Dr. David Lenz
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Dr. David Lenz |
Projektmitarbeiter |
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Telefon: 0641 / 99-22655 Sekretariat: 0641 / 99-22641 E-Mail: David Lenz Anschrift: Justus-Liebig-Universität Gießen Fachbereich Wirtschaftwissenschaften Professur für Statistik und Ökonometrie Licher Str. 64 35394 Gießen
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Responsibility
- Postdoctoral Researcher DynTOBI Project
- DynTOBI - Dynamische Textdaten-basierte Output-Indikatoren als Basis einer neuen Innovationsmetrik
(TOBI - dynamic text data based output indicators as basis for a novel innovation metric) - In cooperation with Centre for European Economic Research (ZEW) in Mannheim
Research Interests
- Data Science
- Machine Learning
- Forecasting
- Deep Learning
- Text Mining
- Neural Networks
- Big Data
- CryptoCurrencies
Grants
- NVIDIA Titan V GPU Grant
Articles in Refereed Journals
- Auzepy, A., Tönjes, E., Lenz, D., & Funk, C. (2023). Evaluating TCFD reporting—A new application of zero-shot analysis to climate-related financial disclosures. PLOS ONE, 18(11), e0288052. Retrieved 2023-11-08, from https://dx.plos.org/10.1371/ journal.pone.0288052 doi: 10.1371/journal.pone.0288052
- Wildnerova, L., Menon, C., Dehghan, R., Kinne, J., & Lenz, D. (2024, July). Which SMEs are greening?: Cross-country evidence from one million web-sites (OECD SME and Entrepreneurship Papers No. 60). Retrieved 2024-11-20, from https://www.oecd-ilibrary.org/economics/which-smes-are -greening ddd00999-en (Series: OECD SME and Entrepreneurship Papers Volume: 60) doi: 10.1787/ddd00999-en
- Kinne, J., Dehghan, R., Schmidt, S., Lenz, D., & Hottenrott, H. (2024). Location factors and ecosystem embedding of sustainability-engaged blockchain companies in the US. A web-based analysis. International Journal of Information Management Data Insights,
4(2), 100287. Retrieved 2024-09-23, from https://linkinghub.elsevier.com/ retrieve/pii/S2667096824000764 doi: 10.1016/j.jjimei.2024.100287 - Dahlke, Johannes, Mathias Beck, Jan Kinne, David Lenz, Robert Dehghan, Martin Wörter, Bernd Ebersberger (2024), Epidemic effects in the diffusion of emerging technologies: evidence from artificial intelligence adoption, Research Policy, Volume 53, Issue 2. http://dx.doi.org/10.1080/17421772.2023.2193222
- Abbasiharofteh, Milad, Miriam Krüger, Jan Kinne, David Lenz und Bernd Resch (2023), The digital layer: alternative data for regional and innovation studies, Spatial Economic Analysis, 1-23. http://dx.doi.org/10.1080/17421772.2023.2193222
- Arifi, Dorian, Bernd Resch, Jan Kinne und David Lenz (2023), Innovation in hyperlink and social media networks: Comparing connection strategies of innovative companies in hyperlink and social media networks, PLOS ONE 18(3): e0283372.
https://doi.org/10.1371/journal.pone.0283372 - Schmidt S, Kinne J, Lauterbach S, Blaschke T, Lenz D, Resch B (2022) "Greenwashing in the US metal industry? A novel approach combining SO2 concentrations from sattelite data, a plant-level firm database and web text mining". Science of The Total Environment, Volume 835
- Dörr JO, Kinne J, Lenz D, Licht G, Winker P (2022) "An integrated data framework for policy guidance during the coronavirus pandemic: Towards real-time decision support for economic policymakers". PLOS ONE 17(2):e0263898.
- D. Lenz, P. Winker (2020), "Measuring the Diffusion of Innovations with Paragraph Vector Topic Models" PLOS ONE. 2020;15(1):1-18
- D. Eugenidis, D. Lenz, C. Leser, F. Schleer-van Gellecom und P. Winker (2020), "Text-mining basierte Analyse der Kapitalmarktreaktionen auf Ad-hoc-Mitteilungen" CORPORATE FINANCE, 2020, 09-10.
- J. Kinne, D.Lenz (2021), "Predicting innovative firms using web mining and deep learning"PLOS ONE. 2021;16(4):1-18
Discussion Papers & Conference Proceedings
- J. Schwierzy, R. Dehghan, S. Schmidt, E. Rodepeter, A. Stoemmer, K. Uctum, J. Kinne, D. Lenz and H. Hottenrott, 2022. "Technology Mapping Using WebAI: The Case of 3D Printing". arXiv preprint arXiv:2201.01125.
- J.Kinne, M. Krüger, D. Lenz, G. Licht, P. Winker (2020),"Corona-Pandemie betrifft Unternehmen unterschiedlich", Tagesaktuelle Webseiten-Analyse zur Reaktion von Unternehmen auf die Corona-Pandemie in Deutschland, ZEW Kurzexpertise Nr. 20-05, Mannheim. Download
- D. Lenz, C. Schulze, M. Guckert (2018),"Real-time Session-Based Recommendations using LSTM with neural Embeddings"Artificial Neural Networks and Machine Learning - ICANN 2018 | SpringerLink.
Expertises
- Dörr, Julian Oliver, Sandra Gottschalk, Jan Kinne, David Lenz and Georg Licht (2020), "Mittelständische Unternehmen in der Corona - Krise im Spiegel ihrer Webseiten", Bundesministerium für Wirtschaft und Energie (BMWi), Mannheim.
Talks
- 2019: Predicting Innovative Firms using Web Mining and Deep Learning, Seminar Webscraping von Unternehmensdaten, Statistisches Bundesamt, Germany
- 2019: Predicting Innovative Firms using Web Mining and Deep Learning, LISH Harvard University Seminar, Cambridge, Massachusetts, Vereinigte Staaten von Amerika (USA)
- 2019: Predicting Innovative Firms using Web Mining and Deep Learning, International Business School Brandeis University Seminar, Waltham, Massachusetts, Vereinigte Staaten von Amerika (USA)
- 2019: Predicting Innovative Firms using Web Mining and Deep Learning, Deutsche Bundesbank Seminar, Deutsche Bundesbank in Frankfurt am Main, Germany
- 2018: Measuring the Diffusion of Innovations with Paragraph Vector Topic Models, 23rd International Conference on Computational Statistics (COMPSTAT), Iasi, Romania
- 2018: Measuring the Diffusion of Innovations with Paragraph Vector Topic Models, 16th ZEW Conference on the Economics of Information and Communication Technologies, ZEW - Zentrum für europäische Wirtschaftsforschung in Mannheim, Germany
- 2018: Measuring the Diffusion of Innovations with Paragraph Vector Topic Models, 20th ZEW Summer Workshop for Young Economists, ZEW - Zentrum für europäische Wirtschaftsforschung in Mannheim, Germany
- 2018: Measuring the Diffusion of Innovations with Paragraph Vector Topic Models, 1st CRoNoS Workshop on Multivariate Data Analysis and Software, Limassol, Cyprus
Repositories