in cooperation with Copa-Cogeca. He is also one of the coordinators of the EU Feed Chain Task Force gathering 41 EU organisations of the feed chain, which is taking care of the maintenance of the EU Catalogue and the Register of feed materials. He is 54 years old, married and has got two children.

Post-Show Report 2019 - Women's Health Innovation Summit

KLC AI Hardware Accelerators 2020-21 (part 3): Edge and automotive, July 2020

  • Motivation

    Today Artificial intelligence (AI) is out of the research laboratory and in the realm of practical engineering applications. AI engineering today is largely about running machine learning (ML) models on digital computers, and these models are typically simulations of brain-inspired models such as neural networks, with deep learning (DL) being the most successful example today. With the plateauing out of CPU performance improvements and the end of Moore’s law, even with multi-core CPU machines, the community has turned to hardware accelerators to run their AI models.

Request a sample

Please complete the form below to receive a sample of this report.

KLC AI Hardware Accelerators 2020-21 (Part 2): Data Centers and HPC, July 2020

  • Motivation

    Artificial intelligence (AI) is out of the research laboratory and is in the realm of practical engineering applications. AI engineering today is largely about running machine learning (ML) models on digital computers, and these models are typically simulations of brain-inspired models such as neural networks, with deep learning (DL) being the most successful example today. With the plateauing out of CPU performance improvements and the end of Moore’s law, even with multi-core CPU machines, the community has turned to hardware accelerators to run their AI models.

Request a sample

Please complete the form below to receive a sample of this report.

KLC Hardware Accelerators 2020-21 (Part 1): Technology and Market Landscapes, July 2020

  • Motivation

    Artificial intelligence (AI) is out of the research laboratory and is in the realm of practical engineering applications. AI engineering today is largely about running machine learning (ML) models on digital computers, and these models are typically simulations of brain-inspired models such as neural networks, with deep learning (DL) being the most successful example today. With the plateauing out of CPU performance improvements and the end of Moore’s law, even with multi-core CPU machines, the community has turned to hardware accelerators to run their AI models.

Request a sample

Please complete the form below to receive a sample of this report.