Experience
Skills
Tools: Python, Jupyter Notebook, AWS SageMaker, Matlab, C/C++
Libraries: PyTorch, Tensorflow, numpy, matplotlib, scikit-learn
Writing/editing: LaTeX, Markdown, Microsoft Office
Teaching and mentoring
Teaching assistant:
Grader:
- Signals and Systems I. (Budapest Univ. of Technology)
- Signals and Systems II. (Budapest Univ. of Technology)
Mentoring:
- Graduate Student Mentor in the Viterbi Graduate Mentorship Program
- Research mentor for an undergraduate student for their thesis on over-parameterized neural networks
Coding and open source software
I support 100% reproducible scientific research and have released code for our work on my GitHub page. The source code for our work on MRAugment: a data augmentation pipeline for MRI can be found here and our results on Minimax lower bounds for transfer learning can be reproduced using the repo here.
During my research projects with MRI data, I have heavily relied on fastMRI dataset from NYU and Facebook AI, and have contributed to the fastMRI repository.
Prior to starting my graduate degree I had a chance to work as a Software Engineering Intern at Bosch Hungary developing automotive navigation software.
Graduate-level courses
Here you can find a list of selected courses I have attended during my graduate studies.
- EE588: Optimization for the Information and Data Sciences (USC)
- ISE633: Large Scale Optimization for Machine Learning
- CSCI567: Machine Learning
- CSCI561: Foundations of Artificial Intelligence
- EE546: Mathematics of High-Dimensional Data
- MATH541A: Introduction to Mathematical Statistics
- EE503: Probability for Electrical Engineers
- EE562: Random Processes in Engineering
- EE585: Linear Systems Theory
- EE591: Magnetic Resonance Imaging and Reconstruction
- CSCI570: Analysis of Algorithms
- CSCI596: Scientific Computing and Visualization
- CSCI585: Database Systems
- CSCI527: Applied Machine Learning for Games