European master in Computer Science for the Human-Centric and Sustainable Industry

Daniela Zaharie is a Professor at the Department of Computer Science from the West University of Timisoara (Romania) with a PhD degree on a topic related to stochastic modelling of neural networks and a Habilitation thesis on the analysis of the behaviour of differential evolution algorithms. Her current research interests include analysis and applications of metaheuristic algorithms, interpretable machine learning models and data mining. She published more than 100 scientific papers,  3 books and was invited speaker at several conferences and summer schools.

She was involved in 19 research projects related to applications of evolutionary algorithms and machine learning in task scheduling, resource allocation in cloud environments, efficient satellite image processing,

computational modelling of various biological processes and the design intelligent systems for assisting the medical diagnosis. Currently she is involved in a project in collaboration with an industrial partner,  aiming to develop software tools for monitoring and planning activities specific to production environments. The main contribution in the context of this project is the design and analysis of heuristic, metaheuristic and hybrid algorithms for jobs scheduling in the case of complex constraints specific to real-world scenarios.

She is/was involved in several managerial activities (doctoral school director, faculty dean, vice-dean). She is associate editor of several journals, including Engineering Applications of Artificial Intelligence. She coordinates two master programmes (“Big Data – Data Science, Analytics and Technologies” and “Bioinformatics”).

In my academic journey, I engaged in diverse internships and projects, showcasing a strong focus on cloud computing and blockchain technology. My initial internship at the Institute e-Austria Timisoara (IeAT) involved implementing scheduling strategies for virtual machine usage, with subsequent publication at the SYNASC conference. During my Master's studies, I collaborated on projects such as SPECS and CloudLightning, where I developed a web service for security analysis and contributed significantly to a decentralized cloud platform. This work resulted in multiple research papers and laid the foundation for my PhD thesis, completed with magna cum laude at the University of Pisa.

Post-PhD, I worked remotely on the FinTechNext project as a part-time post-doctoral researcher for University College Cork. Here, I designed and implemented a blockchain module for a Tokenization platform, earning recognition for my contributions. Simultaneously, as a Lecturer at the West University of Timisoara, I supervised numerous theses, achieving high marks and presenting results at the SYNASC conference. Additionally, my involvement in the SERRANO research project focuses on advancing cloud service orchestration, applying insights from prior achievements to new use cases.

A full-time technology addict, passionate entrepreneur, university lecturer and experienced both low/high-level software engineer, full-stack developer and security researcher in academia. With a demonstrated history in working in the higher education industry, complemented by strong educational background (with BSc degrees in Mathematics and Informatics, as well as Computer Engineering, followed by a MSc in Artificial Intelligence and Distributed Computing, and a PhD in Computer Security), numerous awards at national and international mathematics and informatics competitions, and valuable research papers published in high-profile peer-reviewed journals and books. An invited speaker to numerous local and global events from various fields of technology and Computer Science.

Dana Gioncu, a Romanian national born on May 12, 1966, holds key positions at the West University of Timisoara (UVT), Romania, serving as a Professor, PhD advisor, and director of master studies on Software Engineering. With a diverse professional journey, she was a software designer and developer before transitioning to academia, holding roles such as Assistant, Lecturer, and Associate Professor at UVT. Currently, she is the Dean of the Faculty of Mathematics and Informatics.

Dana has been the Director of the Institute e-Austria Timisoara since 2002, a research spin-off of UVT. Her contributions have been recognized with awards like the IBM Award (2009) and the Maria-Sybilla-Merian Award in Germany (2005). She has actively participated in EC Committees, contributing as an expert evaluator for various R&D programs.

As Editor-in-chief of "Scalable Computing: Practice and Experience" and a member of several editorial boards, Dana plays a pivotal role in advancing computational methods. She is a founding member of ARCAS (Romanian Association for Promoting Advanced Computational Methods in Scientific Research) and is affiliated with IEEE and OW2. Her multifaceted involvement underscores a commitment to academic excellence, research, and the promotion of advanced computational methods.

My main scientific and academic area of interest revolves around Artificial Intelligence and Distributed Systems. Specifically, the application of various machine learning methods for the processing and analysis of data from distributed systems. This expertise also includes the application of Deep Learning methods for georeferenced satellite image processing (ex. Instance detection and semantic segmentation).

I have actively participated in the design and implementation of various services for distributed data processing including but not limited to performance monitoring, system log processing, IoT data processing, large scale image processing. These services where later used in conjunction with other components in various large-scale research platforms mainly for autonomous event handling and performance analysis.

All developed analytics platforms also contained various hyper-parameter optimization and performance analysis reporting mechanisms. These features where added so that onboarding of end-users with limited AI/ML background could be done in a shorter timeframe. 

Proficient use of many technologies related to artificial intelligence and machine learning such as: scikit-learn, tensorflow, keras, numpy/pandas, deap, JADE, SPADE, pyTorch, pyod etc.

Proficient use of many Big Data technologies including Hadoop/YARN, Spark, Kafka, Dask, Redis, Flink, Flume, ELK stack etc.

Teaching activities in the last 7 years:

• Basic of Computer Science (Lab)
• Graph theory and combinatorics (Lab)
• Software engineering (Lab)
• Computer Graphics (Lab)
• Intelligent Systems (Course/Lab)
• Multi-Agent Systems (Course/Lab)
• Web Technologies (Course/Lab)
• Big Data Applications (Course/Lab)
• Video Game Programming (Course /Lab)

He obtained a Ph.D. in Computer Science from West University of Timișoara, specializing in multiple pattern matching techniques for security applications using heterogeneous systems. His research introduces a novel CPU/GPU architecture for pattern-matching automata, demonstrating effectiveness in intrusion detection and DNA profiling. The M.S. in Artificial Intelligence and Distributed Computing explores dictionary-based compression algorithms, presenting a parallel implementation of Lempel-Ziv-Welch. The B.S. in Computer Science includes a dissertation on static code analysis using multipattern search algorithms.

With professional experience as a Software Developer/Cloud Engineer/DevOps at Cloudbase Solutions and a Software Developer at Continental Automotive, he contributed to cloud computing, Linux integration, and safety-critical solutions for automotive systems. Teaching roles include a teaching assistant and lecturer in various computer science courses.

Fluent in Romanian and proficient in German and English, he possesses technical skills in C, CUDA, Python, Shell scripting, and Linux. Volunteer work with AIESEC Timisoara showcases involvement in leadership, international exchange, and student training projects.