José Bonomi-Barufi | Botânica | Best Researcher Award

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Dr. José Bonomi-Barufi | Botânica | Best Researcher Award

👨‍🏫Profile Summary

O autor possui uma sólida formação acadêmica, com graduação e mestrado em Ciências Biológicas pela Universidade de São Paulo, concluídos em 2002 e 2004, respectivamente. Além disso, obteve licenciatura em Ciências Biológicas na mesma instituição em 2004. Sua experiência concentra-se na área de Botânica, com ênfase em algas. Ele lecionou como professor temporário na Universidade Católica de Brasília em 2005. Completou seu doutorado em Ciências (Botânica) pela Universidade de São Paulo em 2010, incluindo um estágio no exterior na Universidad de Málaga, Espanha, em 2007, especializando-se em fotobiologia e biologia molecular de macroalgas. Entre agosto de 2010 e agosto de 2011, trabalhou no Centro de Pesquisas e Desenvolvimento Leopoldo Américo Miguez de Melo, CENPES, Petrobras, na linha de pesquisa de bioprocessos de microalgas para obtenção de biocombustíveis. Desde agosto de 2011, é docente do Departamento de Botânica da Universidade Federal de Santa Catarina. Ele também é membro do Grupo de Pesquisa do CNPq Biologia, Cultivo e Biotecnologia de Microalgas – Santa Catarina, sediado na UFSC, e faz parte do Núcleo Permanente dos Programas de Pós-Graduação em Biotecnologia e Biociências e de Pós-Graduação em Biologia de Fungos, Algas e Plantas, ambos da UFSC. Sua pesquisa atual abrange fotobiologia, ecofisiologia, fotossíntese e biorremediação de macro ou microalgas.

🌐 Professional Profiles

🎓 Formação Acadêmica/Titulação:

Pós-Doutorado: 🇪🇸, Pós-Doutorado: 🇪🇸, Doutorado em Ciências (Botânica): 🇧🇷🇪🇸, Mestrado em Ciências Biológicas (Botânica): 🇧🇷, Graduação em Ciências Biológicas: 🇧🇷

👨‍🏫 Experiência Profissional:

  • Professor Associado: 🇧🇷
  • Professor Temporário: 🇧🇷

📚 Atividades de Ensino:

  • Ministra disciplinas nos cursos de graduação em Ciências Biológicas e Oceanografia.

🔬 Pesquisa e Desenvolvimento:

Pesquisa nas áreas de ecofisiologia, fotobiologia, fotossíntese e biorremediação de algas. Membro de grupos de pesquisa e núcleos permanentes de programas de pós-graduação.

💼 Outras Atividades:

Membro do colegiado do curso de graduação em Ciências Biológicas. Ministra disciplinas de Biologia de Vegetais Aquáticos, Diversidade e Evolução de Organismos Fotossintetizantes e Fungos, e Fitobentos. Realizou estágios e atividades técnico-científicas no Instituto de Biociências da Universidade de São Paulo. Monitor de disciplinas de graduação e participação em atividades técnico-científicas durante a graduação e pós-graduação.

🔍 Linhas de Pesquisa:

  • Ecofisiologia, Fotobiologia, Algas, Fotossíntese, Biorremediação

🔬 Projetos de Pesquisa:

Perspectivas da Fotoproteção a partir de Algas Marinhas: Toxicidade, Aplicações e Ecofisiologia (2018 – Atual) Coordenador: José Bonomi Barufi Descrição: Este projeto visa explorar diferentes aspectos relacionados à fotoproteção utilizando algas marinhas. Envolve a avaliação dos efeitos dos filtros solares na fisiologia de macroalgas, o papel da radiação UV na ocupação de nichos e a regulação da síntese de compostos fotoprotetores, entre outros. Status: Em andamento Filogenética Molecular do Gênero Sargassum na Costa de Santa Catarina (2014 – 2016) Coordenador: José Bonomi Barufi Descrição: Este projeto realizou uma análise filogenética de espécimes de algas pardas do gênero Sargassum coletadas na costa de Santa Catarina, utilizando técnicas de análise molecular para identificação e estabelecimento de filogenias. Status: Concluído Produção de Biomassa de Microalgas em Escala Piloto para a Obtenção de Biodiesel (2014 – Atual) Coordenador: José Bonomi Barufi Descrição: Este projeto busca desenvolver pesquisas científicas e tecnológicas visando a produção de biodiesel a partir de microalgas cultivadas em escala piloto, incluindo a otimização da produção de biomassa e a bioacumulação de lipídios adequados. Status: Em andamento Efeito da Latitude na Concentração e Diversidade de Compostos Fotoprotetores de Algas Marinhas do Brasil (2013 – 2016) Coordenador: José Bonomi Barufi Descrição: Este projeto investiga o potencial das algas brasileiras na fotoproteção contra a radiação UV, com foco nos compostos fotoprotetores. Analisa o efeito da latitude na concentração desses compostos ao longo da costa brasileira. Status: Concluído

 

📚Top Noted Publication

  1. Sissini et al. (2017): Discusses likely scenarios regarding the floating Sargassum (Phaeophyceae) in the South Atlantic Ocean.

 

  1. Souza et al. (2019): Explores how microorganism-based larval diets affect mosquito development, size, and nutritional reserves, focusing on the yellow fever mosquito Aedes aegypti.

 

  1. Barufi et al. (2011): Investigates the effects of nitrogen supply on the accumulation of photosynthetic pigments and photoprotectors in Gracilaria tenuistipitata cultured under UV radiation.

 

  1. Gouvêa et al. (2017): Examines the interactive effects of marine heatwaves and eutrophication on the ecophysiology of a widespread and ecologically important macroalga.

 

  1. Horta et al. (2016): Provides current knowledge about rhodoliths in Brazil and discusses potential impacts of climate change on them.

 

  1. Vega et al. (2020): Explores cyanobacteria and red macroalgae as potential sources of antioxidants and UV radiation-absorbing compounds for cosmeceutical applications.

 

  1. Scherner et al. (2012): Investigates the photosynthetic response of two seaweed species along an urban pollution gradient, providing evidence of selection of pollution-tolerant species.

 

  1. Scherner et al. (2013): Determines salinity critical threshold values for photosynthesis of two cosmopolitan seaweed species, providing baselines for potential shifts in seaweed assemblages.

 

  1. Figueroa et al. (2014): Studies the short-term effects of increasing CO2, nitrate, and temperature on three Mediterranean macroalgae, focusing on their biochemical composition.

 

  1. Schneider et al. (2020): Discusses the photoprotection properties of marine photosynthetic organisms grown in high ultraviolet exposure areas, particularly for cosmeceutical applications.

 

  1. Carvalho et al. (2020): Investigates environmental drivers of rhodolith beds and epiphyte community along the South Western Atlantic coast.

 

  1. Vega et al. (2021): Explores mycosporine-like amino acids from red macroalgae as UV-photoprotectors with potential cosmeceutical applications.

 

Hajar Hakkoum | Machine learning | Best Scholar Award

Posted on by Young Scientists Awards

Dr. Hajar Hakkoum | Machine learning |Best Scholar Award

👨‍🏫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 Salé, Morocco.

🌐 Professional Profiles

📚 Education:

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

🔍 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 Informática, 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).

🏆 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: “A different language is a different vision of life.” Reading: “A reader lives a thousand lives before he dies.” Running: “Exercise is a tribute to the heart.”

 

📚Top Noted Publication

  1. “Interpretability 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. “Assessing 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. “Ensemble 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. “Artificial 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. “A 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. “Global 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. “Evaluating 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.