Prof. Dr. Mathias Kraus

Juniorprofessur für Data Analytics

Lebenslauf

Mathias Kraus studierte von 2010 bis 2016 Mathematik im Bachelor- und Informatik im Masterprogramm am Karlsruher Institut für Technologie (KIT). Nach seinem Masterabschluss arbeitete er 2017 als wissenschaftlicher Mitarbeiter an der Albert-Ludwigs-Universität Freiburg und von 2018 bis 2020 als Doktoratsstudent an der ETH Zürich, wo er im Oktober 2020 erfolgreich seine Promotion zum Thema „Deep Learning in Business Analytics: Methods and Applications“ abschloss. Zwischenzeitlich verbrachte er einen Forschungsaufenthalt an der University of Texas at Austin. Seit Februar 2021 ist Mathias Kraus Inhaber der Juniorprofessur für Data-Analytics am Institut für Wirtschaftsinformatik.

Das Hauptziel seiner Forschung ist es, Datenquellen aus dem Gesundheitswesen durch den Einsatz von Data-Analytics Methoden zu nutzen, um bessere Behandlungen von Patienten zu erreichen. Zu diesem Zweck entwickelt er innovative Methoden aus den Bereichen Statistik, maschinelles Lernen und Big Data, um zur Erforschung von datengesteuerter Entscheidungshilfe im Gesundheitsmanagement beizutragen. Seine Arbeiten wurden in führenden Fachzeitschriften der Wirtschaftsinformtik, Operations Research, sowie auf Informatik-Konferenzen veröffentlicht.

Mehr Informationen finden Sie auch auf der Website der Juniorprofessur.

Aktuelle Publikationen

2024

Bérubé C., Lehmann VF., Maritsch M., Kraus M., Feuerriegel S., Wortmann F., Züger T., Stettler C., Fleisch E., Baki Kocaballi A., Kowatsch T.:
Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial
In: JMIR Human Factors 11 (2024), Art.Nr.: e42823
ISSN: 2292-9495
DOI: 10.2196/42823
Kraus M., Hambauer N., Müller K., Kröckel P., Ulapane N., De Caigny A., De Bock K., Coussement K.:
Coupling Neural Networks Between Clusters for Better Personalized Care
In: Proceedings of the 57th Hawaii International Conference on System Sciences 2024
Open Access: https://hdl.handle.net/10125/106821
Schimanski T., Reding A., Reding N., Bingler J., Kraus M., Leippold M.:
Bridging the gap in ESG measurement: Using NLP to quantify environmental, social, and governance communication
In: Finance Research Letters 61 (2024), Art.Nr.: 104979
ISSN: 1544-6123
DOI: 10.1016/j.frl.2024.104979
Zilker S., Weinzierl S., Kraus M., Zschech P., Matzner M.:
A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis (forthcoming)
In: Health Care Management Science (2024)
ISSN: 1386-9620

