Anestis Gkanogiannis

Anestis Gkanogiannis

Machine/Deep Learning and Bioinfornatics Scientist

Biography

Anestis Gkanogiannis is an award winning Researcher in the fields of Artificial Intelligence, Machine Learning and Bioinformatics. With strong analytical skills and problem solving capabilities. Expert in algorithm construction and prototype software development. With ability to learn fast, fill knowledge gaps and adapt to new technologies and challenges.

NGS data analysis, Genomic Selection, Computational Biology, Artificial Intelligence, Machine Learning, Deep Learning, Big Data Mining, Classification, Clustering, Linear Classifiers, Information Retrieval, Algorithms and Data Structures, Image Retrieval, Multimodal Retrieval, Medical Retrieval, Hierarchical Classification, Large Scale Classification

Download my resumé.

Interests
  • Bioinformatics, Computational Biology
  • Machine/Deep Learning
  • Artificial Intelligence
  • Information Retrieval
  • Electronics prototyping (RPi, Arduino)
Education
  • PhD in Machine Learning, 2011

    Athens University of Economics and Business

  • MSc in Machine Learning, 2005

    Athens University of Economics and Business

  • BSc in Physics, 2003

    University of Crete

Skills

bioinformatics
Bioinformatics

90%

artificial-intelligence
AI

90%

machine-learning
Machine Learning

90%

deep-learning
Deep Learning

80%

rpi
Electronics

80%

hpc
HPC

80%

java
Java

90%

R

80%

c
C/C++

80%

linux
UNIX/Linux

90%

mathematics
Mathematics

80%

physics
Physics

80%

Experience

 
 
 
 
 
Gene Regulation, Stem Cells and Cancer, CRG
Bioinformatician
Apr 2022 – Present Barcelona, Catalonia, Spain
Cancer metabolism, NGS data analysis (RNA-Seq, ATAC-Seq, Nanopore, WGS, etc.), Nextflow pipeline development, Bioconductor R packages development, CRISPR gene screening, Computational Biology
 
 
 
 
 
Agrobiodiversity Research Area, CIAT/CGIAR
Research Scientist / Bioinformatics Head
Jan 2017 – Mar 2021 Cali, Valle de Cauca, Colombia
Plant Genomics/Genetics/Bioinformatics (Cassava, Rice, Bean, Forages), NGS data analysis, Computational Biology, Population Genomics, GWAS, Genomic Selection, Machine Learning, Artificial Intelligence, Big Data
 
 
 
 
 
Intégration de données (Equipe ID), CIRAD/BIOS/AGAP
Post-Doctorate Research Fellow
Jun 2015 – Dec 2016 Montpellier, Languedoc-Roussillon-Midi-Pyrénées, France
Research Scientist, Computational Biology, Software Analyst/Developer, Web Developer: RTB (Roots Tubers and Banana).
 
 
 
 
 
Laboratoire de Génomique et Biochimie du Métabolisme(LGBM), CEA/DSV/IG/Genoscope/LGBM
Post-Doctorate Research Fellow
Jun 2013 – Jun 2015 Evry, Île-de-France, France
Research Scientist, Machine Learning and Computational Biology, Software Analyst/Developer (in cooperation with CEA LIST): MetaTarget
 
 
 
 
 
University of New Brunswick, Faculty of Computer Science
Post-Doctorate Research Fellow
Oct 2011 – Mar 2013 Fredericton, NB, Canada
Research Scientist, Machine Learning, Artificial Intelligence, Software Analyst/Developer and Project Manager, I-AID: Intelligent Analysis of Information and Dissemination

Publications

(2022). Methylation in the CHH Context Allows to Predict Recombination in Rice. Int. J. Mol. Sci. 2022, 23(20), 12505;.

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(2022). Characterization of cassava ORANGE proteins and their capability to increase provitamin A carotenoids accumulation. PLoS ONE 17(1): e0262412..

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(2021). The diversity, origin and evolution of wild and landrace varieties of M.esculenta from RADSeq data. In Preparation.

(2021). Two dense genetic maps and WhiteFly infestation QTLs from RADSeq and WGS data of two F1 M.esculenta populations. In Preparation.

(2019). NOISYmputer: genotype imputation in bi-parental populations for noisy low-coverage next-generation sequencing data. In bioRxiv 658237.

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