Sergiu Nisioi is an Associate Professor at the Faculty of Mathematics and Computer Science, University of Bucharest, with expertise in computational linguistics, machine translation, and text simplification. He bridges cognitive science with NLP through eye-tracking and EEG research, while also exploring sound art and digital autonomy via initiatives like HYPHA.ro. Current projects include PN-IV-P2-2.1-TE-2023-2007 (text complexity/readability), Legal Document Processing , and Europarl Dialectal Corpora Research spans computational psycholinguistics , LSTM-based translation models , and algorithmic composition for sound art His work integrates interdisciplinary methodologies, combining EEG signal processing for architecture data with the University of Architecture, and DSP for ecological projects at chlorophylla.live.
Lect. dr. Alina Cristiana Gavriluţ is a faculty member at the Faculty of Mathematics, Al. I. Cuza University of Iaşi, Romania. Her research focuses on advanced topics in mathematical analysis, including set multifunctions, non-additive measures, fuzzy integrals, and their applications in physics and complex systems. She has authored/co-authored multiple books and over 30 peer-reviewed papers in journals like Fuzzy Sets and Systems , Entropy , and Reports on Mathematical Physics . Her work explores regularity properties of set multifunctions, integrability in non-additive settings, and interdisciplinary applications in areas like fractal information, quantum mechanics, and neurosciences. She has also contributed to theoretical frameworks connecting mathematical physics with complex systems, including studies on non-differentiable entropy and spacetime manifolds. Dr. Gavriluţ collaborates extensively with researchers in mathematics and physics, notably Maricel Agop and Anca Croitoru. Her research has implications for diverse fields, from image processing to neuronal network modeling. She actively participates in international conferences, presenting findings on set-valued integration, fractal systems, and transdisciplinary approaches to complex phenomena. Education: PhD in Mathematics from Al. I. Cuza University (year not specified). Key Areas: Mathematical analysis, measure theory, fuzzy set theory, complex systems, non-differentiable dynamics. Grants/Awards: Not explicitly listed in the provided texts, but her prolific publication record indicates sustained academic engagement. Labs/Teams: Affiliated with the Mathematics department at Al. I. Cuza University, contributing to research groups in functional analysis and mathematical physics.
Dr. Onet-Marian Zsuzsanna is a Lecturer in the Department of Computer Science at Babes-Bolyai University in Cluj-Napoca, Romania. Her research focuses on applying machine learning techniques to software engineering challenges, particularly software defect prediction and restructuring. Academic Rank: Lecturer Affiliation: Babes-Bolyai University Department: Computer Science Email: zsuzsanna.onet@ubbcluj.ro Research Interests : Dr. Marian specializes in developing machine learning models (clustering, association rules, reinforcement learning) for software defect detection, package-level restructuring, and test order optimization. She also explores applications of Formal Concept Analysis in text summarization and music pattern discovery. Recent Publications highlight her work in source-code embeddings, unsupervised learning for software analysis, and comparative studies of online/traditional learning environments. Her methods often integrate domain-specific metrics and AI-driven optimization. Contact : zsuzsanna.onet@ubbcluj.ro | Office: Teodor Mihaly street, Room 440
Cristian Mihaescu is a Lecturer at the Department of Computer Science and Engineering (DCTI), University of Craiova, within the Faculty of Automatic Control, Computers and Electronics. He is actively involved in teaching and research related to machine learning, distributed systems, and educational data mining. Teaching: Data Structures and Algorithms, Parallel and Distributed Algorithms, Machine Learning, Distributed Systems Engineering Research Focus: Machine learning applications in education, social network analysis, and compiler optimization Technological Interests: Microservices, data mining, and intelligent system design
Adrian ALEXANDRESCU is an Associate Professor at the Department of Computer Science and Engineering within the Faculty of Automatic Control and Computer Engineering at “Gheorghe Asachi” Technical University of Iași. He is a member of the Open Infrastructure Research Center and specializes in interdisciplinary research areas such as blockchain technology, distributed systems, artificial intelligence (genetic algorithms, neural networks), and IoT applications. His work bridges theoretical computer science with practical implementations in education, healthcare, and smart technologies. His research interests focus on leveraging blockchain for secure transactions, optimizing distributed systems, and enhancing e-learning through gamification. He has contributed to projects involving sensor networks for health monitoring, real-time driver sobriety tracking, and decentralized identity management systems. His academic contributions span over 20 years, with notable work on genetic algorithms for task mapping in heterogeneous systems and cloud-based solutions for ambient assisted living. Dr. ALEXANDRESCU’s publications emphasize blockchain’s role in trustless systems, IoT-driven healthcare environments, and AI-driven solutions for education and logistics. His work on decentralized article retrieval systems and plagiarism detection frameworks underscores his commitment to ethical and efficient digital ecosystems. Despite no explicitly listed awards, his prolific publication record reflects sustained academic excellence. He advises on projects at the intersection of cloud computing, distributed architectures, and smart technologies. His labs and collaborations focus on developing scalable solutions for real-world challenges such as secure real estate transactions and community-driven academic publishing systems.