2023

De Bock KW., Coussement K., Caigny AD., Słowiński R., Baesens B., Boute RN., Choi TM., Delen D., Kraus M., Lessmann S., Maldonado S., Martens D., Óskarsdóttir M., Vairetti C., Verbeke W., Weber R.:
Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda
In: European Journal of Operational Research (2023)
ISSN: 0377-2217
DOI: 10.1016/j.ejor.2023.09.026
Keller G., Rachunek K., Springer F., Kraus M.:
Evaluation of a newly designed deep learning-based algorithm for automated assessment of scapholunate distance in wrist radiography as a surrogate parameter for scapholunate ligament rupture and the correlation with arthroscopy
In: La Radiologia Medica (2023)
ISSN: 0033-8362
DOI: 10.1007/s11547-023-01720-8
Kraus M., Tschernutter D., Weinzierl S., Zschech P.:
Interpretable generalized additive neural networks
In: European Journal of Operational Research (2023)
ISSN: 0377-2217
DOI: 10.1016/j.ejor.2023.06.032
Lehmann V., Foll S., Maritsch M., van Weenen E., Kraus M., Lagger S., Odermatt K., Albrecht C., Fleisch E., Zueger T., Wortmann F., Stettler C.:
Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data
In: Diabetes Care 46 (2023), S. 993-997
ISSN: 0149-5992
DOI: 10.2337/dc22-2290
Lehmann V., Zueger T., Maritsch M., Kraus M., Albrecht C., Berube C., Feuerriegel S., Wortmann F., Kowatsch T., Styger N., Lagger S., Laimer M., Fleisch E., Stettler C.:
Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes
In: Diabetes Obesity & Metabolism (2023)
ISSN: 1462-8902
DOI: 10.1111/dom.15021
Maritsch M., Föll S., Lehmann V., Styger N., Bérubé C., Kraus M., Feuerriegel S., Kowatsch T., Züger T., Fleisch E., Wortmann F., Stettler C.:
Smartwatches for non-invasive hypoglycaemia detection during cognitive and psychomotor stress
In: Diabetes Obesity & Metabolism (2023)
ISSN: 1462-8902
DOI: 10.1111/dom.15402
Schallmoser S., Zueger T., Kraus M., Saar-Tsechansky M., Stettler C., Feuerriegel S.:
Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study
In: Journal of Medical Internet Research 25 (2023), S. e42181-
ISSN: 1438-8871
DOI: 10.2196/42181
Stammbach D., Webersinke N., Bingler JA., Kraus M., Leippold M.:
Environmental Claim Detection
61st Annual Meeting of the Association for Computational Linguistics (Toronto, 9. Juli 2023 - 14. Juli 2023)
In: Association for Computational Linguistics (Hrsg.): Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) 2023
Open Access: https://aclanthology.org/2023.acl-short.91
URL: https://aclanthology.org/2023.acl-short.91
Vaghefi SA., Stammbach D., Muccione V., Bingler J., Ni J., Kraus M., Allen S., Colesanti-Senni C., Wekhof T., Schimanski T., Gostlow G., Yu T., Wang Q., Webersinke N., Huggel C., Leippold M.:
ChatClimate: Grounding conversational AI in climate science
In: Communications Earth & Environment 4 (2023), Art.Nr.: 480
ISSN: 2662-4435
DOI: 10.1038/s43247-023-01084-x
Zilker S., Weinzierl S., Zschech P., Kraus M., Matzner M.:
Best of both worlds: Combining predictive power with interpretable and explainable results for patient pathway prediction
European Conference on Information Systems (Kristiansand, 13. Juni 2023 - 16. Juni 2023)
In: Proceedings of the 31st European Conference on Information Systems 2023
Zipfel J., Verworner F., Fischer M., Wieland U., Kraus M., Zschech P.:
Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models
In: Computers & Industrial Engineering 177 (2023), Art.Nr.: 109045
ISSN: 0360-8352
DOI: 10.1016/j.cie.2023.109045
van Weenen E., Banholzer N., Föll S., Zueger T., Fontana FY., Skroce K., Hayes C., Kraus M., Feuerriegel S., Lehmann V., Scott SN., Wortmann F., Stettler C.:
Glycaemic patterns of male professional athletes with type 1 diabetes during exercise, recovery and sleep: Retrospective, observational study over an entire competitive season
In: Diabetes Obesity & Metabolism (2023)
ISSN: 1462-8902
DOI: 10.1111/dom.15147

2022

Bingler JA., Kraus M., Leippold M., Webersinke N.:
Cheap Talk and Cherry-Picking: What ClimateBert has to say on Corporate Climate Risk Disclosures
In: Finance Research Letters 47 (2022), S. 102776
ISSN: 1544-6123
DOI: 10.1016/j.frl.2022.102776
Bingler JA., Kraus M., Leippold M., Webersinke N.:
Cheap Talk in Corporate Climate Commitments: The effectiveness of climate initiatives
In: Social Science Research Network (2022)
DOI: 10.2139/ssrn.3998435
(Working Paper)
Harl M., Herchenbach M., Kruschel S., Hambauer N., Zschech P., Kraus M.:
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision
In: Proceedings of the 17th International Conference on Wirtschaftsinformatik (WI) 2022
Open Access: https://aisel.aisnet.org/wi2022/student_track/student_track/33/
URL: https://aisel.aisnet.org/wi2022/student_track/student_track/33/
Hershcovich D., Webersinke N., Kraus M., Bingler JA., Leippold M.:
Towards Climate Awareness in NLP Research
EMNLP 2022: The 2022 Conference on Empirical Methods in Natural Language Processing (Abu Dhabi, 7. Dezember 2022 - 11. Dezember 2022)
In: Association for Computational Linguistics (Hrsg.): Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing 2022
Open Access: https://aclanthology.org/2022.emnlp-main.159
URL: https://aclanthology.org/2022.emnlp-main.159
Lorenz R., Kraus M., Wolf H., Feuerriegel S., Netland TH.:
Selecting advanced analytics in manufacturing: a decision support model
In: Production Planning & Control (2022)
ISSN: 0953-7287
DOI: 10.1080/09537287.2022.2126951
Webersinke N., Kraus M., Bingler JA., Leippold M.:
ClimateBert: A Pretrained Language Model for Climate-Related Text
AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges (Arlington, VA, 17. November 2022 - 19. November 2022)
In: Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges 2022
DOI: 10.2139/ssrn.4229146
URL: https://www.climatechange.ai/papers/aaaifss2022/12
Zschech P., Weinzierl S., Hambauer N., Zilker S., Kraus M.:
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
European Conference on Information Systems (Timisoara, 5. Juli 2022 - 9. Juli 2022)
In: Proceedings of the 30th European Conference on Information Systems 2022
Zueger T., Schallmoser S., Kraus M., Saar-Tsechansky M., Feuerriegel S., Stettler C.:
Machine Learning for Predicting the Risk of Transition from Prediabetes to Diabetes
In: Diabetes Technology & Therapeutics (2022)
ISSN: 1520-9156
DOI: 10.1089/dia.2022.0210

