Hajar Hakkoum | Machine learning | Best Scholar Award

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Dr. Hajar Hakkoum | Machine learning |Best Scholar Award

πŸ‘¨β€πŸ«Profile Summary

In my current role as a Postdoctoral Researcher at INRAE in Versailles, France, I specialize in plant cell cycle dynamics through image analysis and compressed fluorescence acquisition. As a Software Engineer at IS Technologies, Courbevoie, France, I contributed to robust server-side components, integrated AI APIs, and enhanced search engine recommendation systems. My Ph.D. in Machine Learning Interpretability from ENSIAS, UM5*, Rabat, involved groundbreaking research in medical AI, including mentoring fellow researchers and contributing to peer-reviewed journals. Proficient in Python, data mining, and deep learning, I possess strengths in scientific writing and presentation skills. Fluent in Arabic, English, French, Spanish, and German, I hold a C1 IELTS certification and received the Best Poster Award at the 15th International Conference on Health Informatics in 2022. Beyond my professional endeavors, I nurture a keen interest in languages, reading, and running. My educational background includes a Software Engineering Degree (Web & Mobile) from ENSIAS, UM5*, Rabat, and completion of Engineering Preparatory Classes at CPGE Salmane Al Faressi in SalΓ©, Morocco.

🌐 Professional Profiles

πŸ“š Education:

Software Engineering Degree (Web & Mobile): ENSIAS, UM5*, Rabat, MAR (2016-2019). Engineering Preparatory Classes: CPGE Salmane Al Faressi, SalΓ©, MAR (2014-2016)

πŸ” Professional Experience:

Postdoctoral Researcher INRAE, Versailles, FR (March 2024 – Present) Conducting image analysis of plant cell cycle dynamics. Implementing compressed fluorescence acquisition techniques.. Software Engineer IS Technologies, Courbevoie, FR (October 2022 – February 2024) Developing robust server-side components using ASP.NET and PostgreSQL. Analyzing and integrating AI APIs for text translation, camera filters, and ChatGPT. Enhancing search engine recommendation systems and assessing employee/user satisfaction using Python. PhD in Machine Learning Interpretability in Medicine ENSIAS, UM5, Rabat, MAR (January 2020 – April 2023) Conducting a systematic literature review on interpretability techniques in medicine. Quantitatively evaluating the interpretability of ML black-box models in medicine. Assessing the impact of categorical feature encoding on ML interpretability techniques in medicine. Guiding two new PhD students in research projects on ML interpretability in Biodiversity and Cybersecurity. Publishing research papers in reputable peer-reviewed journals and conferences emphasizing the significance of interpretability in medical AI. Contributing to the peer-review process within the medical AI domain as a reviewer for the Scientific African journal (Q2 with IF: 2.9). PhD Internship (ERASMUS) Facultad de InformΓ‘tica, Murcia, SP (January – June 2022) Investigating the impact of categorical data on interpretability techniques. Participating in interdisciplinary discussions to bridge the gap between ML and domain experts’ needs. Projects and Internships: Final Degree Project – Research Initiation: Interpretability of ANNs for breast cancer diagnosis (Python). Third Year Project – Handwritten Digits and French Numbers Image Recognition using CNNs and collected images (Python). Second Year Internship – ChatBot development for Banks Q/A (.NET). First Year Internship – Checks Amounts Validation for Banks (C#, Azure APIs).

πŸ† Certificates:

IELTS: C1 (8.5 Reading & Listening, 7.5 Speaking, and 6.5 Writing). Best Poster Award: 15th International Conference on Health Informatics (2022)

🎯 Interests:

Languages: β€œA different language is a different vision of life.” Reading: β€œA reader lives a thousand lives before he dies.” Running: β€œExercise is a tribute to the heart.”

 

πŸ“šTop Noted Publication

  1. β€œInterpretability in the medical field: A systematic mapping and review study” (2022, Applied Soft Computing):
    • This paper likely serves as a comprehensive review and mapping study on the topic of interpretability in the medical field, possibly summarizing existing research and identifying trends or gaps in the literature.

 

  1. β€œAssessing and comparing interpretability techniques for artificial neural networks breast cancer classification” (2021, Computer Methods in Biomechanics and Biomedical Engineering):
    • The focus here is on assessing and comparing interpretability techniques specifically applied to artificial neural networks for breast cancer classification. The paper likely delves into the methods used to make these complex models interpretable.

 

  1. β€œEnsemble blood glucose prediction in diabetes mellitus: A review” (2022, Computers in Biology and Medicine):
    • This study appears to be a review on ensemble methods for predicting blood glucose levels in the context of diabetes mellitus, exploring the various techniques employed in aggregating predictions for improved accuracy.

 

  1. β€œArtificial neural networks interpretation using LIME for breast cancer diagnosis” (2020, Trends and Innovations in Information Systems and Technologies):
    • This paper seems to focus on the interpretability of artificial neural networks for breast cancer diagnosis, specifically using the Local Interpretable Model-agnostic Explanations (LIME) technique.

 

  1. β€œA Systematic Map of Interpretability in Medicine” (2022, HEALTHINF):
    • This paper likely provides a systematic map, possibly a visual representation, of interpretability in medicine. It could outline the landscape of interpretability techniques, their applications, and potential challenges in the medical field.

 

  1. β€œGlobal and local interpretability techniques of supervised machine learning black box models for numerical medical data” (2024, Engineering Applications of Artificial Intelligence):
    • This study seems to explore both global and local interpretability techniques applied to supervised machine learning models for numerical medical data. The emphasis is likely on making black-box models more understandable and transparent.

