Gholamreza Anbarjafari | Generative AI | Best Researcher Award

Posted on by Best Paper Award

Prof. Gholamreza Anbarjafari | Generative AI | Best Researcher Award

Professor at Estonian Business School Estonia.

Professor Gholamreza Anbarjafari, also known as Shahab, is a distinguished AI scientist with over 15 years of experience in leading and developing cutting-edge solutions in AI, Generative AI, Machine Learning, Medical Signal Processing, and Computer Vision. He has held prominent academic positions, including Professor and Head of the iCV Lab at the University of Tartu and Visiting Professor roles at Yildiz Technical University and Estonian Business School. His professional journey includes significant contributions to industry, notably as Director of AI at PwC Finland, where he leads AI and GenAI initiatives across the Nordic region. An IEEE Senior Member, Professor Anbarjafari has been recognized with several awards, including the Best Lecturer award and the Best Paper award by ETRI Journal. His research has garnered substantial funding, and he has a robust publication record with a high h-index and numerous citations, reflecting his impact in the field.​

Publication Profile

ORCID

Scopus

Google Scholar

Education

  • Ph.D. in Electrical and Electronic Engineering
    Eastern Mediterranean University, Cyprus (January 2011)
    Thesis: Probability Distribution Function Based Face Recognition Boosted by Data Fusion
    Supervisor: Prof. Hasan Demirel

  • M.Sc. in Electrical and Electronic Engineering
    Eastern Mediterranean University, Cyprus (June 2008)
    Thesis: A New Face Recognition System Based on Colour Statistics
    Supervisor: Prof. Hasan Demirel ​

  • B.Sc. in Electrical and Electronic Engineering (High Honors)
    Eastern Mediterranean University, Cyprus (January 2007)
    Project: Explorer Robot
    Supervisor: Dr. Mustafa K. Uyguroğlu

Professional Experience

  • Director of AI, PwC Finland (March 2020 – Present)
    Leads AI and Generative AI (GenAI) strategy development, spearheading initiatives across the Nordic region. Provides consultancy on AI, GenAI, and computer vision solutions to diverse industries, including FinTech, AgriTech, EduTech, and SecurTech. Developed multiple LLM and deep learning-based solutions for clients in HRTech, banking, and cybersecurity sectors.​

  • Visiting Professor, Estonian Business School, Tallinn, Estonia (September 2024 – Present)
    Assists in developing AI-related grants and R&D projects.​

  • Visiting Professor, Yildiz Technical University, Istanbul, Turkey (September 2020 – May 2024)
    Taught Artificial Intelligence courses for postgraduate students and consulted on smart city and digitalization projects.

  • Discovery Search Team Lead, Rakuten Inc., via iCV Lab, Tartu, Estonia (September 2018 – August 2020)
    Led research on multimodal and multilingual discovery search for e-commerce, analyzing user behaviors during e-shopping.​

  • Co-founder and Chief Technology Officer (CTO), Alpha3D, Tallinn, Estonia (November 2019 – September 2023)
    Developed 3D content creation using stable diffusion models for AR applications.​

  • Professor/Head of iCV Lab, University of Tartu, Tartu, Estonia (September 2013 – April 2024)
    Led research in human behavior analysis, conducting R&D on data-driven AI solutions for various DeepTech applications. Secured over €10 million in funding from 47 contracts, collaborating with notable partners such as Veriff, Clevon, AuveTech, and others.

Research Interest 

Professor Anbarjafari’s research encompasses a broad spectrum of areas within artificial intelligence and computer vision, including:​

  • AI/Generative AI & Machine Learning

  • Computer Vision & Affective Computing

  • Natural Language Processing (NLP)

  • DeepTech Solutions across various domains

  • Human-Robot Interaction

  • Medical Signal Processing (EEG and ECG analysis)

  • 3D Modeling and Visualization

  • Image and Video Super Resolution

  • Biometric Recognition

  • Image Compression and Watermarking​

Author Metrics

  • h-index: 44

  • i10-index: 115

  • Total Citations: 8,271

Top Noted Publication

1. Action Recognition Using Single-Pixel Time-of-Flight Detection

Authors: I. Ofodile, A. Helmi, A. Clapés, E. Avots, K. M. Peensoo, S. M. Valdma, G. Anbarjafari, et al.
Journal: Entropy, Vol. 21, Issue 4, Article 414 (2019)
Citations: 16
Summary: This paper explores a novel method for action recognition using single-pixel Time-of-Flight (ToF) detection rather than conventional RGB or depth cameras. The study emphasizes:

  • Utilizing a ToF sensor for motion capture by detecting temporal variations in backscattered light.

  • A compressed sensing approach that allows action recognition from a limited number of photons (i.e., low-light or cost-constrained scenarios).

  • Demonstrated high accuracy in recognizing actions like waving, walking, and jumping using neural networks trained on ToF data.

2. An Objective No-Reference Measure of Illumination Assessment

Author: G. Anbarjafari
Journal: Measurement Science Review, Vol. 15, Issue 6, pp. 319–326 (2015)
Citations: 16
Summary: This paper proposes a no-reference (NR) metric for evaluating the illumination quality of digital images. Key contributions include:

  • Development of an algorithm that assesses brightness consistency and contrast without requiring a reference image.

  • Use of statistical parameters from histogram analysis to predict human visual satisfaction with image lighting.

3. Prediction of sgRNA Off-Target Activity in CRISPR/Cas9 Gene Editing Using Graph Convolution Network

Authors: P. K. Vinodkumar, C. Ozcinar, G. Anbarjafari
Journal: Entropy, Vol. 23, Issue 5, Article 608 (2021)
Citations: 15
Summary: This study applies graph convolutional networks (GCNs) to predict off-target effects of sgRNA sequences in CRISPR/Cas9 gene editing. Contributions:

  • Modeling nucleotide sequences as graphs to capture spatial and relational properties.

  • Achieves superior prediction accuracy over traditional machine learning methods.

  • Addresses a critical concern in genome editing: unintended mutations.

4. Size-Dictionary Interpolation for Robot’s Adjustment

Authors: M. Daneshmand, A. Aabloo, G. Anbarjafari
Journal: Frontiers in Bioengineering and Biotechnology, Vol. 3, Article 63 (2015)
Citations: 15
Summary: This research presents an adaptive algorithm for robot movement adjustment based on size-dictionary interpolation. Highlights:

  • A size-dictionary is created from previously observed environmental objects and used to adjust robot motion dynamically.

  • Enables robots to adapt quickly to new object dimensions without full reprocessing.

5. Multifunctionality of Polypyrrole Polyethylene Oxide Composites: Concurrent Sensing, Actuation and Energy Storage

Authors: N. Q. Khuyen, R. Kiefer, Z. Zondaka, G. Anbarjafari, A. L. Peikolainen, T. F. Otero, et al.
Journal: Polymers, Vol. 12, Issue 9, Article 2060 (2020)
Citations: 14
Summary: The paper investigates polypyrrole–polyethylene oxide (PPy–PEO) composites with integrated functionalities for:

  • Sensing (via electrical resistance changes),

  • Actuation (due to electrochemical expansion/contraction), and

  • Energy storage (as supercapacitor materials). The work includes both material synthesis and experimental validation.

Conclusion

Professor Gholamreza Anbarjafari is exceptionally qualified for the Best Researcher Award. His interdisciplinary research, academic leadership, and tangible impact in both academia and industry mark him as a pioneer in the field of Generative AI and applied computer vision.

While he could further strengthen his global outreach and public engagement, his credentials, innovation, and contribution trajectory make him a highly deserving recipient of such recognition.