Emiliano Mankolli

Msc.

Departament:
Kërkimi Shkencor
Contact:
emiliano_mankolli@universitetipolis.edu.al

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ACADEMIC PROFILE

Emiliano Mankolli is a distinguished university lecturer and the Head of Data Science and Machine Learning at Ritech International AG. He completed his Bachelor’s and Master’s degrees in Mathematics and Computer Science from the University of Tirana in 2012, graduating at the top of his class. In 2019, Mankolli embarked on his PhD research, focusing on “Optimization Methods for Machine Learning Applications” under the guidance of Prof. Vassil Guliashki. His research interests encompass Machine Learning and Optimization, with a primary emphasis on developing algorithms that enhance and optimize the hiring process for various companies within the recruitment industry. This year, he successfully defended his doctoral thesis at the Bulgarian Academy of Sciences in Sofia and continues to apply his research findings to various startups.

Academic Career

Mankolli’s academic career began in 2014 at the “Aleksandër Moisiu” University of Durrës, where he specialized in Algorithms, Artificial Intelligence, and Machine Learning. Following his time at “Aleksandër Moisiu” University, he expanded his academic contributions by joining the faculty at Western Balkan University. Since June 2024, Mankolli has been a valued member of the academic staff at Polis University, where he covers subjects related to Data Science, Machine Learning, and AI.

INDUSTRY EXPERIENCE

Mankolli’s industry experience began in 2016 when he joined PaperClicks as a data scientist. For the past five years, he has been leading the Data Science and Machine Learning team at Ritech International AG. He and his team worked on a project that has been successfully implemented by one of the world’s largest logistics companies in docking centers across several US ports.

With his extensive academic background, industry experience, and innovative research, Emiliano Mankolli continues to make significant contributions to the fields of Data Science and Machine Learning, driving advancements in recruitment optimization and beyond

LATEST PUBLICATIONS

Mankolli E. M., Guliashki V. G. (2020), “Machine Learning and Natural Language Processing: Review of Models and Optimization Problems”, Proceedings of 12th ICT Innovations Conference 2020, held on 24-26 September 2020 in Skopje, Republic of North Macedonia, “Machine Learning and Applications”, Vesna Dimitrova, Ivica Dimitrovski (editors), Springer, Volume 1316 of the Communications in Computer and Information Science series (CCIS), ISBN: 978-3-030-62097-4, ISSN: 1865-0937, SJR (0.188), Computer Science – Quartile Q3, pp. 71-86 https://link.springer.com/chapter/10.1007/978-3-030-62098-1_7

Mankolli E., Guliashki V. (2021) “A Hybrid Machine Learning Method for Text Analysis to Determine Job Titles”, TELSIKS 2021, Proceedings of papers of the “15th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services”, October 20-22, 2021, Niš, Serbia, IEEE Catalog Number: CFP21488-USB, ISBN: 978-1-6654-2912-2 (IEEE), pp. 380-385, doi: 10.1109/TELSIKS52058.2021.9606341. https://ieeexplore.ieee.org/document/9606341

Mankolli E., Reducing the complexity of candidate selection using Natural Language Processing, In: Proceedings of 29-th IEEE International Conference on Systems, Signals and Image Processing “IWSSIP 2022”, June 01 – 03, 2022, Sofia, Bulgaria, pp. 1-4,

doi: 10.1109/IWSSIP55020.2022.9854488. https://ieeexplore.ieee.org/document/9854488

Mankolli E., S. Bushati, Candidate Engagement Success Prediction Using Machine Learning and Natural Language Processing Techniques, In: Proceedings of 24th Conference on Control Systems and Computer Science (CSCS), May 24-26, 2023, Bucharest, Romania, pp. 431-435, doi: 10.1109/CSCS59211.2023.00074. https://ieeexplore.ieee.org/document/10214773

Guliashki V., E. Mankolli and S. Bushati, (2023), “A machine learning approach improving university campus security”, IEEE International Workshop on Technologies for Defense and Security TechDefense 2023, November 20-22, 2023, Rome, Italy, pp.341-345. IEEETechDefense2023_FinalProgram.pdf

Emiliano Mankolli

Msc.