Gabriel DANCIU is a Lecturer at the Department of Electronic and Computers, Faculty of Electrical Engineering and Computer Science, Technical University of Banat. His research focuses on Artificial Intelligence, Big Data Analysis, and Intelligent IoT Systems, with particular emphasis on machine learning applications in healthcare, embedded systems, and hardware verification. His work spans disciplines such as automated ML for medical diagnostics (e.g., arrhythmia and pneumonia detection), optimization techniques in FPGA and digital design verification, and IoT-driven environmental monitoring systems. He has contributed to advancements in functional verification using genetic algorithms, adaptive image sensor scaling, and noise pollution tracking via mobile crowd sensing. Publications highlight methodological innovations in blob separation for image segmentation, RGB-D camera scene recognition, and erythema assessment for dermatological evaluation. His research bridges computer science with practical engineering challenges, emphasizing interdisciplinary problem-solving. No scientific awards or grants are explicitly mentioned in the provided texts. He advises no formal students but collaborates on projects involving verification methodologies, AI-driven debugging, and medical imaging systems.
Lazar Alexandru is an Associate Professor at the Technical University of Iasi, specializing in control systems and electrical engineering. His research focuses on predictive control, model-free algorithms, and their applications in automotive systems, electric machines, and robotics. He has published extensively on topics such as motor control, vehicle platooning, and industrial automation. His work emphasizes practical implementation and simulation using tools like MATLAB and LabVIEW. Research interests include advanced control strategies for electric drives, nonlinear systems, and data-driven methods. His contributions span theoretical development and experimental validation, with a particular emphasis on automotive and aerospace applications. His articles explore predictive current control, model-free adaptive systems, and real-time control architectures. Despite his prolific output, no awards or grants are explicitly mentioned in the text. Advising details are also unavailable, though his work suggests involvement in graduate research projects.
Dr. Sanda-Maria Avram is a faculty member at Babeș-Bolyai University in Cluj-Napoca, Romania, where she teaches in the Faculty of Mathematics and Computer Science. She currently instructs courses in Operating Systems for bachelor's students and oversees the elaboration of Bachelor Theses. Her teaching approach emphasizes practical application and student engagement in both undergraduate and master's programs, having recently completed the sixth iteration of her Adaptive Web Design course with 81 master's students across six sections. Dr. Avram's research spans multiple domains with a current focus on authorship attribution through artificial intelligence, natural language processing, and stylometry. Her work aims to achieve and surpass AI-based results through mathematical approaches. Previously, her research encompassed knowledge discovery, data visualization, web mining, e-learning systems, and networking. She has developed several educational platforms including SINE, PULSE, and PULSE Extended for student evaluation in laboratory settings, addressing the specific needs of Romanian universities where laboratory sessions are mandatory and guided by a single tutor. Her publication record reveals a strong trend toward applying computational methods to solve practical problems in education and networking. Recent work focuses on linguistic analysis and authorship attribution using advanced AI techniques, while earlier publications address educational analytics and network routing protocols. She has demonstrated that integrated analytics using authentication data provide more reliable insights for e-learning environments than standard tools like Google Analytics. Dr. Avram leads a student research group focused on mathematical approaches to authorship attribution, encouraging students with strong mathematical skills to collaborate. She actively maintains academic profiles on Google Scholar, ResearchGate, and Academia.edu (under the names SandaDragos and SandaMariaAvram), where she has established a network of academic collaborators and shares her research findings with the broader scholarly community.
