Discovering the Future
2022
ADVANCE
Unprecedented platform to bring the top-notch expertise in the targeted STEM-related fields in Armenia
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PARTNERS
General Information
About the program

The ADVANCE STEM Research grant program sets ground for development of scientific directions in STEM-related fields in Armenia. This pioneering platform invites distinguished scientists from all around the world to lead new teams comprised of ambitious Armenian researchers. The experience and network these emerging Armenian researchers gain in the program boosts their professional growth, enables effective global collaboration, and significantly increases research output in target fields. The comprehensive financial and logistical support provided by FAST and its partners works toward putting Armenia on the map of cutting-edge scientific research worldwide.

We provide 2 to 4-year grants to the research groups formed in Armenia around a project proposed and led by a distinguished scientist from abroad called an international Principal Investigator (PI). Priority is given to research projects both in the field of AI/ML and in other STEM-related disciplines with the use of AI.

Aiming to foster inter-institutional and interdisciplinary connections, we put together diverse and multi-faceted research groups, where there is a lot to learn from each other. We encourage qualified and motivated researchers from any institution in Armenia and beyond to apply. Please note that researchers holding administrative positions in the government and/or top management or leadership positions at academic institutions are welcome to apply but are not eligible for monetary compensation. Eligibility for travel of such team members can be considered if justified by the project needs. You can find out more about our projects and open calls in the “Research Projects” section.

The research is conducted primarily in Armenia. The PIs both visit Armenia and supervise the group’s work remotely. The PIs also teach at local universities, helping to nurture aspiring researchers in relevant fields of study.

Granting scheme

The grant includes the following budget lines: 

  • PI’s travel expenses and coverage of expenses during their visit to Armenia, 
  • Salaries for the local researchers, International travel costs for the local researchers’ participation in the conferences, collaborative research activities abroad, or other capacity-building events, 
  • Laboratory materials, consumables, 
  • Publications in journals and, if applicable, patenting costs.

3
PIs engaged
13
local researchers funded
3
intensive courses organized
Participants
Dr. Garabed Antranikian
Principal Investigator (PI), Biotechnology Project
Dr. Anna Poladyan
Senior Researcher Biotechnology Project
Dr. Hovik Panosyan
Expert Biotechnology Project
Dr. Sargis Aghayan
Senior Researcher Biotechnology Project
Dr. Karen Trchunyan
Senior Researcher Biotechnology Project
Dr. Ani Paloyan
Senior Researcher Biotechnology Project
Ms. Ella Minasyan
Junior Researcher Biotechnology Project
Ms. Diana Ghevondyan
Junior Researcher Biotechnology Project
Dr. Arnak Dalalyan
Principal Investigator (PI) Machine Learning Project
Dr. Arshak Minasyan
Senior Researcher Machine Learning Project
Dr. Sona Hunanyan
Senior Researcher Machine Learning Project
Mr. Tigran Galstyan
Junior Researcher Machine Learning Project
Mr. Henrik Sergoyan
MSc. Student Machine Learning Project
Mr. Khachatur Khechoyan
MSc. Student Machine Learning Project
Dr. Naira Hovakimyan
International Principal Investigator (PI) Computational Agriculture Project
Dr. Vardan Urutyan
Local Principal Investigator (PI) Computational Agriculture
Dr. Emil Gevorgyan
Team Lead Computational Agriculture
Mr. Christoph Elias Aoun
Senior Researcher Computational Agriculture Project
Mr. Andranik Ugujyan
Junior Researcher Computational Agriculture Project
Ms. Seda Janazyan
Master's Student Computational Agriculture Project
Ms. Anna Avetisyan
Master's Student Computational Agriculture Project
Research projects
Computational Agriculture for Improved Food Systems and Resilient Policies in Armenia

Title: Computational Agriculture for improved food systems and resilient policies in Armenia

Principal Investigator: Prof. Naira Hovakimyan

University: University of Illinois at Urbana-Champaign (UIUC), USA

Local Supervisor: Dr. Vardan Urutyan, Armenian National Agrarian University (ANAU)

Research team: Emil Gevorgyan, Christoph Aoun Elias, Andranik Ugujyan, Seda Janazyan, Anna Avetisyan

Duration: 2021 - 2025

Project Importance

Climate change and food security are the most significant challenges the humanity currently faces. The demand for bioenergy and agricultural products increases at a global scale which leads to the questions of how much food and energy can we produce and what are the environmental impacts associated with changes in agricultural land use and management.

Armenia is facing severe challenges of climate change and food insecurity, and the country’s progress in transforming the agricultural management system was derailed recently by double shocks of COVID-19 outbreak and Artsakh war. In 2021, the poverty rate reached 26.4% of the population, almost 11% of which are extremely or very poor. Despite these facts, little expertise is available for optimizing agricultural management and developing an intelligent agricultural management system. Among several reasons for the existence of the above-mentioned challenges, it is also worth mentioning that: (a) there is very poor capacity in Armenia to deal with profound research models for the optimization of agricultural management systems; (b) there is a lack of a country-specific structured database for running such research models; and (c) there is weak connection between academia and policy-making and thus, lack of knowledge-based approaches to political decision-making. Additionally, there is a lack of individuals with expertise in rigorous science and software engineering standards in Armenia.

Research Aim

The research will help ANAU scientists and young academicians to understand comprehensive simulations by using platforms which allow the evaluation of the effects of various management practices under different weather and soil conditions in a timely and cost-effective manner. ANAU researchers design and implement a wide range of academic activities varying from crop production, sustainability of agro-ecosystems, and food value chains to biosciences, climate change and adaptation, and food security.

This novel research is conducted in close collaboration with the cross-disciplinary team of “Optimization of Agricultural Management for Soil Carbon Sequestration Using Deep Reinforcement Learning and Large-Scale Simulations” project implemented by the University of Illinois at Urbana-Champaign, KTH Department of Sustainable Development, and Stockholm University, Department of Physical Geography. In the first stage of cooperation, ANAU scholars will make great use of the Agricultural Production Systems sIMulator (APSIM) platform model.

Expected Outcomes

In this joint learning and testing process, the ANAU research team is focusing on the following outcomes:

  • Analyzing and extending knowledge on the internationally recognized open source APSIM platform for modeling and simulation of Armenian agricultural system,

  • Gaining practical experience in the use of APSIM modules enabling the simulation of systems for a diverse range of plant, animal, soil, climate, and management interactions,

  • Selecting relevant data and information and identifying the gaps to run such models for Armenia,

  • Building a simulator to test, model, and simulate, for instance, soil-plant interactions for different regions in Armenia.

To achieve these outcomes, the researchers will seek to build strong and lasting ties with Dr. Naira Hovakimyan, a W. Grafton, and Lillian B. Wilkins Professor of University of Illinois at Urbana-Champaign and her team. During the first year, the research team will put an emphasis on analyzing platforms for modeling and simulation (especially APSIM), collecting and comparing relevant data for running such models, and identifying and filling data gaps and creating necessary databases. In the second year, the research team will concentrate on running the APSIM model for Armenia, analyzing the results and gaining knowledge on deep reinforcement learning (RL) and large-scale soil and crop simulations, as well as improving the models. Presenting key findings and recommendations to relevant decision-makers, publishing results in international journals and exploring funding opportunities for the next stage will be objectives for the third research year.