Authored Online Courses

Authored Courses

Python Primer (2022): This course provides an introduction to Python, and was created with support from Texas A&M Institute of Data Science and the Texas A&M University Department of Mathematics. For this course I provide Jupyter Notebooks demonstrating the material and provide links to additional resources, and I also provide a number of short videos, corresponding to the notebooks providing additional explanation.
After completing this primer you should be able to do the following:
  • Learn where to find tutorials, documentation on Python as well as community developed packages.
  • Use comments and learn how to incorporate them into the help system.
  • Write and evaluate expressions using variable names.
  • Use basic data structures including strings, lists, tuples, sets, and dictionaries, among others.
  • Understand and use various control flow mechanisms, including looping, functions and exceptions.
  • Write functions, understand return values and argument lists, including positional and keyword arguments.
  • Understand binding and copies, as well as interning/small integer caching.
  • Learn about name resolution, blocks, scope and name spaces.
  • Find and use modules and packages.
  • Learn about objects, classes and inheritance, including multiple inheritance.
R Primer (2021): This course provides an introduction to R, and was created with support from Texas A&M Institute of Data Science as part of the Biomedical Data Science Online Training Program. For this course I provide Jupyter Notebooks demonstrating the material and provide links to additional resources, and I also provide a number of short videos, corresponding to the notebooks providing additional explanation.
After completing this primer you should be able to do the following:
  • Create and manage R projects using the R console, RStudio, RStudio Cloud, Google Colab, and Jupyter Notebook
  • Use the built-in help system to find details on functions, packages and data sets
  • Write and evaluate expressions using variable names
  • Understand name resolution and conflicts
  • Use basic data structures including vectors, lists, factors and data frames, among others.
  • Understand and use various control flow mechanisms, and how to replace looping structures using apply
  • Write functions, understand argument lists, use named and unnamed arguments including default values
  • Understand and utilize short-circuit and lazy evaluation
  • Find, install and load packages from CRAN
  • Read/Write basic custom data files
  • Visualize data using ggplot and other plotting tools

Workshops Co-Organized

Workshops Co-Organized

Uncertainty Quantification: Theory Meets Practice (Nov 5, 2021)

Goals and Scope: The Texas A&M Institute of Data Science (TAMIDS) is hosting a one-day workshop to bring together TAMU researchers across different colleges and disciplines with common interests in the intersection of Uncertainty Quantification and Machine Learning. The multidisciplinary interactive workshop will feature ranges of topics from theory to applications, and offer a venue to potentially seed new collaborations and help strengthen TAMU's competitive position in the field through presenting research results, sharing open problems, and demonstrating tools and software in machine learning and artificial intelligence as these pertain to uncertainty quantification and probabilistic computational methods.

Organizers:

  • Matthew Hielsberg (Institute for Scientific Computation and Department of Mathematics)
  • Arash Noshadravan (Department of Civil and Environmental Engineering)
  • Rui Tuo (Department of Industrial and Systems Engineering)

Publications

Publications

S. Foucart, M. Hielsberg, G. Mullendore, G. Petrova, P. Wojtaszczyk, Optimal Algorithms for Computing Average Temperatures, Mathematics of Climate and Weather Forecasting, 5:34-44, 2019.

R. Prazenica, M. Hielsberg, R. Sharpley and A. Kurdila, 3-D Implicit Terrain Mapping and Path Planning for Autonomous MAV Flight in Urban Environments, AIAA Guidance, Navigation, and Control Conference (GNC), Aug 2013 (AIAA-2013-4792).

M. Hielsberg, R. Tsai, P. Guo and C. Chen, Visibility-Based Urban Exploration and Learning Using Point Clouds, ICES REPORT 13-06, The Institute for Computational Engineering and Sciences, The University of Texas at Austin, March 2013.

R. DeVore, G. Petrova, M. Hielsberg, L. Owens, B. Clack and A. Sood, Processing Terrain Point Cloud Data, SIAM J. Imaging Sci., 6(1):1-31, 2013.

R. Prazenica, A. Kurdila, R. Sharpley, P. Binev, M. Hielsberg, J. Lane, and J. Evers, Vision-based receding horizon control for micro air vehicles in urban environments (preprint).

P. Binev, R. DeVore, M. Hielsberg, L.S. Johnson, B. Karaivanov, B. Lane and R. Sharpley, Geometric Encoding of Natural and Urban Terrains (preprint)

A. Thies, B. Philips, P. Binev, R. DeVore, M. Hielsberg, L.S. Johnson, B. Karaivanov, B. Lane, R. Sharpley Smooth, Piecewise-Polynomial Terrain Representation Using Nontraditional Metrics, STTR Final Report, Schafer Corporation Contract No. W911NF-04-C-0060, U.S. Army Research Office, March 2005.

Selected Talks

Selected Talks

Data Analyses in Python, Open Source Open Science Workshop 2020, Texas A&M University - September 13, 2020.

Processing and Compressing Terrain Data, ARO MURI: Dynamic Modeling of 3D Urban Terrain, Year 5 Review, University of South Carolina - November 5, 2012.

Processing Terrain Point Cloud Data, ARO MURI: Dynamic Modeling of 3D Urban Terrain, Year 4 Review, University of California, Irvine - January 10, 2012.

