Markus Lange-Hegermann

markus.lange-hegermann@th-owl.de
ID
71761

20 Publikationen

Alle markieren

[20]
2024 | Konferenz - Poster | ELSA-ID: 11605
Trilling-Haasler, M., Tebbe, J., Lange-Hegermann, M., & Schneider, J. (2024). Yeast filtration with rotating membrane filtration –  a new approach for an economical recovery of beer form surplus yeast . 39th EBC Congress 2024, Lille.
ELSA
 
[19]
2024 | Konferenzband - Beitrag | ELSA-ID: 12815 | OA
Tebbe, J., Zimmer, C., Steland, A., Lange-Hegermann, M., & Mies, F. (2024). Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning. In International Conference on Artificial Intelligence and Statistics (AISTATS), Vol. 238 (pp. 1333–1341). MLResearchPress .
ELSA | Download (ext.) | WoS
 
[18]
2024 | Konferenz - Vortrag | ELSA-ID: 12816
Cheng, K. Y., Pazmino, S., Bergh, B., Lange-Hegermann, M., & Schreiweis, B. (2024). An Image Retrieval Pipeline in a Medical Data Integration Center. In 19th World Congress on Medical and Health Informatics (MEDINFO) (Vol. 310, pp. 1388–1389). IOS Press, Incorporated. https://doi.org/10.3233/SHTI231208
ELSA | DOI | WoS | PubMed | Europe PMC
 
[17]
2024 | Zeitschriftenaufsatz (wiss.) | ELSA-ID: 12822
Cheng, K. Y., Lange-Hegermann, M., Hövener, J.-B., & Schreiweis, B. (2024). Instance-level medical image classification for text-based retrieval in a medical data integration center. Computational and Structural Biotechnology Journal, 24, 434–450. https://doi.org/10.1016/j.csbj.2024.06.006
ELSA | DOI | WoS | PubMed | Europe PMC
 
[16]
2023 | Konferenz - Poster | ELSA-ID: 11381
Hernández Rodriguez, T., Ramm, S., Lange-Hegermann, M., & Frahm, B. (n.d.). A systematic, model-based workflow for risk-based decision making in upstream development. 14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology, Berlin, Germany. DECHEMA e.V.
ELSA
 
[15]
2023 | Konferenz - Vortrag | ELSA-ID: 11383
Hernández Rodriguez, T., Posch, C., Pörtner, R., Lange-Hegermann, M., Wurm, F. M., & Frahm, B. (2023). Model-assisted design strategies for bioprocesses – Advanced statistical methods in industrial upstream cell culture. 14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology, Berlin, Germany. DECHEMA e.V.
ELSA
 
[14]
2023 | Konferenz - Poster | ELSA-ID: 10201 | OA
Hernández Rodriguez, T., Ramm, S., Lange-Hegermann, M., & Frahm, B. (2023). A systematic, model-based workflow for risk-based decision making in upstream development. BioProcessingDays 2023, Recklinghausen, Germany.
ELSA | Download (ext.)
 
[13]
2023 | Konferenzband - Beitrag | ELSA-ID: 9930
Lange-Hegermann, M., Schmohl, T., Watanabe, A., Schelling, K., Heiss, S., & Rubart, J. (2023). KI-basierte Erstellung individualisierter Mathematikaufgaben für MINT-Fächer. In T. Schmohl, A. Watanabe, & K. Schelling (Eds.), Künstliche Intelligenz in der Hochschulbildung: Chancen und Grenzen des KI-gestützten Lernens und Lehrens (Vol. 4, pp. 161–172). transcript Verlag. https://doi.org/10.14361/9783839457696-009
ELSA | DOI
 
[12]
2023 | Zeitschriftenaufsatz (wiss.) | ELSA-ID: 12811
Shayan, H., Krycki, K., Doemeland, M., & Lange-Hegermann, M. (2023). PGNAA Spectral Classification of Metal With Density Estimations. IEEE Transactions on Nuclear Science, 70(6), 1171–1177. https://doi.org/10.1109/tns.2023.3242626
ELSA | DOI | WoS
 
[11]
2023 | Konferenzband - Beitrag | ELSA-ID: 12828
Härkönen, M., Lange-Hegermann, M., & Raiţă, B. (2023). Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients. In 40th International Conference on Machine Learning (Vol. 202). MLResearchPress .
ELSA | WoS
 
