Adriana Birlutiu

Adriana Birlutiu is a Lecturer in the Computer Science Department at 1 December 1918 University of Alba Iulia, Romania. Her expertise lies in machine learning, computer vision, bioinformatics, and transfer learning, with a recent focus on porcelain-industry optimisation.

Education

• Ph.D., Radboud University Nijmegen, Netherlands (2011)
• M.Sc., Babeș-Bolyai University of Cluj-Napoca & University of Lorraine (Erasmus), 2005
• B.Sc., Babeș-Bolyai University of Cluj-Napoca, 2004

Research Interests

Adriana's research spans machine learning, deep learning, computer vision, and bioinformatics. She has contributed to preference learning, domain adaptation, protein–protein interaction prediction, and automated quality control in porcelain manufacturing. Her recent projects integrate deep neural networks with industrial computer-vision systems to detect defects and recognise characters on ceramic surfaces.

Publication Trends

Across 15 recent publications (2010-2019), Adriana has consistently explored transfer learning, multi-task learning, and Bayesian methods. Articles cluster around two major axes: biomedical applications (protein networks, cancer relapse prediction, respiratory-motion modelling for radiotherapy) and industrial AI (porcelain defect detection, character recognition). The work shows a clear evolution from theoretical machine-learning foundations to practical, domain-specific implementations.

Grants & Projects

• SIVAP (2016-2018): Intelligent ML & computer-vision system for porcelain manufacturing optimisation, UEFISCDI PN-III-P2-2.1-BG-2016-0333.
• CMRCC (2017-2018): Computational Models for Reproducing Ceramics Colors, UEFISCDI PN-III-P2-2.1-PED-2016-1835.

Student Supervision & Mentoring

Adriana has supervised more than 25 undergraduate and master’s theses. Her students have won multiple awards at national conferences such as In-Extenso and SCCSS-IEECC, covering topics from automated defect detection to web applications for academic scheduling.

Teaching Responsibilities

She teaches courses including Machine Learning, Mathematical Software, Fundamental Algorithms, Object-Oriented Databases, and Modelling and Simulation at both undergraduate and master levels.