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.
Liana Stanescu is a Professor at the Faculty of Automatic Control, Computers and Electronics at the University of Craiova, Romania. Her research spans interdisciplinary domains at the intersection of computer science and medical technology. Email: liana.stanescu@ucv.ro Her research interests focus on artificial intelligence applications in software engineering and medical imaging, including: Automated testing frameworks Deep learning for medical image classification Graph-based volumetric segmentation techniques Comparative database analysis Recent publications highlight trends in AI-driven testing methodologies, hybrid medical-educational systems, and advanced database architectures. Key technologies explored include Petri Nets for dynamic system modeling and LiFi communication protocols. No formal scientific awards are publicly documented. She has developed practical tools for hospital automation, educational platforms, and medical data analysis systems.
Lecturer Catalin V Rusu is a faculty member at the Transylvanian Institute of Neuroscience (TINS) , where he directs the Applied Computational Intelligence Laboratory . His research spans biologically inspired neural systems , spiking neural networks , and machine learning applications in structural diagnostics . Rusu teaches courses like Advanced Programming Methods and Object-Oriented Programming . Research Interests Modeling cortical microcircuits and self-organizing neural systems Spiking neural networks for autonomous robotics (ROBBY framework) Damage detection in beam structures using frequency shifts and ML Predictive modeling for student attention and academic progress Probabilistic planning and reinforcement learning Teaching Requirements Students must bring laptops with Java/Python environments (JDK 8+/Python3) and IDEs (IntelliJ/PyCharm) Git setup required for collaborative coding sessions Hands-on sessions with extended durations (100 minutes with breaks) Contact Transylvanian Institute of Neuroscience (TINS) Str. Ploiești 33, Cluj-Napoca, Romania Email: rusu@bet-you-can-guess-it.much-obvious-very-wow
COCULESCU CRISTINA is a Full Professor and Director of the Department of Informatics, Statistics, Mathematics at the Romanian-American University of Bucharest , Faculty of Managerial Informatics. She holds a habilitation certificate in Cybernetics and Statistics (2021), a PhD in Cybernetics and Economic Statistics (2004), and multiple degrees in Mathematics and Economics. Education: BSc in Mathematics (University of Craiova, 1996) MSc in Algebra (University of Bucharest, 1997) MSc in Cybernetics and Economic Forecasting (Academy of Economic Studies, Bucharest, 2002) Research Focus: Applied mathematics in economics, econometrics, artificial intelligence, and energy consumption modeling. Her work spans renewable energy analysis using panel data, e-learning security, and neural network applications in telework adaptability studies. Professional Affiliations: Romanian Statistical Society (RSS), Eurasia Business and Economics Society (EBES), International Institute of Informatics and Systemics (IIIS), European Academy of Management (EURAM).