[10]
2023 | Konferenzband - Beitrag | ELSA-ID: 10787
Tebbe, J., Pawlik, T., Trilling-Haasler, M., Löbner, J., Lange-Hegermann, M., & Schneider, J. (2023). Holistic optimization of a dynamic cross-flow filtration process towards a cyber-physical system. In J. Jasperneite, L. Wisniewski, K. Fung Man, & Institute of Electrical and Electronics Engineers (Eds.), 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) (pp. 1–7). IEEE. https://doi.org/10.1109/INDIN51400.2023.10217913
ELSA | DOI | WoS
 
[9]
2022 | Zeitschriftenaufsatz (wiss.) | ELSA-ID: 11377
Hernández Rodriguez, T., Sekulic, A., Lange-Hegermann, M., & Frahm, B. (2022). Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. Processes, 10(5), Article 883. https://doi.org/10.3390/pr10050883
ELSA | DOI
 
[8]
2022 | Konferenz - Poster | ELSA-ID: 7932
Hernández Rodriguez, T., Ramm, S., Lange-Hegermann, M., & Frahm, B. (n.d.). A systematic, model-based workflow for risk-based decision making in upstream development. 5th annual Bioprocessing Summit Europe, Barcelona, Spain.
ELSA
 
[7]
2022 | Sammelwerk - Beitrag | ELSA-ID: 10193
Hernández Rodriguez, T., Sekulic, A., Lange-Hegermann, M., & Frahm, B. (2022). Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design. In R. Pörtner & J. Möller (Eds.), Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing: Vol. special issue (pp. 21–48). MDPI. https://doi.org/10.3390/pr10050883
ELSA | DOI
 
[6]
2022 | Konferenz - Poster | ELSA-ID: 10198
Hernández Rodriguez, T., Pörtner, R., Lange-Hegermann, M., Wurm, F. M., & Frahm, B. (n.d.). A systematic, model-based approach for decision making in upstream development – Considerations regarding clone selection and cell expansion. 27th Meeting of the European Society for Animal Cell Technology (ESACT): Advanced Cell Technologies: Making Protein, Cell, and Gene Therapies a Reality, Lisbon, Portugal .
ELSA
 
[5]
2022 | Konferenzband - Beitrag | ELSA-ID: 12804
Besginow, A., & Lange-Hegermann, M. (2022). Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh, & Neural Information Processing Systems Foundation (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022) (Vol. 35, pp. 29386–29399). Curran Associates, Inc.
ELSA | WoS
 
[4]
2021 | Konferenzband - Beitrag | ELSA-ID: 7581 | OA
Lange-Hegermann, M., Schmohl, T., Watanabe, A., Heiss, S., & Rubart, J. (2021). AI-based STEM education: Generating individualized exercises in mathematics. New Perspectives in Science Education (Vol. 10, pp. 385–390). Bologna: Libreriauniversitaria.it.
ELSA | Download (ext.)
 
[3]
2021 | Konferenzband - Beitrag | ELSA-ID: 5620 | OA
Lange-Hegermann, M., Schmohl, T., Watanabe, A., Heiss, S., & Rubart, J. (2021). AI-Based Stem Education: Generating Individualized Exercises in Mathematics. New Perspectives in Science Education (pp. 385–390). Bologna: Libreriauniversitaria.it. https://doi.org/10.26352/F318_2384-9509
ELSA | DOI | Download (ext.)
 
[2]
2021 | Konferenzband - Beitrag | ELSA-ID: 12786
Lange-Hegermann, M. (2021). Linearly Constrained Gaussian Processes with Boundary Conditions. In A. Banerjee & K. Fukumizu (Eds.), 24th International Conference on Artificial Intelligence and Statistics (AISTATS) (Vol. 130). MLResearchPress .
ELSA | WoS
 
[1]
2020 | Konferenzband - Beitrag | ELSA-ID: 12812
Berns, F., Lange-Hegermann, M., & Beecks, C. (2020). Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. In H. Panetto, K. Madani, & A. Smirnov (Eds.), Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1 (pp. 87–92). SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0010130300870092
ELSA | DOI | WoS
 
