Scholarships & Prizes

Amir Mosavi (Hungary) is among the laurates of MAB Young Scientists Awards 2017

2017. július 27.
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Since 1989 UNESCO's Man and the Biosphere Programme has been providing young researchers each year with awards of up to US$ 5,000 in support of their research on ecosystems, natural resources and biodiversity.

The MAB Programme has been granting awards of up to $5,000 each since 1989 to encourage young researchers to undertake work on ecosystems, natural resources and biodiversity.

On 13 June 2017 the International Coordinating Council of the Man and the Biosphere (MAB) Programme of UNESCO has named the laurates of the Young Scientists Awards 2017:

Amirhosein Mosavi (Hungary): Monitoring and assessing sustainability in Biosphere Reserves utilizing sustainable business models.

Isma Merad (Algeria): Impact of human activity on Lake El Mellah (El Kala, Algeria): health assessment using the Cerastoderma glaucum (Mollusca, Bivalvia) as a sentinel species.

Stella Marlène B. F.Sokpon (Benin): Involvement of local communities in ecotourism activities and attitudes regarding conservation at the Pendjari biosphere.

Marie Florence Sandrine Ngo Ngwe (Cameroon): Genetic and biochemical features of forests at the Dja Biosphere Reserve to improve knowledge and conservation of its biodiversity.

Luiza Abdurasulova (Kazakhstan): Study on the biodiversity, monitoring and management of insects of the Karatau Biosphere Reserve.

Eduardo Luna Sanchez (Mexico): Case studies of organizations that implement natural resource management projects in the Sierra Gorda Biosphere Reserve

Adeeb Hayyan (Malaysia): Physiochemical properties and cellular toxicity of oil plant seeds available in Tasik Chini Biosphere Reserve as an alternative for conventional natural oils with regards to nutraceutical and food industry.

Dr. Amir Mosavi’s Biography and Research

Amir_Mosavi_kepDr. Mosavi is a data scientist at Obuda University where he forms a research group with the collaboration of IEEE members, Dr. Dineva Adrienn, and Prof. Annamaria Varkonyi-koczy for the advancement of the low emission hybrid engines of the future. Dr. Mosavi is also a visiting professor of Business Informatics at Budapest Business School. In his early research career, for his graduate studies in Computer Science and Environmental Science, Dr. Mosavi, moved to UK, and then to Canada. Passionate about travelling and eager for international collaboration, he has managed, so far, more than 15 international research stays at the world’s well-known research institutes in the fields of Decision Sciences and Data Sciences, his main research interests. He has also travelled to more than 55 countries on a survey for his book “Predict It” to investigate the environmental decision-making behavior in different countries under the influence of the cultural and geopolitical factors.

Dr. Amir Mosavi, is recognized as a leading sustainability scientist, prized by the German Federal Minister of Education and Research, Professor Johanna Wanka, for his novelty in research and dedication to sustainable development. He is ranked among the top sustainability potentials who received the Green Talents Award for his original research on the advancement of the accurate prediction tools utilized to anticipate the climate change management and disaster reduction policies. He reasons that humanity requires accurate prediction and decision support tools to be able to analyze and reduce the climate change impacts.

Dr. Mosavi highly contributed to the research on climate change risk reduction by coining the term “predictive-decision model”. His commercial data-driven computation platform provides the state-of-the-art consultations to policy makers, worldwide, for making informed decisions. Through the accurate anticipation of the consequences of the potential decisions a “predictive-decision model” suggests wiser decisions in an automated manner. Having the advanced tools for processing the potential decisions in the climate change realm is, in fact, vital as often the environmentally-friendly choices contradict with the economic and political interests of the nations. In this context Dr. Mosavi’s idea is to support organizations and governments through introducing novel business models which are sustainable and can maintain the profitability. His novel concept has been particularly found to be suitable to address the sustainability issues of the UNESCO's Biosphere reserves. With the aid of data sciences, the past events are analyzed in order to provide the vision about the future decisions and actions. In fact with the aid of his predictive models scientists can better study the potential effects of the human behavior to develop and grow in a more sustainable direction. His high research impacts has shown that Dr. Mosavi’s novel approach of integration of the predictive models and decision-analysis is revolutionizing the way decision-support systems function today.

The juries of the numerous awards including MAB UNESCO Young Scientist Award, Future Talents Award of TU-Darmstadt, GoStyria Award of University of Graz, Bauhaus University of Weimar Research Fellowship Award, Campus France Research Award, and Estonian Dora & Estophilus Award found Dr. Mosavi’s research on prediction models surprisingly novel and scientifically rigorous which blazes new trails, and departs from the mainstream with a remarkable track record. His proposed data-driven decision-support system for climate change adaptation and risk reduction has indeed extensive potential for transforming latest discoveries into a major innovation in decision science and prediction. Recently the Norwegian University of Science and Technology through a research fund provided by the European Research Consortium for Informatics and Mathematics supported Dr. Mosavi’s research, enabling him to take his innovative concept to the next level with a fruitful collaboration with the leading data scientists such as Prof. Pinar Ozturk and Prof. Hai Thanh Nguyen.

More on his research:

More details about MAB Young Scientists Awards are available here.



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