Mr. Elson Cibaku has amassed extensive professional experience in both academic and industry settings. Currently, he serves as a Research Assistant at the New Jersey Institute of Technology (NJIT), where he collaborates on cutting-edge research projects, resulting in impactful papers on topics such as power systems, vaccine distribution, and optimization problems. From September 2021 to May 2023, he also worked as a Teaching Assistant at NJIT, where he developed instructional materials and conducted grading and assessment activities for various engineering courses. Prior to his time at NJIT, Mr. Cibaku was a Lead Software Engineer at Impala Digital in Tirana, Albania, from February 2021 to August 2021. In this role, he orchestrated tech stack adoption, led cross-functional teams, and developed a C# cache library that significantly improved system performance. Before that, he served as a Senior Software Engineer at Facilization from September 2013 to February 2021, where he led critical projects, such as developing a scalable backend for Virtual IBAN and optimizing SQL queries for the Bank of Albania’s Credit Registry, earning the Innovation Award in 2018. Additionally, Mr. Cibaku has experience as a Lecturer at the University of Tirana from October 2016 to August 2021, where he taught various computer science courses and supervised master and undergraduate students. His diverse professional background highlights his expertise in software engineering, research, and teaching, underscoring his commitment to advancing technology and data science.
Research Interests:
Mr. Elson Cibaku’s research interests lie at the intersection of industrial engineering, computer science, and data science. His current research focuses on leveraging advanced machine learning and deep learning methodologies to enhance efficiency in power systems, optimize vaccine distribution, and solve complex multi-pickup and delivery problems. He is particularly interested in the applications of graph attention networks, distributed optimization, and reinforcement learning in these domains. Additionally, Mr. Cibaku’s work encompasses statistical methods in data science, stochastic programming, optimization techniques for data engineering, and advanced topics in operations research. His diverse research portfolio reflects a strong commitment to addressing real-world challenges through innovative technological solutions.
Skills:
Mr. Elson Cibaku possesses a diverse and comprehensive skill set, underpinned by his proficiency in multiple programming languages including Python, C#, and Java. His expertise in machine learning spans supervised learning (regression and classification), deep learning (neural networks, transformers, generative models, transfer learning, and NLP models), unsupervised learning (clustering, dimensionality reduction, and anomaly detection), reinforcement learning (policy gradient methods, deep Q-learning, PPO, and TD learning), and generative adversarial networks. In data processing and visualization, Mr. Cibaku is adept with tools such as Tableau, Matplotlib, Looker Studio, and Power BI. His knowledge in data technologies encompasses data mining, the big data life cycle, Google Big Query, data warehousing, and data structures. In the realm of web development, he is skilled in .NET Core, ASP.NET Web API, and CQRS. His database management capabilities include working with relational databases (SQL Server, Oracle Database, and PostgreSQL), indexing strategies, data modeling, performance tuning, optimization, and NoSQL databases (MongoDB). Additionally, he is proficient in cloud services such as Microsoft Azure, Google Cloud Platform, and Amazon Web Services, and has experience with application servers including Keycloak, WildFly, NGINX, and Microsoft IIS. Other tools in his repertoire include Redis and DevExpress XAF. Mr. Cibaku holds several certifications, including MCSA: SQL 2016 Business Intelligence Development, MCPS: Microsoft Certified Professional, and certifications in implementing and developing data warehouses with Microsoft SQL Server. This extensive skill set positions him as a versatile and knowledgeable professional in the fields of computer science and data engineering.
Publications:
Boosting Efficiency in State Estimation of Power Systems by Leveraging Attention Mechanism
- Authors: E. Cibaku, F. Gama, S. W. Park
- Journal: Energy and AI
- Volume: 16
- Article Number: 100369