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I’m an AI Engineer with over six years of experience working in computer vision, natural language processing, and signal processing. I have a Master’s degree in Artificial Intelligence from Institut Polytechnique de Paris, one of the top universities in the world. As a certified PMI-Agile Practitioner (PMI-ACP), I focus on working smarter and leading teams effectively to deliver high-quality results. I enjoy using AI to solve real-world problems and always look for ways to learn more about new technologies. I also like sharing what I know and helping others grow in the field.
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- GPA: 14.5/20 (Grade: A)
- Master Thesis: Borehole Sonic Data Classification.
- One of the top 50 universities in the world.
- GPA: 3.48/4
- member of dean's list several times
- GPA: 9.9/10
- a member of the group preparing for the National Olympiad in Physics and
Mathematics
- a group leader, responsible for extra curriculum activities.
- Leading a deep learning science team
- Designing and building deep learning models for computer vision, NLP, and time series tasks
- Working with stakeholders to plan projects, understand needs, and lead the work
- Mentoring interns and helping them grow their skills
- Giving talks and presentations at webinars
- Exploring new AI tools and ideas to improve our work
- Writing research papers and working on patents.
- Created predictive models and data visualizations.
- Developed and advised data-driven solutions.
- Master Thesis: Borehole Sonic Data Classification using ML
• Created predictive models and data visualizations
• Consulted on effective data-driven solutions
• Guided undergraduate students through laboratory experiments
• Programmed AVR/ARM microcontrollers using C language
• Led up to 25 students aged 15 to 22 in a summer camp
• Organized summer camp activities
- Prepared up to 80 high school students in Mathematics and Physics for their final exam.
- All my students passed their exams successfully
- Helped high school students to prepare for national exams (NAEC) in math and physics
• 3nd place out of more than 100 teams.
• Created sentiment analysis classifier using SOTA deep learning algorithms.
Article
• 2nd place out of 120 teams
• Created recommender system algorithm using SOTA deep learning algorithms.
Article
• Winner in insurance business category.
• Created a churn prediction model.
Article
• Winner of Aging Independently category.
• Predicting their activities based on specific time.
Article
• Competition in Mathematical Logical problems
•
2000Lari scholarship
Article
• One of the 50 finalist in Georgia
Sharing knowledge and experiences in AI, Technology, and Personal Development
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Research papers and academic contributions in AI and Machine Learning
This paper proposes TAUNet, a spatio-temporal model using bidirectional convolutional LSTMs to detect corrosion defects in wellbore images by capturing spatial and temporal continuity. It introduces temporal entropy loss and temporal bridging postprocessing to ensure consistent, continuous predictions. Custom data augmentation further improves generalization. The framework significantly boosts segmentation accuracy and could benefit other fields needing temporal coherence.
The paper introduce machine learning workflow which can automatically identify many important geomechanical and geological features, such as breakouts, natural fractures, drilling fractures, stress effects and intrinsic anisotropic effects using borehole sonic and image data with the help of machine learning.
This paper proposes a survey of neuro-symbolic approaches that combine Rule Mining and Knowledge Graph Embeddings to predict missing facts in incomplete knowledge bases. Rule Mining discovers logical patterns, while Knowledge Graph Embeddings map entities to vector spaces. By integrating these methods, the paper aims to leverage their complementary strengths for more accurate fact prediction.