 

  1. β€œEvaluating Interpretability of Multilayer Perceptron and Support Vector Machines for Breast Cancer Classification” (2022 IEEE/ACS 19th International Conference on Computer Systems and…):
    • This paper likely evaluates the interpretability of two different machine learning models, Multilayer Perceptron and Support Vector Machines, in the context of breast cancer classification.

 

Simo Maduna | Molecular Ecology | Young Scientist Award

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 Dr. Simo Maduna | Molecular Ecology| Young Scientist Award

 

πŸ‘¨β€πŸ« 🌍 Dr. Simo Maduna , a versatile geneticist and educator, holds a Ph.D. in Genetics from Stellenbosch University, where they also achieved a Master’s degree with Cum Laude honors. Their academic journey includes a Certificate in Higher Education Teaching from Harvard University, showcasing a commitment to pedagogical excellence. Currently an Extraordinary Senior Lecturer at Stellenbosch University and a Researcher at The Norwegian Institute of Bioeconomy Research, they bring a wealth of experience. With a diverse background, including teaching at Somerset West Private High School and facilitating courses at Stellenbosch University,  is dedicated to both research and education. Their extensive research contributions in environmental genetics and numerous grants underscore their commitment to advancing scientific knowledge. 🧬🌍 #Genetics #Researcher #Education

🌐 Professional Profiles

πŸŽ“ Educational Background

Ph.D. in Genetics, Stellenbosch University, 2017, Master of Science in Genetics (Cum Laude), Stellenbosch University, 2014, Certificate in Higher Education Teaching, Harvard University, 2021, Bachelor of Science (Hons.) in Genetics, Stellenbosch University, 2012, Bachelor of Science in Molecular Biology and Biotechnology, Stellenbosch University, 2011, Certificate in Music, Stellenbosch University and University of South Africa

πŸ‘¨β€πŸ”¬ Professional Experience

Current Positions:

Researcher at The Norwegian Institute of Bioeconomy Research Svanhovd, Environment and Natural Resources Division, Jul 2022 – Present, Extraordinary Senior Lecturer at Stellenbosch University, Genetics Department, Jul 2022 – Present

Previous Positions:

Postdoctoral Researcher at The Norwegian Institute of Bioeconomy Research Svanhovd, Environment and Natural Resources Division, Jul 2018 – Jun 2022, Postgraduate Co-Supervisor at Stellenbosch University, Genetics Department, Apr 2013 – Jun 2022, Teaching Assistant at Stellenbosch University, Genetics, Mar 2012 – Sep 2015

πŸ… Grants, Awards, and Memberships

1st Rufford Small Grant, 2022–2023 IDEA Wild Small Equipment Grant, 2022 & 2018 National Research Foundation Award, 2013–2017 Ph.D. Merit Bursary, Stellenbosch University, 2015–2017 Save Our Seas Foundation Small Grant, 2015 Golden Key Membership, 2011 Studie Trust Bursary – The Sazal Inzalo Foundation, 2010–2012 The DG Murray Trust Bursary, 2009

πŸ‘¨β€πŸ« Teaching Experience

Teacher at Somerset West Private High School, Jul 2017 – Dec 2017 Lecturer at Stellenbosch University, Genetics Department Facilitator at Stellenbosch University, Department of Genetics, Feb 2012 – Nov 2014

πŸ” Continuous Improvement Programs

Acoustic Telemetric Workshop (VEMCO) Shark Dissection Workshop Computational Molecular Evolution Bioinformatics applied: Introduction to Statistics and Methods Real-time PCR Training Course Fluidigm (Biomarker HD) Gene Expression and SNP Genotyping Training Acoustic Telemetry Workshop (VEMCO) Landscape Genomics Introduction to Evolutionary Quantitative Genetics 8th NCEP Conservation Teaching & Learning Studio (2021) Genetics and Society: A Course for Educators Eukaryotic Genome Assembly using PacBio and Hi-C UPED 650: Online and Blended Learning Ambios Effective Camera Trapping Distance Learning Course Ambios Effective Bird ID Course Species distribution and ecological niche modeling in R

πŸ“‘ Additional Experience

Reviewer and Editorial Board Member for various journals and publications. This individual possesses a rich academic background, extensive teaching experience, and a notable record in genetic research, education, and environmental conservation.

 

πŸ“šTop Noted Publication

Species identification and comparative population genetics of four coastal houndsharks based on novel NGS‐mined microsatellites Paper Published in 2017 Cited by 33

Microsatellite cross-species amplification and utility in southern African elasmobranchs: a valuable resource for fisheries management and conservation Paper Published in 2014 Cited by 32

When two oceans meet: regional population genetics of an exploited coastal shark, Mustelus mustelus Paper Published in 2015 Cited by 27

Differential gene flow patterns for two commercially exploited shark species, tope (Galeorhinus galeus) and common smoothhound (Mustelus mustelus) along the south–west coast of β€¦ Paper Published in 2015 Cited by 26

Quantification of grazing efficacy, growth and health score of different lumpfish (Cyclopterus lumpus L.) families: possible size and gender effects Paper Published in 2021 Cited by 22

Molecular research on the systematically challenging smoothhound shark genus Mustelus: a synthesis of the past 30 years Paper Published in 2017 Cited by 6

DB, Spetland, F., Lindberg, KS, 2021. Quantification of grazin g efficacy, growth and health score of different lumpfish ( Cyclopterus lumpus L.) families: possible size and β€¦Paper Published in 2021 Cited by 19

Mitogenomics of the suborder Cottoidei (Teleostei: Perciformes): Improved assemblies, mitogenome features, phylogeny, and ecological implications Paper Published  Cited by 6

Characterization of the complete mitochondrial genome of the common smoothhound shark, Mustelus mustelus (Carcharhiniformes: Triakidae) Paper Published in 2018 Cited by 5