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20 Publikationen

Alle markieren

[20]
2024 | Konferenz - Poster | ELSA-ID: 11605
Trilling-Haasler, M., Tebbe, J., Lange-Hegermann, M., & Schneider, J. (2024). Yeast filtration with rotating membrane filtration –  a new approach for an economical recovery of beer form surplus yeast . 39th EBC Congress 2024, Lille.
ELSA
 
[19]
2024 | Konferenzband - Beitrag | ELSA-ID: 12815 | OA
Tebbe, J., Zimmer, C., Steland, A., Lange-Hegermann, M., & Mies, F. (2024). Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning. In International Conference on Artificial Intelligence and Statistics (AISTATS), Vol. 238 (pp. 1333–1341). MLResearchPress .
ELSA | Download (ext.) | WoS
 
[18]
2024 | Konferenz - Vortrag | ELSA-ID: 12816
Cheng, K. Y., Pazmino, S., Bergh, B., Lange-Hegermann, M., & Schreiweis, B. (2024). An Image Retrieval Pipeline in a Medical Data Integration Center. In 19th World Congress on Medical and Health Informatics (MEDINFO) (Vol. 310, pp. 1388–1389). IOS Press, Incorporated. https://doi.org/10.3233/SHTI231208
ELSA | DOI | WoS | PubMed | Europe PMC
 
[17]
2024 | Zeitschriftenaufsatz (wiss.) | ELSA-ID: 12822
Cheng, K. Y., Lange-Hegermann, M., Hövener, J.-B., & Schreiweis, B. (2024). Instance-level medical image classification for text-based retrieval in a medical data integration center. Computational and Structural Biotechnology Journal, 24, 434–450. https://doi.org/10.1016/j.csbj.2024.06.006
ELSA | DOI | WoS | PubMed | Europe PMC
 
[16]
2023 | Konferenz - Poster | ELSA-ID: 11381
Hernández Rodriguez, T., Ramm, S., Lange-Hegermann, M., & Frahm, B. (n.d.). A systematic, model-based workflow for risk-based decision making in upstream development. 14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology, Berlin, Germany. DECHEMA e.V.
ELSA
 
[15]
2023 | Konferenz - Vortrag | ELSA-ID: 11383
Hernández Rodriguez, T., Posch, C., Pörtner, R., Lange-Hegermann, M., Wurm, F. M., & Frahm, B. (2023). Model-assisted design strategies for bioprocesses – Advanced statistical methods in industrial upstream cell culture. 14th European Congress of Chemical Engineering and 7th European Congress of Applied Biotechnology, Berlin, Germany. DECHEMA e.V.
ELSA
 
[14]
2023 | Konferenz - Poster | ELSA-ID: 10201 | OA
Hernández Rodriguez, T., Ramm, S., Lange-Hegermann, M., & Frahm, B. (2023). A systematic, model-based workflow for risk-based decision making in upstream development. BioProcessingDays 2023, Recklinghausen, Germany.
ELSA | Download (ext.)
 
[13]
2023 | Konferenzband - Beitrag | ELSA-ID: 9930
Lange-Hegermann, M., Schmohl, T., Watanabe, A., Schelling, K., Heiss, S., & Rubart, J. (2023). KI-basierte Erstellung individualisierter Mathematikaufgaben für MINT-Fächer. In T. Schmohl, A. Watanabe, & K. Schelling (Eds.), Künstliche Intelligenz in der Hochschulbildung: Chancen und Grenzen des KI-gestützten Lernens und Lehrens (Vol. 4, pp. 161–172). transcript Verlag. https://doi.org/10.14361/9783839457696-009
ELSA | DOI
 
[12]
2023 | Zeitschriftenaufsatz (wiss.) | ELSA-ID: 12811
Shayan, H., Krycki, K., Doemeland, M., & Lange-Hegermann, M. (2023). PGNAA Spectral Classification of Metal With Density Estimations. IEEE Transactions on Nuclear Science, 70(6), 1171–1177. https://doi.org/10.1109/tns.2023.3242626
ELSA | DOI | WoS
 
[11]
2023 | Konferenzband - Beitrag | ELSA-ID: 12828
Härkönen, M., Lange-Hegermann, M., & Raiţă, B. (2023). Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients. In 40th International Conference on Machine Learning (Vol. 202). MLResearchPress .
ELSA | WoS
 
