Clemens Heitzinger

Associate Prof. Dr.techn. Dipl.-Ing.

Research Areas

Partial Differential Equations, Stochastic Differential Equations, Stochastic finite element methods, Multi-body problems, Machine Learning

About

Reinforcement learning; Bayesian methods for ordinary- and partial-differential-equation models; large language models.

Role

Associate Professor
Machine Learning, E194-06

Contact

Courses

2023W

Bachelor Thesis in Computer Science / 194.112 / PR
CAIML seminar / 192.138 / SE
Generative AI / 194.154 / VU
Introduction to Machine Learning / 194.025 / VU
Project in Computer Science 1 / 194.145 / PR
Seminar for PhD Students / 194.110 / SE

2024S

Bachelor Thesis in Computer Science / 194.112 / PR
CAIML seminar / 192.131 / SE
Interdisciplinary Project in Data Science / 194.147 / PR
Privatissimum for Doctor's Thesis / 194.028 / PV
Project in Computer Science 1 / 194.145 / PR
Reinforcement Learning / 194.163 / VU
Scientific Research and Writing / 193.052 / SE
Seminar for PhD Students / 194.110 / SE

Projects

Reliable reinforcement learning for sustainable enery systems
2023 – 2025 / Austrian Research Promotion Agency (FFG)

Publications

A Short Introduction to Artificial Intelligence: Methods, Success Stories, and Current Limitations / Clemens Heitzinger, & Stefan Woltran. (2024). A Short Introduction to Artificial Intelligence: Methods, Success Stories, and Current Limitations. In H. Werthner, C. Ghezzi, & J. Kramer (Eds.), Introduction to Digital Humanism : A Textbook (pp. 135–149). Springer. https://doi.org/10.1007/978-3-031-45304-5_9
Improving sensor interoperability between contactless and contact-based fingerprints using pose correction and unwarping / Ruzicka, L., Dominik Söllinger, Kohn, B., Heitzinger, C., Uhl, A., & Strobl, B. (2023). Improving sensor interoperability between contactless and contact-based fingerprints using pose correction and unwarping. IET Biometrics, 2023, Article 7519499. https://doi.org/10.1049/2023/7519499
Development of a reinforcement learning algorithm to optimize corticosteroid therapy in critically ill patients with sepsis / Bologheanu, R., Kapral, L., Laxar, D., Maleczek, M., Dibiasi, C., Zeiner, S., Agibetov, A., Ercole, A., Thoral, P., Elbers, P., Clemens Heitzinger, & Kimberger, O. (2023). Development of a reinforcement learning algorithm to optimize corticosteroid therapy in critically ill patients with sepsis. Journal of Clinical Medicine, 12(4), Article 1513. https://doi.org/10.3390/jcm12041513
Bayesian estimation of physical and geometrical parameters for nanocapacitor array biosensors / Stadlbauer, B., Cossettini, A., Morales Escalante, J. A., Pasterk, D., Scarbolo, P., Taghizadeh, L., Heitzinger, C., & Selmi, L. (2019). Bayesian estimation of physical and geometrical parameters for nanocapacitor array biosensors. Journal of Computational Physics, 397, Article 108874. https://doi.org/10.1016/j.jcp.2019.108874

Awards

PDE Models for Nanotechnology
2013 / START Prize / Austria / Website
START-Preis
2013 / START-Programm / Austria
Erwin Schrödinger Fellowship (FWF)
2003 / Schrödinger-Stipendium / Austria

And more…

Soon, this page will include additional information such as reference projects, activities as journal reviewer and editor, memberships in councils and committees, and other research activities.

Until then, please visit Clemens Heitzinger’s research profile in TISS .

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