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
5
courses/workshops 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
Research projects
Novel deep neural networks for inspection of solar panel anomalies using multimodal images/videos

Title: Novel deep neural networks for inspection of solar panel anomalies using multimodal images/videos 

Principal Investigator: Prof. Sos Agaian

University: City University of New York

Research team: Recruitment in progress

Hosting Institution: Yerevan State University, Faculty of Informatics and Applied Mathematics (IAM).

Project Importance

According to the latest reports, renewable energy sources such as photovoltaic (PV or solar panels) and wind energy systems will generate 88% of worldwide electricity demand by 2050. The installation of PV generation plants has been rapidly increasing every year, with PV capacity reaching 77 GW in the US, and over 500 GW worldwide.

The Armenian government has adopted several laws on developing domestic, especially renewable, energy resources and implementing energy efficiency measures. And with their global availability, reliability, easy installation, and pollution-free energy generation, renewable PV energy sources are rising in popularity in Armenia and abroad. However, PV modules usually suffer from temperature, rain, wind, and other environmental, damage and mechanical damage during transportation and installation, which could shorten their lifespan. In addition, the damage to PV modules could affect the entire PV system, leading to economic efficiency and energy loss problems as growing plant sizes render manual inspection impractical. So, fast, reliable, automatic, regularly nondestructive inspection, and monitoring and maintaining PV modules is essential for an efficient operation with minimal energy loss and maximal lifespan.

Remote sensing of PV has been addressing these pain points through different techniques such as electroluminescence (EL) images, infrared (IR) images, and color image techniques. These methods provide a fast capture of PV images but require specialists to inspect the obtained images visually, and have a long processing time due to a large amount of data. As a result, intensive research is required to speed up damage inspection and localization in PV images. Multimodal images based on machine learning methods face three key challenges: large variations in the visual appearance of objects, complex background noise, and small geographic objects in high-resolution images. Moreover, collecting labeled data is expensive, often requires experts, and scales poorly with the number of tasks. And even though traditional approaches work well for few-shot learning, they are likely to ignore the spatial information encoded within feature maps, making the model very sensitive to background clutter on image examples. Therefore, it is essential to research few-shot learning for multimodal drone sensing image analysis and interpretation.

Expected Results

The project will significantly impact the development of AI technology with application in Armenia.

  • Extend knowledge on creating solar panels' multimodal-multi camera analysis and management systems.

  • Generate new knowledge and information about special damages caused to solar panels by Armenia’s climate, dust, and wind.

  • Better understand the complexities of renewable PV energy sources. Gain large-scale knowledge about deep neural networks for detection, localization, and classification of solar panel damages using multimodal images/videos.

  • Deliver baseline data for subsequent targeted studies using more specialized sampling and study designs such as mark/recapture and patch occupancy.

  • Learn how to use known databases, generate so-called Armenian baseline/relevant data and information, and identify the gaps to run such models for Armenia.

  • Increase practical experience in creating novel machine learning modules, including transformers and a few-shot learning system.

  • Transform the developed technology and database accurately and inexpensively, and catalyze many fields of ecology, renewable PV energy, global warming, and carbon dioxide emission into "big data" sciences.

  • Publish several research papers in international top machine learning and computer vision journals.

Eligibility Criteria and Selection Process

To be eligible for the project, the applicant is expected to:

  • Have/or to be in the process of obtaining a master’s degree in Data Science, Computer Science, Mathematics, Statistics or a related field,

  • Be familiar with Neural Networks,

  • Be familiar with Image Processing,

  • Have a good working knowledge of Python programming language.


To apply for the research project, the applicants should fill in the APPLICATION FORM along with all the supporting documents (CV, Motivation letter, a Letter of Recommendation). FAST shortlists eligible candidates for review by the PI, who then identifies the finalists to be interviewed by the independent Evaluation Committee. The deadline for applications is September 25, 2022.