[10]
2023 | Konferenzband - Beitrag | ELSA-ID: 10787
Tebbe, J., Pawlik, T., Trilling-Haasler, M., Löbner, J., Lange-Hegermann, M., & Schneider, J. (2023). Holistic optimization of a dynamic cross-flow filtration process towards a cyber-physical system. In J. Jasperneite, L. Wisniewski, K. Fung Man, & Institute of Electrical and Electronics Engineers (Eds.), 2023 IEEE 21st International Conference on Industrial Informatics (INDIN) (pp. 1–7). IEEE. https://doi.org/10.1109/INDIN51400.2023.10217913
ELSA | DOI | WoS
 
[9]
2022 | Zeitschriftenaufsatz (wiss.) | ELSA-ID: 11377
Hernández Rodriguez, T., Sekulic, A., Lange-Hegermann, M., & Frahm, B. (2022). Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design. Processes, 10(5), Article 883. https://doi.org/10.3390/pr10050883
ELSA | DOI
 
[8]
2022 | Konferenz - Poster | ELSA-ID: 7932
Hernández Rodriguez, T., Ramm, S., Lange-Hegermann, M., & Frahm, B. (n.d.). A systematic, model-based workflow for risk-based decision making in upstream development. 5th annual Bioprocessing Summit Europe, Barcelona, Spain.
ELSA
 
[7]
2022 | Sammelwerk - Beitrag | ELSA-ID: 10193
Hernández Rodriguez, T., Sekulic, A., Lange-Hegermann, M., & Frahm, B. (2022). Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design. In R. Pörtner & J. Möller (Eds.), Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing: Vol. special issue (pp. 21–48). MDPI. https://doi.org/10.3390/pr10050883
ELSA | DOI
 
[6]
2022 | Konferenz - Poster | ELSA-ID: 10198
Hernández Rodriguez, T., Pörtner, R., Lange-Hegermann, M., Wurm, F. M., & Frahm, B. (n.d.). A systematic, model-based approach for decision making in upstream development – Considerations regarding clone selection and cell expansion. 27th Meeting of the European Society for Animal Cell Technology (ESACT): Advanced Cell Technologies: Making Protein, Cell, and Gene Therapies a Reality, Lisbon, Portugal .
ELSA
 
[5]
2022 | Konferenzband - Beitrag | ELSA-ID: 12804
Besginow, A., & Lange-Hegermann, M. (2022). Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh, & Neural Information Processing Systems Foundation (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022) (Vol. 35, pp. 29386–29399). Curran Associates, Inc.
ELSA | WoS
 
[4]
2021 | Konferenzband - Beitrag | ELSA-ID: 7581 | OA
Lange-Hegermann, M., Schmohl, T., Watanabe, A., Heiss, S., & Rubart, J. (2021). AI-based STEM education: Generating individualized exercises in mathematics. New Perspectives in Science Education (Vol. 10, pp. 385–390). Bologna: Libreriauniversitaria.it.
ELSA | Download (ext.)
 
[3]
2021 | Konferenzband - Beitrag | ELSA-ID: 5620 | OA
Lange-Hegermann, M., Schmohl, T., Watanabe, A., Heiss, S., & Rubart, J. (2021). AI-Based Stem Education: Generating Individualized Exercises in Mathematics. New Perspectives in Science Education (pp. 385–390). Bologna: Libreriauniversitaria.it. https://doi.org/10.26352/F318_2384-9509
ELSA | DOI | Download (ext.)
 
[2]
2021 | Konferenzband - Beitrag | ELSA-ID: 12786
Lange-Hegermann, M. (2021). Linearly Constrained Gaussian Processes with Boundary Conditions. In A. Banerjee & K. Fukumizu (Eds.), 24th International Conference on Artificial Intelligence and Statistics (AISTATS) (Vol. 130). MLResearchPress .
ELSA | WoS
 
[1]
2020 | Konferenzband - Beitrag | ELSA-ID: 12812
Berns, F., Lange-Hegermann, M., & Beecks, C. (2020). Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0. In H. Panetto, K. Madani, & A. Smirnov (Eds.), Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1 (pp. 87–92). SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0010130300870092
ELSA | DOI | WoS
 
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Darstellung / Sortierung

Export / Einbettung