2021

Bingler JA., Kraus M., Leippold M.:
Cheap Talk and Cherry-Picking: What ClimateBert has to say on Corporate Climate Risk Disclosures
In: Social Science Research Network (2021)
ISSN: 1556-5068
DOI: 10.2139/ssrn.3796152
Maag B., Feuerriegel S., Kraus M., Saar-Tsechansky M., Züger T.:
Modeling longitudinal dynamics of comorbidities
2021 ACM Conference on Health, Inference, and Learning, CHIL 2021 (, 8. April 2021 - 9. April 2021)
In: ACM CHIL 2021 - Proceedings of the 2021 ACM Conference on Health, Inference, and Learning 2021
DOI: 10.1145/3450439.3451871
Ozyurt Y., Kraus M., Hatt T., Feuerriegel S.:
AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units
27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 (Virtual, 14. August 2021 - 18. August 2021)
In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2021
DOI: 10.1145/3447548.3467143
la Torre AMD., van Weenen E., Kraus M., Weiler S., Feuerriegel S., Burden AM.:
A network analysis of drug combinations associated with acute generalized exanthematous pustulosis (AGEP)
In: Journal of Clinical Medicine 10 (2021), Art.Nr.: 4486
ISSN: 2077-0383
DOI: 10.3390/jcm10194486

2020

Ducci F., Kraus M., Feuerriegel S.:
Cascade-LSTM: A Tree-Structured Neural Classifier for Detecting Misinformation Cascades
26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020 (, 23. August 2020 - 27. August 2020)
In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2020
DOI: 10.1145/3394486.3403317
Kraus M., Feuerriegel S., Oztekin A.:
Deep learning in business analytics and operations research: Models, applications and managerial implications
In: European Journal of Operational Research 281 (2020), S. 628-641
ISSN: 0377-2217
DOI: 10.1016/j.ejor.2019.09.018
Maritsch M., Föll S., Lehmann V., Bérubé C., Kraus M., Feuerriegel S., Kowatsch T., Züger T., Stettler C., Fleisch E., Wortmann F.:
Towards wearable-based hypoglycemia detection and warning in diabetes
2020 ACM CHI Conference on Human Factors in Computing Systems, CHI EA 2020 (Honolulu, HI, 25. April 2020 - 30. April 2020)
In: Conference on Human Factors in Computing Systems - Proceedings 2020
DOI: 10.1145/3334480.3382808
Zueger T., Gloor M., Lehmann V., Melmer A., Kraus M., Feuerriegel S., Laimer M., Stettler C.:
White coat adherence effect on glucose control in adult individuals with diabetes
In: Diabetes Research and Clinical Practice 168 (2020), Art.Nr.: 108392
ISSN: 0168-8227
DOI: 10.1016/j.diabres.2020.108392

2019

Kraus M., Feuerriegel S.:
Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences
In: Decision Support Systems 125 (2019), Art.Nr.: 113100
ISSN: 0167-9236
DOI: 10.1016/j.dss.2019.113100
Kraus M., Feuerriegel S.:
Personalized purchase prediction of market baskets with Wasserstein-based sequence matching
25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 (Anchorage, AK, 4. August 2019 - 8. August 2019)
In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2019
DOI: 10.1145/3292500.3330791
Kraus M., Feuerriegel S.:
Sentiment analysis based on rhetorical structure theory:Learning deep neural networks from discourse trees
In: Expert Systems With Applications 118 (2019), S. 65-79
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2018.10.002
Maritsch M., Berube C., Kraus M., Lehmann V., Zuger T., Feuerriegel S., Kowatsch T., Wortmann F.:
Improving heart rate variability measurements from consumer smartwatches with machine learning
2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2019 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2019 (London, 9. September 2019 - 13. September 2019)
In: UbiComp/ISWC 2019- - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers 2019
DOI: 10.1145/3341162.3346276
Wolf H., Lorenz R., Kraus M., Feuerriegel S., Netland TH.:
Bringing Advanced Analytics to Manufacturing: A Systematic Mapping
IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2019 (Austin, TX, 1. September 2019 - 5. September 2019)
In: Farhad Ameri, Kathryn E. Stecke, Gregor von Cieminski, Dimitris Kiritsis (Hrsg.): IFIP Advances in Information and Communication Technology 2019
DOI: 10.1007/978-3-030-30000-5_42

2018

Kratzwald B., Ilic S., Kraus M., Feuerriegel S., Prendinger H.:
Deep learning for affective computing: Text-based emotion recognition in decision support
In: Decision Support Systems 115 (2018), S. 24-35
ISSN: 0167-9236
DOI: 10.1016/j.dss.2018.09.002

2017

Kraus M., Feuerriegel S.:
Decision support from financial disclosures with deep neural networks and transfer learning
In: Decision Support Systems 104 (2017), S. 38-48
ISSN: 0167-9236
DOI: 10.1016/j.dss.2017.10.001