Emily Mo-Hellenbrand, M.Sc.: Difference between revisions
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== Research Interests == | == Research Interests == | ||
* High performance computing (HPC). Invasive computing. Hardware-aware programming. | * High performance computing (HPC). Invasive computing. Hardware/Resource-aware programming. | ||
* Hyperbolic problems. Finite volume methods. Tsunami/weather/climate simulations. | * Hyperbolic problems. Finite volume methods. Tsunami/weather/climate simulations. | ||
* Inverse problems. Bayesian inference. Machine learning/data mining. | * Inverse problems. Bayesian inference. Machine learning/data mining. | ||
Revision as of 11:19, 23 October 2015
- Address:
- Technische Universität München
- Institut für Informatik, Lehrstuhl 5
- Boltzmannstr. 3
- 85748 Garching b. München
Office: MI 02.05.057
Email: 
Phone: (089) 289 18 630
Fax: (089) 289 18 607
Office Hours: by arrangement
Student research & Hiwi projects available. Contact for details.
Short CV
- 2005-2007: Bachelor of Engineering in Electrical Engineering - Microelectronics, Stony Brook University (State University of New York), United States
- 2007-2010: Applications Developer, JPMorgan Chase, New York, United States
- 2011-2013: Master of Science in Computational Science and Engineering (CSE), Technical University of Munich, Germany
- 2014-present : Research associate / PhD candidate, Chair of Scientific Computing in Computer Science (SCCS), Technical University of Munich, Germany
Research Interests
- High performance computing (HPC). Invasive computing. Hardware/Resource-aware programming.
- Hyperbolic problems. Finite volume methods. Tsunami/weather/climate simulations.
- Inverse problems. Bayesian inference. Machine learning/data mining.
- Model order reduction. Sparse grids.
- Fault-tolerant applications.
Research Projects
- Deutsche Forschungsgemeinschaft (DFG) funded. Collaboration between Universität Erlangen-Nürnberg, Karlsruhe Institute of Technology and Technische Universität München.
Conference Talks
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Teaching
Winter 14/15
- Scientific Computing Lab - WS14
- Hauptseminar Computational Aspects of Machine Learning - WS14
- CSE Seminar Case Studies - WS14
Summer 15
Student Projects
- P. Gómez: Adaptive Construction of Surrogate Models Based on Sparse Grid Interpolants for Bayesian Inverse Problems, IDP. (Finished July 2015)
- K. Sampa Subbarao: Elastic Machine Learning with Scalable Multivariate Extrapolation, Guided Research. (Active)