Liviu P. Dinu is a Professor at the Faculty of Mathematics and Computer Science, University of Bucharest, with a career spanning three decades. He holds a PhD (2003) and Dr. habil (2014) in Computer Science and has served as Director of the Human Language Technologies Research Center. His work bridges computational linguistics, natural language processing, and bioinformatics. PhD: 2003, University of Bucharest (supervisor: Solomon Marcus) Dr. habil: 2014, University of Bucharest Bachelor’s: 1994, University of Bucharest His research explores computational methods for historical linguistics, including cognate identification, semantic change analysis, and proto-word reconstruction. He also investigates applications of NLP in mental health (depression/gambling detection), financial text analysis, and DNA sequence similarity. Recent work focuses on multimodal transformers, few-shot learning, and AI-generated text detection. Article trends reveal interdisciplinary work in historical linguistics , mental health informatics , and bioinformatics . He employs rank distance , ensemble methods , and deep learning frameworks across applications from semantic divergence to emotion detection in Romanian tweets. Scientific awards include the Grigore C. Moisil Prize (2007, Romanian Academy) and In Hoc Signo Vinces Prize (2005, National Research Council) . He has supervised 7 completed PhDs and mentors ongoing students at the Interdisciplinary Doctoral School (ISDS) and Computer Science Doctoral School. Current projects involve multi-agent systems for authorship attribution , algorithms for graph structure identification , and linguistic tools for historical analysis . His lab collaborates internationally on DNA sequence analysis and cross-lingual semantic laws research.
Mihaela Elena Breaban serves as Professor and Director of the Doctoral School at the Faculty of Computer Science, Alexandru Ioan Cuza University of Iasi, Romania. Her academic leadership extends to coordinating doctoral studies while maintaining active research in computational intelligence and data analysis. She holds office C416 at General Berthelot 16, 700483 - Iasi, and maintains international research collaborations, notably as a member of the Data Science & Soft Computing Group at Goldsmiths, University of London. Her research spans Evolutionary Computation (as part of ECODAM research group), Machine Learning and Big Data Analytics , Databases , and Constraint satisfaction . Breaban's work focuses on developing evolutionary algorithms for projection pursuit, clustering techniques, and optimization problems. Her methodological contributions include PSO-kMeans hybridization, nonlinear projection pursuit for outlier detection, and multi-objective evolutionary approaches for feature selection. These techniques have found applications in medical imaging, real estate dynamics modeling, and vehicle routing problems. Analysis of her publication record reveals a consistent trajectory from foundational work in evolutionary algorithms and constraint satisfaction toward increasingly sophisticated applications in medical diagnostics and big data analytics. Her recent publications demonstrate a strong emphasis on deep learning approaches for brain tumor segmentation and lung disease detection from CT scans, representing a significant shift toward biomedical applications of computational intelligence techniques. Scientific recognition includes: Winner of the ImageCLEFmed Tuberculosis competition As Director of the Doctoral School, Professor Breaban oversees doctoral education across the Faculty of Computer Science. Her teaching portfolio spans undergraduate and graduate levels, including Probability and Statistics (Info 1), Databases (Info 2), Genetic Algorithms (Info 3), Nature-inspired Optimization Models (Master OC), Data Mining (Master OC), and Big Data Analytics (Master ISS, OC, SD). While specific grant information isn't detailed in available sources, her international collaborations and competition wins suggest successful research funding. She actively participates in research communities through membership in the ECODAM research group at the Faculty of Computer Science, Iasi, and the Data Science & Soft Computing Group at Goldsmiths, University of London. These affiliations facilitate cross-institutional research on evolutionary computation and data science applications.
Madalina Raschip is an Associate Professor at the Faculty of Computer Science, Alexandru Ioan Cuza University of Iasi , Romania. Her academic work spans constraint satisfaction, evolutionary computation, hybrid metaheuristics, and data mining. She earned her PhD at the same university and completed post-doctoral studies at the University of Neuchatel, Switzerland, supported by a Sciex Fellowship. Research Interests : Constraint satisfaction, evolutionary algorithms, hybrid optimization, data mining, graph neural networks, and automated algorithm selection. Teaching : Offers courses in Artificial Intelligence, Data Structures, Deep Learning in NLP, and Experimental Analysis of Algorithms. Publications : Focus on hybrid metaheuristics, constraint programming, sorting networks, and biomedical applications. Her work includes collaborations on medical data analysis, ant colony systems, and SAT solving. Awards : Sciex Fellowship for postdoctoral research. Research Groups : Member of the Evolutionary Computing Research Group and DECO (Data Engineering for Constraints Optimization).
