Gabriel Burcea

Gabriel Burcea

London, England, United Kingdom
712 followers 500+ connections

Activity

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Experience

  • Cognizant Graphic
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    London Area, United Kingdom

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    London, England, United Kingdom

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    London, England, United Kingdom

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    London, United Kingdom

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    USA

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    Targu Mures

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Education

  • Coursera

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    Theoretical and practical exercises in Machine Learning and Big Data

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    I have gained skills such as Machine Learning & Statistical Data Mining, Big Data Applications, Data Science Research Topics, Data Programming, Data Visualisation, Natural Language Processing, and Geometric Data Analysis by undertaking an MSc Course in Data Science.

    In particular, I have developed my knowledge by performing practical and research work in a variety of real-world Data Science applications based on the newest software and hardware technologies (Spark, Hadoop ecosystem, R…

    I have gained skills such as Machine Learning & Statistical Data Mining, Big Data Applications, Data Science Research Topics, Data Programming, Data Visualisation, Natural Language Processing, and Geometric Data Analysis by undertaking an MSc Course in Data Science.

    In particular, I have developed my knowledge by performing practical and research work in a variety of real-world Data Science applications based on the newest software and hardware technologies (Spark, Hadoop ecosystem, R, Python, SQL, running on a cluster of 10 resourceful servers which form the Big Data Management and Analytics resource dedicated to the Data Science MSc programme and research). Moreover, I have made part of a research team that was attached to Kings College and Institute of Psychiatry in predicting the time of remission in psychotic patients using Machine Learning techniques.

    The grades l obtained in the MSc studies are good and very good.

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    Statistics module (MSD I and MSD II) represented 50 % of the courses.
    Statistical analyses: descriptive analysis such as t-test, data manipulation, correlations, logistic regression, multilevel regression analysis and hypothesis testing; knowledge of data management and data manipulation. The projects entailed analysis of health datasets in UK as well as European ones on internet youth bullying

Licenses & Certifications

Volunteer Experience

  • Intern and Researcher

    Livada Oprhan Care

    - 6 years 3 months

    Civil Rights and Social Action

  • DataKind Graphic

    Data Scientis Volunteer

    DataKind

    - 1 month

    Poverty Alleviation

    I, with a team of data scientist, have been analyzing two data sets provided by Christian Aid organizations. The purpose of the project entailed in rethinking and giving insights on the resilience Index developed by Christian Aid. From cleaning the data to data visualization and application of Random Forest Christian Aid was able to spot challenges, improvements they may take into account in data collection, rethinking the questions and redefining the resilience index the organization worked…

    I, with a team of data scientist, have been analyzing two data sets provided by Christian Aid organizations. The purpose of the project entailed in rethinking and giving insights on the resilience Index developed by Christian Aid. From cleaning the data to data visualization and application of Random Forest Christian Aid was able to spot challenges, improvements they may take into account in data collection, rethinking the questions and redefining the resilience index the organization worked with over the last 2 years.

  • Open Data Science Conference (ODSC) Graphic

    Participant

    Open Data Science Conference (ODSC)

    - 1 month

    Participated in different kind of workshops related to Data Visualisation, Natural Language Processing, Machine Learning, Big Data.

    R, Python, SparkR, TensorFlow were few of the software/environments used in this workshops.

Publications

  • Variation in global COVID-19 symptoms by geography and by chronic disease: A global survey using the COVID-19 Symptom Mapper

    The Lancet/eClinicalMedicine

    Summary

    Background

    COVID-19 is typically characterised by a triad of symptoms: cough, fever and loss of taste and smell, however, this varies globally. This study examines variations in COVID-19 symptom profiles based on underlying chronic disease and geographical location.
    Methods

    Using a global online symptom survey of 78,299 responders in 190 countries between 09/04/2020 and 22/09/2020, we conducted an exploratory study to examine symptom profiles associated with a…

    Summary

    Background

    COVID-19 is typically characterised by a triad of symptoms: cough, fever and loss of taste and smell, however, this varies globally. This study examines variations in COVID-19 symptom profiles based on underlying chronic disease and geographical location.
    Methods

    Using a global online symptom survey of 78,299 responders in 190 countries between 09/04/2020 and 22/09/2020, we conducted an exploratory study to examine symptom profiles associated with a positive COVID-19 test result by country and underlying chronic disease (single, co- or multi-morbidities) using statistical and machine learning methods.
    Findings

    From the results of 7980 COVID-19 tested positive responders, we find that symptom patterns differ by country. For example, India reported a lower proportion of headache (22.8% vs 47.8%, p<1e-13) and itchy eyes (7.3% vs. 16.5%, p=2e-8) than other countries. As with geographic location, we find people differed in their reported symptoms if they suffered from specific chronic diseases. For example, COVID-19 positive responders with asthma (25.3% vs. 13.7%, p=7e-6) were more likely to report shortness of breath compared to those with no underlying chronic disease.
    Interpretation

    We have identified variation in COVID-19 symptom profiles depending on geographic location and underlying chronic disease. Failure to reflect this symptom variation in public health messaging may contribute to asymptomatic COVID-19 spread and put patients with chronic diseases at a greater risk of infection. Future work should focus on symptom profile variation in the emerging variants of the SARS-CoV-2 virus. This is crucial to speed up clinical diagnosis, predict prognostic outcomes and target treatment.

    See publication
  • Self-Reported Academic Achievement of Romanian Institutionalized Children: The Effect of Staff Punishments, Institutional, and Individual Characteristics in Child Maltreatment in Long-Term Residential Centers: History, Research, and Current Implications

    Springer

    Other authors

Languages

  • English

    Full professional proficiency

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