Contact
| Name | Arman Arzani |
|---|---|
| Position | Researcher |
| Phone | +49-201-183-6364 |
| Fax | +49-201-183-4176 |
| arman.arzani@uni-due.de | |
| Address | Schützenbahn 70 Building SA 45127 Essen |
| Room | SA-329 |
Research Interest
- Natural Language Processing
- Deep learning
- Large Language Models
- Topic Modelling
- Information Retrieval
- Knowledge Transfer & Innovation
Education
- Since 05.2020 PhD Student, Computer Science – University of Duisburg-Essen, Germany
- 2020 Master of Science, Angewandte Informatik – University of Duisburg-Essen, Germany
- 2014 Bachelor of Science, Computer Software Engineering – Sheikh Bahaei University, Iran
Publications
2024 |
Arman Arzani, Marcus Handte, Pedro José Marrón: Discovering Potential Founders within Academic Institutions. In: International Journal of Data Science and Analytics, 2024, ISSN: 2364-4168. (Type: Journal Article | Abstract | Links)@article{Arzani2024,<jats:title>Abstract</jats:title><jats:p>Technology transfer is central to the development of an iconic entrepreneurial university. To foster knowledge transfer, many universities undergo a scouting process by their innovation coaches. The goal is to find staff members and students, who have the knowledge, expertise, and the potential to found startups by transforming their research results into a product. Since there is no systematic approach to measure the innovation potential of university members based on their academic activities, the scouting process is typically subjective and relies heavily on the experience of the innovation coaches. In this paper, we study the discovery of potential founders to support the scouting process using a data-driven approach. We create a novel data set by integrating the founder profiles with the academic activities from 8 universities across 5 countries. We explain the process of data integration as well as feature engineering. By applying machine learning methods, we investigate the classification accuracy of founders based on their academic background. Our analysis shows that using a random forest (RF), it is possible to differentiate founders and non-founders with an average accuracy of 79%. This accuracy remains mostly stable when applying an RF trained on one university to another, suggesting the existence of a generic founder profile. The detailed analysis indicates a high significance of the career path as well as patent- and grant-related features among others. Furthermore, we show that using a RF, it is possible to exploit these features to predict the future founding probability up to 3 years in advance with an accuracy of 80%. Finally, by analyzing the academic disciplines of founders we show that the patent documents have more influence on the startup’s core orientation than the publications.</jats:p> |
2023 |
Arman Arzani, Marcus Handte, Matteo Zella, Pedro José Marrón: Exploiting Topic Modelling for the Identification of Untapped Scientific Collaborations. In: 2023 the 7th International Conference on Information System and Data Mining (ICISDM), pp. 73–81, Association for Computing Machinery, Atlanta, USA, 2023, ISBN: 9798400700637. (Type: Proceedings Article | Abstract | Links)@inproceedings{10.1145/3603765.3603774,Finding potential collaborators has become a challenge due to the growing number of scientists in organizations such as universities, research institutes, or companies. Collaboration Recommendation Systems (CRSs) have been developed to help researchers identify possible collaboration partners, but they often rely on citation graphs or paper abstracts which may not be readily available in organizational databases or online sources. However, scientific publication titles provide consistent bibliometric data that can provide insights into research areas. TOMOSCO is a topic modelling framework that uses transformer-based methods to extract research area information from small amounts of text, such as publication titles or brief project descriptions. TOMOSCO can classify, cluster, and match research topics across different disciplines, uncovering relationships among scientists and suggesting potential interdisciplinary collaborations. In experiments, TOMOSCO was able to identify existing collaborations with over 90% accuracy based solely on publication titles and propose new collaborations based on previously unseen publications and project descriptions. |
Arman Arzani, Marcus Handte, Pedro José Marrón: Challenges in Implementing a University-Based Innovation Search Engine. In: Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KDIR, pp. 477-486, INSTICC SciTePress, 2023, ISSN: 2184-3228. (Type: Proceedings Article | Links)@inproceedings{10.5220/0012263100003598, |
Arman Arzani, Marcus Handte, Matteo Zella, Pedro José Marrón: Discovering Potential Founders Based on Academic Background. In: Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KMIS, pp. 117-125, INSTICC SciTePress, 2023, ISSN: 2184-3228, (Best Poster Award). (Type: Proceedings Article | Links)@inproceedings{10.5220/0012156200003598, |