LiDAR Simulation, DTRA/NSF Algorithms for Threat Detection, University of South Carolina - December 19, 2010

Overview of ATD Hyperspectral Data, DTRA/NSF Algorithms for Threat Detection, University of South Carolina - December 19, 2010.

LiDAR Overview, DTRA/NSF Seminar Series, Texas A&M University - November 30, 2010

Simulation and Experimentation, ARO MURI: Dynamic Modeling of 3D Urban Terrain, Year 3 Review, University of Texas at Austin - September 28, 2010.

Transitioning MURI Algorithms to NATO Collaborators, Dynamic Modeling of 3D Urban Terrain, Yr 3, University of Texas, Austin, TX, 2010. (with P. Binev, M. Hielsberg, S. Johnson, and R. Sharpley).

Simulation, Data Acquisition and Dynamic Data Sets, ARO MURI: Dynamic Modeling of 3D Urban Terrain, Year 1 Review, University of South Carolina - November 20, 2008.

IMI Simulation Capabilities, Model Classes, Approximation, and Metrics for Dynamic Processing of Urban Terrain Data, Rice University - September 10, 2007.

Worked Projects and Other Contributions

Worked Projects

G. Petrova, Generalized Gauss-Radau and Gauss-Lobatto formulas with Jacobi weight functions, BIT Numerical Mathematics, 57(1) (2017), 191--206.

Data Assimilation and Parameter Estimation for Parametric Partial Differential Equations, PI: R. DeVore, ONR, 2016-2019.

Numerical Recovery, PI: R. DeVore, ONR, 2015-2018.

H. Nguyen, G. Petrova, Extended Gaussian Type Cubatures for the Ball, J. Comp. Applied Math., 290 (2015), 209--223.

Numerical Methods for Solving Parametric PDEs, PI: DeVore, Co-PI's: G. Petrova, A. Bonito, ONR, 2011-2015.

New Theory and Algorithms for Scalable Data Fusion, PI: R. DeVore, AFOSR, 2009-2013.

Fast Computational Algorithms in High Dimensions, PI: R. DeVore, ONR, 2009-2012.

An ATD Proposal. Fast Point Cloud Surface Reconstruction Algorithms, PI: DeVore, Co-PI's G. Petrova, S. Schaefer, NSF, 2009-2012.

Phase II SBIR: Innovative Micro Air Vehicles & Control Techniques for Urban Environments, PI: R. Sharpley, Radiance Technologies/DOD-Air Force Research Laboratory, 2009-2011.

Fundamental Questions In Compressed Sensing, PI: R. DeVore, ONR, 2008-2009.

Phase I SBIR: Innovative Micro Air Vehicles & Control Techniques for Urban Environments, PI: R. Sharpley, Wright-Patterson AFRL, SBIR with Radiance Technologies, 2008.

Dynamic Modeling of 3D Urban Terrain, Army Research Office Multi-University Research Initiative, with participants South Carolina (lead), Texas A&M, Virginia Tech, Princeton, Rice, UC-Irvine, UCLA and Univ. of Texas, 2007-2013.

Software for Generating Geometrically and Topologically Accurate Urban Terrain Models Using Implicit Methods, PI: R. Sharpley, ARO Phase II STTR with Radiance Technologies, 2007-2009.

Model Classes, Approximation, and Metrics for Dynamic Processing of Urban Terrain Data, R. DeVore- PI, R. Sharpley co-PI, ARO MURI, South Carolina (lead) with Texas A&M, Virginia Tech, Princeton, Texas, UCLA, & UC-Irvine, 2007-2009.

Software for Generating Geometrically & Topologically Accurate Urban Terrain Models Using Implicit Methods, PI: R. DeVore, co-PI: R. Sharpley, ARO Phase I STTR with Radiance Technologies, 2006-2008.

Advanced Mathematical Methods for Processing Large Data Sets, R. DeVore-PI, P. Binev/R.Sharpley co-PI's, (DOD/ARO), 2005-2008.

Multiresolution Methods for Vision-Based Guidance, Navigation and Control, PI: R. Sharpley, (AFOSR/Florida- lead institution together with CMU, USC, Virginia Tech), 2003-2008.

Active Vision for Control of Agile Autonomous Flight (AVCAAF), Air Force Office of Scientific Research, with Univ. of Florida (lead), Carnegie Mellon Univ, Univ of South Carolina, Virginia Tech and Eglin Air Force Base, 2003-2008.

Acknowledgments

Acknowledgments

Y. Lu, Visual Navigation for Robots in Urban and Indoor Environments, Doctoral dissertation, Texas A&M University, 2015.

Y. Li, Algorithms and data representations for emerging non-volatile memories, Doctoral dissertation, Texas A&M University, 2014.

B. Stroustrup, The C++ Programming Language, Errata, 2013.

PCL Community Data Repositories, 2013.

B. Karaivanov, Polygonal Representation of 3D Urban Terrain Point-Cloud Data, 2011.

M. Walters, Iterated point-line configurations in projective planes, Doctoral dissertation, Texas A&M University, 2009.

B. Karaivanov, Hybrid Surface Reconstruction from Point Cloud Data, Dynamic Modeling of 3D Urban Terrain, Yr 1, University of South Carolina, Columbia, SC, 2008. (with P. Binev, R. DeVore, M. Hielsberg, S. Johnson, L. Owens, and R. Sharpley)

IMI Provides Research Opportunities for Undergraduate Students, COSMOS, 2002.